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Data Visualization: Why Does It Matter in Businesses?

Data Visualization

Data visualization refers to a graphical representation of information that displays patterns and trends while allowing viewers to gain rapid insights. Data visualization is critical for businesses to quickly detect trends in data that would otherwise be time-consuming. However, making sense of the quintillion bytes of daily data is difficult without data proliferation, including data visualization.


Understanding data is beneficial to every professional business; hence, data visualization is spreading to all fields where data exists. For any company, data is its most valuable asset. One can effectively communicate their points and use that knowledge by using visualization.

"Data visualization is a great way to simplify data and show it in a form that is understandable, insightful, and actionable. Data visualization is being increasingly seen as the vital final step of any successful data-driven analytics plan."

Caroline Lee, CocoSign

Why Does Data Visualization Matter?

As we acquire more and more data, data visualization becomes increasingly critical, to the point where we are nearly drowning in information, and it is difficult to distinguish what is significant and what is not—for example, the product development program for a new automobile or plane. Analyzing test data is vital, but a massive amount of information is created with each test drive or flight, making processing at the required speed challenging. Visualization tools help comprehend complex data and detect patterns and anomalies.

  • Visual information accounts for 90% of the information sent to the brain.
  • Data visualizations can shorten business meetings by 24%.
  • Managers who use visual data recovery tools are 28 percent more likely than those who rely on managed reporting and dashboards to find timely information.
  • 48% of these managers can find the data they need without the help of I.T. staff.
  • For every dollar spent on business intelligence with data visualization skills, $13.01 will be returned.

Let us look into some of the benefits of data visualization in businesses.

The Creditable Impact of Data Visualizations on Business

While big data is ruling industries, business intelligence transforms much of this data into actionable data points. As a result, data visualization plays a role in transferring information to the human brain by swiftly presenting data.

Visualization has a lot of aesthetic importance in representing and conveying a clear message. Businesses that rely solely on data will eventually go out of business if data visualization is not implemented. Data visualization's competitive benefits can make or kill enterprises. It is critical to know that there are no shortcuts to making faster and better judgments without visualizing the data.

Data Visualization Helps You Take Better Decisions Based on Data

Unlike meetings that focus on text or numbers, business meetings that address visual data tend to be shorter and easier to reach consensus on. Data visualization speeds up decision-making and allows viewers to better understand patterns and trends.

The benefits of data analytics can be accessed across all departments, right from admin to IT, sales to marketing. Even if they are not experts at reading data, your sales team can better understand consumer behavior and impressions if the correct data visualization solutions are in place. With the proper training and tools in place, you can build specialists using data visualization, a combination of technical analytics, and artistic narrative.

When visualizations are created to meet your business goals, you will obtain the best results. For example, some data visualizations help in analysis, while others make the data visually appealing. Some are created to demonstrate concepts, processes, or tactics to various audiences. You can create your own based on your specific goals, data types, and stakeholder requirements.

Data Visualization Is a Medium to Tell a Data Story to the Viewers

Data visualization can also tell a story about data to the audience. The visualization can convey facts in an easy-to-understand format while creating an account and bringing the audience to a predetermined conclusion. This data tale should have a strong start, a basic storyline, and a logical decision like any other story.

If a data analyst is tasked with creating a data visualization for company leaders that details the profitability of various items, the data story could begin with the profits and losses of multiple products before moving on to advice on how to address the failures. Create your own depending on your specific goals, data types, and stakeholder requirements.

Data Visualization Helps You Gather Data Faster and Saves Time

Data visualization is a much faster way to get insights from data than examining a chart. Using data visualization, business meetings can take decisions faster, saving time. When the long meetings are cut short by utilizing data visualization, businesses can dedicate more time to other core business activities.

Summing up

To develop excellent data visualizations, various tools and methodologies are available. You and your team must comprehend the fundamental principles and choose appropriate tools. Above all, the data must be presented accurately. Layouts, colors, text, and dashboards must be appropriately crafted to build data visualizations that best help your business objectives.

Frequently Asked Questions


Why is data visualization important?

Presenting data in a visual or graphical style is known as data visualization. It allows decision-makers to see analytics in a graphic format, making it easier to grasp complex topics or spot new patterns.

What are some of the types of data visualization?

There are several types of data visualization. They are line graphs, scatter plots, pie charts, heat maps, area charts, histograms, and choropleths.

What are the main goals of data visualization?

The main goals of data visualization are to understand your audience, stick to a strict timeline, and deliver a compelling story.

Spotlight

IQVIA

IQVIA (NYSE:IQV) is a leading global provider of advanced analytics, technology solutions, and clinical research services to the life sciences industry. IQVIA creates intelligent connections across all aspects of healthcare through its analytics, transformative technology, big data resources and extensive domain expertise. IQVIA Connected Intelligence™ delivers powerful insights with speed and agility — enabling customers to accelerate the clinical development and commercialization of innovative medical treatments that improve healthcare outcomes for patients. With approximately 86,000 employees, IQVIA conducts operations in more than 100 countries.

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Big Data Management, Data Visualization, Data Architecture

The Future of Big Data: What to Expect in 2022?

Article | August 18, 2022

Data management professionals getting the central stage in business, focus on ethics increasing, pressure on Big Tech affecting the landscape of the public web data industry. Thesewill be among the most prominent trends in the big data industry in 2022, according to a public data gathering solutions provider, Oxylabs. Their experts predict what to expect in the year ahead. Growing Markets for External Data Tomas Montvilas, Chief Commercial Officer at Oxylabs, says that more industries will discover the benefits of using external data in the upcoming year. He lists a few: “The market of SaaS products that use external data to provide insights for their clients will grow further in 2022. The successful IPOs of companies like Semrush, Similarweb, Zoominfo and others are driving further investments in the field, and we are likely to see more stars emerging,” - Tomas says. Another important area he sees for the web scraping industry’s growth is cybersecurity. Cyber threats are becoming more advanced and require new measures of defense. This is where web monitoring and scraping technologies come in. “Constant monitoring of both the public and dark web can help identify malicious sites and programs early. It can also help catch data leaks sooner by finding data sets when they go for sale on the dark web and recognize the actions of hacker groups. Meanwhile, proxies can help with email security by allowing you to scan emails from different IP addresses," he explains. Data Management Role in Business Further Increasing With the recent explosion in digitizing everything, data management and analytics have become pivotal in business. Data departments have been experiencing exponential growth during the past few years and the growth will continue well into 2022. Gediminas Rickevicius, Vice President of Global Partnerships at Oxylabs, notes that the increasing importance of data departments can be easily illustrated by budgeting trends. According to several recent surveys Oxylabs conducted in the UK's finance and ecommerce industries, most data departments are expecting to increase their budgets (51% ecommerce, 43% financial services).Another trend Gediminas predicts for data departments will be the increasing outsourcing of automated public web data gathering tools. There will be several reasons for this. First of all, it is obvious that as companies become dependent on external data, manual data gathering processes are simply not sufficient. Another important factor is the current job market landscape. “With “the great resignation” and lack of human resources being the dominant topics of 2021, it became even harder to find in-house professionals that could dedicate all their time to maintaining and adjusting web scraping infrastructure. Outsourcing this task allows optimizing resources and focusing on data analysis rather than acquisition.” - says Gediminas. Pressure for Big Tech Could Affect Web Data Industry Recent years have been marked by the growing pressure on Big Tech from governments around the world. 2022 will be no different; there will likely be a push for new regulations, especially around personal data and its acquisition and aggregation.According to Denas Grybauskas, Head of Legal at Oxylabs, the data gathering industry should not turn a blind eye to these processes. In light of government pressure, some big tech companies might already be in the process of restricting access to public web data, which could affect many businesses. “Some companies are preparing for the old death tactic of pointing fingers. At least in accordance with the leaked emails, Meta (Facebook) is planning to do in terms of personal data leaks and data scraping companies - to shift the attention from leaks by stating that personal data got out in the wild not due to Facebook’s mistakes, but those of scrapers”, - Denas says. Moving Towards Industry Self-regulation When it comes to the strategic development of the data gathering industry, ethics and legal implications will remain the hot topics in 2022, pushing the industry to continue raising the standards. Ethical proxy acquisition and strong KYC practices will dominate the conversation, predicts Julius Cerniauskas, CEO of Oxylabs.He explains that, as with most new technologies, web scraping is developing faster than the regulations that could safeguard it from potential misuse cases. Therefore, the industry itself has to take the lead in developing self-regulation guidelines and standards for the proper use of technology. “For several reasons, the issue is set to become more mainstream in 2022. First of all, as the largest industry players are setting the tone, smaller players are likely to follow. Secondly, brands that use proxy services are putting more emphasis on the nature of proxies too, as potential misuse could damage their reputation as well,” - says Julius. Authors:Julius Cerniauskas, CEO, Oxylabs, Tomas Montvilas, Chief Commercial Officer, Oxylabs,Gediminas Rickevičius, VP of Global Partnerships, Oxylabs,Denas Grybauskas, Head of Legal, Oxylabs

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Business Intelligence, Big Data Management, Big Data

Data Virtualization: A Dive into the Virtual Data Lake

Article | July 18, 2023

No matter if you own a retail business, a financial services company, or an online advertising business, data is the most essential resource for contemporary businesses. Businesses are becoming more aware of the significance of their data for business analytics, machine learning, and artificial intelligence across all industries. Smart companies are investing in innovative approaches to derive value from their data, with the goals of gaining a deeper understanding of the requirements and actions of their customers, developing more personalized goods and services, and making strategic choices that will provide them with a competitive advantage in the years to come. Business data warehouses have been utilized for all kinds of business analytics for many decades, and there is a rich ecosystem that revolves around SQL and relational databases. Now, a competitor has entered the picture. Data lakes were developed for the purpose of storing large amounts of data to be used in the training of AI models and predictive analytics. For most businesses, a data lake is an essential component of any digital transformation strategy. However, getting data ready and accessible for creating insights in a controllable manner remains one of the most complicated, expensive, and time-consuming procedures. While data lakes have been around for a long time, new tools and technologies are emerging, and a new set of capabilities are being introduced to data lakes to make them more cost-effective and more widely used. Why Should Businesses Opt for Virtual Data Lakes and Data Virtualization? Data virtualization provides a novel approach to data lakes; modern enterprises have begun to use logical data lake architecture, which is a blended method based on a physical data lake but includes a virtual data layer to create a virtual data lake. Data virtualization combines data from several sources, locations, and formats without requiring replication. In a process that gives many applications and users unified data services, a single "virtual" data layer is created. There are many reasons and benefits for adding a virtual data lake and data virtualization, but we will have a look at the top three reasons that will benefit your business. Reduced Infrastructure Costs Database virtualization can save you money by eliminating the need for additional servers, operating systems, electricity, application licensing, network switches, tools, and storage. Lower Labor Costs Database virtualization makes the work of a database IT administrator considerably easier by simplifying the backup process and enabling them to handle several databases at once. Data Quality Marketers are nervous about the quality and accuracy of the data that they have. According to Singular, in 2019, 13% responded that accuracy was their top concern. And 12% reported having too much data. Database virtualization improves data quality by eliminating replication. Virtual Data Lake and Marketing Leaders Customer data is both challenging as well as an opportunity for marketers. If your company depends on data-driven marketing on any scale and expects to retain a competitive edge, there is no other option: it is time to invest in a virtual data lake. In the omnichannel era, identity resolution is critical to consumer data management. Without it, business marketers would be unable to develop compelling customer experiences. Marketers could be wondering, "A data what?" Consider data lakes in this manner: They provide marketers with important information about the consumer journey as well as immediate responses about marketing performance across various channels and platforms. Most marketers lack insight into performance because they lack the time and technology to filter through all of the sources of that information. A virtual data lake is one solution. Marketers can reliably answer basic questions like, "How are customers engaging with our goods and services, and where is that occurring in the customer journey?" using a data lake. "At what point do our conversion rates begin to decline?" The capacity to detect and solve these sorts of errors at scale and speed—with precise attribution and without double-counting—is invaluable. Marketers can also use data lakes to develop appropriate standards and get background knowledge of activity performance. This provides insight into marketing ROI and acts as a resource for any future marketing initiatives and activities. Empowering Customer Data Platform Using Data Virtualization Businesses are concentrating more than ever on their online operations, which means they are spending more on digital transformation. This involves concentrating on "The Customer," their requirements and insights. Customers have a choice; switching is simple, and customer loyalty is inexpensive, making it even more crucial to know your customer and satisfy their requirements. Data virtualization implies that the customer data platform (CDP) serves as a single data layer that is abstracted from the data source's data format or schemas. The CDP offers just the data selected by the user with no bulk data duplication. This eliminates the need for a data integrator to put up a predetermined schema or fixed field mappings for various event types. Retail Businesses are Leveraging Data Virtualization Retailers have been servicing an increasingly unpredictable customer base over the last two decades. They have the ability to do research, check ratings, compare notes among their personal and professional networks, and switch brands. They now expect to connect with retail businesses in the same way that they interact with social networks. To accomplish so, both established as well as modern retail businesses must use hybrid strategies that combine physical and virtual businesses. In order to achieve this, retail businesses are taking the help of data virtualization to provide seamless experiences across online and in-store environments. How Does Data Virtualization Help in the Elimination of Data Silos? To address these data-silo challenges, several businesses are adopting a much more advanced data integration strategy: data virtualization. In reality, data virtualization and data lakes overlap in many aspects. Both architectures start with the assumption that all data should be accessible to end users. Broad access to big data volumes is employed in both systems to better enable BI and analytics as well as other emerging trends like artificial intelligence and machine learning. Data Virtualization can address a number of big data pain points with features such as query pushdown, caching, and query optimization. Data virtualization enables businesses to access data from various sources such as data warehouses, NoSQL databases, and data lakes without requiring physical data transportation thanks to a virtual layer that covers the complexities of source data from the end user. A couple of use cases where data virtualization can eliminate data silos are: Agile Business Intelligence Legacy BI solutions are now unable to meet the rising enterprise BI requirements. Businesses now need to compete more aggressively. As a result, they must improve the agility of their processes. Data virtualization can improve system agility by integrating data on-demand. Moreover, it offers uniform access to data in a unified layer that can be merged, processed, and cleaned. Businesses may also employ data virtualization to build consistent BI reports for analysis with reduced data structures and instantly provide insights to key decision-makers. Virtual Operational Data Store The Virtual Operational Data Store (VODS) is another noteworthy use of data virtualization. Users can utilize VODS to execute additional operations on the data analyzed by data virtualization, like monitoring, reporting, and control. GPS applications are a perfect example of VODS. Travelers can utilize these applications to get the shortest route to a certain location. A VODS takes data from a variety of data repositories and generates reports on the fly. So, the traveler gets information from a variety of sources without having to worry about which one is the main source. Closing Lines Data warehouses and virtual data lakes are both effective methods for controlling huge amounts of data and advancing to advanced ML analytics. Virtual data lakes are a relatively new technique for storing massive amounts of data on commercial clouds like Amazon S3 and Azure Blob. While dealing with ML workloads, the capacity of a virtual data lake and data virtualization to harness more data from diverse sources in much less time is what makes it a preferable solution. It not only allows users to cooperate and analyze data in new ways, but it also accelerates decision-making. When you require business-friendly and well-engineered data displays for your customers, it makes a strong business case. Through data virtualization, IT can swiftly deploy and repeat a new data set as client needs change. When you need real-time information or want to federate data from numerous sources, data virtualization can let you connect to it rapidly and provide it fresh each time. Frequently Asked Questions What Exactly Is a “Virtual Data Lake?” A virtual data lake is connected to or disconnected from data sources as required by the applications that are using it. It stores data summaries in the sources such that applications can explore the data as if it were a single data collection and obtain entire items as required. What Is the Difference Between a Data Hub and a Data Lake? Data Lakes and Data Hubs (Datahub) are two types of storage systems. A data lake is a collection of raw data that is primarily unstructured. On the other hand, a data hub, is made up of a central storage system whose data is distributed throughout several areas in a star architecture. Does Data Virtualization Store Data? It is critical to understand that data virtualization doesn't at all replicate data from source systems; rather, it saves metadata and integration logic for viewing.

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Business Intelligence, Big Data Management, Big Data

Data Integration Platform: Leveraging the Power of Data

Article | August 17, 2023

Data is not stored in a single database, file system, data lake, or repository. Data generated in a system of record must meet various business requirements, connect with other data sources, and then be utilized for analytics, customer-facing apps, or internal procedures. A well-established data integration solution provides a unified picture of data received from various places and formats. This can also happen when two organizations merge or when internal applications are consolidated. Data integration can also facilitate the development of a more complete data warehouse, resulting in more accurate and effective analysis. Data integration establishes the foundation for effective Business Intelligence (BI) and decision-making. Data Integration as a Tool for Business Strategy The gathering, analysis, and integration of data is essential to the success of businesses. Let’s have a look at the way in which data integration technology enables business strategy. Set Data-Integration Goals These objectives should be part of the larger company objective. A thorough awareness of your consumers, for example, is corporate goal. To do this, your integration strategy should aim to embed customer data into your service, sales, and marketing platforms. Improved Financial Data Management A robust data integration strategy allows you to monitor and manage vital financial and operational data through simple dashboards that combine all business and financial data into a single platform. Any effective financial management system will include basic accounting capabilities that will enable you to track revenue and spending, assets and liabilities, and amortizations in order to provide accurate financial reports. Enhanced Marketing Analytics Data about competitors, industry trends, consumer behavior, and campaign performance should drive your marketing strategy. Update often as fresh data becomes available. By assessing your marketing tools and channels, you can determine the optimum time, place, and technique to advertise your company. Gather data from social media, email marketing tools, CRMs, CMSs, and other platforms for marketing analytics. This also allows you to evaluate where you should spend additional resources to improve the consistency of your marketing effort. Save Time and Resources Business intelligence experts have a huge workload of sifting through business data. Analysts worry less when teams have direct access to essential data. This frees them to concentrate on difficult, valuable data sets. Without data integration platforms, even the simplest business report requires manual processing of all sources, creating code or automatically uploading data to the database, and exhausting systematization. Not to mention the challenge to monitor and correct any human factor errors. This will be completely eliminated by integration automation. How AI Is Enhancing the Data Integration Process? Data Mapping: Businesses can map data faster using AI for insights generation and decision-making. Autonomous learning: An ML-based data integration process enables autonomous learning to discover patterns and trends in the stored data. Big data processing: Machine learning (ML) makes it possible to quickly and accurately transform unstructured and inconsistent data into desirable formats. Closing Lines Regardless of the size of your company or resources, processing and managing data correctly can expand vision of your business and customers. As organizations rely on data analytics and business intelligence, data integration will become more user-friendly in the coming years. Data integration is an unavoidable aspect of every organization's digital transformation path. Implement the most current data integration techniques to stay ahead of your competition.

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Business Intelligence, Big Data Management, Data Science

Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Article | May 2, 2023

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 Visualization, Data Architecture

Data Virtualization: A Dive into the Virtual Data Lake

Article | August 18, 2022

Data management professionals getting the central stage in business, focus on ethics increasing, pressure on Big Tech affecting the landscape of the public web data industry. Thesewill be among the most prominent trends in the big data industry in 2022, according to a public data gathering solutions provider, Oxylabs. Their experts predict what to expect in the year ahead. Growing Markets for External Data Tomas Montvilas, Chief Commercial Officer at Oxylabs, says that more industries will discover the benefits of using external data in the upcoming year. He lists a few: “The market of SaaS products that use external data to provide insights for their clients will grow further in 2022. The successful IPOs of companies like Semrush, Similarweb, Zoominfo and others are driving further investments in the field, and we are likely to see more stars emerging,” - Tomas says. Another important area he sees for the web scraping industry’s growth is cybersecurity. Cyber threats are becoming more advanced and require new measures of defense. This is where web monitoring and scraping technologies come in. “Constant monitoring of both the public and dark web can help identify malicious sites and programs early. It can also help catch data leaks sooner by finding data sets when they go for sale on the dark web and recognize the actions of hacker groups. Meanwhile, proxies can help with email security by allowing you to scan emails from different IP addresses," he explains. Data Management Role in Business Further Increasing With the recent explosion in digitizing everything, data management and analytics have become pivotal in business. Data departments have been experiencing exponential growth during the past few years and the growth will continue well into 2022. Gediminas Rickevicius, Vice President of Global Partnerships at Oxylabs, notes that the increasing importance of data departments can be easily illustrated by budgeting trends. According to several recent surveys Oxylabs conducted in the UK's finance and ecommerce industries, most data departments are expecting to increase their budgets (51% ecommerce, 43% financial services).Another trend Gediminas predicts for data departments will be the increasing outsourcing of automated public web data gathering tools. There will be several reasons for this. First of all, it is obvious that as companies become dependent on external data, manual data gathering processes are simply not sufficient. Another important factor is the current job market landscape. “With “the great resignation” and lack of human resources being the dominant topics of 2021, it became even harder to find in-house professionals that could dedicate all their time to maintaining and adjusting web scraping infrastructure. Outsourcing this task allows optimizing resources and focusing on data analysis rather than acquisition.” - says Gediminas. Pressure for Big Tech Could Affect Web Data Industry Recent years have been marked by the growing pressure on Big Tech from governments around the world. 2022 will be no different; there will likely be a push for new regulations, especially around personal data and its acquisition and aggregation.According to Denas Grybauskas, Head of Legal at Oxylabs, the data gathering industry should not turn a blind eye to these processes. In light of government pressure, some big tech companies might already be in the process of restricting access to public web data, which could affect many businesses. “Some companies are preparing for the old death tactic of pointing fingers. At least in accordance with the leaked emails, Meta (Facebook) is planning to do in terms of personal data leaks and data scraping companies - to shift the attention from leaks by stating that personal data got out in the wild not due to Facebook’s mistakes, but those of scrapers”, - Denas says. Moving Towards Industry Self-regulation When it comes to the strategic development of the data gathering industry, ethics and legal implications will remain the hot topics in 2022, pushing the industry to continue raising the standards. Ethical proxy acquisition and strong KYC practices will dominate the conversation, predicts Julius Cerniauskas, CEO of Oxylabs.He explains that, as with most new technologies, web scraping is developing faster than the regulations that could safeguard it from potential misuse cases. Therefore, the industry itself has to take the lead in developing self-regulation guidelines and standards for the proper use of technology. “For several reasons, the issue is set to become more mainstream in 2022. First of all, as the largest industry players are setting the tone, smaller players are likely to follow. Secondly, brands that use proxy services are putting more emphasis on the nature of proxies too, as potential misuse could damage their reputation as well,” - says Julius. Authors:Julius Cerniauskas, CEO, Oxylabs, Tomas Montvilas, Chief Commercial Officer, Oxylabs,Gediminas Rickevičius, VP of Global Partnerships, Oxylabs,Denas Grybauskas, Head of Legal, Oxylabs

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Business Intelligence, Big Data Management, Big Data

Leveraging Big Data for Competitive Advantage: Benefits

Article | July 18, 2023

No matter if you own a retail business, a financial services company, or an online advertising business, data is the most essential resource for contemporary businesses. Businesses are becoming more aware of the significance of their data for business analytics, machine learning, and artificial intelligence across all industries. Smart companies are investing in innovative approaches to derive value from their data, with the goals of gaining a deeper understanding of the requirements and actions of their customers, developing more personalized goods and services, and making strategic choices that will provide them with a competitive advantage in the years to come. Business data warehouses have been utilized for all kinds of business analytics for many decades, and there is a rich ecosystem that revolves around SQL and relational databases. Now, a competitor has entered the picture. Data lakes were developed for the purpose of storing large amounts of data to be used in the training of AI models and predictive analytics. For most businesses, a data lake is an essential component of any digital transformation strategy. However, getting data ready and accessible for creating insights in a controllable manner remains one of the most complicated, expensive, and time-consuming procedures. While data lakes have been around for a long time, new tools and technologies are emerging, and a new set of capabilities are being introduced to data lakes to make them more cost-effective and more widely used. Why Should Businesses Opt for Virtual Data Lakes and Data Virtualization? Data virtualization provides a novel approach to data lakes; modern enterprises have begun to use logical data lake architecture, which is a blended method based on a physical data lake but includes a virtual data layer to create a virtual data lake. Data virtualization combines data from several sources, locations, and formats without requiring replication. In a process that gives many applications and users unified data services, a single "virtual" data layer is created. There are many reasons and benefits for adding a virtual data lake and data virtualization, but we will have a look at the top three reasons that will benefit your business. Reduced Infrastructure Costs Database virtualization can save you money by eliminating the need for additional servers, operating systems, electricity, application licensing, network switches, tools, and storage. Lower Labor Costs Database virtualization makes the work of a database IT administrator considerably easier by simplifying the backup process and enabling them to handle several databases at once. Data Quality Marketers are nervous about the quality and accuracy of the data that they have. According to Singular, in 2019, 13% responded that accuracy was their top concern. And 12% reported having too much data. Database virtualization improves data quality by eliminating replication. Virtual Data Lake and Marketing Leaders Customer data is both challenging as well as an opportunity for marketers. If your company depends on data-driven marketing on any scale and expects to retain a competitive edge, there is no other option: it is time to invest in a virtual data lake. In the omnichannel era, identity resolution is critical to consumer data management. Without it, business marketers would be unable to develop compelling customer experiences. Marketers could be wondering, "A data what?" Consider data lakes in this manner: They provide marketers with important information about the consumer journey as well as immediate responses about marketing performance across various channels and platforms. Most marketers lack insight into performance because they lack the time and technology to filter through all of the sources of that information. A virtual data lake is one solution. Marketers can reliably answer basic questions like, "How are customers engaging with our goods and services, and where is that occurring in the customer journey?" using a data lake. "At what point do our conversion rates begin to decline?" The capacity to detect and solve these sorts of errors at scale and speed—with precise attribution and without double-counting—is invaluable. Marketers can also use data lakes to develop appropriate standards and get background knowledge of activity performance. This provides insight into marketing ROI and acts as a resource for any future marketing initiatives and activities. Empowering Customer Data Platform Using Data Virtualization Businesses are concentrating more than ever on their online operations, which means they are spending more on digital transformation. This involves concentrating on "The Customer," their requirements and insights. Customers have a choice; switching is simple, and customer loyalty is inexpensive, making it even more crucial to know your customer and satisfy their requirements. Data virtualization implies that the customer data platform (CDP) serves as a single data layer that is abstracted from the data source's data format or schemas. The CDP offers just the data selected by the user with no bulk data duplication. This eliminates the need for a data integrator to put up a predetermined schema or fixed field mappings for various event types. Retail Businesses are Leveraging Data Virtualization Retailers have been servicing an increasingly unpredictable customer base over the last two decades. They have the ability to do research, check ratings, compare notes among their personal and professional networks, and switch brands. They now expect to connect with retail businesses in the same way that they interact with social networks. To accomplish so, both established as well as modern retail businesses must use hybrid strategies that combine physical and virtual businesses. In order to achieve this, retail businesses are taking the help of data virtualization to provide seamless experiences across online and in-store environments. How Does Data Virtualization Help in the Elimination of Data Silos? To address these data-silo challenges, several businesses are adopting a much more advanced data integration strategy: data virtualization. In reality, data virtualization and data lakes overlap in many aspects. Both architectures start with the assumption that all data should be accessible to end users. Broad access to big data volumes is employed in both systems to better enable BI and analytics as well as other emerging trends like artificial intelligence and machine learning. Data Virtualization can address a number of big data pain points with features such as query pushdown, caching, and query optimization. Data virtualization enables businesses to access data from various sources such as data warehouses, NoSQL databases, and data lakes without requiring physical data transportation thanks to a virtual layer that covers the complexities of source data from the end user. A couple of use cases where data virtualization can eliminate data silos are: Agile Business Intelligence Legacy BI solutions are now unable to meet the rising enterprise BI requirements. Businesses now need to compete more aggressively. As a result, they must improve the agility of their processes. Data virtualization can improve system agility by integrating data on-demand. Moreover, it offers uniform access to data in a unified layer that can be merged, processed, and cleaned. Businesses may also employ data virtualization to build consistent BI reports for analysis with reduced data structures and instantly provide insights to key decision-makers. Virtual Operational Data Store The Virtual Operational Data Store (VODS) is another noteworthy use of data virtualization. Users can utilize VODS to execute additional operations on the data analyzed by data virtualization, like monitoring, reporting, and control. GPS applications are a perfect example of VODS. Travelers can utilize these applications to get the shortest route to a certain location. A VODS takes data from a variety of data repositories and generates reports on the fly. So, the traveler gets information from a variety of sources without having to worry about which one is the main source. Closing Lines Data warehouses and virtual data lakes are both effective methods for controlling huge amounts of data and advancing to advanced ML analytics. Virtual data lakes are a relatively new technique for storing massive amounts of data on commercial clouds like Amazon S3 and Azure Blob. While dealing with ML workloads, the capacity of a virtual data lake and data virtualization to harness more data from diverse sources in much less time is what makes it a preferable solution. It not only allows users to cooperate and analyze data in new ways, but it also accelerates decision-making. When you require business-friendly and well-engineered data displays for your customers, it makes a strong business case. Through data virtualization, IT can swiftly deploy and repeat a new data set as client needs change. When you need real-time information or want to federate data from numerous sources, data virtualization can let you connect to it rapidly and provide it fresh each time. Frequently Asked Questions What Exactly Is a “Virtual Data Lake?” A virtual data lake is connected to or disconnected from data sources as required by the applications that are using it. It stores data summaries in the sources such that applications can explore the data as if it were a single data collection and obtain entire items as required. What Is the Difference Between a Data Hub and a Data Lake? Data Lakes and Data Hubs (Datahub) are two types of storage systems. A data lake is a collection of raw data that is primarily unstructured. On the other hand, a data hub, is made up of a central storage system whose data is distributed throughout several areas in a star architecture. Does Data Virtualization Store Data? It is critical to understand that data virtualization doesn't at all replicate data from source systems; rather, it saves metadata and integration logic for viewing.

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Business Intelligence, Big Data Management, Big Data

Choose the Right BI Solution: Top Business Intelligence Companies

Article | August 17, 2023

Data is not stored in a single database, file system, data lake, or repository. Data generated in a system of record must meet various business requirements, connect with other data sources, and then be utilized for analytics, customer-facing apps, or internal procedures. A well-established data integration solution provides a unified picture of data received from various places and formats. This can also happen when two organizations merge or when internal applications are consolidated. Data integration can also facilitate the development of a more complete data warehouse, resulting in more accurate and effective analysis. Data integration establishes the foundation for effective Business Intelligence (BI) and decision-making. Data Integration as a Tool for Business Strategy The gathering, analysis, and integration of data is essential to the success of businesses. Let’s have a look at the way in which data integration technology enables business strategy. Set Data-Integration Goals These objectives should be part of the larger company objective. A thorough awareness of your consumers, for example, is corporate goal. To do this, your integration strategy should aim to embed customer data into your service, sales, and marketing platforms. Improved Financial Data Management A robust data integration strategy allows you to monitor and manage vital financial and operational data through simple dashboards that combine all business and financial data into a single platform. Any effective financial management system will include basic accounting capabilities that will enable you to track revenue and spending, assets and liabilities, and amortizations in order to provide accurate financial reports. Enhanced Marketing Analytics Data about competitors, industry trends, consumer behavior, and campaign performance should drive your marketing strategy. Update often as fresh data becomes available. By assessing your marketing tools and channels, you can determine the optimum time, place, and technique to advertise your company. Gather data from social media, email marketing tools, CRMs, CMSs, and other platforms for marketing analytics. This also allows you to evaluate where you should spend additional resources to improve the consistency of your marketing effort. Save Time and Resources Business intelligence experts have a huge workload of sifting through business data. Analysts worry less when teams have direct access to essential data. This frees them to concentrate on difficult, valuable data sets. Without data integration platforms, even the simplest business report requires manual processing of all sources, creating code or automatically uploading data to the database, and exhausting systematization. Not to mention the challenge to monitor and correct any human factor errors. This will be completely eliminated by integration automation. How AI Is Enhancing the Data Integration Process? Data Mapping: Businesses can map data faster using AI for insights generation and decision-making. Autonomous learning: An ML-based data integration process enables autonomous learning to discover patterns and trends in the stored data. Big data processing: Machine learning (ML) makes it possible to quickly and accurately transform unstructured and inconsistent data into desirable formats. Closing Lines Regardless of the size of your company or resources, processing and managing data correctly can expand vision of your business and customers. As organizations rely on data analytics and business intelligence, data integration will become more user-friendly in the coming years. Data integration is an unavoidable aspect of every organization's digital transformation path. Implement the most current data integration techniques to stay ahead of your competition.

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Business Intelligence, Big Data Management, Data Science

Predictive Maintenance with Industrial Big Data: Reactive to Proactive Strategies

Article | May 2, 2023

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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IQVIA

IQVIA (NYSE:IQV) is a leading global provider of advanced analytics, technology solutions, and clinical research services to the life sciences industry. IQVIA creates intelligent connections across all aspects of healthcare through its analytics, transformative technology, big data resources and extensive domain expertise. IQVIA Connected Intelligence™ delivers powerful insights with speed and agility — enabling customers to accelerate the clinical development and commercialization of innovative medical treatments that improve healthcare outcomes for patients. With approximately 86,000 employees, IQVIA conducts operations in more than 100 countries.

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NetApp Empowers Secure Cloud Sovereignty with StorageGRID

NetApp | November 08, 2023

NetApp introduces StorageGRID for VMware Sovereign Cloud, enhancing data storage and security for sovereign cloud customers. NetApp's Object Storage plugin for VMware Cloud Director enables seamless integration of StorageGRID for secure Object Storage for unstructured data. NetApp's Sovereign Cloud integration ensures data sovereignty, security, and data value while adhering to regulatory standards. NetApp, a prominent global cloud-led, data-centric software company, has recently introduced NetApp StorageGRID for VMware Sovereign Cloud. This NetApp plugin offering for VMware Cloud Director Object Storage Extension empowers sovereign cloud customers to cost-efficiently secure, store, protect, and preserve unstructured data while adhering to global data privacy and residency regulations. Additionally, NetApp has also unveiled the latest release of NetApp ONTAP Tools for VMware vSphere (OTV 10.0), which is designed to streamline and centralize enterprise data management within multi-tenant vSphere environments. The concept of sovereignty has emerged as a vital facet of cloud computing for entities that handle highly sensitive data, including national and state governments, as well as tightly regulated sectors like finance and healthcare. In this context, national governments are increasingly exploring ways to enhance their digital economic capabilities and reduce their reliance on multinational corporations for cloud services. NetApp's newly introduced Object Storage plugin for VMware Cloud Director offers Cloud Service Providers a seamless means to integrate StorageGRID as their primary Object Storage solution to provide secure Object Storage for unstructured data to their customers. This integration provides StorageGRID services into the familiar VMware Cloud Director user interface, thereby minimizing training requirements and accelerating time to revenue for partners. A noteworthy feature of StorageGRID is its universal compatibility and native support for industry-standard APIs, such as the Amazon S3 API, facilitating smooth interoperability across diverse cloud environments. Enhanced functionalities like automated lifecycle management further ensure cost-effective data protection, storage, and high availability for unstructured data within VMware environments. The integration of NetApp's Sovereign Cloud with Cloud Director empowers providers to offer customers: Robust assurance that sensitive data, including metadata, remains under sovereign control, safeguarding against potential access by foreign authorities that may infringe upon data privacy laws. Heightened security and compliance measures that protect applications and data from evolving cybersecurity threats, all while maintaining continuous compliance with infrastructure, trusted local, established frameworks, and local experts. A future-proof infrastructure capable of swiftly reacting to evolving data privacy regulations, security challenges, and geopolitical dynamics. The ability to unlock the value of data through secure data sharing and analysis, fostering innovation without compromising privacy laws and ensuring data integrity to derive accurate insights. VMware Sovereign Cloud providers are dedicated to designing and operating cloud solutions rooted in modern, software-defined architectures that embody the core principles and best practices outlined in the VMware Sovereign Cloud framework. Workloads within VMware Sovereign Cloud environments are often characterized by a diverse range of data sets, including transactional workloads and substantial volumes of unstructured data, all requiring cost-effective and integrated management that is compliant with regulated standards for sovereign and regulated customers. In addition to the aforementioned advancements, NetApp also announced a collaborative effort with VMware aimed at modernizing API integrations between NetApp ONTAP and VMware vSphere. This integration empowers VMware administrators to streamline the management and operations of NetApp ONTAP-based data management platforms within multi-tenant vSphere environments, all while allowing users to leverage a new micro-services-based architecture that offers enhanced scalability and availability. With the latest releases of NetApp ONTAP and ONTAP Tools for vSphere, NetApp has significantly made protection, provisioning, and securing modern VMware environments at scale faster and easier, all while maintaining a centralized point of visibility and control through vSphere. NetApp ONTAP Tools for VMware provides two key benefits to customers: A redefined architecture featuring VMware vSphere APIs for Storage Awareness (VASA) integration, simplifying policy-driven operations and enabling cloud-like scalability. An automation-enabled framework driven by an API-first approach, allowing IT teams to seamlessly integrate with existing tools and construct end-to-end workflows for easy consumption of features and capabilities.

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Salesforce Unveils Einstein 1 Platform: Transforming CRM Experiences

Salesforce | September 14, 2023

Salesforce introduces the groundbreaking Einstein 1 Platform, built on a robust metadata framework. The Einstein 1 Data Cloud supports large-scale data and high-speed automation, unifying customer data, enterprise content, and more. The latest iteration of Einstein includes Einstein Copilot and Einstein Copilot Studio. On September 12, 2023, Salesforce unveiled the Einstein 1 Platform, introducing significant enhancements to the Salesforce Data Cloud and Einstein AI capabilities. The platform is built on Salesforce's underlying metadata framework. Einstein 1 is a reliable AI platform for customer-centric companies that empowers organizations to securely connect diverse datasets, enabling the creation of AI-driven applications using low-code development and the delivery of entirely novel CRM experiences. Salesforce's original metadata framework plays a crucial role in helping companies organize and comprehend data across various Salesforce applications. 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The complexity of enterprise data systems has increased, and previous computing revolutions, such as cloud computing, social media, and mobile technologies, have generated isolated pockets of customer data. Furthermore, Salesforce ensures automatic upgrades three times a year, with the metadata framework safeguarding integrations, customizations, and security models from disruptions. This enables organizations to seamlessly incorporate, expand, and evolve their use of Salesforce as the platform evolves. The Einstein 1 Data Cloud, which supports large-scale data and high-speed automation, paves the way for a new era of data-driven AI applications. This real-time hyperscale data engine combines and harmonizes customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data, culminating in a unified customer view. Currently, the platform is already processing a staggering 30 trillion transactions per month and connecting and unifying 100 billion records daily. The Data Cloud is now natively integrated with the Einstein 1 Platform, and this integration unlocks previously isolated data sources, enabling the creation of comprehensive customer profiles and the delivery of entirely fresh CRM experiences. The Einstein 1 Platform has been expanded to support thousands of metadata-enabled objects per customer, each able to manage trillions of rows. Furthermore, Marketing Cloud and Commerce Cloud, which joined Salesforce's Customer 360 portfolio through acquisitions, have been reengineered onto the Einstein 1 Platform. Now, massive volumes of data from external systems can be seamlessly integrated into the platform and transformed into actionable Salesforce objects. Automation at scale is achieved by triggering flows in response to changes in any object, even events from IoT devices or AI predictions, at a rate of up to 20,000 events per second. These flows can interact with any enterprise system, including legacy systems, through MuleSoft. Analytics also benefit from this scalability, as Salesforce provides a range of insights and analytics solutions, including reports and dashboards, Tableau, CRM analytics, and Marketing Cloud reports. With the Einstein 1 Platform's common metadata schema and access model, these solutions can operate on the same data at scale, delivering valuable insights for various use cases. Salesforce has additionally made Data Cloud accessible at no cost to every customer with Enterprise Edition or higher. This allows customers to commence data ingestion, harmonization, and exploration, leveraging Data Cloud and Tableau to extend the influence of their data across all business segments and kickstart their AI journey. Salesforce's latest iteration of Einstein introduces a conversational AI assistant to every CRM application and customer experience. This includes: Einstein Copilot: This is an out-of-the-box conversational AI assistant integrated into every Salesforce application's user experience. Einstein Copilot enhances productivity by assisting users within their workflow, enabling natural language inquiries, and providing pertinent, trustworthy responses grounded in proprietary company data from the Data Cloud. Furthermore, Einstein Copilot proactively takes action and offers additional options beyond the user's query. Einstein Copilot Studio: This feature enables companies to create a new generation of AI-powered apps with custom prompts, skills, and AI models. This can help accelerate sales processes, streamline customer service, auto-generate websites based on personalized browsing history, or transform natural language prompts into code. Einstein Copilot Studio offers configurability to make Einstein Copilot available across consumer-facing channels such as websites and messaging platforms like Slack, WhatsApp, or SMS. Both Einstein Copilot and Einstein Copilot Studio operate within the secure Einstein Trust Layer, an AI architecture seamlessly integrated into the Einstein 1 Platform. This architecture ensures that teams can leverage generative AI while maintaining stringent data privacy and security standards. The metadata framework within the Einstein 1 Platform expedites AI adoption by providing a flexible, dynamic, and context-rich environment for machine learning algorithms. Metadata describes the structure, relationships, and behaviors of data within the system, allowing AI models to better grasp the context of customer interactions, business processes, and interaction outcomes. This understanding enables fine-tuning of large language models over time, delivering continually improved results.

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Big Data Management

Google Cloud and Bloomberg Unite to Accelerate Customers Data Strategies

Bloomberg | November 06, 2023

Bloomberg and Google Cloud integrate Data License Plus (DL+) with BigQuery for efficient data access and analytics. Customers can access fully modeled data within BigQuery, eliminating data preparation time. Mackenzie Investments adopts DL+ ESG Manager to host the acquisition, management, and publishing of Multi-vendor ESG data. Bloomberg has unveiled a new offering designed to accelerate the data strategies of Google Cloud customers by integrating Bloomberg's cloud-based data management solution, Data License Plus (DL+), with Google Cloud's fully managed, serverless data warehouse, BigQuery. Now, with access to Bloomberg's extensive experience modeling, managing, and delivering vast quantities of complex content, mutual customers can receive their Bloomberg Data License (DL) data, entirely modeled and seamlessly combined within BigQuery. As a result, organizations can leverage the advanced analytics capabilities of Google Cloud to extract more value from critical business information quickly and efficiently with minimal data wrangling. Through this extended collaboration, customers can harness the powerful analytics features of BigQuery and tap into Bloomberg's extensive collection of datasets available through Data License to power their most essential workloads. Bloomberg's Data License content offers a wide variety, including reference, pricing, ESG, regulatory, estimates, fundamentals, and historical data, supporting operational, quantitative, and investment research workflows, covering over 70 million securities and 40,000 data fields. Key benefits include: Direct Access to Bloomberg Data in BigQuery: Bloomberg customers can seamlessly access Bloomberg Data License content within BigQuery, allowing for scalable use across their organization. This eliminates the time-consuming tasks of ingesting and structuring third-party datasets, thereby accelerating the time-to-value for analytics projects. Elimination of Data Barriers: Google Cloud and Bloomberg will make Bloomberg's DL+ solution available to mutual customers via BigQuery. This allows for the delivery of fully modeled Bloomberg data and multi-vendor ESG content within their analytics workloads. In a recent announcement, Bloomberg revealed that Mackenzie Investments has selected DL+ ESG Manager to host the acquisition, management, and publishing of multi-vendor ESG data. This move positions Mackenzie Investments to implement ESG investing strategies more efficiently and develop sophisticated ESG-focused insights and investment products, with BigQuery playing a central role in powering these analytics workloads moving forward. Don Huff, the Global Head of Client Services and Operations at Bloomberg Data Management Services, stated that as capital markets firms are in the process of migrating their workloads to the Cloud, their customers require efficient access to high-quality data in a preferred environment. He expressed excitement about extending their partnership with Google Cloud, aiming to stay at the forefront of innovation in financial data management and to enhance their customers' enterprise analytics capabilities. Stephen Orban, the VP of Migrations, ISVs, and Marketplace at Google Cloud, stated that Google Cloud and Bloomberg share a common commitment to empowering customers making data-driven decisions to power their businesses. He mentioned that the expanded alliance between the two companies would allow customers to effortlessly integrate Bloomberg's leading datasets with their own data within BigQuery. This would simplify the process of conducting analytics with valuable insights related to financial markets, regulations, ESG, and other critical business information.

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Big Data Management

NetApp Empowers Secure Cloud Sovereignty with StorageGRID

NetApp | November 08, 2023

NetApp introduces StorageGRID for VMware Sovereign Cloud, enhancing data storage and security for sovereign cloud customers. NetApp's Object Storage plugin for VMware Cloud Director enables seamless integration of StorageGRID for secure Object Storage for unstructured data. NetApp's Sovereign Cloud integration ensures data sovereignty, security, and data value while adhering to regulatory standards. NetApp, a prominent global cloud-led, data-centric software company, has recently introduced NetApp StorageGRID for VMware Sovereign Cloud. This NetApp plugin offering for VMware Cloud Director Object Storage Extension empowers sovereign cloud customers to cost-efficiently secure, store, protect, and preserve unstructured data while adhering to global data privacy and residency regulations. Additionally, NetApp has also unveiled the latest release of NetApp ONTAP Tools for VMware vSphere (OTV 10.0), which is designed to streamline and centralize enterprise data management within multi-tenant vSphere environments. The concept of sovereignty has emerged as a vital facet of cloud computing for entities that handle highly sensitive data, including national and state governments, as well as tightly regulated sectors like finance and healthcare. In this context, national governments are increasingly exploring ways to enhance their digital economic capabilities and reduce their reliance on multinational corporations for cloud services. NetApp's newly introduced Object Storage plugin for VMware Cloud Director offers Cloud Service Providers a seamless means to integrate StorageGRID as their primary Object Storage solution to provide secure Object Storage for unstructured data to their customers. This integration provides StorageGRID services into the familiar VMware Cloud Director user interface, thereby minimizing training requirements and accelerating time to revenue for partners. A noteworthy feature of StorageGRID is its universal compatibility and native support for industry-standard APIs, such as the Amazon S3 API, facilitating smooth interoperability across diverse cloud environments. Enhanced functionalities like automated lifecycle management further ensure cost-effective data protection, storage, and high availability for unstructured data within VMware environments. The integration of NetApp's Sovereign Cloud with Cloud Director empowers providers to offer customers: Robust assurance that sensitive data, including metadata, remains under sovereign control, safeguarding against potential access by foreign authorities that may infringe upon data privacy laws. Heightened security and compliance measures that protect applications and data from evolving cybersecurity threats, all while maintaining continuous compliance with infrastructure, trusted local, established frameworks, and local experts. A future-proof infrastructure capable of swiftly reacting to evolving data privacy regulations, security challenges, and geopolitical dynamics. The ability to unlock the value of data through secure data sharing and analysis, fostering innovation without compromising privacy laws and ensuring data integrity to derive accurate insights. VMware Sovereign Cloud providers are dedicated to designing and operating cloud solutions rooted in modern, software-defined architectures that embody the core principles and best practices outlined in the VMware Sovereign Cloud framework. Workloads within VMware Sovereign Cloud environments are often characterized by a diverse range of data sets, including transactional workloads and substantial volumes of unstructured data, all requiring cost-effective and integrated management that is compliant with regulated standards for sovereign and regulated customers. In addition to the aforementioned advancements, NetApp also announced a collaborative effort with VMware aimed at modernizing API integrations between NetApp ONTAP and VMware vSphere. This integration empowers VMware administrators to streamline the management and operations of NetApp ONTAP-based data management platforms within multi-tenant vSphere environments, all while allowing users to leverage a new micro-services-based architecture that offers enhanced scalability and availability. With the latest releases of NetApp ONTAP and ONTAP Tools for vSphere, NetApp has significantly made protection, provisioning, and securing modern VMware environments at scale faster and easier, all while maintaining a centralized point of visibility and control through vSphere. NetApp ONTAP Tools for VMware provides two key benefits to customers: A redefined architecture featuring VMware vSphere APIs for Storage Awareness (VASA) integration, simplifying policy-driven operations and enabling cloud-like scalability. An automation-enabled framework driven by an API-first approach, allowing IT teams to seamlessly integrate with existing tools and construct end-to-end workflows for easy consumption of features and capabilities.

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Data Visualization

Salesforce Unveils Einstein 1 Platform: Transforming CRM Experiences

Salesforce | September 14, 2023

Salesforce introduces the groundbreaking Einstein 1 Platform, built on a robust metadata framework. The Einstein 1 Data Cloud supports large-scale data and high-speed automation, unifying customer data, enterprise content, and more. The latest iteration of Einstein includes Einstein Copilot and Einstein Copilot Studio. On September 12, 2023, Salesforce unveiled the Einstein 1 Platform, introducing significant enhancements to the Salesforce Data Cloud and Einstein AI capabilities. The platform is built on Salesforce's underlying metadata framework. Einstein 1 is a reliable AI platform for customer-centric companies that empowers organizations to securely connect diverse datasets, enabling the creation of AI-driven applications using low-code development and the delivery of entirely novel CRM experiences. Salesforce's original metadata framework plays a crucial role in helping companies organize and comprehend data across various Salesforce applications. This is like establishing a common language to facilitate communication among different applications built on the core platform. It then maps data from disparate systems to the Salesforce metadata framework, thus creating a unified view of enterprise data. This approach allows organizations to tailor user experiences and leverage data for various purposes using low-code platform services, including Einstein for AI predictions and content generation, Flow for automation, and Lightning for user interfaces. Importantly, these customizations are readily accessible to other core applications within the organization, eliminating the need for costly and fragile integration code. In today's business landscape, customer data is exceedingly fragmented. On average, companies employ a staggering 1,061 different applications, yet only 29% of them are integrated. The complexity of enterprise data systems has increased, and previous computing revolutions, such as cloud computing, social media, and mobile technologies, have generated isolated pockets of customer data. Furthermore, Salesforce ensures automatic upgrades three times a year, with the metadata framework safeguarding integrations, customizations, and security models from disruptions. This enables organizations to seamlessly incorporate, expand, and evolve their use of Salesforce as the platform evolves. The Einstein 1 Data Cloud, which supports large-scale data and high-speed automation, paves the way for a new era of data-driven AI applications. This real-time hyperscale data engine combines and harmonizes customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data, culminating in a unified customer view. Currently, the platform is already processing a staggering 30 trillion transactions per month and connecting and unifying 100 billion records daily. The Data Cloud is now natively integrated with the Einstein 1 Platform, and this integration unlocks previously isolated data sources, enabling the creation of comprehensive customer profiles and the delivery of entirely fresh CRM experiences. The Einstein 1 Platform has been expanded to support thousands of metadata-enabled objects per customer, each able to manage trillions of rows. Furthermore, Marketing Cloud and Commerce Cloud, which joined Salesforce's Customer 360 portfolio through acquisitions, have been reengineered onto the Einstein 1 Platform. Now, massive volumes of data from external systems can be seamlessly integrated into the platform and transformed into actionable Salesforce objects. Automation at scale is achieved by triggering flows in response to changes in any object, even events from IoT devices or AI predictions, at a rate of up to 20,000 events per second. These flows can interact with any enterprise system, including legacy systems, through MuleSoft. Analytics also benefit from this scalability, as Salesforce provides a range of insights and analytics solutions, including reports and dashboards, Tableau, CRM analytics, and Marketing Cloud reports. With the Einstein 1 Platform's common metadata schema and access model, these solutions can operate on the same data at scale, delivering valuable insights for various use cases. Salesforce has additionally made Data Cloud accessible at no cost to every customer with Enterprise Edition or higher. This allows customers to commence data ingestion, harmonization, and exploration, leveraging Data Cloud and Tableau to extend the influence of their data across all business segments and kickstart their AI journey. Salesforce's latest iteration of Einstein introduces a conversational AI assistant to every CRM application and customer experience. This includes: Einstein Copilot: This is an out-of-the-box conversational AI assistant integrated into every Salesforce application's user experience. Einstein Copilot enhances productivity by assisting users within their workflow, enabling natural language inquiries, and providing pertinent, trustworthy responses grounded in proprietary company data from the Data Cloud. Furthermore, Einstein Copilot proactively takes action and offers additional options beyond the user's query. Einstein Copilot Studio: This feature enables companies to create a new generation of AI-powered apps with custom prompts, skills, and AI models. This can help accelerate sales processes, streamline customer service, auto-generate websites based on personalized browsing history, or transform natural language prompts into code. Einstein Copilot Studio offers configurability to make Einstein Copilot available across consumer-facing channels such as websites and messaging platforms like Slack, WhatsApp, or SMS. Both Einstein Copilot and Einstein Copilot Studio operate within the secure Einstein Trust Layer, an AI architecture seamlessly integrated into the Einstein 1 Platform. This architecture ensures that teams can leverage generative AI while maintaining stringent data privacy and security standards. The metadata framework within the Einstein 1 Platform expedites AI adoption by providing a flexible, dynamic, and context-rich environment for machine learning algorithms. Metadata describes the structure, relationships, and behaviors of data within the system, allowing AI models to better grasp the context of customer interactions, business processes, and interaction outcomes. This understanding enables fine-tuning of large language models over time, delivering continually improved results.

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Big Data Management

Google Cloud and Bloomberg Unite to Accelerate Customers Data Strategies

Bloomberg | November 06, 2023

Bloomberg and Google Cloud integrate Data License Plus (DL+) with BigQuery for efficient data access and analytics. Customers can access fully modeled data within BigQuery, eliminating data preparation time. Mackenzie Investments adopts DL+ ESG Manager to host the acquisition, management, and publishing of Multi-vendor ESG data. Bloomberg has unveiled a new offering designed to accelerate the data strategies of Google Cloud customers by integrating Bloomberg's cloud-based data management solution, Data License Plus (DL+), with Google Cloud's fully managed, serverless data warehouse, BigQuery. Now, with access to Bloomberg's extensive experience modeling, managing, and delivering vast quantities of complex content, mutual customers can receive their Bloomberg Data License (DL) data, entirely modeled and seamlessly combined within BigQuery. As a result, organizations can leverage the advanced analytics capabilities of Google Cloud to extract more value from critical business information quickly and efficiently with minimal data wrangling. Through this extended collaboration, customers can harness the powerful analytics features of BigQuery and tap into Bloomberg's extensive collection of datasets available through Data License to power their most essential workloads. Bloomberg's Data License content offers a wide variety, including reference, pricing, ESG, regulatory, estimates, fundamentals, and historical data, supporting operational, quantitative, and investment research workflows, covering over 70 million securities and 40,000 data fields. Key benefits include: Direct Access to Bloomberg Data in BigQuery: Bloomberg customers can seamlessly access Bloomberg Data License content within BigQuery, allowing for scalable use across their organization. This eliminates the time-consuming tasks of ingesting and structuring third-party datasets, thereby accelerating the time-to-value for analytics projects. Elimination of Data Barriers: Google Cloud and Bloomberg will make Bloomberg's DL+ solution available to mutual customers via BigQuery. This allows for the delivery of fully modeled Bloomberg data and multi-vendor ESG content within their analytics workloads. In a recent announcement, Bloomberg revealed that Mackenzie Investments has selected DL+ ESG Manager to host the acquisition, management, and publishing of multi-vendor ESG data. This move positions Mackenzie Investments to implement ESG investing strategies more efficiently and develop sophisticated ESG-focused insights and investment products, with BigQuery playing a central role in powering these analytics workloads moving forward. Don Huff, the Global Head of Client Services and Operations at Bloomberg Data Management Services, stated that as capital markets firms are in the process of migrating their workloads to the Cloud, their customers require efficient access to high-quality data in a preferred environment. He expressed excitement about extending their partnership with Google Cloud, aiming to stay at the forefront of innovation in financial data management and to enhance their customers' enterprise analytics capabilities. Stephen Orban, the VP of Migrations, ISVs, and Marketplace at Google Cloud, stated that Google Cloud and Bloomberg share a common commitment to empowering customers making data-driven decisions to power their businesses. He mentioned that the expanded alliance between the two companies would allow customers to effortlessly integrate Bloomberg's leading datasets with their own data within BigQuery. This would simplify the process of conducting analytics with valuable insights related to financial markets, regulations, ESG, and other critical business information.

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