TL;DR
Data intelligence is the layer that makes enterprise data understandable, traceable and trusted, and high-performing teams run it as a daily practice, not a catalog project. They start from the decisions that matter and agree one business definition for every shared KPI. They treat metadata and lineage as live signals, so a broken number is traced to its source in minutes.
Every important dataset has a named owner who answers for its quality. They measure trust and reuse instead of counting tables, and they give AI agents the same governed context people use. A single-platform shop can lean on its native catalog, while a multi-cloud or regulated estate needs one governed layer across every platform.
Key Takeaways
Data intelligence connects metadata, lineage, quality, business definitions and governed access so people and AI can trust an answer.
High-performing teams begin with a few decision-critical KPIs and agree one definition for each before buying more tooling.
Lineage and quality checks act as live operating signals rather than documentation nobody reads after launch.
Named owners and data products make trust a job someone is accountable for, rather than a vague hope.
AI agents raise the stakes, because Gartner expects 60% of AI projects without AI-ready data to be abandoned through 2026.
Progress is measured in time to trusted data, KPI consistency and certified-data adoption, not in catalog size.
Watch on YouTube
Business Ontology on Microsoft Fabric: Global Pharma Case Study
A short Kanerika clip on how a global pharma company replaced conflicting dashboard KPIs with one governed business ontology, the exact problem this guide opens with.
Why Do Two Dashboards Report Different Revenue Numbers?
Picture a Monday leadership review. Finance shows quarterly revenue from its dashboard, while sales shows a different figure, and both teams swear their number is right.
Nobody in the room is lying. Finance counts revenue when an invoice is recognized, while sales counts it when a contract is signed. One dashboard also excludes refunds, while the other does not.
Two analysts spend the afternoon pulling SQL and chasing an extract someone refreshed by hand. The decision the meeting was meant to make slips a week.
This is the everyday problem data intelligence exists to solve. The cause is rarely missing data, though. It is missing shared meaning, visible lineage and clear ownership.
Teams that avoid this scene share a handful of habits. One global pharmaceutical company that adopted them cut investigation and reconciliation time by 70%. This guide shows those habits and how to put them in place.
What Is Data Intelligence?
In plain terms, it means knowing what your data means, where it came from and whether to trust it before anyone acts. It sits between raw storage, such as a cloud data warehouse , and the reports or AI tools people use every day. Every answer inherits its quality.
IBM describes an approach that combines data management and metadata management with tools such as AI and ML . The aim, in IBM’s definition , is “to help organizations understand how enterprise data is produced and used.”
This guide uses the term in that practical sense. In other words, it is the practice and platform layer that makes enterprise data understandable, traceable and trusted for people and AI. Its working parts include metadata, lineage and quality, as well as business definitions and governed access.
Before going further, one naming note avoids confusion. Databricks branded its platform the Data Intelligence Platform, and as of October 2026 its product page calls it the Databricks Data + AI Platform . That is one vendor’s product, covered in our guide to the Databricks Data Intelligence Platform . The discipline described here, however, applies whatever platform you run.
Data Intelligence vs Business Intelligence vs Data Analytics vs Data Governance
These four terms overlap, so teams often use them interchangeably and then argue about scope. The cleanest way to separate them is by the question each one answers.
Business intelligence tells you what happened. Analytics, by comparison, explains why and what might happen next. Governance sets the rules, while the intelligence layer makes the data behind all three understandable and trustworthy.
Discipline Question it answers Typical owner Main output
Business intelligence What happened, and where? BI and reporting team Dashboards and scheduled reports Data analytics Why did it happen, and what comes next? Analysts and data scientists Analyses, models and forecasts Data governance Who may use which data, under what rules? Governance council and data stewards Policies, roles and access standards Data intelligence What does this data mean, where did it come from, and can I trust it? Data platform team with domain owners Shared definitions, lineage, quality signals and governed context
In practice, governance writes the rulebook and this layer turns it into something people can see and use while they work. Our guide to data governance best practices covers the rulebook side in depth, especially ownership and policy design.
Kanerika Service
Data Governance Consulting Services
Kanerika helps teams define ownership, access policies and quality rules, then turn them into working controls that people and AI tools actually follow.
Explore Data Governance Services Types of Data Intelligence
The type depends on the question that trusted data must answer, so the labels mirror the familiar analytics ladder. Each one needs the same foundation, but the payoff and the urgency differ.
Type Question Example
Descriptive What happened? Monthly revenue by region from one certified dataset Diagnostic Why did it happen? Tracing a margin drop to a changed discount rule Predictive What is likely next? Forecasting churn from usage and support history Prescriptive What should we do? Recommending which accounts a retention team calls first Real-time What is happening at this moment? Flagging a failed pipeline before a morning report refreshes
The higher a question sits on that ladder, the more often an action runs on the data without a person checking it. A descriptive report waits for someone to read it, while a prescriptive model sends its call list straight to a retention team. If that model reads an untrusted definition of an active customer, the team calls the wrong accounts first.
Where Data Catalogs, Data Fabric and Data Observability Fit
Vendors often pitch several neighboring ideas as the whole answer. Each is useful, but none covers the whole practice on its own.
Concept What it does Role in the bigger picture
Data catalog Indexes datasets so people can search and understand them The discovery layer; see our guide to the enterprise data catalog Data fabric and data mesh Architecture choices for connecting or decentralizing data Shape how the layer is organized; compare them in data fabric vs data mesh Data observability Monitors freshness, volume and schema changes in pipelines Feeds the quality and trust layer; see data observability tools
Without owners, a catalog becomes a stale index. Observability without business definitions tells you a table changed but not whether the KPI is still right. The value appears when the pieces work together.
How Data Intelligence Works: Six Layers From Raw Data to a Trusted Answer
Think of the system as a supply chain for meaning. Raw records go in at one end, while a number that someone can defend in a board meeting comes out at the other.
Six layers do the work, and they fall into three jobs. Layers one and two capture data and its context. After that, the middle two establish trust, and finally the last two put that trusted data to use.
Sources. ERP, CRM, product logs, files and SaaS tools feed the platform through data ingestion pipelines. Nothing here is trusted until later layers check it.Metadata and lineage. Then the platform records what each dataset holds, who changed it and how it flows into reports. Good data lineage shows every hop from source table to dashboard tile.Quality and trust. Automated checks, built on a data quality framework , test completeness, freshness and valid values, then publish a trust status that users can see.Business context. A semantic layer or ontology defines what “revenue” or “active customer” means once, so that everyone uses the same meaning.Governed access. Policies decide who can see which rows and columns, while every access is logged for audit.Consumption and feedback. BI tools, notebooks and AI agents query the trusted layer, then their usage flows back as signals about what matters most.
The order matters less than the connections. A quality check means little unless lineage shows which reports it affects. Similarly, a business definition helps nobody if the AI assistant cannot read it.
Worked Example: Tracing a Disputed Revenue Number
Return to the Monday meeting once more, and run it through the six layers. With the layer in place, the reconciliation looks very different from before.
To begin with, an analyst opens the finance dashboard’s revenue tile and follows its lineage to the source tables.
Lineage shows the sales dashboard reads a different extract, refreshed by hand earlier in the week.
The quality layer has already flagged that extract as stale, but nobody had been watching the alert.
Meanwhile, the semantic layer shows two definitions of revenue in use, one gross and one net of refunds.
After that, the revenue owner confirms the certified definition, and the sales dashboard is repointed to the governed dataset.
Both teams see the same number from that point on, and the stale extract is retired so the gap cannot reopen. Any refresh the data still needs runs as a scheduled job in the team’s data orchestration tools .
What took two analysts most of an afternoon instead becomes a short, documented trace. More importantly, the fix sticks, because the root cause is closed rather than patched.
Checklist
Data Governance Checklist
A downloadable checklist to set up owners, definitions, quality rules and access policies for your first decision-critical domain.
Get the Checklist → What High-Performing Teams Do Differently
Strong data teams are separated from average ones by habits they run every week, whatever the platform they bought.
Average teams tend to catalog everything, let each dashboard define its own KPIs and fix data after someone complains. High-performing teams invert each of those habits, so six practices show up again and again.
1. They Start From Decisions, Not Data Volume
Strong teams ask which decisions hurt most when the numbers disagree. For example, pricing, revenue recognition and inventory planning usually top the list.
They make the data behind those few decisions trustworthy first, then expand. A team that tries to document every table in the warehouse before helping anyone usually loses funding before it helps a single decision.
2. They Keep One Set of Business Definitions
Every shared KPI gets one written definition, one owner and one place where it is computed. Dashboards and AI tools read that definition instead of rebuilding it in their own logic.
This is also where a semantic layer or a business ontology earns its keep. The semantic layer standardizes metrics for reporting, while an ontology models the entities and relationships behind them, such as customers, contracts and products. Our comparison of semantic layer vs ontology explains when you need one, the other or both.
The short video below walks through why AI agents in particular need both pieces.
Watch on YouTube
Semantic Layer vs Ontology: Why Enterprise AI Agents Need Both
Kanerika explains how a semantic layer standardizes metrics while an ontology models the business behind them, and why AI agents need both to answer correctly.
3. They Treat Metadata and Lineage as Live Signals
For most organizations, metadata is documentation written once and then left to drift. High performers wire it into daily operations instead.
When a source schema changes, lineage tells the owner which reports and models will break, before users notice. When a quality check from your data observability tools fails, the trust badge on every affected dashboard changes with it. Our guide to metadata management tools covers the tooling side.
4. They Name Owners and Manage Data as Products
Each important dataset has a named owner in the business rather than just the engineer who built the pipeline. That owner also signs off on the definition, the quality rules and who may use it.
Packaging that dataset as a data product , with documentation, a quality promise and a support contact, makes it reusable across teams so others stop rebuilding it. It also gives the governance rules a human face, which is how data governance best practices survive contact with busy people.
5. They Measure Trust and Reuse
Average teams report how many tables they have cataloged. High performers report outcomes, and the clearest one is how long a disputed number takes to settle.
Take the Monday revenue dispute from the opening. Before, two analysts spent an afternoon tracing filters and extracts, and the decision slipped a week. With one certified definition and visible lineage, the owner traces the gap in minutes and the meeting moves on.
Issue resolution time is one of the five measures in the metrics section below, which also shows how to capture each one.
6. They Give AI Agents Governed Context
An AI assistant that queries raw tables can return a confident but wrong answer. Strong teams therefore point their agents at the same definitions, lineage and access rules that people use, which is the core of AI-ready data .
The agent then knows that “revenue” means net revenue and that one table is deprecated. It also knows a sales user may not see payroll columns. In the end, governed context is what lets an organization trust an AI answer enough to act on it.
Taken together, these six habits explain why some teams resolve a disputed number in minutes while others lose a week. None of them requires a particular vendor, but each one requires a decision about ownership.
Why Data Intelligence Matters More Once AI Agents Query Your Data
For years, bad data mostly produced a confusing dashboard that a human could question before acting. AI agents remove that safety net, because they act on whatever context they are given.
The everyday benefits are still real. Decisions move faster when nobody needs to reconcile two reports first, so analysts get that time back for actual analysis. The same lineage and access rules that save that time also produce the audit trail regulators ask for, which lowers compliance risk.
63% of organizations do not have, or are unsure they have, the right data management practices for AI. That finding comes from a Gartner survey, published in a February 2025 Gartner release . Its prediction that “through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data” covers a window that closes on December 31, 2026.
What AI-Ready Data Requires
Getting AI-ready data in place is the work this guide describes. It means shared definitions, visible lineage, measured quality and governed access, applied especially to the data your agents will touch.
This is also why governance and AI teams end up sharing one backlog. When an agent gives a wrong answer, the fix is often a missing definition or a stale table, not a better model.
Consider an agent asked for a customer count. If the catalog does not mark the old customer table as deprecated, the agent quotes it with full confidence. If access rules live only inside a BI tool, the same agent can surface a restricted column to a user who should never see it.
On-Demand Webinar
Data Security Risks in AI: How Microsoft Purview Protects You
An on-demand session on governing the data AI tools can reach, with access policies, classification and audit in Microsoft Purview.
Watch the Webinar → Data Intelligence Platform Capabilities to Look For
Every vendor demo looks impressive, so evaluate a platform by the problems it must solve for you. The tests below also come straight from the six layers and practices described earlier.
Run each test on your own data, with your own disputed KPI, rather than on a polished sample dataset. The red flags are the signs a tool will become shelfware.
Capability What to test Red flag
Discovery and search Can a business user find the certified revenue dataset without help? Search returns every copy with no trust signal Automated lineage Does lineage reach from source columns to dashboard tiles without manual mapping? Lineage stops before it reaches the dashboard Data quality Can rules run on a schedule and show results next to the data? Quality lives in a separate tool nobody opens Business definitions Can one KPI definition be reused by BI tools and AI agents? Definitions are free-text notes only Governed access Are row and column policies enforced and every access logged? Access control depends on copying data Openness Can other engines and clouds read the metadata? Metadata is locked to one engine
A platform does not need to win every row. It needs to handle the rows that match your most painful decision, and to connect with whatever handles the rest.
Platform-Native, Independent Catalog or Custom Layer?
As of October 2026, Databricks, Microsoft and Snowflake each document their own governance and catalog layer. The real choice, then, is whether that native layer is enough, or whether you need something that spans platforms.
The descriptions below come from each vendor’s own documentation, so they show stated capabilities rather than a ranking.
Option What the vendor documents Best fit
Databricks Unity Catalog A unified governance layer for data and AI with access control, lineage tracking, audit logging, and row and column filters Estates centered on Databricks Microsoft OneLake catalog with Purview Unified Catalog OneLake catalog for discovery and governance status in Fabric; Purview adds governance domains, data products, data quality and access policies Microsoft-centered estates Snowflake Horizon Catalog Semantic context, sensitive data protection, data quality, end-to-end lineage and AI guardrails, with Iceberg REST access for Spark, Flink and Trino Snowflake-centered estates Independent or custom catalog layer Connects metadata across several platforms under one governance model Multi-cloud or multi-vendor estates
In Unity Catalog, for example, a row filter is a SQL user-defined function that evaluates each row at query time. You attach it with ALTER TABLE ... SET ROW FILTER, while a column mask uses ALTER COLUMN ... SET MASK. The Databricks guide to row filters and column masks , updated October 9, 2026, walks through both.
Which Route Fits Your Estate?
The scenario, more than the feature list, decides which route high-performing teams take.
Your situation What high-performing teams do
Single-platform shop Use the native catalog, and spend the effort on definitions and owners Microsoft estate Pair the OneLake catalog with Microsoft Purview governance so Fabric and non-Fabric data share one governance model Multi-cloud estate Put one governed metadata layer across platforms instead of syncing several native catalogs by hand Regulated industry Prioritize access policies, audit logs and lineage evidence before discovery features
In every row, the tool is a smaller decision than the operating model. The definitions, owners and measures are what high-performing teams carry from one platform to the next.
Datasheet
Microsoft Fabric Ontology for Governed Analytics and AI
A datasheet on modeling shared business entities and KPI definitions on Fabric so BI reports and AI agents read the same governed context.
View the Datasheet → Where It Pays Off: Examples by Industry
The pattern is the same in every industry: a few decisions depend on numbers that several teams produce differently. What changes, though, is which decision hurts most.
The table below pairs each industry with a common starting point, such as churn in retail or risk reporting in banking. Each one is narrow enough to prove value in a single domain before expanding.
Industry Decision that needs trusted data What the layer provides
Retail and consumer Which customers are about to lapse One customer definition across ecommerce, stores and loyalty data Healthcare and pharma Which KPI belongs in a regulatory or executive report Certified definitions with lineage evidence for auditors Financial services Exposure and risk reporting Column-level access policies and traceable calculations Manufacturing Yield, scrap and supplier quality Shared product and plant hierarchies across ERP and shop-floor systems Logistics On-time delivery and cost per shipment Consistent event definitions across carriers and warehouses Telecom Churn and network-driven revenue loss, a core use of data analytics in telecom Joined customer, billing and network data under one owner
Retention shows the pattern most clearly, and it extends beyond commercial customers.
Customer Retention : Donorbox to track giving patterns, flag lapsed supporters, and trigger personalized re-engagement campaigns before donors disengage entirely.
In each case, the first win is a number that two teams stop arguing about, rather than a new tool. Retention work in particular depends on one agreed definition of an active customer.
How to Measure Whether Data Intelligence Is Working
If a program cannot show progress in business terms, it will be cut at the next budget review. The fix is to measure outcomes people feel rather than activity the data team performs.
Five measures cover most programs, so start with these. Capture a baseline before the work starts, because a later improvement means little without one.
Metric What it means How to capture it
Time to find trusted data How long a user needs to locate a certified dataset for a question Short timed tasks with real users, repeated each quarter Issue resolution time How long a disputed or broken number takes to trace and fix Ticket timestamps from report to root cause KPI consistency How often the same KPI matches across reports Automated comparison of headline metrics across dashboards Certified-data adoption Share of report and query usage that hits certified datasets Platform usage logs Governed AI and decision answers Share of AI answers and major decisions traced to governed sources Agent query logs and decision records
These measures pair well with operational analytics , which already tracks how the business runs day to day. Then put the metrics on the same dashboard leaders already read.
What Drives Cost and Effort
Four factors drive most of the effort: source systems, existing quality debt, the tools that must read shared definitions, and regulated data. Quality debt is usually the one that dominates, especially in larger estates.
A team might plan to certify its revenue KPI in one quarter, then discover that CRM and billing store customer IDs differently. Matching those records and agreeing which system wins can take longer than writing the definition itself, so the timeline slips. A data quality framework helps size that cleanup before you commit to a timeline.
A simple return formula keeps the business case honest. Every input below is illustrative, so replace each one with figures from your own baseline.
Component How to calculate it (illustrative inputs)
Hours saved Analyst hours saved on reconciliation x loaded hourly cost Decision value Value of faster or avoided wrong decisions Compliance savings Avoided compliance and audit cost Total cost Platform cost + people cost Annual return Hours saved + decision value + compliance savings – total cost
Keep the decision-value term conservative. Reviewers trust a case built mostly on hours saved, which you can measure, far more than one built on estimated revenue upside.
Common Mistakes and Challenges to Avoid
Failed programs usually fail in familiar ways. Knowing the patterns before you start is the cheapest insurance available.
Catalog-only rollouts. An enterprise data catalog goes live, but nobody owns the entries, so search results go stale within months.No named owners. When everyone is responsible for a dataset, nobody fixes it.Boiling the ocean. Documenting every table before helping one decision burns budget and patience.Skills gaps. Lineage, semantic modeling and quality engineering need people who understand the data as well as the business.Weak change management. When analysts keep their private extracts, the certified dataset sits unused.
Regulation is the challenge teams most often leave too late. Privacy and sector rules, from GDPR and CCPA to HIPAA, shape who may see which data and what audit evidence you keep. Building access policies and lineage evidence in from the start is far cheaper than retrofitting them after an audit finding.
A Four-Stage Roadmap to Trusted Data
A roadmap works best when each stage delivers something a business user notices. Four stages keep the scope small while keeping the proof visible. The same staging logic scales up in our enterprise transformation roadmap .
Pick one decision-critical domain. Choose the area where disputed numbers cost the most, such as revenue or inventory, and run a short readiness check on its sources and owners.Agree definitions and owners. Write one definition for each shared KPI in that domain, name an owner for each, then model the entities behind them.Connect BI and AI to the trusted layer. Repoint dashboards and AI assistants to the certified datasets, then retire the private extracts.Measure, then expand. Compare the five metrics against the baseline, publish the result, and only then pick the next domain.
The second domain moves faster than the first, because the definitions, owners and habits already exist. That compounding effect is why small, real starts beat large, theoretical ones.
How Kanerika Builds Trusted Data Foundations
Kanerika’s data governance consulting team delivers this work in the same staged way the roadmap describes. We assess one domain, model its shared definitions, connect BI and AI to the governed layer, and measure the change before expanding.
Each engagement follows IMPACT, our six-phase method of Initiate, Map, Plan, Activate, Check and Track. For catalog and glossary work, we use KANGovern, our accelerator built on Microsoft Purview and Databricks Unity Catalog. It automates data discovery across 150+ sources and adds classification and lineage tracking.
A global pharmaceutical enterprise, for example, shows what that looks like. Its KPI definitions and business rules lived inside individual Power BI dashboards, so the same metric returned different numbers depending on the report.
Kanerika built one governed ontology on Microsoft Fabric and OneLake with Fabric IQ Ontology, modeling more than 40 entities and relationships. More than 85 KPIs were then standardized across Power BI. The team also extended the ontology into Fabric AI and Copilot, so conversational queries read the same definitions. As a result, investigation and reconciliation time fell by 70%.
Case Study
70% Less Investigation Time with One Governed Ontology on Fabric
A global pharmaceutical enterprise moved KPI definitions out of individual Power BI dashboards into one governed ontology, standardizing 85+ KPIs and cutting investigation and reconciliation time by 70%.
Read the Case Study → Our delivery runs under ISO 27001, ISO 27701, SOC 2 and ISO 9001 certifications, with CMMI Level 3 process maturity. That matters when the data in scope is regulated or commercially sensitive.
In the end, the disputed revenue number from the Monday meeting is a solvable problem. High-performing teams solve it by agreeing what their data means, showing where it comes from and naming who owns it. They also measure whether people and AI trust the result, and the Monday meeting goes back to being about the decision.
Frequently Asked Questions
What is data intelligence?
Data intelligence is the practice and platform layer that makes enterprise data understandable, traceable and trusted for people and AI. It combines metadata, lineage, quality checks, shared business definitions and governed access. The result is that anyone, or any AI agent, can see what a number means, where it came from and whether it is safe to act on.
What is an example of data intelligence?
A common example is resolving two dashboards that show different revenue figures. With lineage, an analyst traces each number back to its source and finds a stale extract and two competing revenue definitions. The owner confirms one certified definition, both dashboards are repointed, and the disagreement cannot quietly return in next month’s report.
Is AI replacing analytics?
AI is changing how people do analytics, not replacing the need for it. Assistants can write queries, summarize trends and answer questions in plain language. Someone still has to define the metrics, check the data and judge whether an answer makes sense. Analysts can then focus on those judgment tasks rather than building reports by hand.
How does data intelligence support AI agents?
AI agents need context to answer correctly. Data intelligence gives them certified business definitions, lineage that shows where data came from, quality status that flags stale tables, and access rules that limit what each user may see. An agent grounded in that governed context returns answers people can trace and trust instead of plausible guesses.
Why is data governance important in a data intelligence platform?
Governance sets the rules for who may use which data and under what conditions. A data intelligence platform turns those rules into working controls, such as row and column policies, audit logs and ownership records. Without governance, discovery and AI features expose data to the wrong people, and trust in every report and answer erodes.
How do you measure data intelligence ROI?
Start with a baseline, then track time to find trusted data, issue resolution time, KPI consistency across reports and certified-data adoption. Convert analyst hours saved on reconciliation into cost using a loaded hourly rate, add avoided compliance cost, and subtract platform and people costs. Keep any estimate of better-decision value conservative and clearly labelled.
Can data intelligence work across multiple cloud platforms?
Yes, but it takes deliberate design. Native catalogs work best inside their own platform, so multi-cloud estates usually add one governed metadata layer that spans every platform, or rely on open formats such as Apache Iceberg with shared catalog access. The goal is one set of definitions and policies rather than several catalogs synced by hand.
How does a data intelligence platform handle sensitive data?
It classifies sensitive fields, then enforces policies such as row filters, column masking and attribute-based access so each user sees only what they are allowed to see. Every access is logged for audit. Lineage shows where sensitive data flows, which helps teams prove compliance and catch copies that have escaped governed storage.
What is the difference between data intelligence and data analytics?
Data analytics examines data to explain why something happened or to predict what comes next. Data intelligence makes the underlying data trustworthy first, by documenting its meaning, tracing its lineage and checking its quality. Analytics produces the insight, while data intelligence ensures the insight rests on data that every team defines and trusts in the same way.
What is the difference between data intelligence and business intelligence?
Business intelligence reports what happened through dashboards and scheduled reports. Data intelligence sits underneath and explains what the data in those reports means, where it came from and whether it can be trusted. Strong business intelligence depends on it, because two dashboards built on different definitions will keep producing conflicting numbers no matter how polished they look.
What is the difference between data intelligence and AI?
AI refers to models and agents that generate predictions, answers or actions. Data intelligence supplies the trusted context those models need, including business definitions, lineage, quality status and access rules. AI without that context can return confident but wrong answers, so the two work as a pair, with data intelligence acting as the foundation AI reasons from.
What is the difference between data intelligence and information?
Information is data that has been organized to answer a question, such as a sales report. Data intelligence is the knowledge about that data itself, meaning how it was produced, how it is defined, who owns it and whether it passed quality checks. It tells you whether the information in front of you deserves your trust.
Why is data intelligence important?
It lets teams make decisions without first reconciling conflicting reports. The main benefits are faster decisions, fewer reconciliation cycles, lower compliance risk and safer use of AI. Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data, which makes trusted, well-described data a requirement rather than a nice extra.
What are the different types of data intelligence?
Data intelligence is often grouped by the question it supports. Descriptive work shows what happened, diagnostic work explains why, predictive work estimates what comes next, and prescriptive work recommends an action. Real-time data intelligence flags events as they happen, such as a failed pipeline. Each type relies on the same trusted definitions and lineage underneath.
What is a data intelligence platform?
A data intelligence platform, sometimes called a data intelligence tool, is software that brings together data discovery, metadata, lineage, quality monitoring, business definitions and access policies. Examples include native catalogs such as Databricks Unity Catalog, Microsoft Purview with the OneLake catalog, and Snowflake Horizon Catalog, as well as independent catalogs that span several platforms.
What are the 4 types of data analytics?
The four types of data analytics are descriptive, diagnostic, predictive and prescriptive. Descriptive analytics reports what happened, diagnostic explains why, predictive estimates what is likely next, and prescriptive recommends what to do. All four depend on trusted inputs, which is why data intelligence practices such as shared definitions and lineage matter before any analytics work begins.
