Transform data into actionable insight

You’re a data leader. The pressure on you is mounting as your organization commits to AI initiatives and leadership looks to you for ROI.

It’s your responsibility to make sure the data used in AI models is reliable, high-quality, trustworthy and compliant with changing regulations. However, you probably have more data than you can manage, and you might not even know where all the data in your organization resides.

That’s where data intelligence comes in. It turns raw data into actionable insights, brings together data governance, quality, lineage and sharing, and gives data users access to reliable, contextual data.

As a data leader, you’re acutely aware of data’s game-changing power and the high expectations that follow. Your organization depends on you to turn data into trusted, actionable insights. But as data explodes in volume and complexity, the obstacles that block your path to success multiply just as fast.  

Overwhelming data 

The volume of data isn’t just large; it can also be unmanageable. Data is coming from everywhere, all the time. And while more data should mean more insights, it often means more noise. Valuable signals get buried. Important decisions get delayed. Also, the data you have may not always be easy to trust. 

Siloed data

You’ve felt the frustration of knowing that the answer is out there—somewhere—but is trapped in a silo, hidden in a system or unavailable because of inconsistent governance. When data is fragmented, even the best tools and talent struggle to deliver results.

Low-quality data

Accessing data is one thing, trusting it is another. Inconsistent formats, missing context and outdated sources don’t just waste time, money and effort; they make it hard for you to have trust in your data. And without trust, innovation stalls.

Data privacy and security

An IBM report states that the average cost of a data breach is USD 4.44 million in 2025. Given these staggering costs, every data-related decision you make carries enormous risk, especially in heavily regulated industries such as finance and healthcare.

Data intelligence helps you tackle these challenges by turning high volumes of fragmented data into clear, actionable insights. Most importantly, it provides answers to some of the crucial data-related questions.

• What data does the organization have and why does it exist?
• Where did this data come from and where does it reside?
• How has data transformed along its journey?
• Who has access to this data?
• How are datasets related to each other?
• Is the data reliable enough to train AI models?

By answering these questions, data intelligence gives organizations deeper insights into their data and how to derive maximum value from it. It empowers self-service analytics and supports key initiatives, including business intelligence and generative AI.

Today, companies are drowning in messy, scattered data, often reacting to it instead of deriving real value from it. Data intelligence delivers several key advantages that address these critical challenges. 

Finds siloed data

Data intelligence helps organizations discover, assess, catalog, curate and govern data assets, wherever they reside. Centralized, unified data catalogs and marketplaces reduce data infrastructure complexity and make it easier for teams to find the data they need. 

By breaking down silos and fostering collaboration, data intelligence drives faster, smarter decision-making. It increases business agility and accelerates time to value by giving the right people access to the right data.

Transforms raw data into actionable intelligence

By using data analytics, data intelligence extracts actionable insights from your data to help you make better decisions. This analysis can take several forms, including predictive analytics (to make future predictions) and prescriptive analytics (to determine the best course of action).

Data intelligence helps users understand what data their organization has and how it can be used, making it easier for teams to connect with the right datasets. 

Makes data more reliable

Without complete data confidence, enterprises struggle to realize the potential of AI. Data intelligence solves this issue by ensuring data quality across every dimension—from accuracy and completeness to consistency and timeliness. The result? Trusted data that delivers transformative business benefits.

“By even incrementally improving data quality, you can empower the organization to create value and minimize risk from your data.”
 

David Feshbach
Global Information Governance and Offering Method Lead
IBM Consulting, Hybrid Cloud and Data
 

Follow these five steps to turn your data challenges into opportunities:

1. Build a complete data inventory and profile

Identify and document the location of all data, including its sources and end users.

2. Run a data intelligence pilot program in a high-impact area

Demonstrate value by improving data governance, quality, lineage and access in one key area.

3. Automate and integrate data governance frameworks

Establish systems for data quality checks, regulatory compliance management and robust data lineage tracking.

4. Set up self-service data access and analytics platforms

Make it easy for the right users to find, understand and trust the data they need.

5. Promote a data-intelligence culture through training and engagement

Encourage a consistent use of data intelligence across all levels of the organization.