You’re a data leader. All eyes are on you and your team as your organization adopts and implements AI.
You have the responsibility to ensure the data used in AI models—and for all other use cases—is reliable, high-quality, accurate, complete, trustworthy, AI-ready, and compliant with ever-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.
Data intelligence helps organizations discover, curate, trust, and access data through cataloging, quality assurance, governance, lineage tracing, and sharing platforms.
To answer these questions, data intelligence uses an interconnected set of processes and tools to automate and streamline metadata management.
This unified process of intelligence gives organizations like yours more insight into their data and how to get the most value from it. Data intelligence thus enables self-service analytics and supports key initiatives for today’s enterprise, including business intelligence and generative AI.
We live in a world where data is everything for businesses, but when data is also everywhere then it can become very easy to lose track of which data is important and useful. What’s more, that data is often siloed and of low quality.
Excessive data
In today’s always-online, always-connected world, the amount of data available to organizations today is, frankly, overwhelming. And that volume is only growing.
Given the sheer amount of data that is available to enterprise businesses, it’s no surprise that much of it is going unused, and data that is collected but never analyzed is of no value to an organization.
Siloed data
Organizations are struggling to apply quality controls and enforce governance policies to this overflow of data. Meanwhile, users can’t always find the right data that they need (if they even know it exists) because of data siloing.
With fragmented data sources that lack both quality control and governance, it’s not surprising that data infrastructures are often far more complicated than they should be.
Low-quality data
A study by Experian revealed 95% of C-level executives believe data quality issues affect their organization’s ability to achieve business objectives.
Even when organizations can access and analyze their data, then, they might not be able to fully trust it, which is a huge problem leading to wasted time, money, and effort.
Fortunately, data intelligence can address all these problems so you can get the greatest value out of your data.
Data privacy and security
An IBM report suggests that the average cost of a data breach has reached USD 4.88 million in 2024, the highest on record. That’s why the privacy and security of an organization’s data—especially those in highly regulated industries such as finance and healthcare—is of utmost importance.
Fortunately, data intelligence can address all these problems so you can get the greatest value out of your data.
With excessive, siloed, and low-quality data abounding, today’s enterprise organizations are often serving at the whims of their data, rather than harnessing it and mining it for its value. That’s why data intelligence is so vital: it’s the solution to these problems, putting you back in charge of your data.
Data intelligence finds siloed data
Through centralized, unified data catalogs and marketplaces, data intelligence helps reduce data infrastructure complexity. It can help organizations discover, assess, catalog, curate, and govern data assets, wherever they reside.
As a result, users throughout your organization can find the right data for their needs, which streamlines operational efficiency and boosts collaboration.
By breaking down these silos and fostering a culture of informed collaboration, data intelligence solutions position your organization to act swiftly and with a deeper understanding of your markets.
Part of the transformative power of a data intelligence solution, then, is its ability to increase business agility and innovative capacity. It can accelerate time-to-value by sharing and distributing the right data assets to the right people with the right access, all via self-service models that ensure data accuracy, completeness, validity, consistency, uniqueness, and timeliness.
Data intelligence transforms raw data into actionable intelligence
By utilizing data analytics, data intelligence extracts actionable insights from your data so you can make better decisions. This data analytics can take several forms, including predictive analytics (using data to make predictions about the future) and prescriptive analytics (using data to determine what to do next).
Data intelligence helps users know what kind of data their organization has and what it can be used for, allowing them to more easily connect with the right datasets for their purposes.
Data intelligence makes data more reliable
Enterprises require end-to-end confidence in their data’s reliability. The data quality tools and practices that data intelligence encompasses help develop this reliability by ensuring their data’s accuracy, completeness, validity, consistency, uniqueness, timeliness and fitness for purpose.
By having easier access to higher-quality data, organizations can finally get to the point of trusting that data, which results in several important, and transformative, benefits for the enterprise.
The benefits of data intelligence include the ability to:
Transform raw data into actionable insights
with AI and machine learning
Accelerate data insights
by unifying data governance, data quality, data lineage and data sharing
Empower data consumers
to consume trustworthy and reliable data, contextualized in natural language with generative AI
What’s more, with the enhanced data quality provided by data intelligence, organizations can more readily discover and assess data quality at scale—wherever it resides, with automated capabilities for data profiling, cleansing, monitoring and more.
All of this leads to a greater competitive advantage, as organizations with data intelligence can harness real-time insights, accelerate data discovery, and prioritize high-caliber data. Being able to track and map your data from creation to consumption further helps to ensure accuracy, trust, and compliance.