What Is Data Intelligence? Functions, Benefits, & Use Cases

This blog was last updated in September 2023

What Is Data Intelligence?

Data intelligence is a system to deliver trustworthy, reliable data. It includes intelligence about data, or metadata. IDC coined the term, stating, “data intelligence helps organizations answer six fundamental questions about data.” These questions are:

• Who is using what data?

• Where is data, and where did it come from (lineage and provenance)?

• When is data being accessed, and when was it last updated?

• Why do we have data? Why do we need to keep (or discard) data?

• How is data being used, or – how should data be used?

• Relationships – what relationships are inherent within data and with data consumers?

  • Source: IDC, Defining Data Intelligence

Data intelligence supports human understanding. By answering key questions around the who, what, where and when of a given data asset, DI paints a picture of why folks might use it, educating on that asset’s reliability and relative value. Insights into how an asset’s been used in the past inform how it might be intelligently applied in the future.

In the fable "The Blind Men and the Elephant," multiple conclusions are drawn by six blind men about the same creature. Similarly, data intelligence provides a comprehensive "big picture" view of complex systems by integrating diverse perspectives.

Data Intelligence vs. Data Analytics

Although these terms are sometimes used interchangeably, they are distinct pillars of modern data management. Each serves a unique role at various stages of the data lifecycle.

The main goal of data intelligence is to effectively manage data as a valuable asset. By contrast, data analytics is focused on the use of data to extract insights and guide decision-making processes. Both are indispensable in today's data-driven landscape, with data intelligence forming the foundation for effective data analytics.

Yet data intelligence is more than a system for judging a single asset alone. It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include data governance, self-service analytics, and more.

Data Intelligence: Origin, Evolution, Use Cases

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. The earliest DI use cases leveraged metadata — EG, popularity rankings reflecting the most used data — to surface assets most useful to others.

Yet finding data is just the beginning. Analysts have questions before queries. They need to know: Who has used this data in the past? How did they use it? How should I use it… if I’m allowed to use it at all?

Examples of Data Intelligence Use Cases

Data intelligence has thus evolved to answer these questions, and today supports a range of use cases. Examples of Data Intelligence use cases include:

• Data governance

• Cloud Transformation

• Cloud Data Migration

• Privacy, Risk and Compliance

• Digital Transformation

• Analytics

Let’s take a closer look at the role of DI in the use case of data governance.

Data Intelligence and Data Governance

Returning to the analyst example: the earliest iterations of DI helped analysts find relevant data. And the support stopped there. Yet analysts needed to know how they could and should use the data… if at all. They said, “If I’m building a report for an executive audience, to guide crucial decision making, I want to make sure the data foundations in that report are solid!”

Data governance has emerged as the answer to this challenge. It addresses vital questions from distinct points of view, empowering all data consumers to know: Is this the right dataset to use…

• … from a compliance perspective?

• … from a quality perspective?

• … from a business perspective?

Data governance formalizes responsibility and authority around data, so roles are clearly defined, and who can do what is transparent to all. Importantly, people are guided to the best data and its most appropriate use. In this way, data governance supports trust and transparency.

Examples of Data Intelligence-Driven Governance Features

So how does data intelligence support governance? Again, metadata is key. Examples of governance features that leverage data intelligence include:

• A business glossary, with automated data classification, to align teams on key terms

• Data lineage tracking and impact analysis reports to show transformation over time

• Trustflags that signal a dataset contains sensitive information, like PII

• Deprecations, which warn users if a dataset has been marked deprecated

• A stewardship dashboard, to track assets most ripe for curation and curation progress

Under an active data governance framework, a Behavioral Analysis Engine will use AI, ML and DI to crawl all data and metadata, spot patterns, and implement solutions.

Data Governance and Data Strategy

Historically, data governance was used defensively as a tool to enforce compliance and ensure audits could be passed. Compliance is essential, especially for regulated industries, but the command-and-control approach to governance that often accompanied this defensive posture created barriers between people and data, and threatened work culture.

Today, enlightened governance leaders are realizing that governance can service a data strategy that plays both offense and defense. In other words, leaders are prioritizing data democratization to ensure people have access to the data they need. Data catalogs then integrate compliance at the point of consumption, so people are alerted to sensitive data where it lives.

Data Intelligence and Metadata

Data intelligence is fueled by metadata. And types of metadata — or data about data — abound. Some high-level metadata categories in a data catalog include:

• Behavioral: Records who is using data, and how they are using it

• Technical: Shows schema or table definitions

• Business: Policies on how to handle different kinds of data appropriately

• Provenance: Shows relationship between two versions of data objects, generated when a new version of a dataset is created (also known as lineage.)

Behavioral metadata is incredibly valuable, as it represents human wisdom around data in an organization. It shows how people use data to glean insights — and learn from each other. How people use data across an enterprise forms a kind of energy: this is the animating spirit of that organization’s unique data intelligence.

As you use a data catalog more extensively, you’ll be exposed to other forms of metadata:

• People metadata: Describes those who work with data, including consumers, curators, stewards, and subject matter experts

• Search metadata: Supports tagging and keywords to help people find relevant data

• Processing metadata: Describes transformations and derivations applied as data is managed through its lifecycle

• Supplier metadata: Important for data acquired from external sources, it includes details about those sources, and subscription or licensing constraints.

  • Source: Introduction to Data Catalogs by Dave Wells

Finally, data catalogs leverage behavioral metadata to glean insights into how humans interact with data. This category synthesizes various metadata types to guide proper usage across all use cases.

Data Intelligence and Active Metadata

Undergirded by ML and AI, and augmented by human intelligence, active metadata gleans internal insights about how people are using data. In this way, it can improve processes, safeguard quality, and synchronize terms across departments. Active metadata offers benefits like:

• Provides context around data’s past usage to support confident self-service analytics

• Identifies the best data to answer a given business question

• Spotlights data pursuant to privacy or regulation laws and guides proper usage

• Translates technical terms into natural human language

• Merges systems based on observed human patterns

In this way, active metadata fuels data intelligence within an enterprise and supports better data management. Active metadata borrows its brilliance from human usage. It gleans insights into how folks use data to empower organizations to manage their data in an increasingly scalable, innovative and efficient manner (Forbes).

What Is a Data Intelligence Software?

Data intelligence software supports a culture of data-driven decision-making. Just as customer relationship management (CRM) software supports improved customer experience, so too DI software supports data culture.

As an organizational discipline, data intelligence manifests in a number of practices, systems, and use cases. It relies on data intelligence software to be managed and optimized. That software typically includes features like:

• Business glossaries and data dictionaries (to store definitions)

• Profiling tools

• Stewardship dashboards

• Data lineage features

• Data cataloging functions, like natural language processing

As data collection and volume surges, enterprises are inundated in both data and its metadata. For this reason, data intelligence software has increasingly leveraged artificial intelligence and machine learning (AI and ML) to automate curation activities, which deliver trustworthy data to those who need it.

Recently, Alation CEO Satyen Sangani spoke about the importance of trusted data and how Alation's Data Intelligence Platform helps organizations manage their data on Bloomberg Technology. Listen below to check out the insights.

How Do Data Intelligence Tools Support Data Culture?

Data intelligence tools contribute significantly to fostering a data-driven culture within organizations. Here are some examples of how they achieve this:

• Business glossaries: Support data literacy by establishing a common language across the enterprise, so people use the correct terms consistently. 

• Stewardship dashboards: Enable stewards to track curation progress and delegate curation activities to others. Sometimes these tools use AI and ML to suggest new business glossary terms and steamline approvals.

• Search & discovery: Like Google for enterprise data, these features connect people to the best data -- and to subject matter experts on that data, supporting collaboration and education.

• Profiling tool: Profiles, which capture metadata, support human understanding of a specific asset, with details about its creators, past users, and transformations.

These tools collectively play a crucial role in cultivating a data-centric environment, ultimately supporting data culture and promoting effective data management practices.

BI and AI for Data Intelligence

Business Intelligence (BI) is explanatory and backward-looking. It surveys the past to explain what happened and why. Artificial Intelligence and Machine Learning (AI & ML) are forward-looking. These future-oriented models are used to make predictions.

Most modern organizations leverage BI in one form or another. First appearing in the 1960s, BI emerged as a means to share information across an enterprise. Today, BI represents a $23 billion market and umbrella term that describes a system for data-driven decision-making.

BI leverages and synthesizes data from analytics, data mining, and visualization tools to deliver quick snapshots of business health to key stakeholders, and empower those people to make better choices. A common BI application is the BI dashboard, which displays key metrics so that leaders have a “big picture view” to inform wise goals and decisions.

Artificial Intelligence, too, is a fast-growing market, valued at $21 billion. Today, modern organizations use AI to glean competitive insights, pulling nuggets of wisdom from a river of data. AI and ML are used in concert to predict possible events and model outcomes. BI, AI, and ML are all plagued by the same challenge: low-quality data.

The BI and AI Problem: Garbage In, Garbage Out

Yet BI and AI systems are only so useful as the data supplied to them. Today, data experts struggle with a common problem called garbage in, garbage out. As lakes of data become oceans, locating that which is trustworthy and reliable grows more difficult — and important. Indeed, as businesses attempt to scale AI and BI programs, small issues around data quality can transmogrify into massive challenges.

“People talk a lot about AI and BI to transform their businesses,” says Alation CEO Satyen Sangani. “But if you want to do either of those things at scale, you’ll need data intelligence.”

How Data Intelligence Helps Businesses Grow

Data intelligence has emerged as the solution to the garbage-in, garbage out problem that’s long stymied AI and BI efforts. Data intelligence is an amalgamation of categories, which include:

• Metadata management

• Data quality

• Data governance

• Master data management

• Data profiling

• Data curation

• Data privacy

Data intelligence adds one more key component to the mix, called active metadata. Coined by Guido De Simoni, senior director at analyst firm Gartner, active metadata describes intelligence around how people use data within an organization.

Business Value and Data Intelligence

Data intelligence represents a strategic asset of immense business value. It empowers organizations to make informed decisions by harnessing insights derived from data. Analyzing data across various aspects of operations helps companies streamline processes and gain a competitive edge.

This, in turn, allows them to personalize offerings to meet customer needs, ultimately bolstering customer loyalty.

Additionally, data intelligence contributes to risk mitigation, fuels innovation, optimizes resource allocation, ensures regulatory compliance, and strengthens cybersecurity measures. It supports digital transformation by turning data into actionable insights that drive business growth, efficiency, and competitiveness.

Data intelligence can help data leaders boost engagement, with dashboards that show how folks are using data across an enterprise.

Types of Data Intelligence

There are five common types of data intelligence, with distinct purposes and applications:

• DescriptiveTo review data and comprehend performance

• Prescriptive To form alternative knowledge and new recommendations

• DiagnosticTo analyze why something happened and determine causes

• PredictiveTo examine historical data and predict future potential incidents

• DecisiveTo gauge data value and recommend new courses of action

Historically, such data intelligence use cases were typically applied to the external world of business operations. But with the rise of active metadata, which gleans internal insights into how data is used, data intelligence is increasingly being applied internally, to increase operational efficiencies, as well.

5 Benefits of Data Intelligence

Data intelligence (DI) supports data experts in their quest to make better decisions. As a system, its goal is simple: to make folks in an organization smarter about the organization, and better equipped to lead its growth. Key high-level benefits include:

1. Adaptive Decision Making

When BI dashboards have accurate, timely information, leaders can make quicker decisions in the moment to stay ahead of the competition. Businesses can adapt their strategy in real time to better anticipate needs and support customers.

A BI dashboard supports of-the-moment decision making.

2. Stronger Data Foundations

How should data be organized and delivered? DI continuously observes processes to streamline operations and better support key stakeholders. Further, by safeguarding quality, DI provides data that is trustworthy and reliable to AI and BI use cases.

3. Operational Efficiencies

Data search & discovery connects people to the data they need. Historically, an analyst would spend up to six weeks just searching for a trustworthy data set. DI sorts wheat from chaff, spotlighting the most trusted assets for wider use, and speeding up operational efficiencies in the process.

4. Augmented Analytics

Why reinvent the wheel? DI empowers analysts to apply augmented analytics to applications, supporting predictive and prescriptive analytics use cases.

5. Transparency Supports Teamwork and Trust

By creating a system around how new truths are proven, DI aligns minds around an organization’s guiding principles and the process for crystallizing them. DI software may borrow from the scientific method to structure its process toward progress.

DI leverages what is known, the collective deduced knowledge of an organization, and integrates this wisdom into the system of data management. The goal is to create an ever-improving system for decision making in support of the business.

How Do You Implement Data Intelligence?

By partnering with a data intelligence platform well suited to your goals, data leaders can integrate DI into their daily business processes. But how do you select the right software and demonstrate its fitness?

We recommend you follow three simple steps. First you craft a plan, choosing realistic goals alongside practical use cases. Next, you test these use cases with the software chosen. Lastly, you launch to the wider organization with a small group, sharing your progress with key leaders along the way, and taking small steps to scale across the organization.

Build a Strategy with Goals and Use Cases

Why does the business want to leverage data intelligence? What will success look like? Your goals should reflect your business’ objectives and clearly define by what metrics you will deem those goals successful.

Your organization’s industry will affect the nature of your goals. Highly regulated industries, like insurance, healthcare, and finance, are traditionally risk averse and subject to compliance audits; historically, their data management strategies were defensive, focused on compliance. Less regulated industries, like retail, often seek to use customer data more proactively, making their strategies more offensive.

But that’s changing. Today, regulated industries seek to play both offense and defense, and leverage customer data more proactively. Similarly, retailers and other less regulated industries are boosting defensive measures to ensure compliance with GDPR and CCPA. Amazon’s GDPR fine of $887M for data-privacy violations has caused many retailers to reconsider their data strategy, and re-prioritize the need for a defense-oriented approach.

Data intelligence software supports a more complex and inclusive data strategy. Software that sorts your data into domains will enable distinct departments to use data in strategically distinct ways.

Field Test Use Cases

Once you’ve defined your goals and use cases, it’s time to put them to the test. Some examples of goals and accompanying use cases include:

• The business wants to make better use of customer data. A use case may challenge analysts to leverage the software to analyze customer data and extract new key insights.

• The business is migrating data to a cloud-data warehouse. A use case may test the chosen software as a migration tool for a select portion of the total data. Does it spotlight legacy dependencies? Does it govern the data as it migrates? What about when it’s relocated to the cloud?

• The business wants to support analysts with data democratization. A use case may onboard a dozen analysts to the new software and invite them to test it out. Does the chosen software make them more productive? Do they have more confidence in their abilities to deliver trustworthy reports and advice?

Implement Data Intelligence Software

You’ve made a solid case to leadership and they’ve given their blessing to the new tool! Once you’ve got the software, it’s time to test it out, assigning key roles and measuring progress. We recommend these four steps:

1. Involve Early Users.These are your data champions! We recommend finding enthusiastic individuals excited to try something new. Train them in the basics of the new tool, share team goals, and invite them to get started.

2. Apply in Iterations.Watch and learn. Who does that? Who doesn’t do what? This is the stage where you suss out who’s suited to what task and measure success. Double down on what’s working, and discard what isn’t.

3. Establish Roles. Now that you’ve used the tool for a bit, you can more clearly assign roles to teams and individuals. Who does what? In this stage, you refine these details.

4. Share Progress. Schedule regular updates to appraise key stakeholders of milestones and progress. Don’t be shy! Invite the larger community, too — you’ll need their support if the tool is to gain traction across the organization.

How Data Intelligence Supports Data Culture: 4 Examples

1. Transforms Data into a Shared Organizational Asset

By spotlighting the best data, data intelligence connects people to assets they can use and trust.

In the past, regulatory pressures have caused leaders to over-focus on protecting the data, creating a generation of data gatekeepers.

Data intelligence embeds compliance into the software, freeing gatekeepers from guarding data, and transforming them into data shopkeepers and educators, responsible for guiding people to the data they need.

2. Creates a System of Transparency and Accountability

Who’s doing what, when, how and why? Answering these questions in a single source supports important conversations and decisions, which in turn supports innovation. Leaders are empowered to set clear goals, and data users are empowered to learn from one another.

3. Improves Decision Making

Historically, organizations have relied on gut feel to make choices. That, or they go with the opinion of the HiPPO (the highest paid person’s opinion!). Data intelligence democratizes access to great data, empowering all people to contribute great ideas. In this way, leaders can move away from “the blind leading the blind” and enforce a decentralized system for decision making that leverages the wisdom of the collective enterprise

4. Aligns Language, Habits, and Values

Culture comes down to simple human questions: What do we believe? Why? Data intelligence establishes a system for key organizational metrics and KPIs, which inform the company’s vision and raison d’etre. So all folks know key organizational values and goals — and can adjust their own focus accordingly.

Bringing data intelligence into your organization can have profound implications on your work culture. By making data central to goals and decision making, leaders cultivate a data culture of collaboration, founded on strategy.

Alation Data Catalog: Platform for Data Intelligence

As creator of the modern data catalog, Alation embeds data intelligence into the platform at every level. Working in concert with AI, ML, and BI, data intelligence optimizes key processes of the catalog, including:

• Metadata management

• Data quality

• Data governance

• Master data management

• Data profiling

• Data curation

• Data privacy

Curious to see what that looks like in action? Grab a quick demo to see how Alation can take your business to the next level.