Business Intelligence, Big Data Management, Big Data
Article | July 18, 2023
Explore the world of predictive analytics for businesses with the help of this comprehensive article. Examine vital predictive analytics events in 2024 to improve business strategy and outcomes.
Contents
1. Introduction to Predictive Analytics
2. Top Predictive Analytics Events in 2024
2.1 Machine Learning Week 2024
2.2 Machine Learning Week Europe 2024
2.3 Marketing Analytics Summit 2024
2.4 Generative AI World 2024
2.5 SMX Paris 2024
2.6 MLconf New York City 2024
2.7 Data Science Salon Austin 2024
2.8 Data Innovation Summit 2024
2.9 Gartner Data & Analytics Conference 2024
2.10 Big Data & AI World 2024
3. Summing Up
1. Introduction to Predictive Analytics
For business executives looking for innovation and long-term vision, 2024 stands out as a critical year in the quickly developing field of data science and analytics. The major predictive analytics events in 2024 throughout the year provide essential forums for change by providing in-depth knowledge of the most recent tactics and trends in the field.
For CEOs and analytics specialists, these conferences are crucial because they offer a special fusion of expertise, teamwork, and creativity needed to successfully negotiate the challenges of the data-driven world. They guarantee to provide participants with the tools and connections required to lead their companies toward expansion and gain a competitive edge. These developments will be crucial in shaping the direction of predictive analytics and corporate strategy going forward, making 2024 a historic year for those leading the charge in digital transformation.
2. Top Predictive Analytics Events in 2024
The field of predictive analytics is developing rapidly and pushing significant developments in several industries. In 2024, professionals who are keen to learn about the newest strategies and ideas will have access to a wide range of top conferences. These conferences provide unmatched chances for networking and knowledge sharing in a variety of industries, guaranteeing that participants stay at the top of predictive analytics.
2.1 Machine Learning Week 2024
Date: June 4-7, 2024
Venue: Sheraton Phoenix Downtown Hotel, Phoenix, AZ
The future of artificial intelligence in business is expected to be drastically changed by Machine Learning Week in 2024. For those at the forefront of machine learning, this will be a vital gathering that offers a combination of keynote addresses, plenary sessions, and practical workshops. It is a must-attend event for anybody hoping to stay ahead in the constantly changing machine learning fields, as it provides a unique chance for experts from a variety of sectors to explore the nexus between AI and business.
Principal Aspects of the Event
Exploring Machine Learning: A perspective on game-changing advances and transformative AI developments
Practical Applications: Workshops and case studies centered on actual implementation in a variety of sectors.
Responsible AI: Using moral principles to inform responsible AI development and application.
Networking and Collaboration: Chances for participants to meet visionaries and leaders in the field.
Who Can Attend ML Week 2024
Data Scientists & Analytics Managers: Individuals working in analytics and data-driven decision-making.
Software Engineers & AI Practitioners: Experts who develop and implement artificial intelligence systems.
Entrepreneurs & Decision-Makers: Professionals who aim to comprehend artificial intelligence and include it into their working life.
2.2 Machine Learning Week Europe 2024
Date: November 18-19, 2024
Venue: Munich, Germany
An important event for utilizing AI, machine learning, and predictive and prescriptive analytics across a variety of industries. With sessions in Tech/Deep Dives, Business and Industry Case Studies, and extensive seminars, this will be one of the top data analytics events of this year that aim to create a collaborative platform for data science experts throughout Europe.
Key Benefits of Attending Machine Learning Week Europe 2024
Practical insights: Learn how machine learning and artificial intelligence are being applied across various industries.
Case Study Learning: Learn from in-depth case studies across a range of industries.
Technical Engagement: Take part in in-depth technical discussions of the newest machine learning innovations.
Professional networking: Network with a group of individuals working in data science.
Trend Awareness: Stay abreast with the newest developments in an area that is evolving quickly to improve knowledge and abilities.
2.3 Marketing Analytics Summit 2024
Date: June 6-7, 2024
Venue: Sheraton Phoenix Downtown Hotel, Phoenix, AZ
The Marketing Analytics Summit 2024 is a premier event of marketing professionals and leaders in digital analytics, with the goal of developing the discipline of marketing analytics. The summit's main goal is to empower participants with the most recent information and resources for using marketing data to inform strategy and decision-making. Phoenix, which is renowned for its dynamic corporate scene, provides a bright backdrop for the event, which has a strong history of innovation and community building.
Keynote Speakers
Lina Mikolajczyk, Director of Analytics
Joe A. Miscavige, Senior Director of Data Distribution Strategy
Ely Rosenstock, Associate Director, Human Health Digital, Data, and Analytics Jim Sterne, Founder
Benefits of Attending the Marketing Analytics Summit 2024
Knowledge Sharing: Learn from more than 1,500 speakers who have made major contributions to the field of marketing analytics.
In-Depth Workshops: Take part in programs aimed at technical mastery and effective marketing insight sharing, such as ‘SQL on GA4 in BQ: BigQuery for Digital Marketers’ and ‘Building World-class Business Dashboards.’
Networking Opportunities: Make meaningful connections with a varied range of business leaders, opinion leaders, and online analysts to develop fruitful professional partnerships.
Career and Business Growth: Gain knowledge from a conference that provides unrivalled chances for personal and entrepreneurial development, having advanced over 20,000 careers and acted as a launchpad for 100 enterprises.
Community Engagement: Be a part of a group that is leading the way in establishing the standards and practices in the marketing analytics sector, having established the Digital Analytics Association with great pride.
2.4 Generative AI World 2024
Date: June 6-7, 2024
Venue: Sheraton Phoenix Downtown Hotel, Phoenix, AZ
Generative AI World 2024 emerges as a pivotal event, set against the dynamic backdrop of Phoenix, AZ. It aims to convert the burgeoning excitement around Generative AI into actionable business strategies and value, positioning itself as a critical summit for data analytics professionals driving digital innovation.
Workshops and Key Speakers
June 4: ‘Deep Learning in Practice: A Hands-On Introduction’ by Bardia Beigi and Prerna Singh
‘Generative AI: From Basic Concepts to Real-World Applications’ by Martin Musiol
Networking Cultivation Platform
Hundreds of CEOs, data scientists, and AI pioneers from a variety of industries will have a common platform to interact at Generative AI World 2024. The goal of the conference is to provide an environment for deep industry networking, lively discussions, and the exchange of significant information, ensuring that participants depart with meaningful relationships and new perspectives.
Noteworthy Focus
The conference's business focus sets it apart from the competition as it seeks to bridge the gap between scholarly research and the real-world application of generative AI in the marketplace. Through hands-on demonstrations and discussions on the most recent developments, attendees will be able to see personally the transformational potential of generative AI. These sessions will highlight real-world applications and the challenges associated with implementing these technologies.
2.5 SMX Paris 2024
Date: March 14-15, 2024
Venue: Étoile Business Center, Paris, France
Immerse in the evolving field of search marketing at SMX Paris 2024. This 13th edition of the conference, taking place in the heart of Paris, is designed to arm professionals with the insights and trends that will define the future of the industry. With keynote speakers Rand Fishkin and Sam Tomlinson leading the charge, attendees will be in for an enlightening experience that will push the boundaries of search marketing into new territories.
Why Attend SMX Paris 2024
The event has been carefully planned to include a wide range of challenges that are important for today's professionals, such as the implications of automation and artificial intelligence in advertising and e-commerce, as well as SEO methods designed for scaling firms.
In addition to being an excellent source of information, the conference serves as a dynamic forum for networking, giving participants the ability to get in touch with thought leaders, entrepreneurs, and other professionals.
For individuals who aspire to be successful in the search marketing industry, SMX Paris is an essential event due to its exceptional combination of in-depth education, professional insights, and networking possibilities.
Who Must Not Miss This Event
A wide range of people in the search marketing and digital advertising industries are the target audience for SMX Paris 2024. The conference is tailored to meet the goals of corporate search marketing experts, agency SEO/SMO/SEA professionals, marketing managers or directors seeking strategic enhancement, as well as webmasters and site administrators focused on optimizing site referencing efficiently. Attending the sessions and workshops will be fruitful for anyone aiming to stay ahead in the ever-changing field of search marketing.
2.6 MLconf New York City 2024
Date: March 28, 2024
Venue: New York City
Professionals from a variety of academic disciplines and industries are invited to attend MLconf New York City 2024 to learn more about the latest developments in the field of machine learning innovation. This conference will provide alternatives for both virtual and in-person attendance that are accessible to a worldwide audience.
Speakers
Prominent speakers on AI research, data science, engineering, and machine learning applications will include Debasmita Das from Mastercard, Arun Krishnaswamy from Zuora, Jeremy Wilken from NVIDIA, Facundo Santiago from Microsoft, and Sanket Gupta from Spotify.
Who Should Attend
A wide range of professionals, including data scientists, engineers, software developers, computer vision specialists, technical leaders, entrepreneurs with start-ups, and academics made up of instructors and students, are an ideal audience for MLconf NYC 2024. By exchanging extensive knowledge, innovative approaches, and networking opportunities, attendees will be positioned to lead the way in machine learning innovation.
2.7 Data Science Salon Austin 2024
Date: March 20-21, 2024
Venue: Austin, TX
Austin, Texas will be the host to the Data Science Salon 2024 once again, providing a platform for thought leaders in the field for a meeting and explore into innovative subjects including predictive analytics, generative AI, and machine learning. Through a unique combination of technical discussions, seminars, and use scenarios, participants will get practical insights from the leaders of machine learning inside a business environment.
Learning and Innovation
Creative Solutions: Participants will take part in talks with eminent speakers—Fatma Tarlaci, Eric Landry, Sreevani Konda, and others—who will share their thoughts on the most recent developments in generative AI, machine learning, and predictive analytics.
Special Addition: The ‘Future of ML & AI Startups + Showcase’ will highlight the inventive spirit of the AI and ML startup community by providing a rare chance to examine VC panels, startup success stories, and a startup display with a $10,000 award.
Exposure to Startup Showcase: Explore innovative AI and ML startups, learn from their success stories, and witness groundbreaking ideas in action, providing inspiration and potential opportunities for investment or collaboration.
2.8 Data Innovation Summit 2024
Date: April 24-25, 2024
Venue: Onsite at Kistamässan, Stockholm | Online through Agorify
Take advantage of the most important Data and AI event of the year, the Data Innovation Summit 2024, and join the latest generation of data, analytics, and AI specialists. This hybrid event, which will be streamed live online via Agorify and held at Stockholm's Kistamässan, will launch a new chapter in the history of data-driven transformation.
Key Benefits of Attending
Discover better from more than 300 sessions covering a broad spectrum of topics from applied AI to IoT, all led by 300 Nordic and international speakers.
Connect with over 3000 attendees from around the world at networking events, expo explores, and the special DIS24 AW & Data After Dark.
Explore more than 100 exhibitors' modern data and AI solutions, which highlight creative initiatives, goods, and technologies.
Who Should Attend
Practitioners in any enterprise looking to speed up AI-driven organizational change.
Technology companies showcasing their most recent breakthroughs and solutions.
Innovative startups looking to expand their networks, become more visible, and find possible partners.
Academics who want to remain up to date on the newest innovations and patterns in the data and artificial intelligence domains.
2.9 Gartner Data & Analytics Conference 2024
Date: March 26-27, 2024
Venue: São Paulo, Brazil
Attracting Chief Data Analytics Officers (CDAOs) and Data & Analytics (D&A) professionals from many industries, the Gartner Data & Analytics Conference 2024 in Sao Paulo, Brazil, becomes the essential gathering place for IT and business executives. This conference provides beneficial insights and connections as it is committed to launching enterprises into a new era of innovation through the strategic use of data, analytics, and artificial intelligence. Participants can anticipate gaining priceless insights and strategies to propel company expansion and success in the dynamic field of data-driven decision-making.
Key Benefits of Participation
Learn important lessons from over 72 sessions covering the most recent research from Gartner, designed to maximize corporate value and decision-making.
Join a global network of thought leaders, D&A executives, and CDAOs to exchange strategies, difficulties, and innovations.
Examine a wide range of subjects that are important to D&A executives, such as maximizing company value, data management best practices, and creative models.
Who Should Attend
Heads of Data & Analytics
Chief Data Officers (CDOs), Chief Analytics Officers (CAOs), Chief Data Analytics Officers (CDAOs)
Analytics and Business Intelligence (BI) Leaders
Information Management and Master Data Management Leaders
Architects and IT Professionals
2.10 Big Data & AI World 2024
Date: March 6-7, 2024
Venue: ExCeL London
Big Data & AI World 2024, a premier event in the UK for cutting-edge technologies and innovative ideas in big data and artificial intelligence, is poised to revolutionize the field of predictive analytics. Scheduled for March 6-7, 2024, at ExCeL London, this conference promises to be an unparalleled convergence of technology enthusiasts eager to drive digital evolution with predictive analytics at the forefront.
Why Should You Be There
Prepare for the next phase of digital transformation by investing in sessions that center on predictive analytics. Optimize with Big Data, improve with AI.
Learn from professionals that share their success stories and insights about predictive analytics in a variety of industries, including media, healthcare, and finance.
The complimentary ticket grants complete access to all Tech Show London events, offering attendees an extensive overview of the technology environment influencing predictive analytics.
3. Summing Up
In 2024, predictive analytics professionals must seize the opportunity at top events like Predictive Analytics Events 2024. These events provide priceless networking opportunities, insights, and resources for understanding the data-driven area of artificial intelligence and machine learning. For B2B executives and specialists, these events are essential for gaining a competitive edge and driving innovation in today's digital era.
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Business Intelligence, Big Data Management, Big Data
Article | July 10, 2023
Introduction
There are many articles explaining advanced methods on AI, Machine Learning or Reinforcement Learning. Yet, when it comes to real life, data scientists often have to deal with smaller, operational tasks, that are not necessarily at the edge of science, such as building simple SQL queries to generate lists of email addresses to target for CRM campaigns. In theory, these tasks should be assigned to someone more suited, such as Business Analysts or Data Analysts, but it is not always the case that the company has people dedicated specifically to those tasks, especially if it’s a smaller structure.
In some cases, these activities might consume so much of our time that we don’t have much left for the stuff that matters, and might end up doing a less than optimal work in both. That said, how should we deal with those tasks? In one hand, not only we usually don’t like doing operational tasks, but they are also a bad use of an expensive professional. On the other hand, someone has to do them, and not everyone has the necessary SQL knowledge for it. Let’s see some ways in which you can deal with them in order to optimize your team’s time.
Reduce
The first and most obvious way of doing less operational tasks is by simply refusing to do them. I know it sounds harsh, and it might be impractical depending on your company and its hierarchy, but it’s worth trying it in some cases. By “refusing”, I mean questioning if that task is really necessary, and trying to find best ways of doing it. Let’s say that every month you have to prepare 3 different reports, for different areas, that contain similar information. You have managed to automate the SQL queries, but you still have to double check the results and eventually add/remove some information upon the user’s request or change something in the charts layout. In this example, you could see if all of the 3 different reports are necessary, or if you could adapt them so they become one report that you send to the 3 different users. Anyways, think of ways through which you can reduce the necessary time for those tasks or, ideally, stop performing them at all.
Empower
Sometimes it can pay to take the time to empower your users to perform some of those tasks themselves. If there is a specific team that demands most of the operational tasks, try encouraging them to use no-code tools, putting it in a way that they fell they will be more autonomous. You can either use already existing solutions or develop them in-house (this could be a great learning opportunity to develop your data scientists’ app-building skills).
Automate
If you notice it’s a task that you can’t get rid of and can’t delegate, then try to automate it as much as possible. For reports, try to migrate them to a data visualization tool such as Tableau or Google Data Studio and synchronize them with your database. If it’s related to ad hoc requests, try to make your SQL queries as flexible as possible, with variable dates and names, so that you don’t have to re-write them every time.
Organize
Especially when you are a manager, you have to prioritize, so you and your team don’t get drowned in the endless operational tasks. In order to do this, set aside one or two days in your week which you will assign to that kind of work, and don’t look at it in the remaining 3–4 days. To achieve this, you will have to adapt your workload by following the previous steps and also manage expectations by taking this smaller amount of work hours when setting deadlines. This also means explaining the paradigm shift to your internal clients, so they can adapt to these new deadlines. This step might require some internal politics, negotiating with your superiors and with other departments.
Conclusion
Once you have mapped all your operational activities, you start by eliminating as much as possible from your pipeline, first by getting rid of unnecessary activities for good, then by delegating them to the teams that request them. Then, whatever is left for you to do, you automate and organize, to make sure you are making time for the relevant work your team has to do. This way you make sure expensive employees’ time is being well spent, maximizing company’s profit.
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Big Data Management, Data Science, Big Data
Article | May 16, 2023
The acronym DMaaS can refer to two related but separate things: data center management-as-a-service referred to here by its other acronym, DCMaaS and data management-as-a-service. The former looks at infrastructure-level questions such as optimization of data flows in a cloud service, the latter refers to master data management and data preparation as applied to federated cloud services.DCMaaS has been under development for some years; DMaaS is slightly younger and is a product of the growing interest in machine learning and big data analytics, along with increasing concern over privacy, security, and compliance in a cloud environment.DMaaS responds to a developing concern over data quality in machine learning due to the large amount of data that must be used for training and the inherent dangers posed by divergence in data structure from multiple sources. To use the rapidly growing array of cloud data, including public cloud information and corporate internal information from hybrid clouds, you must aggregate data in a normalized way so it can be available for model training and processing with ML algorithms. As data volumes and data diversity increase, this becomes increasingly difficult.
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Big Data Management, Data Visualization, Data Architecture
Article | August 18, 2022
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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