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If 2023 was the year of AI discovery and 2024 was that of AI experimentation, then 2025 will be the year that organisations seek to maximise AI-driven efficiencies and leverage AI for competitive advantage. Primary among these is the need to ensure the data that will power their AI strategies is fit for purpose.
Companies are leaning into delivering on data intelligence and governance initiatives in 2025 according to our recent State of Data Intelligence research. Data intelligence software is continuously evolving to enable organizations to efficiently and effectively advance new data initiatives.
By 2024 , 60% of the data used for the development of AI and analytics solutions will be synthetically generated. By 2025, 80% of data and analytics governance initiatives focused on business outcomes, rather than data standards, will be considered essential business capabilities.
Searching for data was the biggest time-sinking culprit followed by managing, analyzing and preparing data. Protecting data came in last place. In 2018, IDC predicted that the collective sum of the world’s data would grow from 33 zettabytes (ZB) to 175 ZB by 2025. That’s a lot of data to manage!
With seven operating centers, nine research facilities, and more than 18,000 staff, the agency continually generates an overwhelming amount of data, which it stores in more than 30 science data repositories across five topical areas — astrophysics, heliophysics, biological science, physical science, earth science, and planetary science.
Every day, organizations of every description are deluged with data from a variety of sources, and attempting to make sense of it all can be overwhelming. By 2025, it’s estimated we’ll have 463 million terabytes of data created every day,” says Lisa Thee, data for good sector lead at Launch Consulting Group in Seattle.
Establish what data you have. Active metadata gives you crucial context around what data you have and how to use it wisely. Active metadata provides the who, what, where, and when of a given asset, showing you where it flows through your pipeline, how that data is used, and who uses it most often.
It also recognised that more and more data was being harvested — but that challenges remained over how to extract truly valuable insight from it. It also set out a detailed plan to make data ‘ an enduring, strategic asset ’, with clear goals to be reached by 2025. What is a data strategy?
As data collection and volume surges, so too does the need for data strategy. As enterprises struggle to juggle all three, data governance offers a vital framework. The world is collectively generating trillions of gigabytes of new data. And one enterprise alone can generate a world of data. The result?
Among the tasks necessary for internal and external compliance is the ability to report on the metadata of an AI model. Metadata includes details specific to an AI model such as: The AI model’s creation (when it was created, who created it, etc.)
Enterprises are making major investments in their data and analytics capabilities, both to help manage growth in data and to cope with emerging data governance and regulatory pressures. According to IDC, by 2025, global data will grow to a whopping 175 zettabytes, and much of that growth will be in the cloud.
Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Use Case #6: DataQuality and Governance The size and complexity of data sources and datasets is making traditional data dictionaries and Entity Relationship Diagrams (ERD) inadequate.
See Roadmap for Data Literacy and Data-Driven Business Transformation: A Gartner Trend Insight Report and also The Future of Data and Analytics: Reengineering the Decision, 2025. measuring value, prioritizing (where to start), and data literacy? where performance and dataquality is imperative?
In 2025, data management is no longer a backend operation. This article dives into five key data management trends that are set to define 2025. AI-driven platforms process vast datasets to identify patterns, automating tasks like metadata tagging, schema creation and data lineage mapping.
However, a closer look reveals that these systems are far more than simple repositories: Data catalogs are at the forefront of bringing AI into your business for at least two reasons. However, lineage information and comprehensive metadata are also crucial to document and assess AI models holistically in the domain of AI governance.
For data management teams, achieving more with fewer resources has become a familiar challenge. While efficiency is a priority, dataquality and security remain non-negotiable. Developing and maintaining data transformation pipelines are among the first tasks to be targeted for automation. Register here!
Specializing in data, their teams are dedicated to ensuring the seamless integration, management, and accessibility of data across multiple facets of the organization. Performance Optimization: A significant portion of the DAGs are dynamically generated based on database metadata.
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