Home > Resources > Articles > Role of Edge Analytics in Smarter Computing & Business Growth

Role of Edge Analytics in Smarter Computing & Business Growth

Aashish Yadav | May 13, 2022 | 385 views

Role of Edge Analytics in Smarter
As businesses are moving towards using more and more data for decision-making, data-driven insights have become the most valuable asset for any company. Today, businesses are feeling the need to process data and access analytics in real-time. In the past, businesses collected data from various IoT devices and sensors, centralized it in a data warehouse or data lake, and then analyzed it to get insights.

What if businesses could bypass the data centralization or integration stage entirely and go straight to the analysis stage? This technique is known as edge analytics. This method allows businesses to accomplish autodidact machine learning, improve data security, and reduce data transfer costs.

With edge analytics and edge computing, businesses can not only generate more sales but also boost efficiency, enhance productivity, and save costs.

Let’s dive deeper into edge analytics, how it complements cloud computing, and why businesses are increasingly opting for it.


How can Edge Analytics Complement Cloud Computing?

Real-time decision-making is still challenging in IoT systems due to factors like bandwidth, latency, power consumption, cost, and various other considerations. This problem, however, can be addressed by using of artificial intelligence in edge analytics, which also makes cloud computing better.

Cloud computing and edge computing are very different approaches and purely depend on the software implemented. These two technologies don’t discredit each other, but rather complement each other.

  • Reduces utilization of data bandwidth or transfer
  • Ends the need for continuous connectivity to the cloud
  • Boosts the real-time performance with faster processing
  • Enhances data security


Common Pitfalls to Dodge with Edge Analytics and Edge Computing

According to Statista, the number of Internet of Things (IoT) devices will reach 30.9 billion units by 2025. Moreover, the global IoT market is expected to grow to $1.6 trillion by 2025.

The cost of transferring and storing all of that data, combined with the lack of a clear advantage, has led many to question whether the IoT is worth the hype. That is why the industry is shifting its focus to edge analytics or computing to fully leverage the data collected from IoT devices. Let’s take a look at some of the challenges that can be addressed with the help of edge analytics:

  • Many industrial IoT solutions require complete uptime.
  • Consumer IoT apps need to process localized events in real-time.
  • A power outage might result in a security breach.
  • Difficulties in adhering to data regulations.


Why You Should Employ Edge Analytics?

“To remain competitive in the post-cloud era, innovative companies are adopting edge computing due to its endless breakthrough capabilities that are not available at the core.”

- David Williams, managing principal at AHEAD.

Edge analytics solutions assist businesses wherever data insights are needed at the edge. It can be used in various industries for numerous things, such as retail customer behavior analysis, remote monitoring and maintenance, detecting fraud at ATMs and other financial sites, and monitoring manufacturing and logistical equipment. Here are some reasons you should choose edge analytics and edge computing for your business.


Saves Time

The prime objective of adopting an edge analytics system is to filter out unnecessary information prior to analysis, and only relevant data is sent via higher-order methods. This saves a lot of time when it comes to processing and uploading data, which makes the complex analytical process done on the cloud a lot more valuable and effective.


Reduces Cost

The use of edge analytics in IoT cuts the cost of data storage and administration. It also saves operating expenses, bandwidth requirements, and resources spent on data processing. All of these things add up to substantial financial savings.


Safeguards Privacy

Edge analytics assists in the preservation of privacy when sensitive or confidential data is gathered by a device, such as GPS data or video streams. This sensitive data is pre-processed on-site rather than being transferred to the cloud for processing. This additional step ensures that only data that complies with privacy laws leaves the device for further analysis.


Reduces Data Analysis Delay

Edge analytics tools enables faster, autonomous decision-making since insights are identified at the data source, preventing latency. It is more effective to analyze data on the defective device itself and shut down the faulty equipment immediately instead of waiting for the data from the equipment to be transferred to a central data analytics environment and waiting for the result.


Solves Connectivity Issues

By making sure that applications are not disrupted by restricted or interrupted network access, edge analytics in IoT helps to safeguard against possible connectivity disruptions in IoT. It is particularly beneficial in rural areas or for minimizing connection costs when utilizing costly technologies such as cellular networks.

 

Industries Leveraging Edge Analytics


Closing Lines

Edge analytics is an exciting field, with businesses in the Internet of Things (IoT) sector growing their expenditures every year. Leading vendors are actively investing in this rapidly growing market. Edge analytics provides measurable business advantages in certain industries such as retail, manufacturing, energy, and logistics by decreasing decision latency, scaling out analytics resources, resolving bandwidth issues, and perhaps reducing expenditures. The potential at the edge leads to a very exciting future of smart computing as sensors get more affordable, applications need more real-time analytics, and developing optimized, cost-effective edge algorithms becomes simpler.


FAQ

What distinguishes edge analytics from regular analytics?

Except for the location of the analysis, edge analytics offers remarkably similar capabilities to regular analytics systems. One significant difference is that edge analytics apps can run on edge devices that can have memory, processing power, or communication.


What are edge devices, and what are some examples?

An edge device serves as an access point to the core networks of businesses or service providers. Some examples include routers, switching devices, integrated access devices (IADs), multiplexers, and other metropolitan area network (MAN) and wide area network (WAN) access devices.


What exactly are edge machines?

Edge ML is a technology that allows smart devices to analyze data locally through local servers or at the device level. This is done with the help of machine and deep learning algorithms, decreasing dependency on cloud networks.

Spotlight

Exabeam

Exabeam is the Smarter SIEM company. We empower enterprises to detect, investigate and respond to cyber attacks more efficiently so their security operations and insider threat teams can work smarter. Security organizations no longer have to live with excessive logging fees, missed distributed attacks and unknown threats, or manual investigations and remediation. With the Exabeam Security Management Platform, analysts can collect unlimited log data, use behavioral analytics to detect attacks, and automate incident response, both on-premises or in the cloud. Exabeam Smart Timelines, sequences of user and device behavior created using machine learning, further reduce the time and specialization required to detect attacker tactics, techniques and procedures

OTHER ARTICLES
BUSINESS STRATEGY

Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Article | July 22, 2022

Explore the benefits of using industrial big data for predictive maintenance strategies. Learn how businesses can shift from reactive to proactive maintenance approaches and optimize operations with the power of predictive analytics. Contents 1 Importance of Predictive Maintenance 2 Challenges of Traditional Reactive Maintenance for Enterprises 3 Emergence of Proactive Strategies for Predictive Maintenance 4 Reactive vs. Proactive Strategies 5 Industrial Big Data Analytics for Predictive Maintenance: Importance and Applications 6 Navigating Implementation Challenges 6.1 Overcoming Data Collection and Pre-processing Challenges 6.2 Addressing Data Integration Challenges 6.3 Model Selection and Implementation Solutions 6.4 Staffing and Training Solutions 7 Leverage Predictive Maintenance for Optimal Operations 8 Final Thoughts 1. Importance of Predictive Maintenance Predictive maintenance (PdM) is a proactive maintenance approach that employs advanced downtime tracking software to evaluate data and predict when maintenance on equipment should be conducted. With PdM constantly monitoring equipment performance and health using sensors, maintenance teams can be alerted when equipment is nearing a breakdown, allowing them to take mitigation measures before any unscheduled downtime occurs. The global predictive maintenance market is expected to expand at a 25.5% CAGR to reach USD 23 billion in 2025 during the forecast period. (Market Research Future) Organizations often prefer PdM as a maintenance management method as it reduces costs with an upfront investment compared to preventive and reactive maintenance. Furthermore, maintenance has become crucial to ensuring smooth system functioning in today's complex industrial environment. Therefore, predictive maintenance is an essential strategy for industrial organizations, as it improves safety and productivity and reduces costs. As industrial equipment becomes more automated and diagnostic tools become more advanced and affordable, more and more plants are taking a proactive approach to maintenance. The immediate goal is to identify and fix problems before they result in a breakdown, while the long-term goal is to reduce unexpected outages and extend asset life. Plants that implement predictive maintenance processes see a 30% increase in equipment mean time between failures (MTBF), on average. This means your equipment is 30% more reliable and 30% more likely to meet performance standards with a predictive maintenance strategy. (Source: FMX) 2. Challenges of Traditional Reactive Maintenance for Enterprises The waning popularity of reactive maintenance is attributed to several inherent limitations, such as exorbitant costs and a heightened likelihood of equipment failure and safety hazards. At the same time, the pursuit of maintaining industrial plants at maximum efficiency with minimal unplanned downtime is an indispensable objective for all maintenance teams. However, the traditional reactive approach, which involves repairing equipment only when it malfunctions, can result in substantial expenses associated with equipment downtime, product waste, and increased equipment replacement and labor costs. To overcome these challenges, organizations can move towards proactive maintenance strategies, which leverage advanced downtime tracking software to anticipate maintenance needs and forestall potential breakdowns. 3. Emergence of Proactive Strategies for Predictive Maintenance The constraints of reactive maintenance have instigated the emergence of proactive approaches, including predictive analytics. It employs real-time data gathered from equipment to predict maintenance needs and employs algorithms to recognize potential issues before they result in debilitating breakdowns. The data collected through sensors and analytics facilitates the establishment of a more thorough and precise assessment of the general well-being of the operation. With such proactive strategies, organizations can: Arrange maintenance undertakings in advance, Curtail downtime, Cut expenses, and Augment equipment reliability and safety 4. Reactive vs. Proactive Strategies As of 2020, 76% of the respondents in the manufacturing sector reported following a proactive maintenance strategy, while 56% used reactive maintenance (run-to-failure). (Source: Statista) Proactive maintenance strategies, such as predictive maintenance, offer many benefits over reactive maintenance, which can be costly and time-consuming. By collecting baseline data and analyzing trends, proactive maintenance strategies can help organizations perform maintenance only when necessary, based on real-world information. However, establishing a proactive maintenance program can be challenging, as limited maintenance resources must be directed to address the most critical equipment failures. Analyzing data from both healthy and faulty equipment can help organizations determine which failures pose the biggest risk to their operation. A proactive maintenance approach may assist in avoiding the fundamental causes of machine failure, addressing issues before they trigger failure, and extending machine life, making it a crucial strategy for any industrial operation. 5. Industrial Big Data Analytics for Predictive Maintenance: Importance and Applications Big data analytics is a key enabler of predictive maintenance strategies. Its capability to process vast amounts of data provides valuable insights into equipment health and performance, making predictive maintenance possible. With their wide-ranging applications, industrial big data analytics tools can predict maintenance needs, optimize schedules, and detect potential problems before they escalate into significant problems. It can also monitor equipment performance, identify areas for improvement, and refine processes to increase equipment reliability and safety. Industrial big data is indispensable in realizing the shift from reactive to proactive predictive maintenance, which is accomplished through the optimal utilization of available datasets. Industrial big data can glean insights into equipment condition, including patterns of maintenance that may not be readily apparent. Moreover, it has the capacity to attain actionable intelligence capable of effecting a closed loop back to the plant floor. Integration of big data technologies with industrial automation is key to this accomplishment. Nevertheless, this transition will necessitate investment in supplementary assets, such as new maintenance processes and employee training. 6. Navigating Implementation Challenges 6.1 Overcoming Data Collection and Pre-processing Challenges One of the primary challenges in implementing industrial big data analytics for predictive maintenance is the collection and pre-processing of data. The voluminous industrial data, which comes in various formats and from multiple sources, makes it necessary for organizations to develop robust data collection and pre-processing strategies to ensure data accuracy and integrity. To achieve this, organizations need to establish sensor and data collection systems and ensure that the data undergoes appropriate cleaning, formatting, and pre-processing to obtain accurate and meaningful results. 6.2 Addressing Data Integration Challenges Integrating data from heterogeneous sources is a daunting challenge that organizations must overcome when implementing industrial big data analytics for predictive maintenance. It involves processing multiple datasets from different sensors and maintenance detection modalities, such as vibration analysis, oil analysis, thermal imaging, and acoustics. While utilizing data from various sources leads to more stable and accurate predictions, it requires additional investments in sensors and data collection, which is generally very hard to achieve in most maintenance systems. A well-crafted data architecture is critical to managing the copious amounts of data that come from different sources, including various equipment, sensors, and systems. Organizations must devise a comprehensive data integration strategy that incorporates relevant data sources to ensure data integrity and completeness. 6.3 Model Selection and Implementation Solutions Selecting appropriate predictive models and implementing them effectively is another significant challenge. To overcome this, organizations need to have an in-depth understanding of the various models available, their strengths and limitations, and their applicability to specific maintenance tasks. They must also possess the necessary expertise to implement the models and seamlessly integrate them into their existing maintenance workflows to achieve timely and accurate results. Furthermore, it is crucial to align the selected models with the organization's business objectives and ensure their ability to deliver the desired outcomes. 6.4 Staffing and Training Solutions In order to ensure successful implementation, organizations must allocate resources toward staffing and training solutions. This entails hiring proficient data scientists and analysts and then providing them with continual training and professional development opportunities. Moreover, it is imperative to have personnel with the requisite technical expertise to manage and maintain the system. Equally crucial is providing training to employees on the system's usage and equipping them with the necessary skills to interpret and analyze data. 7. Leverage Predictive Maintenance for Optimal Operations Predictive maintenance is widely acknowledged among plant operators as the quintessential maintenance vision due to its manifold advantages, such as higher overall equipment effectiveness (OEE) owing to a reduced frequency of repairs. Furthermore, predictive maintenance data analytics facilitate cost savings by enabling optimal scheduling of repairs and minimizing planned downtimes. It also enhances employees' productivity by providing valuable insights on the appropriate time for component replacement. Additionally, timely monitoring and addressing potential problems can augment workplace safety, which is paramount for ensuring employee well-being. In a survey of 500 plants that implemented a predictive maintenance program, there was an average increase in equipment availability of 30%. Simply implementing predictive maintenance will ensure your equipment is running when you need it to run. (Source: FMX) By synchronizing real-time equipment data with the maintenance management system, organizations can proactively prevent equipment breakdowns. Successful implementation of predictive maintenance data analytic strategies can substantially reduce the time and effort spent on maintaining equipment, as well as the consumption of spare parts and supplies for unplanned maintenance. Consequently, there will be fewer instances of breakdowns and equipment failures, ultimately leading to significant cost savings. On average, predictive maintenance reduced normal operating costs by 50%. (Source: FMX) 8. Final Thoughts Traditional reactive maintenance approaches need to be revised in today's industrial landscape. Proactive strategies, such as predictive maintenance, are necessary to maintain equipment health and performance. Real-time predictive maintenance using big data collected from equipment can help prevent costly downtime, waste, equipment replacement, and labor expenses, thus enhancing safety and productivity. The shift from reactive to proactive maintenance is crucial for organizations, and industrial big data analytics is vital for realizing this transition. Although big data analytics applications for predictive maintenance pose challenges, they can be overcome with the right measures. Ultimately, the effective implementation of big data analytics solutions is a vital enabler of big data predictive maintenance strategies and an essential tool for any industrial plant seeking to optimize its maintenance approach. By embracing predictive maintenance strategies and leveraging the power of industrial big data and analytics, organizations can ensure the longevity and reliability of their equipment, enhancing productivity and profitability.

Read More
BUSINESS STRATEGY

Implementing Big Data and AI: Best Practices and Strategies for 2023

Article | May 31, 2022

Discover the latest strategies and best practices for implementing big data and AI into your organization for 2023. Gain insights on leading Big Data and AI solution providers to drive business growth. Contents 1 Establishing a Relationship between Big Data and AI 2 Importance of Big Data and AI in 2023 3 Key Challenges in Implementing Big Data and AI 4 Best Practices and Strategies for Big Data and AI Implementation 4.1 Building a Data Strategy 4.2 Implementing a Data Governance Framework 4.3 Leveraging Cloud Computing 4.4 Developing a Data Science and AI Roadmap 4.5 Leveraging Established Agile Methodologies 4.6 Prototyping Through Sandboxing 5 Top AI and Big Data Companies to Look For in 2023 6 Conclusion 1. Establishing a Relationship between Big Data and AI The relationship between AI and big data is mutually beneficial, as AI requires vast amounts of data to enhance its decision-making abilities, while big data analytics benefits from AI for superior analysis. This union enables the implementation of advanced analytics, such as predictive analysis, resulting in the optimization of business efficiency by anticipating emerging trends, scrutinizing consumer behavior, automating customer segmentation, customizing digital campaigns, and utilizing decision support systems propelled by big data, AI, and predictive analytics. This integration empowers organizations to become data-driven, resulting in significant improvements in business performance. 2. Importance of Big Data and AI in 2023 In the year 2023, it is anticipated that the utilization of big data analytics and artificial intelligence (AI) will profoundly impact diverse industries. The investment in big data analytics will be primarily driven by the need for data compliance, security, and mobilization, ultimately aiming to achieve real-time analysis. Therefore, businesses seeking to excel in this area must be prepared to adopt cloud technology and make significant advancements in computing power and data processing methods. Recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks. (Source: Forbes) The banking, retail, manufacturing, finance, healthcare, and government sectors have already made substantial investments in big data analytics, which have resulted in the forecasting of trends, enhancing business recommendations, and increasing profits. In addition, AI technology will make significant advancements in 2023, including democratization, making it accessible to a broader user population. This shift will enable customers to wield authority, and businesses will be able to use AI to better meet their specific and individualized business requirements. Finally, a significant shift likely to be witnessed in the AI field in 2023 is the move to a more industrialized, embedded type of architecture, where actual business users may begin utilizing algorithms. According to a recent study, 61% of respondents believe that AI will have a significant impact on their industry within the next three to five years. (Source: Deloitte Insights Report) 3. Key Challenges in Implementing Big Data and AI 97.2% of business executives say their organizations are investing in big data and AI projects. These executives cite their desire to become “nimble, data-driven businesses” as the reason for these investments, as 54.4% say that their companies’ inability to do this was the biggest threat they faced. In addition, 79.4% say they’re afraid that other, more data-driven companies will disrupt and outperform them. (Source: Zippia) Implementing big data analytics and artificial intelligence (AI) presents various challenges that businesses must tackle to realize their full potential. One such obstacle is the intricate nature of the data, which could be either structured or unstructured and necessitate specialized tools and techniques for processing and analysis. Moreover, companies must ensure data quality, completeness, and integrity to facilitate accurate analysis and decision-making. Another substantial challenge in implementing big data and AI is the requirement for skilled personnel with expertise in data science, machine learning, and related technologies. To stay up-to-date on the latest tools and techniques, companies must invest in ongoing training and development programs for their employees. Ethical and legal concerns surrounding data privacy, security, and transparency must also be addressed, especially after recent data breaches and privacy scandals. Integrating big data and AI into existing IT systems can be a challenging and time-consuming process that necessitates careful planning and coordination to ensure smooth integration and minimize disruption. Lastly, the high cost of implementing these technologies can be a significant barrier, especially for smaller businesses or those with limited IT budgets. To overcome these challenges, companies must be strategic, prioritize use cases, and develop a clear implementation roadmap while leveraging third-party tools and services to minimize costs and maximize ROI. 4. Best Practices and Strategies for Big Data and AI Implementation 24% of companies use big data analytics. While 97.2% of companies say they’re investing in big data and AI projects, just 24% describe their organizations as data-driven. (Source: Zippia) 4.1 Building a Data Strategy One of the biggest challenges in building a data strategy is identifying the most relevant data sources and data types for the organization’s specific business objectives. The sheer volume and diversity of data available can further complicate this. The key to addressing this challenge is thoroughly assessing the organization’s data assets and prioritizing them based on their business value. This involves: Identifying the key business objectives and Determining which data sources and data types are most relevant to achieving those objectives 4.2 Implementing a Data Governance Framework Establishing a data governance framework involving all stakeholders is crucial for ensuring agreement on data quality, privacy, and security standards. However, implementing such a framework can be daunting due to the divergent priorities and perspectives of stakeholders on good data governance. So, to overcome this challenge, clear guidelines and processes must be established: Creating a data governance council Defining roles and responsibilities Involving all stakeholders in the development and implementation of guidelines Data quality management, privacy, and security processes should be established to maintain high data governance standards Organizations can improve the effectiveness of their data governance initiatives by aligning all stakeholders and ensuring their commitment to maintaining optimal data governance standards. 4.3 Leveraging Cloud Computing It is essential to carefully select a cloud provider that aligns with the organization's security and compliance requirements. In addition, robust data security and compliance controls should be implemented: Establishing data encryption and access controls Implementing data backup and recovery procedures Regularly conducting security and compliance audits By following these practices, organizations can ensure their big data and AI projects are secure and compliant. 4.4 Developing a Data Science and AI Roadmap The obstacles to developing a data science and AI roadmap lie in identifying the most pertinent use cases that cater to the specific business objectives of an organization. This difficulty is further compounded by the potential divergence of priorities and perspectives among various stakeholders concerning the definition of a successful use case. Hence, it is imperative to establish unambiguous guidelines for identifying and prioritizing use cases that align with their respective business values. This entails: Identifying the key business objectives Carefully ascertaining which use cases are most pertinent to realizing those objectives Meticulously delineating the success criteria for each use case 4.5 Leveraging Established Agile Methodologies Leveraging well-established agile methodologies is critical in successfully implementing large-scale big data and AI projects. By defining a precise project scope and goals, prioritizing tasks, and fostering consistent communication and collaboration, enterprises can effectively execute AI and big data analytics initiatives leveraging agile methodologies Such an approach provides teams with a clear understanding of their responsibilities, facilitates seamless communication, and promotes continuous improvement throughout the project lifecycle, resulting in a more efficient and effective implementation 4.6 Prototyping Through Sandboxing Establishing clear guidelines and processes is crucial to overcome the challenge of creating prototypes through sandboxing that are representative of the production environment and can meet the organization's requirements. It includes: Defining the scope and objectives of the prototype, Meticulously selecting the appropriate tools and technologies Guaranteeing that the prototype is an authentic reflection of the production environment Additionally, conducting thorough testing and evaluation is necessary to ensure that the prototype can be scaled effectively to meet the organization's needs. 5. Top AI and Big Data Companies to Look For in 2023 H2O.ai H2O.ai is a leading provider of artificial intelligence (AI) and machine learning (ML) software. It provides a platform for businesses to use artificial intelligence and data-driven insights to drive innovation and growth. The software offers a suite of tools and algorithms to help users build predictive models, analyze data, and gain insights that inform business decisions. With a user-friendly interface and a robust set of features, H2O.ai is a valuable tool for businesses looking to leverage the power of machine learning to stay ahead of the competition. ThoughtSpot ThoughtSpot is a leading search and AI-driven analytics platform that enables businesses to quickly and easily analyze complex data sets. The platform offers a range of features, including advanced analytics, customizable visualizations, and collaborative capabilities. It is designed to make data analytics accessible to anyone within an organization, regardless of technical expertise. The platform is also highly customizable, allowing businesses to tailor it to meet their specific needs and integrate it with their existing data infrastructure. Treasure Data Treasure Data is a cloud-based enterprise data management platform that helps businesses collect, store, and analyze their data to gain valuable insights. Its platform includes a suite of powerful tools for data collection, storage, processing, and analysis, including a flexible data pipeline, a powerful data management console, and a range of analytics tools. The platform is also highly scalable, capable of handling massive amounts of data and processing millions of events per second, making it suitable for businesses of all sizes and industries. Denodo Denodo is a leading data virtualization software company that provides a unified platform for integrating and delivering data across multiple sources and formats in real time. The platform offers unmatched performance and unified access to a broad range of enterprise, big data, cloud, and unstructured sources. It also provides agile data service provisioning and governance at less than half the cost of traditional data integration. In addition, its data virtualization technology simplifies the complexity of data sources and creates a virtual layer of data services accessible to any application or user, regardless of the data’s location or format. Pendo.io Pendo.io is a leading cloud-based platform that provides product analytics, user feedback, and guidance for digital products. It allows businesses to make data-driven decisions about their products and optimize their customer journey. The platform empowers companies to transform product intelligence into actionable insights rapidly and at scale, enabling a new generation of businesses that prioritize product development. TigerGraph TigerGraph is a graph database and analytics platform that allows businesses to gain deeper insights and make better decisions by analyzing connected data. It is designed to handle complex data sets and perform advanced graph analytics at scale. The platform offers a range of graph analytics algorithms that can be applied to a variety of use cases, including fraud detection, recommendation engines, supply chain optimization, and social network analysis. Solix Technologies, Inc. Solix Technologies, Inc. is a leading big data management and analysis software solution provider that empowers data-driven enterprises to achieve their Information Lifecycle Management (ILM) goals. Its flagship product, Solix Big Data Suite, provides an ILM framework for Enterprise Archiving and Enterprise Data Lake applications utilizing Apache Hadoop as an enterprise data repository. In addition, the Solix Enterprise Data Management Suite (Solix EDMS) helps organizations implement database archiving, test data management, data masking and application retirement across all enterprise data. Reltio Reltio is a leading provider of cloud-based master data management (MDM) solutions that enable organizations to create a unified view of their data across all sources and formats. The platform combines MDM with big data analytics and machine learning to provide a single source of truth for data-driven decision-making. The solution offers a range of features, including data modeling, data quality management, data governance, and data analytics. dbt Labs dbt Labs is a cloud-based data transformation software platform that helps analysts and engineers manage the entire analytics engineering workflow, from data ingestion to analysis. The platform enables users to transform and model raw data into analysis-ready data sets using a SQL-based language. With its modular and scalable approach, dbt Labs makes it easier for data teams to collaborate and manage their data pipelines. Rockset Rockset is a real-time indexing database platform that allows businesses to run fast queries on data from multiple sources without needing to manage the underlying infrastructure. It supports various data types, including structured, semi-structured, and nested data, making it flexible and versatile. In addition, the serverless platform is built on a cloud-native architecture, making it easy to scale up or down as needed. With Rockset, users can build real-time applications and dashboards, perform ad hoc analysis, and create data-driven workflows. 6. Conclusion The relationship between big data and AI is mutually beneficial, given the fact that AI requires copious amounts of data to refine its decision-making capabilities, while big data analytics derives immense value from AI for advanced analysis. As a result, the integration of big data analytics and AI is projected to profoundly impact diverse industries in 2023. Nevertheless, adopting these technologies poses multifarious challenges, necessitating businesses to adopt a strategic approach and develop a comprehensive implementation roadmap to optimize ROI and minimize expenses. Ultimately, the successful implementation of big data and AI strategies can enable organizations to become data-driven, culminating in substantial improvements in business performance.

Read More
BUSINESS STRATEGY

Big Data in Healthcare: Improving Patient Outcomes

Article | April 4, 2022

Explore the impact of big data on the healthcare industry and how it is being used to improve patient outcomes. Discover how big data is being leveraged to enhance overall healthcare delivery. Contents 1. Introduction 1.1 Role of Big Data in Healthcare 1.2 The Importance of Patient Outcomes 2. How Big Data Improves Patient Outcomes 2.1 Personalized Medicine and Treatment Plans 2.2 Early Disease Detection and Prevention 2.3 Improved Patient Safety and Reduced Medical Errors 3. Challenges and Considerations While Using Big Data in Healthcare 4. Final thoughts 1. Introduction In today's constantly evolving healthcare industry, the significance of big data cannot be overstated. Its multifaceted nature makes it a valuable asset to healthcare providers in their efforts to enhance patient outcomes and reduce business costs. When harnessed effectively, big data in healthcare provides companies with the insights they need to personalize healthcare, streamline customer service processes, and improve their practices for interacting with patients. This results in a more tailored and thorough experience for customers, ultimately leading to better care. 1.1 Role of Big Data in Healthcare Big data pertains to vast collections of structured and unstructured data in the healthcare industry. One of the primary sources of big data in healthcare is electronic health records (EHRs), which contain: Patient’s medical history Demographics Medications Test results Analyzing this data can: Facilitate informed decision-making Improve patient outcomes Reduce healthcare costs Integrating structured and unstructured data can add significant value to healthcare organizations, and Big Data Analytics (BDA) is the tool used to extract information from big data. Big Data Analytics (BDA) can extract information and create trends, and in healthcare, it can identify clusters, correlations, and predictive models from large datasets. However, privacy and security concerns and ensuring data accuracy and reliability are significant challenges that must be addressed. 1.2 The Importance of Patient Outcomes Patient outcomes are the consequences of healthcare interventions or treatments on a patient's health status and are essential in evaluating healthcare systems and guiding healthcare decision-making. However, the current healthcare system's focus on volume rather than value has led to fragmented payment and delivery systems that fall short in terms of quality, outcomes, costs, and equity. To overcome these shortcomings, a learning healthcare system is necessary to continuously apply knowledge for improved patient outcomes and affordability. However, access to timely guidance is limited, and organizational and technological limitations pose significant challenges in measuring patient-centered outcomes. 2. How Big Data Improves Patient Outcomes Big data in healthcare engenders a substantial impact by facilitating the delivery of treatment that is both efficient and effective. This innovative approach to healthcare enables the identification of high-risk patients, prediction of disease outbreaks, management of hospital performance, and improvement of treatment effectiveness. Thanks to modern technology, the collection of electronic data is now a seamless process, thus empowering healthcare professionals to create data-driven solutions to improve patient outcomes. 2.1 Personalized Medicine and Treatment Plans Big data can revolutionize personalized medicine and treatment plans by analyzing vast patient data to create tailored treatment plans for each patient, resulting in better outcomes, fewer side effects, and faster recovery times. 2.2 Early Disease Detection and Prevention Big data analytics in healthcare allow for early interventions and treatments by identifying patterns and trends that indicate disease onset. This improves patient outcomes and reduces healthcare costs. Real-time patient data monitoring and predictive analytics enable timely action to prevent complications. 2.3 Improved Patient Safety and Reduced Medical Errors Big data analytics can help healthcare providers identify safety risks like medication errors, misdiagnoses, and adverse reactions, improving patient safety and reducing medical errors. This can lead to cost savings and better patient outcomes. 3. Challenges and Considerations While Using Big Data in Healthcare In order to maximize the potential advantages, organizations must address significant challenges of big data in healthcare, like privacy and security concerns, data accuracy and reliability, and expertise and technology requirements. Safeguards like encryption, access controls, and data de-identification can mitigate privacy and security risks Ensuring data accuracy and reliability requires standardized data collection, cleaning, and validation procedures Additionally, healthcare organizations must prioritize the recruitment of qualified professionals with expertise in data management, and analysis is crucial The adoption of advanced technologies such as artificial intelligence and machine learning can support effective analysis and interpretation of big data in healthcare 4. Final Thoughts The impact of big data on healthcare is profound, and the healthcare sector possesses the possibility of a paradigm shift by leveraging the potential of big data to augment patient outcomes and curtail costs. Nevertheless, implementing big data entails formidable challenges that necessitate their resolution to fully unleash healthcare data technology's benefits. Notably, handling voluminous and heterogeneous datasets in real time requires state-of-the-art technological solutions. To attain the maximal benefits of big data in healthcare, organizations must proactively address these challenges by implementing risk-mitigating measures and fully capitalizing on big data's potential.

Read More
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Navigating Big Data Integration: Challenges and Strategies

Article | April 13, 2023

Explore the complexities of integrating Big Data into your organization. Learn effective strategies for overcoming challenges to optimize your data integration process and maximize business outcomes. Contents 1 Introduction 2 Challenges in Big Data Integration 2.1 Data Volume, Velocity, and Variety Challenges 2.2 Integration with Legacy Systems and Data Silos 2.3 Technical Challenges 2.4 Organizational Challenges 3 Overcoming Integration Challenges: Strategies 3.1 Conducting Thorough Analysis of Data Infrastructure 3.2 Prioritizing Projects Based On Business Needs 3.3 Implementing Scalable and Flexible Solutions 3.4 Establishing Robust Data Governance Practices 4 Conclusion 1. Introduction Big data integration is a critical component of effective data management for organizations of all sizes. While some CIOs may believe that consolidating legacy data sources into a single platform can solve integration challenges, the reality is often more complex. Data is vast and usually spread across multiple sources, making integration a daunting task. Nearly 25% of businesses struggle with integrating new applications with their old systems. That’s because legacy system integration isn’t always easy to achieve. (Source: Gartner) Thus, to tackle big data integration effectively, it's essential to understand how it fits into the organization's overall data management strategy and determine the policies governing the integration process. In addition, there are several technical challenges involved in data integration, including ensuring all components work well together, reflecting trends in big data analytics, and finding skilled big data engineers and analysts. 2. Challenges in Big Data Integration 2.1 Data Volume, Velocity, and Variety Challenges In order to effectively integrate big data, companies must address the three key components of volume, variety, and velocity. Coordinating and managing massive amounts of data is both logistically challenging and costly, especially with large volumes. Working with multiple data sources is also a major hurdle that necessitates advanced analytics resources and expertise. Large datasets can take weeks to process, making real-time data analytics an arduous task. This becomes particularly challenging when dealing with intricate and extensive datasets, where velocity poses a significant obstacle. Attempting to apply a uniform analytical process to all data sets may be impractical, further impeding progress. 2.2 Integration with Legacy Systems and Data Silos According to a report, 25% of organizations have more than 50 unique data silos, and these prevent companies from harnessing their data for their business. (Source: 451 Research) The integration of legacy systems presents a significant challenge for companies, as it entails various difficulties, such as high maintenance costs, data silos, compliance issues, weaker data security, and a lack of integration with new systems. The maintenance of legacy systems is both expensive and futile, leaving a company with outdated technology and a tarnished reputation due to potential breaches. Furthermore, legacy systems may fail to meet evolving compliance regulations such as GDPR and lack appropriate data security measures. Over time, data silos can develop due to organizational structures and company culture, leading to difficulties in achieving effective data integration. Siloed data obstructs departments from accessing the full benefits of new systems, impeding technological growth within a company. Additionally, legacy systems may not be compatible with new systems, causing further communication issues. 2.3 Technical Challenges Selecting the Right Big Data Integration Tools Choosing the right tools, technologies and big data integration services is crucial to meet specific business needs. It can be challenging to keep up with the constantly evolving technology landscape, making it important to stay up-to-date with the latest trends and innovations. The decision-making process should involve a thorough evaluation of existing tools and technologies to determine their effectiveness and relevance to the integration process. Failure to choose the appropriate tools and technologies can lead to inefficiencies, longer processing times, and increased costs. Ensuring Different Systems and Data Formats Compatibility It is estimated that around 85% of big data projects will fail to meet all their objectives, illustrating the scale of the challenge that businesses face when trying to get a handle on complex and disparate data from across the enterprise. (Source: Gartner) In integrating big data, it is common to have different systems and data formats that need to be integrated. Ensuring compatibility between these different systems and data formats can be a challenge. A solution-based approach to this challenge is to use data integration platforms that provide support for a wide range of data formats and systems. This ensures that the integration process is seamless and efficient. Addressing Issues of Data Quality and Completeness To integrate big data successfully, it's essential to address issues related to data quality and completeness. Only accurate or complete data can lead to correct insights and precise decision-making, which can benefit businesses. Developing comprehensive data quality management strategies that include data profiling, cleansing, and validation is necessary to overcome this challenge. These strategies ensure that the data being integrated is accurate and complete, leading to better actionable insights and business intelligence. 2.4 Organizational Challenges Developing Comprehensive Integration Strategy Developing a clear and comprehensive integration strategy for big data can be challenging, but it is essential for success. The developed strategy should clearly outline the business objectives and the scope of the integration effort as well as identify the key stakeholders involved. Additionally, it should define the technical requirements and resources necessary to support the integration effort. Building Cross-Functional Teams to Support Integration Efforts Building cross-functional teams for successful data integration can be challenging due to identifying the right individuals with diverse skill sets and navigating complex technical environments. However, it is crucial to form teams comprising members from various departments, including IT, data science, and administration, who collaborate to identify business needs, devise an integration strategy, and implement integration solutions. Building such teams promotes effective communication and coordination across departments and stakeholders, enabling organizations to leverage data assets effectively. 3. Overcoming Integration Challenges: Strategies 3.1 Conducting Thorough Analysis of Data Infrastructure Conducting a thorough analysis of existing data infrastructure and systems is the first step in any data integration effort. This analysis should identify the strengths and weaknesses of the existing infrastructure and systems. This information can be used to develop a comprehensive integration strategy that addresses existing challenges and identifies opportunities for improvement. 3.2 Prioritizing Projects Based On Business Needs It is crucial to prioritize, and sequence integration projects based on business needs to leverage the benefits of data integration. This approach ensures that resources are allocated appropriately and the most critical projects are addressed first. Conducting a thorough cost-benefit analysis is an effective way to determine the value and impact of each project to prioritize and plan accordingly. 3.3 Implementing Scalable and Flexible Solutions In orderto accommodate the ever-increasing amount of data and evolving business requirements, it is essential to implement scalable and flexible integration solutions. This approach ensures that the integration process remains efficient and can adapt to changing needs. Modern data integration platforms that support cloud-based solutions, real-time data processing, and flexible data models can be adopted to achieve this. 3.4 Establishing Robust Data Governance Practices Establishing robust data governance practices ensures data is managed effectively throughout the integration process. This involves defining clear policies, procedures, and standards for data management across the entire data lifecycle, from acquisition to disposition. Additionally, data quality and security controls should be implemented, and employees must be trained on data governance best practices. Organizations can effectively manage data by establishing these practices throughout the integration process. It includes defining data ownership, establishing policies, and implementing quality controls. Ultimately, this approach ensures that data is accurate, complete, and reliable and that the organization is compliant with any relevant regulations or standards. 4. Conclusion Integrating big data represents a formidable obstacle for many organizations, yet with the proper strategies in place, these challenges can be surmounted, enabling businesses to unleash the full potential of their data assets. It is paramount that organizations possess a comprehensive and lucid understanding of both the technical and organizational challenges inherent in integrating big data. Businesses must prioritize data integration and processing initiatives based on their commercial requirements, employ scalable and flexible solutions, and establish robust data governance practices. By doing so, they can acquire invaluable insights that drive business growth and innovation, improve operational efficiency, and enhance their competitiveness in the market.

Read More
BUSINESS STRATEGY

Metadata Driven Data Fabric

Article | July 22, 2022

Explore the benefits of using industrial big data for predictive maintenance strategies. Learn how businesses can shift from reactive to proactive maintenance approaches and optimize operations with the power of predictive analytics. Contents 1 Importance of Predictive Maintenance 2 Challenges of Traditional Reactive Maintenance for Enterprises 3 Emergence of Proactive Strategies for Predictive Maintenance 4 Reactive vs. Proactive Strategies 5 Industrial Big Data Analytics for Predictive Maintenance: Importance and Applications 6 Navigating Implementation Challenges 6.1 Overcoming Data Collection and Pre-processing Challenges 6.2 Addressing Data Integration Challenges 6.3 Model Selection and Implementation Solutions 6.4 Staffing and Training Solutions 7 Leverage Predictive Maintenance for Optimal Operations 8 Final Thoughts 1. Importance of Predictive Maintenance Predictive maintenance (PdM) is a proactive maintenance approach that employs advanced downtime tracking software to evaluate data and predict when maintenance on equipment should be conducted. With PdM constantly monitoring equipment performance and health using sensors, maintenance teams can be alerted when equipment is nearing a breakdown, allowing them to take mitigation measures before any unscheduled downtime occurs. The global predictive maintenance market is expected to expand at a 25.5% CAGR to reach USD 23 billion in 2025 during the forecast period. (Market Research Future) Organizations often prefer PdM as a maintenance management method as it reduces costs with an upfront investment compared to preventive and reactive maintenance. Furthermore, maintenance has become crucial to ensuring smooth system functioning in today's complex industrial environment. Therefore, predictive maintenance is an essential strategy for industrial organizations, as it improves safety and productivity and reduces costs. As industrial equipment becomes more automated and diagnostic tools become more advanced and affordable, more and more plants are taking a proactive approach to maintenance. The immediate goal is to identify and fix problems before they result in a breakdown, while the long-term goal is to reduce unexpected outages and extend asset life. Plants that implement predictive maintenance processes see a 30% increase in equipment mean time between failures (MTBF), on average. This means your equipment is 30% more reliable and 30% more likely to meet performance standards with a predictive maintenance strategy. (Source: FMX) 2. Challenges of Traditional Reactive Maintenance for Enterprises The waning popularity of reactive maintenance is attributed to several inherent limitations, such as exorbitant costs and a heightened likelihood of equipment failure and safety hazards. At the same time, the pursuit of maintaining industrial plants at maximum efficiency with minimal unplanned downtime is an indispensable objective for all maintenance teams. However, the traditional reactive approach, which involves repairing equipment only when it malfunctions, can result in substantial expenses associated with equipment downtime, product waste, and increased equipment replacement and labor costs. To overcome these challenges, organizations can move towards proactive maintenance strategies, which leverage advanced downtime tracking software to anticipate maintenance needs and forestall potential breakdowns. 3. Emergence of Proactive Strategies for Predictive Maintenance The constraints of reactive maintenance have instigated the emergence of proactive approaches, including predictive analytics. It employs real-time data gathered from equipment to predict maintenance needs and employs algorithms to recognize potential issues before they result in debilitating breakdowns. The data collected through sensors and analytics facilitates the establishment of a more thorough and precise assessment of the general well-being of the operation. With such proactive strategies, organizations can: Arrange maintenance undertakings in advance, Curtail downtime, Cut expenses, and Augment equipment reliability and safety 4. Reactive vs. Proactive Strategies As of 2020, 76% of the respondents in the manufacturing sector reported following a proactive maintenance strategy, while 56% used reactive maintenance (run-to-failure). (Source: Statista) Proactive maintenance strategies, such as predictive maintenance, offer many benefits over reactive maintenance, which can be costly and time-consuming. By collecting baseline data and analyzing trends, proactive maintenance strategies can help organizations perform maintenance only when necessary, based on real-world information. However, establishing a proactive maintenance program can be challenging, as limited maintenance resources must be directed to address the most critical equipment failures. Analyzing data from both healthy and faulty equipment can help organizations determine which failures pose the biggest risk to their operation. A proactive maintenance approach may assist in avoiding the fundamental causes of machine failure, addressing issues before they trigger failure, and extending machine life, making it a crucial strategy for any industrial operation. 5. Industrial Big Data Analytics for Predictive Maintenance: Importance and Applications Big data analytics is a key enabler of predictive maintenance strategies. Its capability to process vast amounts of data provides valuable insights into equipment health and performance, making predictive maintenance possible. With their wide-ranging applications, industrial big data analytics tools can predict maintenance needs, optimize schedules, and detect potential problems before they escalate into significant problems. It can also monitor equipment performance, identify areas for improvement, and refine processes to increase equipment reliability and safety. Industrial big data is indispensable in realizing the shift from reactive to proactive predictive maintenance, which is accomplished through the optimal utilization of available datasets. Industrial big data can glean insights into equipment condition, including patterns of maintenance that may not be readily apparent. Moreover, it has the capacity to attain actionable intelligence capable of effecting a closed loop back to the plant floor. Integration of big data technologies with industrial automation is key to this accomplishment. Nevertheless, this transition will necessitate investment in supplementary assets, such as new maintenance processes and employee training. 6. Navigating Implementation Challenges 6.1 Overcoming Data Collection and Pre-processing Challenges One of the primary challenges in implementing industrial big data analytics for predictive maintenance is the collection and pre-processing of data. The voluminous industrial data, which comes in various formats and from multiple sources, makes it necessary for organizations to develop robust data collection and pre-processing strategies to ensure data accuracy and integrity. To achieve this, organizations need to establish sensor and data collection systems and ensure that the data undergoes appropriate cleaning, formatting, and pre-processing to obtain accurate and meaningful results. 6.2 Addressing Data Integration Challenges Integrating data from heterogeneous sources is a daunting challenge that organizations must overcome when implementing industrial big data analytics for predictive maintenance. It involves processing multiple datasets from different sensors and maintenance detection modalities, such as vibration analysis, oil analysis, thermal imaging, and acoustics. While utilizing data from various sources leads to more stable and accurate predictions, it requires additional investments in sensors and data collection, which is generally very hard to achieve in most maintenance systems. A well-crafted data architecture is critical to managing the copious amounts of data that come from different sources, including various equipment, sensors, and systems. Organizations must devise a comprehensive data integration strategy that incorporates relevant data sources to ensure data integrity and completeness. 6.3 Model Selection and Implementation Solutions Selecting appropriate predictive models and implementing them effectively is another significant challenge. To overcome this, organizations need to have an in-depth understanding of the various models available, their strengths and limitations, and their applicability to specific maintenance tasks. They must also possess the necessary expertise to implement the models and seamlessly integrate them into their existing maintenance workflows to achieve timely and accurate results. Furthermore, it is crucial to align the selected models with the organization's business objectives and ensure their ability to deliver the desired outcomes. 6.4 Staffing and Training Solutions In order to ensure successful implementation, organizations must allocate resources toward staffing and training solutions. This entails hiring proficient data scientists and analysts and then providing them with continual training and professional development opportunities. Moreover, it is imperative to have personnel with the requisite technical expertise to manage and maintain the system. Equally crucial is providing training to employees on the system's usage and equipping them with the necessary skills to interpret and analyze data. 7. Leverage Predictive Maintenance for Optimal Operations Predictive maintenance is widely acknowledged among plant operators as the quintessential maintenance vision due to its manifold advantages, such as higher overall equipment effectiveness (OEE) owing to a reduced frequency of repairs. Furthermore, predictive maintenance data analytics facilitate cost savings by enabling optimal scheduling of repairs and minimizing planned downtimes. It also enhances employees' productivity by providing valuable insights on the appropriate time for component replacement. Additionally, timely monitoring and addressing potential problems can augment workplace safety, which is paramount for ensuring employee well-being. In a survey of 500 plants that implemented a predictive maintenance program, there was an average increase in equipment availability of 30%. Simply implementing predictive maintenance will ensure your equipment is running when you need it to run. (Source: FMX) By synchronizing real-time equipment data with the maintenance management system, organizations can proactively prevent equipment breakdowns. Successful implementation of predictive maintenance data analytic strategies can substantially reduce the time and effort spent on maintaining equipment, as well as the consumption of spare parts and supplies for unplanned maintenance. Consequently, there will be fewer instances of breakdowns and equipment failures, ultimately leading to significant cost savings. On average, predictive maintenance reduced normal operating costs by 50%. (Source: FMX) 8. Final Thoughts Traditional reactive maintenance approaches need to be revised in today's industrial landscape. Proactive strategies, such as predictive maintenance, are necessary to maintain equipment health and performance. Real-time predictive maintenance using big data collected from equipment can help prevent costly downtime, waste, equipment replacement, and labor expenses, thus enhancing safety and productivity. The shift from reactive to proactive maintenance is crucial for organizations, and industrial big data analytics is vital for realizing this transition. Although big data analytics applications for predictive maintenance pose challenges, they can be overcome with the right measures. Ultimately, the effective implementation of big data analytics solutions is a vital enabler of big data predictive maintenance strategies and an essential tool for any industrial plant seeking to optimize its maintenance approach. By embracing predictive maintenance strategies and leveraging the power of industrial big data and analytics, organizations can ensure the longevity and reliability of their equipment, enhancing productivity and profitability.

Read More
BUSINESS STRATEGY

Maximize ROI with Marketing Analytics Technology

Article | May 31, 2022

Discover the latest strategies and best practices for implementing big data and AI into your organization for 2023. Gain insights on leading Big Data and AI solution providers to drive business growth. Contents 1 Establishing a Relationship between Big Data and AI 2 Importance of Big Data and AI in 2023 3 Key Challenges in Implementing Big Data and AI 4 Best Practices and Strategies for Big Data and AI Implementation 4.1 Building a Data Strategy 4.2 Implementing a Data Governance Framework 4.3 Leveraging Cloud Computing 4.4 Developing a Data Science and AI Roadmap 4.5 Leveraging Established Agile Methodologies 4.6 Prototyping Through Sandboxing 5 Top AI and Big Data Companies to Look For in 2023 6 Conclusion 1. Establishing a Relationship between Big Data and AI The relationship between AI and big data is mutually beneficial, as AI requires vast amounts of data to enhance its decision-making abilities, while big data analytics benefits from AI for superior analysis. This union enables the implementation of advanced analytics, such as predictive analysis, resulting in the optimization of business efficiency by anticipating emerging trends, scrutinizing consumer behavior, automating customer segmentation, customizing digital campaigns, and utilizing decision support systems propelled by big data, AI, and predictive analytics. This integration empowers organizations to become data-driven, resulting in significant improvements in business performance. 2. Importance of Big Data and AI in 2023 In the year 2023, it is anticipated that the utilization of big data analytics and artificial intelligence (AI) will profoundly impact diverse industries. The investment in big data analytics will be primarily driven by the need for data compliance, security, and mobilization, ultimately aiming to achieve real-time analysis. Therefore, businesses seeking to excel in this area must be prepared to adopt cloud technology and make significant advancements in computing power and data processing methods. Recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks. (Source: Forbes) The banking, retail, manufacturing, finance, healthcare, and government sectors have already made substantial investments in big data analytics, which have resulted in the forecasting of trends, enhancing business recommendations, and increasing profits. In addition, AI technology will make significant advancements in 2023, including democratization, making it accessible to a broader user population. This shift will enable customers to wield authority, and businesses will be able to use AI to better meet their specific and individualized business requirements. Finally, a significant shift likely to be witnessed in the AI field in 2023 is the move to a more industrialized, embedded type of architecture, where actual business users may begin utilizing algorithms. According to a recent study, 61% of respondents believe that AI will have a significant impact on their industry within the next three to five years. (Source: Deloitte Insights Report) 3. Key Challenges in Implementing Big Data and AI 97.2% of business executives say their organizations are investing in big data and AI projects. These executives cite their desire to become “nimble, data-driven businesses” as the reason for these investments, as 54.4% say that their companies’ inability to do this was the biggest threat they faced. In addition, 79.4% say they’re afraid that other, more data-driven companies will disrupt and outperform them. (Source: Zippia) Implementing big data analytics and artificial intelligence (AI) presents various challenges that businesses must tackle to realize their full potential. One such obstacle is the intricate nature of the data, which could be either structured or unstructured and necessitate specialized tools and techniques for processing and analysis. Moreover, companies must ensure data quality, completeness, and integrity to facilitate accurate analysis and decision-making. Another substantial challenge in implementing big data and AI is the requirement for skilled personnel with expertise in data science, machine learning, and related technologies. To stay up-to-date on the latest tools and techniques, companies must invest in ongoing training and development programs for their employees. Ethical and legal concerns surrounding data privacy, security, and transparency must also be addressed, especially after recent data breaches and privacy scandals. Integrating big data and AI into existing IT systems can be a challenging and time-consuming process that necessitates careful planning and coordination to ensure smooth integration and minimize disruption. Lastly, the high cost of implementing these technologies can be a significant barrier, especially for smaller businesses or those with limited IT budgets. To overcome these challenges, companies must be strategic, prioritize use cases, and develop a clear implementation roadmap while leveraging third-party tools and services to minimize costs and maximize ROI. 4. Best Practices and Strategies for Big Data and AI Implementation 24% of companies use big data analytics. While 97.2% of companies say they’re investing in big data and AI projects, just 24% describe their organizations as data-driven. (Source: Zippia) 4.1 Building a Data Strategy One of the biggest challenges in building a data strategy is identifying the most relevant data sources and data types for the organization’s specific business objectives. The sheer volume and diversity of data available can further complicate this. The key to addressing this challenge is thoroughly assessing the organization’s data assets and prioritizing them based on their business value. This involves: Identifying the key business objectives and Determining which data sources and data types are most relevant to achieving those objectives 4.2 Implementing a Data Governance Framework Establishing a data governance framework involving all stakeholders is crucial for ensuring agreement on data quality, privacy, and security standards. However, implementing such a framework can be daunting due to the divergent priorities and perspectives of stakeholders on good data governance. So, to overcome this challenge, clear guidelines and processes must be established: Creating a data governance council Defining roles and responsibilities Involving all stakeholders in the development and implementation of guidelines Data quality management, privacy, and security processes should be established to maintain high data governance standards Organizations can improve the effectiveness of their data governance initiatives by aligning all stakeholders and ensuring their commitment to maintaining optimal data governance standards. 4.3 Leveraging Cloud Computing It is essential to carefully select a cloud provider that aligns with the organization's security and compliance requirements. In addition, robust data security and compliance controls should be implemented: Establishing data encryption and access controls Implementing data backup and recovery procedures Regularly conducting security and compliance audits By following these practices, organizations can ensure their big data and AI projects are secure and compliant. 4.4 Developing a Data Science and AI Roadmap The obstacles to developing a data science and AI roadmap lie in identifying the most pertinent use cases that cater to the specific business objectives of an organization. This difficulty is further compounded by the potential divergence of priorities and perspectives among various stakeholders concerning the definition of a successful use case. Hence, it is imperative to establish unambiguous guidelines for identifying and prioritizing use cases that align with their respective business values. This entails: Identifying the key business objectives Carefully ascertaining which use cases are most pertinent to realizing those objectives Meticulously delineating the success criteria for each use case 4.5 Leveraging Established Agile Methodologies Leveraging well-established agile methodologies is critical in successfully implementing large-scale big data and AI projects. By defining a precise project scope and goals, prioritizing tasks, and fostering consistent communication and collaboration, enterprises can effectively execute AI and big data analytics initiatives leveraging agile methodologies Such an approach provides teams with a clear understanding of their responsibilities, facilitates seamless communication, and promotes continuous improvement throughout the project lifecycle, resulting in a more efficient and effective implementation 4.6 Prototyping Through Sandboxing Establishing clear guidelines and processes is crucial to overcome the challenge of creating prototypes through sandboxing that are representative of the production environment and can meet the organization's requirements. It includes: Defining the scope and objectives of the prototype, Meticulously selecting the appropriate tools and technologies Guaranteeing that the prototype is an authentic reflection of the production environment Additionally, conducting thorough testing and evaluation is necessary to ensure that the prototype can be scaled effectively to meet the organization's needs. 5. Top AI and Big Data Companies to Look For in 2023 H2O.ai H2O.ai is a leading provider of artificial intelligence (AI) and machine learning (ML) software. It provides a platform for businesses to use artificial intelligence and data-driven insights to drive innovation and growth. The software offers a suite of tools and algorithms to help users build predictive models, analyze data, and gain insights that inform business decisions. With a user-friendly interface and a robust set of features, H2O.ai is a valuable tool for businesses looking to leverage the power of machine learning to stay ahead of the competition. ThoughtSpot ThoughtSpot is a leading search and AI-driven analytics platform that enables businesses to quickly and easily analyze complex data sets. The platform offers a range of features, including advanced analytics, customizable visualizations, and collaborative capabilities. It is designed to make data analytics accessible to anyone within an organization, regardless of technical expertise. The platform is also highly customizable, allowing businesses to tailor it to meet their specific needs and integrate it with their existing data infrastructure. Treasure Data Treasure Data is a cloud-based enterprise data management platform that helps businesses collect, store, and analyze their data to gain valuable insights. Its platform includes a suite of powerful tools for data collection, storage, processing, and analysis, including a flexible data pipeline, a powerful data management console, and a range of analytics tools. The platform is also highly scalable, capable of handling massive amounts of data and processing millions of events per second, making it suitable for businesses of all sizes and industries. Denodo Denodo is a leading data virtualization software company that provides a unified platform for integrating and delivering data across multiple sources and formats in real time. The platform offers unmatched performance and unified access to a broad range of enterprise, big data, cloud, and unstructured sources. It also provides agile data service provisioning and governance at less than half the cost of traditional data integration. In addition, its data virtualization technology simplifies the complexity of data sources and creates a virtual layer of data services accessible to any application or user, regardless of the data’s location or format. Pendo.io Pendo.io is a leading cloud-based platform that provides product analytics, user feedback, and guidance for digital products. It allows businesses to make data-driven decisions about their products and optimize their customer journey. The platform empowers companies to transform product intelligence into actionable insights rapidly and at scale, enabling a new generation of businesses that prioritize product development. TigerGraph TigerGraph is a graph database and analytics platform that allows businesses to gain deeper insights and make better decisions by analyzing connected data. It is designed to handle complex data sets and perform advanced graph analytics at scale. The platform offers a range of graph analytics algorithms that can be applied to a variety of use cases, including fraud detection, recommendation engines, supply chain optimization, and social network analysis. Solix Technologies, Inc. Solix Technologies, Inc. is a leading big data management and analysis software solution provider that empowers data-driven enterprises to achieve their Information Lifecycle Management (ILM) goals. Its flagship product, Solix Big Data Suite, provides an ILM framework for Enterprise Archiving and Enterprise Data Lake applications utilizing Apache Hadoop as an enterprise data repository. In addition, the Solix Enterprise Data Management Suite (Solix EDMS) helps organizations implement database archiving, test data management, data masking and application retirement across all enterprise data. Reltio Reltio is a leading provider of cloud-based master data management (MDM) solutions that enable organizations to create a unified view of their data across all sources and formats. The platform combines MDM with big data analytics and machine learning to provide a single source of truth for data-driven decision-making. The solution offers a range of features, including data modeling, data quality management, data governance, and data analytics. dbt Labs dbt Labs is a cloud-based data transformation software platform that helps analysts and engineers manage the entire analytics engineering workflow, from data ingestion to analysis. The platform enables users to transform and model raw data into analysis-ready data sets using a SQL-based language. With its modular and scalable approach, dbt Labs makes it easier for data teams to collaborate and manage their data pipelines. Rockset Rockset is a real-time indexing database platform that allows businesses to run fast queries on data from multiple sources without needing to manage the underlying infrastructure. It supports various data types, including structured, semi-structured, and nested data, making it flexible and versatile. In addition, the serverless platform is built on a cloud-native architecture, making it easy to scale up or down as needed. With Rockset, users can build real-time applications and dashboards, perform ad hoc analysis, and create data-driven workflows. 6. Conclusion The relationship between big data and AI is mutually beneficial, given the fact that AI requires copious amounts of data to refine its decision-making capabilities, while big data analytics derives immense value from AI for advanced analysis. As a result, the integration of big data analytics and AI is projected to profoundly impact diverse industries in 2023. Nevertheless, adopting these technologies poses multifarious challenges, necessitating businesses to adopt a strategic approach and develop a comprehensive implementation roadmap to optimize ROI and minimize expenses. Ultimately, the successful implementation of big data and AI strategies can enable organizations to become data-driven, culminating in substantial improvements in business performance.

Read More
BUSINESS STRATEGY

Importance of Encryption in the Business World

Article | April 4, 2022

Explore the impact of big data on the healthcare industry and how it is being used to improve patient outcomes. Discover how big data is being leveraged to enhance overall healthcare delivery. Contents 1. Introduction 1.1 Role of Big Data in Healthcare 1.2 The Importance of Patient Outcomes 2. How Big Data Improves Patient Outcomes 2.1 Personalized Medicine and Treatment Plans 2.2 Early Disease Detection and Prevention 2.3 Improved Patient Safety and Reduced Medical Errors 3. Challenges and Considerations While Using Big Data in Healthcare 4. Final thoughts 1. Introduction In today's constantly evolving healthcare industry, the significance of big data cannot be overstated. Its multifaceted nature makes it a valuable asset to healthcare providers in their efforts to enhance patient outcomes and reduce business costs. When harnessed effectively, big data in healthcare provides companies with the insights they need to personalize healthcare, streamline customer service processes, and improve their practices for interacting with patients. This results in a more tailored and thorough experience for customers, ultimately leading to better care. 1.1 Role of Big Data in Healthcare Big data pertains to vast collections of structured and unstructured data in the healthcare industry. One of the primary sources of big data in healthcare is electronic health records (EHRs), which contain: Patient’s medical history Demographics Medications Test results Analyzing this data can: Facilitate informed decision-making Improve patient outcomes Reduce healthcare costs Integrating structured and unstructured data can add significant value to healthcare organizations, and Big Data Analytics (BDA) is the tool used to extract information from big data. Big Data Analytics (BDA) can extract information and create trends, and in healthcare, it can identify clusters, correlations, and predictive models from large datasets. However, privacy and security concerns and ensuring data accuracy and reliability are significant challenges that must be addressed. 1.2 The Importance of Patient Outcomes Patient outcomes are the consequences of healthcare interventions or treatments on a patient's health status and are essential in evaluating healthcare systems and guiding healthcare decision-making. However, the current healthcare system's focus on volume rather than value has led to fragmented payment and delivery systems that fall short in terms of quality, outcomes, costs, and equity. To overcome these shortcomings, a learning healthcare system is necessary to continuously apply knowledge for improved patient outcomes and affordability. However, access to timely guidance is limited, and organizational and technological limitations pose significant challenges in measuring patient-centered outcomes. 2. How Big Data Improves Patient Outcomes Big data in healthcare engenders a substantial impact by facilitating the delivery of treatment that is both efficient and effective. This innovative approach to healthcare enables the identification of high-risk patients, prediction of disease outbreaks, management of hospital performance, and improvement of treatment effectiveness. Thanks to modern technology, the collection of electronic data is now a seamless process, thus empowering healthcare professionals to create data-driven solutions to improve patient outcomes. 2.1 Personalized Medicine and Treatment Plans Big data can revolutionize personalized medicine and treatment plans by analyzing vast patient data to create tailored treatment plans for each patient, resulting in better outcomes, fewer side effects, and faster recovery times. 2.2 Early Disease Detection and Prevention Big data analytics in healthcare allow for early interventions and treatments by identifying patterns and trends that indicate disease onset. This improves patient outcomes and reduces healthcare costs. Real-time patient data monitoring and predictive analytics enable timely action to prevent complications. 2.3 Improved Patient Safety and Reduced Medical Errors Big data analytics can help healthcare providers identify safety risks like medication errors, misdiagnoses, and adverse reactions, improving patient safety and reducing medical errors. This can lead to cost savings and better patient outcomes. 3. Challenges and Considerations While Using Big Data in Healthcare In order to maximize the potential advantages, organizations must address significant challenges of big data in healthcare, like privacy and security concerns, data accuracy and reliability, and expertise and technology requirements. Safeguards like encryption, access controls, and data de-identification can mitigate privacy and security risks Ensuring data accuracy and reliability requires standardized data collection, cleaning, and validation procedures Additionally, healthcare organizations must prioritize the recruitment of qualified professionals with expertise in data management, and analysis is crucial The adoption of advanced technologies such as artificial intelligence and machine learning can support effective analysis and interpretation of big data in healthcare 4. Final Thoughts The impact of big data on healthcare is profound, and the healthcare sector possesses the possibility of a paradigm shift by leveraging the potential of big data to augment patient outcomes and curtail costs. Nevertheless, implementing big data entails formidable challenges that necessitate their resolution to fully unleash healthcare data technology's benefits. Notably, handling voluminous and heterogeneous datasets in real time requires state-of-the-art technological solutions. To attain the maximal benefits of big data in healthcare, organizations must proactively address these challenges by implementing risk-mitigating measures and fully capitalizing on big data's potential.

Read More
BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Navigating Big Data Integration: Challenges and Strategies

Article | April 13, 2023

Explore the complexities of integrating Big Data into your organization. Learn effective strategies for overcoming challenges to optimize your data integration process and maximize business outcomes. Contents 1 Introduction 2 Challenges in Big Data Integration 2.1 Data Volume, Velocity, and Variety Challenges 2.2 Integration with Legacy Systems and Data Silos 2.3 Technical Challenges 2.4 Organizational Challenges 3 Overcoming Integration Challenges: Strategies 3.1 Conducting Thorough Analysis of Data Infrastructure 3.2 Prioritizing Projects Based On Business Needs 3.3 Implementing Scalable and Flexible Solutions 3.4 Establishing Robust Data Governance Practices 4 Conclusion 1. Introduction Big data integration is a critical component of effective data management for organizations of all sizes. While some CIOs may believe that consolidating legacy data sources into a single platform can solve integration challenges, the reality is often more complex. Data is vast and usually spread across multiple sources, making integration a daunting task. Nearly 25% of businesses struggle with integrating new applications with their old systems. That’s because legacy system integration isn’t always easy to achieve. (Source: Gartner) Thus, to tackle big data integration effectively, it's essential to understand how it fits into the organization's overall data management strategy and determine the policies governing the integration process. In addition, there are several technical challenges involved in data integration, including ensuring all components work well together, reflecting trends in big data analytics, and finding skilled big data engineers and analysts. 2. Challenges in Big Data Integration 2.1 Data Volume, Velocity, and Variety Challenges In order to effectively integrate big data, companies must address the three key components of volume, variety, and velocity. Coordinating and managing massive amounts of data is both logistically challenging and costly, especially with large volumes. Working with multiple data sources is also a major hurdle that necessitates advanced analytics resources and expertise. Large datasets can take weeks to process, making real-time data analytics an arduous task. This becomes particularly challenging when dealing with intricate and extensive datasets, where velocity poses a significant obstacle. Attempting to apply a uniform analytical process to all data sets may be impractical, further impeding progress. 2.2 Integration with Legacy Systems and Data Silos According to a report, 25% of organizations have more than 50 unique data silos, and these prevent companies from harnessing their data for their business. (Source: 451 Research) The integration of legacy systems presents a significant challenge for companies, as it entails various difficulties, such as high maintenance costs, data silos, compliance issues, weaker data security, and a lack of integration with new systems. The maintenance of legacy systems is both expensive and futile, leaving a company with outdated technology and a tarnished reputation due to potential breaches. Furthermore, legacy systems may fail to meet evolving compliance regulations such as GDPR and lack appropriate data security measures. Over time, data silos can develop due to organizational structures and company culture, leading to difficulties in achieving effective data integration. Siloed data obstructs departments from accessing the full benefits of new systems, impeding technological growth within a company. Additionally, legacy systems may not be compatible with new systems, causing further communication issues. 2.3 Technical Challenges Selecting the Right Big Data Integration Tools Choosing the right tools, technologies and big data integration services is crucial to meet specific business needs. It can be challenging to keep up with the constantly evolving technology landscape, making it important to stay up-to-date with the latest trends and innovations. The decision-making process should involve a thorough evaluation of existing tools and technologies to determine their effectiveness and relevance to the integration process. Failure to choose the appropriate tools and technologies can lead to inefficiencies, longer processing times, and increased costs. Ensuring Different Systems and Data Formats Compatibility It is estimated that around 85% of big data projects will fail to meet all their objectives, illustrating the scale of the challenge that businesses face when trying to get a handle on complex and disparate data from across the enterprise. (Source: Gartner) In integrating big data, it is common to have different systems and data formats that need to be integrated. Ensuring compatibility between these different systems and data formats can be a challenge. A solution-based approach to this challenge is to use data integration platforms that provide support for a wide range of data formats and systems. This ensures that the integration process is seamless and efficient. Addressing Issues of Data Quality and Completeness To integrate big data successfully, it's essential to address issues related to data quality and completeness. Only accurate or complete data can lead to correct insights and precise decision-making, which can benefit businesses. Developing comprehensive data quality management strategies that include data profiling, cleansing, and validation is necessary to overcome this challenge. These strategies ensure that the data being integrated is accurate and complete, leading to better actionable insights and business intelligence. 2.4 Organizational Challenges Developing Comprehensive Integration Strategy Developing a clear and comprehensive integration strategy for big data can be challenging, but it is essential for success. The developed strategy should clearly outline the business objectives and the scope of the integration effort as well as identify the key stakeholders involved. Additionally, it should define the technical requirements and resources necessary to support the integration effort. Building Cross-Functional Teams to Support Integration Efforts Building cross-functional teams for successful data integration can be challenging due to identifying the right individuals with diverse skill sets and navigating complex technical environments. However, it is crucial to form teams comprising members from various departments, including IT, data science, and administration, who collaborate to identify business needs, devise an integration strategy, and implement integration solutions. Building such teams promotes effective communication and coordination across departments and stakeholders, enabling organizations to leverage data assets effectively. 3. Overcoming Integration Challenges: Strategies 3.1 Conducting Thorough Analysis of Data Infrastructure Conducting a thorough analysis of existing data infrastructure and systems is the first step in any data integration effort. This analysis should identify the strengths and weaknesses of the existing infrastructure and systems. This information can be used to develop a comprehensive integration strategy that addresses existing challenges and identifies opportunities for improvement. 3.2 Prioritizing Projects Based On Business Needs It is crucial to prioritize, and sequence integration projects based on business needs to leverage the benefits of data integration. This approach ensures that resources are allocated appropriately and the most critical projects are addressed first. Conducting a thorough cost-benefit analysis is an effective way to determine the value and impact of each project to prioritize and plan accordingly. 3.3 Implementing Scalable and Flexible Solutions In orderto accommodate the ever-increasing amount of data and evolving business requirements, it is essential to implement scalable and flexible integration solutions. This approach ensures that the integration process remains efficient and can adapt to changing needs. Modern data integration platforms that support cloud-based solutions, real-time data processing, and flexible data models can be adopted to achieve this. 3.4 Establishing Robust Data Governance Practices Establishing robust data governance practices ensures data is managed effectively throughout the integration process. This involves defining clear policies, procedures, and standards for data management across the entire data lifecycle, from acquisition to disposition. Additionally, data quality and security controls should be implemented, and employees must be trained on data governance best practices. Organizations can effectively manage data by establishing these practices throughout the integration process. It includes defining data ownership, establishing policies, and implementing quality controls. Ultimately, this approach ensures that data is accurate, complete, and reliable and that the organization is compliant with any relevant regulations or standards. 4. Conclusion Integrating big data represents a formidable obstacle for many organizations, yet with the proper strategies in place, these challenges can be surmounted, enabling businesses to unleash the full potential of their data assets. It is paramount that organizations possess a comprehensive and lucid understanding of both the technical and organizational challenges inherent in integrating big data. Businesses must prioritize data integration and processing initiatives based on their commercial requirements, employ scalable and flexible solutions, and establish robust data governance practices. By doing so, they can acquire invaluable insights that drive business growth and innovation, improve operational efficiency, and enhance their competitiveness in the market.

Read More
MORE ARTICLES

Spotlight

Exabeam

Exabeam is the Smarter SIEM company. We empower enterprises to detect, investigate and respond to cyber attacks more efficiently so their security operations and insider threat teams can work smarter. Security organizations no longer have to live with excessive logging fees, missed distributed attacks and unknown threats, or manual investigations and remediation. With the Exabeam Security Management Platform, analysts can collect unlimited log data, use behavioral analytics to detect attacks, and automate incident response, both on-premises or in the cloud. Exabeam Smart Timelines, sequences of user and device behavior created using machine learning, further reduce the time and specialization required to detect attacker tactics, techniques and procedures

Related News

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, BUSINESS STRATEGY

Latest Release of the Ocient Hyperscale Data Warehouse Simplifies Data Pipelines and Accelerates Query Performance for Growing Customer Base

Businesswire | May 26, 2023

Ocient, the leading hyperscale data analytics solutions company, today announced the version 22 release of its flagship product, the Ocient Hyperscale Data Warehouse (OHDW). This latest release builds upon Ocient’s hyperscale data integration and data movement capabilities while adding new real-time analytics features, query performance enhancements, and support for additional business intelligence (BI) tools. Ocient’s ability to continuously stream, load, and transform large scale datasets and execute machine learning directly within the database enables customers to execute complex, continuous data integration, transformation, and exploration alongside real-time analytics and OLAP-style workloads from a single solution stack. Native support for ETL and ELT workloads – Version 22 of the OHDW delivers improved performance for loading, streaming, and extract, load, transform (ELT) workloads. Organizations challenged with continuous data movement and transformation at scale can streamline complicated loading processes for complex data types and eliminate the need for standalone tools like Spark and Informatica that add latency and overhead to hyperscale workloads. Once data is transformed into a relational format and loaded or streamed, Ocient’s ELT capabilities support massive joins, groupings, and aggregations that enable customers to optimize query performance, activate transformed datasets, and prepare variations of source data for data science and operational data stores without needing to move or copy data. New real-time analytics features – Version 22 marks the general availability of Hyperloglog (HLL) sketches for Ocient’s suite of real-time analytics capabilities so customers can create rollups of data using approximations on aggregated metrics and accelerate query processing without sacrificing performance. Ocient’s ability to execute compute-intensive log-level aggregation, high concurrency queries, and always-on data streaming for real-time analytics alongside complex, compute-intensive OLAP-style workloads enables customers to streamline operations and consolidate a variety of mixed analytical workloads onto a single platform. Query performance enhancements – To enable customers to enrich data at scale while lowering latency and reducing costs, Ocient version 22 delivers query performance enhancements via I/O pushdown and join optimizations on hyperscale tables with hundreds of billions of rows. When combined with Ocient’s support for semi-structured and multidimensional data types, these enhancements enable Ocient to accelerate query plans on highly complex data sets while tightly controlling costs. Support for integrations with Metabase and Superset – Ocient version 22 offers new integrations with Metabase and Superset for easy data visualization and integration into existing customer environments. Both integrations leverage Ocient’s geospatial analytics and in-database machine learning (ML) so customers can quickly visualize and act on ML model results without the need for additional ELT processes or requirements. “In today’s economic environment, enterprises looking to scale their data and analytics requirements must pursue innovation while bringing greater efficiencies and cost savings to their business,” said Chris Gladwin, CEO at Ocient. “With version 22, Ocient is enabling customers with the capabilities they need to increase price performance, consolidate multiple resource-intensive tools into a single solution, and unlock new opportunities for product development and real-time decision intelligence to help grow their business.” From piloting new solutions to full production deployments, Ocient helps customers simplify complex data pipelines while facilitating the time-intensive task of engineering new end-to-end solutions with comprehensive migration support and services. When consolidating multiple databases and tools into a single solution, Ocient’s Customer Solutions team ensures customers move from pilot to production in weeks to months, with ROI delivered in a year or less. Ocient provides flexibility to deploy on premises and on cloud with Google or AWS, or as a fully managed solution in OcientCloud. To learn more about the Ocient Hyperscale Data Warehouse, click here. About Ocient Ocient is the leading hyperscale data analytics solutions company that enables organizations to unlock value by analyzing trillions of data records at performance levels and costs previously unattainable. Leading organizations around the world trust Ocient’s team of industry experts to design and deploy proven complex solutions that enable and fast-track new revenue opportunities and streamline operations. Ocient’s pilot-to-production solutions are rapidly deployed on premises, in the Ocient Cloud or in the public cloud, with little to no resource-intensive integration. Ocient is a carbon-neutral company, headquartered in Chicago, and backed by leading investors including Greycroft, OCA Ventures and In-Q-Tel. For more information, please visit www.ocient.com.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, BUSINESS STRATEGY

TIBCO Intelligently Unifies Data with Key Enhancements to Cohesive Data Integration Offerings

Businesswire | June 06, 2023

TIBCO, a global leader in enterprise connectivity, empowers the world’s most innovative companies to solve their complex data challenges and make mission-critical business decisions with greater confidence and impact. Today, TIBCO announced enhancements to its data integration capabilities, assisting customers in unifying data intelligently and delivering on requirements for better access, trust, and control. These advancements provide a highly cohesive suite of data management solutions to enable a data integration architecture that connects disparate data sources, breaks down silos, and improves overall data quality. "While we can't underestimate the power of data, we also can't overlook the complexities some companies face in accessing quality data," said Ali Ahmed, general manager, TIBCO, a business unit of Cloud Software Group. "If organizations are to meet this challenge head-on, they must embrace a data integration framework, which enables them to unify data intelligently and better position themselves as a data-driven organization to make faster, smarter decisions." TIBCO's data integration framework delivers data to everyone, supporting cloud-native application development using diverse data, and automating data access. These new enhancements are central to creating balanced, smart, automated data capabilities for the citizen data engineer in a low-code, no-code environment. Updates expand on comprehensive data management capabilities, capitalize on advancements in machine learning operations, and deliver industry-tailored solutions to enable faster adoption. This builds on recent updates to TIBCO EBX®, a leading Master Data Management (MDM) platform. With our most recent launch, EBX® 6.1, enhancements continue to lead the MDM landscape trends with new social and collaborative features, extended hierarchies capabilities, new modes to display data, and leading business objects. EBX begins the year with nine leadership rankings, including being recognized as a leader by five analyst firms in the 2022 and 2023 report years. TIBCO® Data Virtualization, another critical component of TIBCO’s data integration framework, now features improved smart mapping to bring more confident and powerful visual insight to valuable data. This unifies all scattered data into a modern data layer to keep up with changing business needs while removing complexities and risks. With TIBCO Data Virtualization, widely spread data is now readily connected in a faster and more accessible manner. The online user interface now includes a redesigned homepage and search features for data cataloging, increased access and security functions, and expanded industry-standard authentication methods for delivering secure, delegated access and device authorization. As a result, users may rapidly and cost-effectively integrate data while generating and maintaining virtualized views and data services. With the TIBCO Data Virtualization application's modular framework, Java enterprise-grade middleware covers all data virtualization development, run time, and administration phases. Data is now genuinely impactful, providing value through insights to enhance revenue, reduce expenses, and improve risk and compliance. As a result of the innovative functionality noted, TIBCO Data Virtualization recently took first place in the 2022 GigaOm Radar Enterprise Data Virtualization Report, the 2022 Top Rated award on TrustRadius, G2's 2023 Leader in the Data Visualization category, along with receiving numerous peer review awards and badges in customer feedback. TIBCO’s data integration architecture is built with TIBCO Cloud™ Integration, a solution that was recently updated with new features to accelerate complex data mapping and transformation. The enhanced smart mapping capabilities are powered by AI to provide automated recommendations based on years of training by user inputs. TIBCO Cloud Integration continues to be recognized for strong data integration capabilities and our broad portfolio of connectors to any data source by analysts, including a recent leadership ranking in the 2023 Gartner® Magic Quadrant™ for iPaaS. Follow @TIBCO on Twitter and on our Facebook, Instagram, and LinkedIn pages to hear the latest TIBCO news and updates. About TIBCO TIBCO, a business unit of Cloud Software Group, unlocks the potential of real-time data for making faster, smarter decisions. Our Connected Intelligence Platform seamlessly connects any application or data source; intelligently unifies data for greater access, trust, and control; and confidently predicts outcomes in real time and at scale. Learn how solutions to our customers' most critical business challenges are made possible by TIBCO at www.tibco.com. TIBCO, TIBCO Cloud, EBX, and the TIBCO logo are trademarks or registered trademarks of Cloud Software Group, Inc. and/or its subsidiaries in the United States and/or other countries. All other product and company names and marks mentioned in this document are the property of their respective owners and are mentioned for identification.

Read More

BIG DATA MANAGEMENT, DATA SCIENCE, BIG DATA

Immuta Brings its Data Security Platform to Snowflake’s Government & Education Data Cloud

Businesswire | June 08, 2023

Immuta, a leader in data security, today announced the launch of its data security platform as a pre-built integration for the Government & Education Data Cloud, launched by Snowflake, the Data Cloud company. The launch of the Government & Education Data Cloud unites Snowflake’s data platform, Snowflake and partner-delivered solutions, and industry-specific datasets to empower educational institutions and public sector agencies at the federal, state, and local levels to spend more time on what matters most: caring for citizens and students. The Snowflake Government & Education Data Cloud makes it possible for government and education institutions to address industry challenges by enabling intra- and cross-agency data sharing and collaboration, establishing 360° views of their stakeholders, monitoring for fraud, waste, and abuse, and leveraging an industry-leading network of public sector partners. “We are looking forward to working with Immuta to improve citizen and student outcomes by giving customers fast, safe, and trusted access to critical education and public sector data,” said Jeff Frazier, Global Head of Public Sector at Snowflake. “Our continued partnership with Immuta will help organizations leverage the data they need to drive meaningful change in their communities.” Within the Government & Education Data Cloud, customers can access industry-specific solutions built by Snowflake’s ecosystem of public sector and education partners to leverage best practices, reduce time-to-value, and increase overall impact. The Immuta Data Security Platform enables joint customers to protect their data on Snowflake’s Government & Education Data Cloud by providing them with automated data discovery and classification, scalable data access controls, dynamic data masking, auditing and reporting, and risk detection indicators. With Immuta and Snowflake, government agencies and educational institutions can securely share data with third parties to effectively collaborate and fully derive value from data in their possession to improve citizen and student outcomes, all while meeting security and compliance standards. “We’re excited to be a launch partner for Snowflake’s Government & Education Data Cloud and have the opportunity to provide customers access to industry-specific solutions that will help them leverage best practices, reduce time-to-value, and increase overall impact,” said Chris Brown, Public Sector CTO, Immuta. “The partnership between Immuta and Snowflake will enable both government and education organizations to derive value and insights from their data, all while protecting it to better serve citizens and students.” Learn more about Snowflake’s Government & Education Data Cloud launch here and partner-tailored solutions for the industry here. Check out our blog to see how Immuta’s integration with the Snowflake Government & Education Data Cloud helps accelerate zero trust while unlocking data’s value. About Immuta Immuta enables organizations to unlock value from their cloud data by protecting it and providing secure access. The Immuta Data Security Platform provides sensitive data discovery, security and access control, data activity monitoring, and has deep integrations with the leading cloud data platforms. Immuta is now trusted by Fortune 500 companies and government agencies around the world to secure their data. Founded in 2015, Immuta is headquartered in Boston, MA. To learn more about Immuta, click here.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, BUSINESS STRATEGY

Latest Release of the Ocient Hyperscale Data Warehouse Simplifies Data Pipelines and Accelerates Query Performance for Growing Customer Base

Businesswire | May 26, 2023

Ocient, the leading hyperscale data analytics solutions company, today announced the version 22 release of its flagship product, the Ocient Hyperscale Data Warehouse (OHDW). This latest release builds upon Ocient’s hyperscale data integration and data movement capabilities while adding new real-time analytics features, query performance enhancements, and support for additional business intelligence (BI) tools. Ocient’s ability to continuously stream, load, and transform large scale datasets and execute machine learning directly within the database enables customers to execute complex, continuous data integration, transformation, and exploration alongside real-time analytics and OLAP-style workloads from a single solution stack. Native support for ETL and ELT workloads – Version 22 of the OHDW delivers improved performance for loading, streaming, and extract, load, transform (ELT) workloads. Organizations challenged with continuous data movement and transformation at scale can streamline complicated loading processes for complex data types and eliminate the need for standalone tools like Spark and Informatica that add latency and overhead to hyperscale workloads. Once data is transformed into a relational format and loaded or streamed, Ocient’s ELT capabilities support massive joins, groupings, and aggregations that enable customers to optimize query performance, activate transformed datasets, and prepare variations of source data for data science and operational data stores without needing to move or copy data. New real-time analytics features – Version 22 marks the general availability of Hyperloglog (HLL) sketches for Ocient’s suite of real-time analytics capabilities so customers can create rollups of data using approximations on aggregated metrics and accelerate query processing without sacrificing performance. Ocient’s ability to execute compute-intensive log-level aggregation, high concurrency queries, and always-on data streaming for real-time analytics alongside complex, compute-intensive OLAP-style workloads enables customers to streamline operations and consolidate a variety of mixed analytical workloads onto a single platform. Query performance enhancements – To enable customers to enrich data at scale while lowering latency and reducing costs, Ocient version 22 delivers query performance enhancements via I/O pushdown and join optimizations on hyperscale tables with hundreds of billions of rows. When combined with Ocient’s support for semi-structured and multidimensional data types, these enhancements enable Ocient to accelerate query plans on highly complex data sets while tightly controlling costs. Support for integrations with Metabase and Superset – Ocient version 22 offers new integrations with Metabase and Superset for easy data visualization and integration into existing customer environments. Both integrations leverage Ocient’s geospatial analytics and in-database machine learning (ML) so customers can quickly visualize and act on ML model results without the need for additional ELT processes or requirements. “In today’s economic environment, enterprises looking to scale their data and analytics requirements must pursue innovation while bringing greater efficiencies and cost savings to their business,” said Chris Gladwin, CEO at Ocient. “With version 22, Ocient is enabling customers with the capabilities they need to increase price performance, consolidate multiple resource-intensive tools into a single solution, and unlock new opportunities for product development and real-time decision intelligence to help grow their business.” From piloting new solutions to full production deployments, Ocient helps customers simplify complex data pipelines while facilitating the time-intensive task of engineering new end-to-end solutions with comprehensive migration support and services. When consolidating multiple databases and tools into a single solution, Ocient’s Customer Solutions team ensures customers move from pilot to production in weeks to months, with ROI delivered in a year or less. Ocient provides flexibility to deploy on premises and on cloud with Google or AWS, or as a fully managed solution in OcientCloud. To learn more about the Ocient Hyperscale Data Warehouse, click here. About Ocient Ocient is the leading hyperscale data analytics solutions company that enables organizations to unlock value by analyzing trillions of data records at performance levels and costs previously unattainable. Leading organizations around the world trust Ocient’s team of industry experts to design and deploy proven complex solutions that enable and fast-track new revenue opportunities and streamline operations. Ocient’s pilot-to-production solutions are rapidly deployed on premises, in the Ocient Cloud or in the public cloud, with little to no resource-intensive integration. Ocient is a carbon-neutral company, headquartered in Chicago, and backed by leading investors including Greycroft, OCA Ventures and In-Q-Tel. For more information, please visit www.ocient.com.

Read More

BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, BUSINESS STRATEGY

TIBCO Intelligently Unifies Data with Key Enhancements to Cohesive Data Integration Offerings

Businesswire | June 06, 2023

TIBCO, a global leader in enterprise connectivity, empowers the world’s most innovative companies to solve their complex data challenges and make mission-critical business decisions with greater confidence and impact. Today, TIBCO announced enhancements to its data integration capabilities, assisting customers in unifying data intelligently and delivering on requirements for better access, trust, and control. These advancements provide a highly cohesive suite of data management solutions to enable a data integration architecture that connects disparate data sources, breaks down silos, and improves overall data quality. "While we can't underestimate the power of data, we also can't overlook the complexities some companies face in accessing quality data," said Ali Ahmed, general manager, TIBCO, a business unit of Cloud Software Group. "If organizations are to meet this challenge head-on, they must embrace a data integration framework, which enables them to unify data intelligently and better position themselves as a data-driven organization to make faster, smarter decisions." TIBCO's data integration framework delivers data to everyone, supporting cloud-native application development using diverse data, and automating data access. These new enhancements are central to creating balanced, smart, automated data capabilities for the citizen data engineer in a low-code, no-code environment. Updates expand on comprehensive data management capabilities, capitalize on advancements in machine learning operations, and deliver industry-tailored solutions to enable faster adoption. This builds on recent updates to TIBCO EBX®, a leading Master Data Management (MDM) platform. With our most recent launch, EBX® 6.1, enhancements continue to lead the MDM landscape trends with new social and collaborative features, extended hierarchies capabilities, new modes to display data, and leading business objects. EBX begins the year with nine leadership rankings, including being recognized as a leader by five analyst firms in the 2022 and 2023 report years. TIBCO® Data Virtualization, another critical component of TIBCO’s data integration framework, now features improved smart mapping to bring more confident and powerful visual insight to valuable data. This unifies all scattered data into a modern data layer to keep up with changing business needs while removing complexities and risks. With TIBCO Data Virtualization, widely spread data is now readily connected in a faster and more accessible manner. The online user interface now includes a redesigned homepage and search features for data cataloging, increased access and security functions, and expanded industry-standard authentication methods for delivering secure, delegated access and device authorization. As a result, users may rapidly and cost-effectively integrate data while generating and maintaining virtualized views and data services. With the TIBCO Data Virtualization application's modular framework, Java enterprise-grade middleware covers all data virtualization development, run time, and administration phases. Data is now genuinely impactful, providing value through insights to enhance revenue, reduce expenses, and improve risk and compliance. As a result of the innovative functionality noted, TIBCO Data Virtualization recently took first place in the 2022 GigaOm Radar Enterprise Data Virtualization Report, the 2022 Top Rated award on TrustRadius, G2's 2023 Leader in the Data Visualization category, along with receiving numerous peer review awards and badges in customer feedback. TIBCO’s data integration architecture is built with TIBCO Cloud™ Integration, a solution that was recently updated with new features to accelerate complex data mapping and transformation. The enhanced smart mapping capabilities are powered by AI to provide automated recommendations based on years of training by user inputs. TIBCO Cloud Integration continues to be recognized for strong data integration capabilities and our broad portfolio of connectors to any data source by analysts, including a recent leadership ranking in the 2023 Gartner® Magic Quadrant™ for iPaaS. Follow @TIBCO on Twitter and on our Facebook, Instagram, and LinkedIn pages to hear the latest TIBCO news and updates. About TIBCO TIBCO, a business unit of Cloud Software Group, unlocks the potential of real-time data for making faster, smarter decisions. Our Connected Intelligence Platform seamlessly connects any application or data source; intelligently unifies data for greater access, trust, and control; and confidently predicts outcomes in real time and at scale. Learn how solutions to our customers' most critical business challenges are made possible by TIBCO at www.tibco.com. TIBCO, TIBCO Cloud, EBX, and the TIBCO logo are trademarks or registered trademarks of Cloud Software Group, Inc. and/or its subsidiaries in the United States and/or other countries. All other product and company names and marks mentioned in this document are the property of their respective owners and are mentioned for identification.

Read More

BIG DATA MANAGEMENT, DATA SCIENCE, BIG DATA

Immuta Brings its Data Security Platform to Snowflake’s Government & Education Data Cloud

Businesswire | June 08, 2023

Immuta, a leader in data security, today announced the launch of its data security platform as a pre-built integration for the Government & Education Data Cloud, launched by Snowflake, the Data Cloud company. The launch of the Government & Education Data Cloud unites Snowflake’s data platform, Snowflake and partner-delivered solutions, and industry-specific datasets to empower educational institutions and public sector agencies at the federal, state, and local levels to spend more time on what matters most: caring for citizens and students. The Snowflake Government & Education Data Cloud makes it possible for government and education institutions to address industry challenges by enabling intra- and cross-agency data sharing and collaboration, establishing 360° views of their stakeholders, monitoring for fraud, waste, and abuse, and leveraging an industry-leading network of public sector partners. “We are looking forward to working with Immuta to improve citizen and student outcomes by giving customers fast, safe, and trusted access to critical education and public sector data,” said Jeff Frazier, Global Head of Public Sector at Snowflake. “Our continued partnership with Immuta will help organizations leverage the data they need to drive meaningful change in their communities.” Within the Government & Education Data Cloud, customers can access industry-specific solutions built by Snowflake’s ecosystem of public sector and education partners to leverage best practices, reduce time-to-value, and increase overall impact. The Immuta Data Security Platform enables joint customers to protect their data on Snowflake’s Government & Education Data Cloud by providing them with automated data discovery and classification, scalable data access controls, dynamic data masking, auditing and reporting, and risk detection indicators. With Immuta and Snowflake, government agencies and educational institutions can securely share data with third parties to effectively collaborate and fully derive value from data in their possession to improve citizen and student outcomes, all while meeting security and compliance standards. “We’re excited to be a launch partner for Snowflake’s Government & Education Data Cloud and have the opportunity to provide customers access to industry-specific solutions that will help them leverage best practices, reduce time-to-value, and increase overall impact,” said Chris Brown, Public Sector CTO, Immuta. “The partnership between Immuta and Snowflake will enable both government and education organizations to derive value and insights from their data, all while protecting it to better serve citizens and students.” Learn more about Snowflake’s Government & Education Data Cloud launch here and partner-tailored solutions for the industry here. Check out our blog to see how Immuta’s integration with the Snowflake Government & Education Data Cloud helps accelerate zero trust while unlocking data’s value. About Immuta Immuta enables organizations to unlock value from their cloud data by protecting it and providing secure access. The Immuta Data Security Platform provides sensitive data discovery, security and access control, data activity monitoring, and has deep integrations with the leading cloud data platforms. Immuta is now trusted by Fortune 500 companies and government agencies around the world to secure their data. Founded in 2015, Immuta is headquartered in Boston, MA. To learn more about Immuta, click here.

Read More

Events