Making the Case for Using Whole Genome Sequencing to Fight Foodborne Illness Globally

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We recently traveled to Geneva to join a meeting of the Codex Alimentarius Commission, an international organization that works to protect consumer health and promote fair practices in food trade. There, we participated in a panel discussion on how best to share WGS globally to fight foodborne illnesses and elicit support from the world’s governments in this effort.

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Kentik

Easily the world’s most powerful network analytics, Kentik® uses real-time flow analysis, uniquely enriched with business and internet context to help enterprises and service providers protect revenue and reputation. Kentik’s SaaS platform is built on a patented big data engine to deliver modern network analytics that is both powerful and easy to use.

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Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Article | July 28, 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.

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BIG DATA MANAGEMENT, DATA SCIENCE, BIG DATA

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

Article | April 28, 2023

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.

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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, DATA SCIENCE

Big Data in Healthcare: Improving Patient Outcomes

Article | May 2, 2023

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.

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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.

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Kentik

Easily the world’s most powerful network analytics, Kentik® uses real-time flow analysis, uniquely enriched with business and internet context to help enterprises and service providers protect revenue and reputation. Kentik’s SaaS platform is built on a patented big data engine to deliver modern network analytics that is both powerful and easy to use.

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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, BUSINESS STRATEGY

MarkLogic 11 Unlocks Value of Complex Data with Industry’s Most Powerful Multi-Model Data Platform

MarkLogic | December 16, 2022

MarkLogic Corporation, a leader in complex data and metadata management and portfolio company of Vector Capital, today announced new features delivered in MarkLogic 11, the latest release of its flagship MarkLogic Server product, that further enhance MarkLogic as a unified data platform with analytics, simplified deployment, management, and auditing—including in the cloud. Data fuels innovation and growth, but organizations are challenged to create business value from a constant stream of new data arriving in real time and from multiple sources. The MarkLogic data platform enables customers to connect and effectively leverage data and metadata as a single data resource. Data coupled with everything known about it means faster insights that accelerate innovation. MarkLogic 11 adds features that enable organizations to analyze and integrate multi-model data in new ways, and to make that data more accessible to developers and endpoints. Support for the increasingly popular GraphQL specification, for example, lets organizations expose multi-model data to BI tooling, and enhanced OpenGIS and GeoSPARQL support makes it easier to query — and tap into new workloads for — geospatial data. MarkLogic 11 also improves the platform’s manageability, auditability, and observability. “MarkLogic 11 is the best data platform for complex data and metadata management, delivering unmatched data agility that will enable customers to get more value from their data and, in turn, make better, more informed decisions. “With the acquisition of Smartlogic last year, we’ve entered a new era for MarkLogic focused on removing complexity and being the single place for breaking down data and knowledge silos." Jeff Casale, CEO of MarkLogic MarkLogic 11 builds on the company’s complex data and metadata management capabilities with: Extended Platform Capabilities: MarkLogic exposes data in a way that's readily usable by existing products. MarkLogic 11 improves interoperability with data ecosystems through support for industry standards such as GraphQL, which eases exposure of multi-model data to BI tooling; OpenGIS and GeoSPARQL, which make it even easier to query geospatial data; and OAuth, which provides a new option for external authentication. Enhanced Multi-model Analytics: Connecting and managing multi-model data is important, but the value of that data cannot be fully exploited until it’s delivered for consumption to the audiences that need it most. The MarkLogic Optic API, introduced in MarkLogic 9, has been extended in MarkLogic 11, including added capabilities for the delivery of multi-model data to BI tools like Tableau and improved support for large analytics, reporting, geospatial data/analysis, and/or export queries with external sort and joins. Flexible Deployment and Easier Management: MarkLogic 11 includes tools that help customers manage growing volumes of data by handling larger result sets at query time and using new adaptive memory algorithms. Support for Docker and Kubernetes enables organizations to deploy MarkLogic clusters in cloud-neutral, containerized environments that use best practices to ensure success. Improved Observability, Auditability, and Manageability: MarkLogic 11 provides enhanced HA with storage failure detection, ensuring availability in the face of storage device/system failures and cloud availability zone failures or brownouts. MarkLogic 11 also enables organizations to monitor the overall health of the system and respond more quickly to failures, and an updated Section 508-compliant UI improves accessibility for all users. The MarkLogic platform and the improvements introduced in MarkLogic 11 are designed to enable organizations to achieve true data agility that lets you quickly and easily respond to change. MarkLogic is hosting a virtual launch event on December 15 at 8 a.m. PT to discuss the new features in detail. The recording will be available on demand. About MarkLogic The MarkLogic data platform gives Global 2000 and public sector organizations a faster, trusted way to unlock value from complex data and achieve data agility. The unified data platform enables organizations to securely connect data and metadata, create and interpret meaning, and consume high-quality contextualized data.

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BIG DATA MANAGEMENT, DATA ARCHITECTURE, BUSINESS STRATEGY

Orion Governance Partners with Qlik to Help Enterprises Solve Their Most Complex Data Challenges

Orion Governance | October 21, 2022

Orion Governance, a leader in Metadata Management solutions and the provider of the Enterprise Information Intelligence Graph (EIIG), the foundation for a self-defined data fabric, announced today it has entered into a technology partnership with Qlik.® This partnership will integrate Orion’s EIIG solution with Qlik Cloud to help enterprises tackle their most challenging data problems. “Orion is thrilled to be a Qlik technology partner. The integration of our EIIG platform enables Qlik users to see key data metrics such as quality score, value score, and trust score right in their Qlik apps. Users can dive into the EIIG platform to see data lineage and get more insight by leveraging metadata analytics such as impact analysis. EIIG's Qlik extension also delivers augmented data quality and allows users to tag all PII data assets right in Qlik apps for data privacy and regulatory compliance.” Niu Bai, Head of Global Business Development at Orion Governance Orion’s Enterprise Information Intelligence Graph (EIIG) automatically ingests metadata from more than 60 technologies to weave the most comprehensive knowledge graph in the industry and build a self-defined data fabric. This data fabric provides the visualizations necessary to catalog, trace, trust and analyze data while promoting confidence, transparency, and governance of the enterprise landscape. “We look forward to customers being able to augment their Qlik analytics experience with Orion’s data metrics to drive more understanding and trust in their data for increased action and impact,” said Josh Good, Vice President, Product Marketing at Qlik. About Orion Governance Orion Governance was founded in 2017 with a mission to disrupt the information management space. The company’s Enterprise Information Intelligence Graph (EIIG) is a cloud based, vendor/technology agnostic SaaS platform that provides the most comprehensive metadata knowledge graph in the industry. The EIIG has persona based visualizations to create a self-defined data fabric with detailed cataloging, traceability, data quality and analytics capabilities; the result of ML/AI automation that enables enterprises to take control of their complex IT landscape in near real-time. Customers include Global 5000 companies in banking insurance, retail, healthcare, telecom and information technology that are challenged by a variety of complexities; regulatory, cloud migration, transformational and silo based constraints. Orion is headquartered in San Mateo, California, with global offices in other US cities, Estonia, Sweden, Singapore, Germany and India.

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BIG DATA MANAGEMENT

Alation Named to Constellation ShortList for Metadata Management, Data Cataloging, & Data Governance for Second Consecutive Year

Alation | August 19, 2021

Alation Inc., the leader in enterprise data catalogs, today announced that it has been selected to the Constellation ShortList™ for Metadata Management, Data Cataloging & Data Governance in Q3 2021. The technology vendors and service providers included in this program deliver critical transformation initiative requirements for early adopter and fast follower organizations. “The ShortList™ is the first place business and technology leaders go for vendor selection, based on the collective view of Constellation’s clients, partners, and analysts who are on the front lines of understanding the technology landscape,” noted R “Ray” Wang, chairman and founder at Constellation Research. “Our analysts know that vendor selection is more of an art than a science and that the listed vendors all play a special role by industry, geography, and size of company. We know these are tough decisions and we hope this helps buyers get a head start. For those who want a detailed analysis, we are there to help with short advisory calls." Additionally, Alation customer Texas Mutual Insurance Company (TXM), the state's leading workers' compensation provider, has been named a finalist in the prestigious 2021 Constellation SuperNova Awards in the digital safety, governance, and privacy efforts category for its implementation of Alation. The SuperNova Awards recognize individuals and teams who are prioritizing disruptive technology and transforming their organizations with digital initiatives, achieving remarkable results, including competitive advantage, cost savings, and quantifiable improvements in agility. Customers like TXM have leaned on Alation to ensure the data behind each analysis is trustworthy and enable them to make critical business decisions rapidly. “It’s time to rethink data governance,” said Satyen Sangani, CEO and co-founder of Alation. “If companies are going to be agile in their decision-making, they need their data to be similarly responsive and agile. They also need to drive down the cost of compliance and regulation. A strong data governance program accelerates strategic decision-making and drives efficiency by putting governance capabilities into the day-to-day workflows of every employee.” Voting for the Constellation SuperNova Awards is open to the public. Polls close in a few short weeks on Sept. 3, 2021. To support TXM, click here to vote. This recognition comes on the heels of Alation being named a Leader in “The Forrester Wave™: Data Governance Solutions, Q3 2021” report and being named Snowflake’s Data Governance Partner of the Year. About Alation Alation is the leader in enterprise data intelligence solutions including data search & discovery, data governance, data stewardship, analytics, and digital transformation. Alation’s initial offering dominates the data catalog market. Thanks to its powerful Behavioral Analysis Engine, inbuilt collaboration capabilities, and open interfaces, Alation combines machine learning with human insight to successfully tackle even the most demanding challenges in data and metadata management. More than 280 enterprises drive data culture, improve decision making, and realize business outcomes with Alation including AbbVie, American Family Insurance, Cisco, Exelon, Fifth Third Bank, Finnair, Munich Re, NASDAQ, New Balance, Parexel, Pfizer, US Foods and Vistaprint. Headquartered in Silicon Valley, Alation was named to Inc. Magazine’s Best Workplaces list and is backed by leading venture capitalists including Blackstone, Costanoa, Data Collective, Dell Technologies, Icon, ISAI Cap, Riverwood, Salesforce, Sanabil, Sapphire, and Snowflake Ventures.

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BUSINESS INTELLIGENCE, BIG DATA MANAGEMENT, BUSINESS STRATEGY

MarkLogic 11 Unlocks Value of Complex Data with Industry’s Most Powerful Multi-Model Data Platform

MarkLogic | December 16, 2022

MarkLogic Corporation, a leader in complex data and metadata management and portfolio company of Vector Capital, today announced new features delivered in MarkLogic 11, the latest release of its flagship MarkLogic Server product, that further enhance MarkLogic as a unified data platform with analytics, simplified deployment, management, and auditing—including in the cloud. Data fuels innovation and growth, but organizations are challenged to create business value from a constant stream of new data arriving in real time and from multiple sources. The MarkLogic data platform enables customers to connect and effectively leverage data and metadata as a single data resource. Data coupled with everything known about it means faster insights that accelerate innovation. MarkLogic 11 adds features that enable organizations to analyze and integrate multi-model data in new ways, and to make that data more accessible to developers and endpoints. Support for the increasingly popular GraphQL specification, for example, lets organizations expose multi-model data to BI tooling, and enhanced OpenGIS and GeoSPARQL support makes it easier to query — and tap into new workloads for — geospatial data. MarkLogic 11 also improves the platform’s manageability, auditability, and observability. “MarkLogic 11 is the best data platform for complex data and metadata management, delivering unmatched data agility that will enable customers to get more value from their data and, in turn, make better, more informed decisions. “With the acquisition of Smartlogic last year, we’ve entered a new era for MarkLogic focused on removing complexity and being the single place for breaking down data and knowledge silos." Jeff Casale, CEO of MarkLogic MarkLogic 11 builds on the company’s complex data and metadata management capabilities with: Extended Platform Capabilities: MarkLogic exposes data in a way that's readily usable by existing products. MarkLogic 11 improves interoperability with data ecosystems through support for industry standards such as GraphQL, which eases exposure of multi-model data to BI tooling; OpenGIS and GeoSPARQL, which make it even easier to query geospatial data; and OAuth, which provides a new option for external authentication. Enhanced Multi-model Analytics: Connecting and managing multi-model data is important, but the value of that data cannot be fully exploited until it’s delivered for consumption to the audiences that need it most. The MarkLogic Optic API, introduced in MarkLogic 9, has been extended in MarkLogic 11, including added capabilities for the delivery of multi-model data to BI tools like Tableau and improved support for large analytics, reporting, geospatial data/analysis, and/or export queries with external sort and joins. Flexible Deployment and Easier Management: MarkLogic 11 includes tools that help customers manage growing volumes of data by handling larger result sets at query time and using new adaptive memory algorithms. Support for Docker and Kubernetes enables organizations to deploy MarkLogic clusters in cloud-neutral, containerized environments that use best practices to ensure success. Improved Observability, Auditability, and Manageability: MarkLogic 11 provides enhanced HA with storage failure detection, ensuring availability in the face of storage device/system failures and cloud availability zone failures or brownouts. MarkLogic 11 also enables organizations to monitor the overall health of the system and respond more quickly to failures, and an updated Section 508-compliant UI improves accessibility for all users. The MarkLogic platform and the improvements introduced in MarkLogic 11 are designed to enable organizations to achieve true data agility that lets you quickly and easily respond to change. MarkLogic is hosting a virtual launch event on December 15 at 8 a.m. PT to discuss the new features in detail. The recording will be available on demand. About MarkLogic The MarkLogic data platform gives Global 2000 and public sector organizations a faster, trusted way to unlock value from complex data and achieve data agility. The unified data platform enables organizations to securely connect data and metadata, create and interpret meaning, and consume high-quality contextualized data.

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BIG DATA MANAGEMENT, DATA ARCHITECTURE, BUSINESS STRATEGY

Orion Governance Partners with Qlik to Help Enterprises Solve Their Most Complex Data Challenges

Orion Governance | October 21, 2022

Orion Governance, a leader in Metadata Management solutions and the provider of the Enterprise Information Intelligence Graph (EIIG), the foundation for a self-defined data fabric, announced today it has entered into a technology partnership with Qlik.® This partnership will integrate Orion’s EIIG solution with Qlik Cloud to help enterprises tackle their most challenging data problems. “Orion is thrilled to be a Qlik technology partner. The integration of our EIIG platform enables Qlik users to see key data metrics such as quality score, value score, and trust score right in their Qlik apps. Users can dive into the EIIG platform to see data lineage and get more insight by leveraging metadata analytics such as impact analysis. EIIG's Qlik extension also delivers augmented data quality and allows users to tag all PII data assets right in Qlik apps for data privacy and regulatory compliance.” Niu Bai, Head of Global Business Development at Orion Governance Orion’s Enterprise Information Intelligence Graph (EIIG) automatically ingests metadata from more than 60 technologies to weave the most comprehensive knowledge graph in the industry and build a self-defined data fabric. This data fabric provides the visualizations necessary to catalog, trace, trust and analyze data while promoting confidence, transparency, and governance of the enterprise landscape. “We look forward to customers being able to augment their Qlik analytics experience with Orion’s data metrics to drive more understanding and trust in their data for increased action and impact,” said Josh Good, Vice President, Product Marketing at Qlik. About Orion Governance Orion Governance was founded in 2017 with a mission to disrupt the information management space. The company’s Enterprise Information Intelligence Graph (EIIG) is a cloud based, vendor/technology agnostic SaaS platform that provides the most comprehensive metadata knowledge graph in the industry. The EIIG has persona based visualizations to create a self-defined data fabric with detailed cataloging, traceability, data quality and analytics capabilities; the result of ML/AI automation that enables enterprises to take control of their complex IT landscape in near real-time. Customers include Global 5000 companies in banking insurance, retail, healthcare, telecom and information technology that are challenged by a variety of complexities; regulatory, cloud migration, transformational and silo based constraints. Orion is headquartered in San Mateo, California, with global offices in other US cities, Estonia, Sweden, Singapore, Germany and India.

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BIG DATA MANAGEMENT

Alation Named to Constellation ShortList for Metadata Management, Data Cataloging, & Data Governance for Second Consecutive Year

Alation | August 19, 2021

Alation Inc., the leader in enterprise data catalogs, today announced that it has been selected to the Constellation ShortList™ for Metadata Management, Data Cataloging & Data Governance in Q3 2021. The technology vendors and service providers included in this program deliver critical transformation initiative requirements for early adopter and fast follower organizations. “The ShortList™ is the first place business and technology leaders go for vendor selection, based on the collective view of Constellation’s clients, partners, and analysts who are on the front lines of understanding the technology landscape,” noted R “Ray” Wang, chairman and founder at Constellation Research. “Our analysts know that vendor selection is more of an art than a science and that the listed vendors all play a special role by industry, geography, and size of company. We know these are tough decisions and we hope this helps buyers get a head start. For those who want a detailed analysis, we are there to help with short advisory calls." Additionally, Alation customer Texas Mutual Insurance Company (TXM), the state's leading workers' compensation provider, has been named a finalist in the prestigious 2021 Constellation SuperNova Awards in the digital safety, governance, and privacy efforts category for its implementation of Alation. The SuperNova Awards recognize individuals and teams who are prioritizing disruptive technology and transforming their organizations with digital initiatives, achieving remarkable results, including competitive advantage, cost savings, and quantifiable improvements in agility. Customers like TXM have leaned on Alation to ensure the data behind each analysis is trustworthy and enable them to make critical business decisions rapidly. “It’s time to rethink data governance,” said Satyen Sangani, CEO and co-founder of Alation. “If companies are going to be agile in their decision-making, they need their data to be similarly responsive and agile. They also need to drive down the cost of compliance and regulation. A strong data governance program accelerates strategic decision-making and drives efficiency by putting governance capabilities into the day-to-day workflows of every employee.” Voting for the Constellation SuperNova Awards is open to the public. Polls close in a few short weeks on Sept. 3, 2021. To support TXM, click here to vote. This recognition comes on the heels of Alation being named a Leader in “The Forrester Wave™: Data Governance Solutions, Q3 2021” report and being named Snowflake’s Data Governance Partner of the Year. About Alation Alation is the leader in enterprise data intelligence solutions including data search & discovery, data governance, data stewardship, analytics, and digital transformation. Alation’s initial offering dominates the data catalog market. Thanks to its powerful Behavioral Analysis Engine, inbuilt collaboration capabilities, and open interfaces, Alation combines machine learning with human insight to successfully tackle even the most demanding challenges in data and metadata management. More than 280 enterprises drive data culture, improve decision making, and realize business outcomes with Alation including AbbVie, American Family Insurance, Cisco, Exelon, Fifth Third Bank, Finnair, Munich Re, NASDAQ, New Balance, Parexel, Pfizer, US Foods and Vistaprint. Headquartered in Silicon Valley, Alation was named to Inc. Magazine’s Best Workplaces list and is backed by leading venture capitalists including Blackstone, Costanoa, Data Collective, Dell Technologies, Icon, ISAI Cap, Riverwood, Salesforce, Sanabil, Sapphire, and Snowflake Ventures.

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