The average organization stores more than 160TB of data, with large organizations often managing more than 340TB. Data volumes continue to grow rapidly, with some enterprises seeing a 63 percent increase in data volume per month.
Trapped inside this data is a goldmine of business value. However, IBM estimates that as little as 32 percent of data is analyzed. The rest is stored, backed up and secured without ever unlocking its potential.
AI can help organizations gain insights from their data, but that data must be clean, structured and accessible. Data is typically scattered across legacy systems with inconsistent formats and definitions. It often contains duplicates, errors and missing values. The effort required to cleanse and integrate the data causes AI projects to stall.
Data intelligence can help organizations understand and unlock the value of their data. It is a disciplined process that combines data management principles with AI and machine learning to help improve how data is captured, cleaned, shared and secured.
Most organizations have tools and processes for data management, the practice of collecting, organizing, securing and storing data to ensure it is accurate and available. Data management covers the entire data lifecycle, combining technologies and policies to make raw data more useful for decision-making.
In most organizations, however, data management practices are themselves siloed. Different departments and teams use different tools to manage their particular datasets, with varying levels of discipline. The problem is often exacerbated by legacy systems, mergers and acquisitions, and a lack of integration. Whatever the cause, the result is duplicate, stale or inconsistent data trapped in isolated repositories.
Data intelligence gives organizations the insight they need to unify and optimize data management processes. It provides a high-level view of organizational data as a whole as well as the details of individual data points. It helps organizations understand why they have certain data, where it came from and who’s using it. It also reveals the relationships between the various datasets.
Data intelligence is a rapidly evolving discipline that’s built on five core pillars:
AI and machine learning add the “intelligence.” AI-powered tools can automatically add metadata using the organization’s own vocabulary. They can transform data lineage into an end-to-end view of complex data stacks. They can make data governance more proactive and scalable, and automate tasks such as cleansing, de-duplication and standardization. They can automatically match, merge and transform disparate data sources and enable real-time synchronization.
Data intelligence can benefit any organization, but organizations with large, complex datasets and the need for real-time decision-making often derive the most value. In financial services, for example, it helps organizations monitor millions of transactions in real time to detect suspicious activity. In healthcare, it aids in the development of predictive models to improve clinical decision-making.
Agentic AI has amplified the need for data intelligence. While AI agents enable autonomous action, their effectiveness depends directly on the quality, structure and accessibility of the underlying data. Data becomes the fuel for agentic AI’s planning and decision-making, making high-quality data and robust governance more critical than ever.
Technologent’s team includes data specialists who can help organizations develop a data intelligence strategy. Our six-prong data framework lays the foundation for secure, well-managed data that can help drive innovation. Contact our experts to discuss your specific challenges, needs and objectives.