Jeffrey Erickson | Senior Writer | January 31, 2025
The term “data intelligence” means different things to different people. Some see it as the intelligence you get from analyzing data, others see it as intelligence about the data being used—where it came from, how it’s stored, secured, shared, and reported on. It is, of course, both of these. For a business to arrive at data analytics and AI outputs that are insightful, trusted, and widely used, there must first be a broad application of data intelligence principles and practices.
Data intelligence is an approach to data analysis and AI that helps ensure an organization’s data is up to date, trusted, and easy for businesspeople to access and use. Data intelligence also covers the tools and skill sets needed to employ that data to drive decisions and action. An organization that calls itself “data-driven” will have invested in the people and technologies that make up a data intelligence process.
Data intelligence is backed by a set of tools and processes often packaged in a cloud platform that enables data experts to collect, curate, and manage data across their transactional databases, warehouses, analytical tools and AI and machine learning (ML) assets. This provides the building blocks for an integrated system for data governance, data quality management, and data integration. The goal is to ensure that the entire data lifecycle is managed effectively—before data is fed to AI models or data analysis and visualization tools.
An organization’s data intelligence process will rely on a broad range of technologies, including data warehouses, data lakes, and ETL tools for storing, updating, and moving data; observability tools for tracking and maintaining data management resources; APIs and integration tools for integrating different systems and data sources; data governance tools for managing data quality, security, and compliance; and finally the analytics/business intelligence (BI) tools, machine learning algorithms, and AI platforms that provide responses, reports, and dashboards. Some data intelligence platforms will encapsulate this entire process and even provide businesses with ready-to-use analytics, including AI and machine learning models geared toward their particular business sectors.
In the end, a data intelligence process is about helping an organization harness its data and derive meaningful insights from it that are then used broadly to make better decisions.
Data intelligence and data analytics are closely intertwined, but they serve different purposes and operate at different scopes within an organization.
Data analytics primarily focuses on examining data and drawing conclusions. Data analytics tools and techniques are used to perform statistical analysis, generate reports, and create models that help to understand the lessons contained in historical data and even predict future outcomes. They do this by identifying patterns, trends, and relationships within data and displaying their findings in visualizations that can inform decision-making.
Data intelligence goes beyond analyzing data. It encompasses the broader set of tools and processes around the entire data lifecycle. These include data governance, data quality, and integration and are intended to help the business trust the findings from those analytics and integrate them into strategies and operational processes.
Key Takeaways
Data intelligence is the discipline within an organization to help ensure data analysis and AI outputs are trusted, well informed, accurate, and widely shared. Think of data intelligence as a business practice built around internal data experts and the tools and technologies they use for sourcing, managing, and sharing data. It seeks to take the mystery out of artificial intelligence-powered data analysis by bringing together business data, ready-to-use analytics, and prebuilt AI and machine learning (ML) models.
The goal: Provide a secure and repeatable process for businesspeople to make confident and insightful decisions. To this end, a data intelligence program is as much about automating data collection and management as it is about using analytical tools. You can see this reality at work in a range of data intelligence platforms available in the cloud or on-premises from providers such as Oracle, Databricks, Microsoft, and others. Some data intelligence platforms incorporate everything from data ingestion and modeling to companywide governance, data analysis and, finally, broad sharing of findings for strategic decision-making. In the case of generative AI, a data intelligence program allows vital AI functions including semantic search and retrieval-augmented generation, or RAG, to work with well-organized and up-to-date information that allows AI models to understand, interpret, and generate high-quality content and insights.
In the realm of data analysis, data intelligence is important because it defines a process for sourcing a wide range of data and managing it in a way that helps ensure the reports and AI outputs based on that data are trusted and widely shared.
For example, data intelligence programs are behind AI and machine learning models that drive initiatives such as smart cities, where massive amounts of data are used to improve urban infrastructure, manage traffic, optimize public transportation, and enhance public safety through real-time data analysis. Data intelligence processes give the energy sector the confidence to forecast demand and optimize energy distribution. In the world of finance, data intelligence helps ensure accurate risk assessments and deliver trusted financial products for individual customers. Data intelligence and machine learning gives these financial institutions the ability to monitor millions of transactions for signs of fraud and even help predict loan defaults.
In the growing field of generative AI, the quality of the data used to train AI models directly affects the quality of their outputs. For AI specialists in large organizations or vendors, a data intelligence process is crucial for helping their models be more valid and reliable, and to support resolution of ethical and legal compliance issues.
A data intelligence process is run through an IT team and seeks to provide self-service interfaces for business users, so that the IT team isn’t a bottleneck for business intelligence and analytics efforts. People involved in the process include database experts, developers, data scientists, and business analysts.
Data intelligence is a holistic approach to data use. An organization’s data intelligence platform will include a wide range of repositories, such as databases or data lakes, on-premises or from cloud providers. A data intelligence process provides access controls such that individuals get the view of data best suited to their roles. That means businesspeople can visually explore the data available to them with the ability to understand its provenance and how it’s being used across the organization. The platform also gives those businesspeople a method to build a data flow for their analysis that transforms, merges, and enriches the data—including by applying machine learning that works across large, complex data sets to discover previously unseen patterns, even make predictions.
Data intelligence provides governance, which fosters collaboration while also restricting access to data based on a person’s role. This allows the system to specify who is authorized to access what and show which content is being used by whom. To increase the adoption of analytics among decision-makers, many data intelligence platforms proactively monitor and update content to increase its value.
Beyond traditional data analytics, data intelligence processes must now help supply quality data to generative AI models; providing models with data that is accurate, recent, and from a variety of sources.
Data intelligence provides a wide range of benefits, making it a prerequisite for any organization that aims to be data-driven—and today, that should be every organization. No matter your business, there’s data that can improve operations and decision-making.
Companies pursuing an AI advantage need a data intelligence program—in fact, data intelligence is a cornerstone of valuable AI systems. High-quality, diverse, and representative data is essential for training AI models, and data intelligence helps ensure that models are exposed to just that sort of data, improving their ability to make accurate predictions. Data intelligence also enables working AI systems to continuously learn and adapt by incorporating new data and feedback into their models to improve their performance and accuracy over time.
Additional benefits include:
Every organization must define what data intelligence means to them. Some data intelligence processes focus on what happens before data is analyzed, its provenance, integration, and security. Other data intelligence processes are more concerned with the type of intelligence that’s drawn from the data.
Below are some examples of the different types of intelligence and insights each can yield.
How any given organization uses its data intelligence practice to gather, organize, analyze, and share data and then report on the results will vary. But in most all cases, data intelligence is the bridge between raw data and smart decisions that advance the business.
In the examples below are some outcomes of the analysis made possible by data intelligence.
If you want trusted, widely used analytics and AI outputs for your business, you’ll need a comprehensive data intelligence platform. Oracle Analytics Platform can help you address the entire analytics process—including data ingestion and modeling, data preparation and enrichment, and visualization and collaboration—without compromising security and governance. The platform comes with machine learning and natural language process technologies that help increase productivity by making data analytics more intuitive and accessible to more people across an organization.
For Oracle Fusion Applications users, all these cloud-based technologies are built into Oracle Fusion Data Intelligence, which brings together business data, ready-to-use analytics, and prebuilt AI and machine learning models to help businesspeople make better decisions informed by a broad spectrum of trusted data.
In the age of generative AI, data intelligence has become more important than ever. Organizations that have invested in a data intelligence process for analytics will have a leg up. For those that haven’t, the promise of generative AI will likely be the catalyst to finally establish data intelligence across their organization. As a discipline and a technology play, data intelligence is set to become smarter and more widely adopted in coming years.
Data intelligence program success depends on turning masses of raw data into valuable insights, predictions, and recommendations. And nothing helps with that chaff-to-wheat process like AI in the cloud. Learn why.
What type of data is intelligence?
Data intelligence isn’t a data type like JSON, graph, or vector data. Rather, it’s an approach to data management that helps to ensure any data being used is up to date, trusted, and easy for businesspeople to find and use.
What are the key components of data intelligence?
The major components of data intelligence are:
How is data intelligence used in business?
Data intelligence is used extensively in business to make operations more efficient, analyze sales trends, evaluate inventory levels, advise on marketing effectiveness, generate customer insights, and much more. It can help decision-makers develop a better understanding of a wide range of collected information and improve business processes.