What Is Data Intelligence?

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.

What Is Data Intelligence?

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 vs. Data Analytics

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 about making it easy for businesspeople and analysts to gather data from trusted sources and use it with their favorite analytical tools and machine learning algorithms.
  • A data intelligence practice establishes processes for how an organization collects, organizes, integrates, and analyzes data and then broadly shares the results for decision-making.
  • Data intelligence platforms help in managing the entire data lifecycle and can encompass databases, data lakes, integration tools, user authentication processes, BI tools, and content management tools.

Data Intelligence Explained

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.

Why Is Data Intelligence Important?

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.

How Does Data Intelligence Work?

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.

Benefits of Data Intelligence

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:

  • Competitive advantage: It just makes sense that organizations that take care with their data and use it to improve their efficiency and market intelligence will be more competitive. The best employ this kind of discipline in everything from long-term strategic planning to daily operations.
  • Cost savings: An organization’s data intelligence process helps reduce waste and lower costs by increasing efficiency. Routes to that include, for example, enabling predictive maintenance to minimize machine breakdowns and improving visibility into resource management.
  • Data-driven culture: Implementing a data intelligence platform and process can help an organization become more confident in its decisions. After all, when you base actions and strategies on quantifiable data, you provide a clear rallying point to align operations.
  • Enhanced customer understanding: When retail and service companies gather and analyze customer data across many touchpoints they can better understand buyer behaviors, preferences, and needs.
  • Improved performance: The right set of data intelligence tools can provide the metrics and KPIs needed to monitor and improve business performance in areas such as sales, productivity, and product quality.
  • Increased efficiency: A data intelligence program automates the collection and management of the data used by analytical tools. That automation helps reduce manual effort and improve the quality of data.
  • Informed decision-making: Whether you’re a baseball manager or a corporate division chief, data intelligence helps leaders bring factual, data-driven information to their decisions to weigh against their intuitions or assumptions, leading to more confidence in strategic decision-making.
  • Innovation: A data intelligence process can help an organization more clearly see market gaps or pinpoint customer needs, helping them to build and test new products, services, and business models.
  • More accurate and powerful AI: Data intelligence acts as a preprocessing and management layer for AI, enhancing data quality, volume, diversity, and context. Better data empowers AI models to deliver more accurate responses and uncover hidden patterns and trends for the business.
  • Predictive capabilities: Beyond uncovering what happened and explaining why, sophisticated machine learning models can forecast trends and behaviors, helping organizations anticipate market demands and customer actions or foresee and act on system failures before they become a problem.
  • Risk management: A data intelligence process can help an organization more clearly see potential risks and vulnerabilities through data analysis, allowing them to assess risks long before they become legal or product issues.

Types of Data Intelligence

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.

  • Descriptive intelligence: This is a type of business intelligence that focuses on summarizing and understanding past data.
    • Purpose. Descriptive intelligence uses historical data to help an organization get a clear understanding of what has happened in the past as a way to tease out what might happen in the future.
    • Tools. A key enabler is software that uses statistical measures to uncover the information hidden in data. It does this by calculating averages, percentages, or other statistical measures and presenting them using data visualization and reporting tools.
    • Examples. Organizations use descriptive intelligence to see trends, such as growth in number of users of a service or past changes in revenue growth by month, quarter, or year.
  • Diagnostic intelligence: This goes beyond describing what has happened, as in descriptive intelligence, to delve into why those events occurred.
    • Purpose. Asks the question, Why? and then analyzes data to help understand the reasons behind a current state of events.
    • Tools. Machine learning algorithms that help provide root cause analysis, segmentation analysis, and comparative analysis are key.
    • Examples. Diagnostic intelligence takes the guesswork out of why something is working—or why it isn’t. For example, what factors led to the success or failure of a product or marketing promotion?
  • Predictive intelligence: This type of business intelligence uses historical data plus statistical models to attempt to predict future outcomes. The system seeks to identify patterns and trends.
    • Purpose. This sophisticated form of data analysis uses machine learning techniques to weigh the likelihood of future outcomes based on historical data.
    • Tools. Predictive intelligence requires machine learning algorithms, and the associated expertise, to enable pattern recognition in large data sets.
    • Examples. A typical use might be recognizing seasonal patterns in sales data that can help predict future peaks and troughs.
  • Prescriptive intelligence: This type of business intelligence goes beyond simply describing or predicting outcomes based on data to providing recommendations and suggesting actions.
    • Purpose. Prescriptive intelligence helps an organization move beyond identifying what has or will likely happen to suggest specific actions that should be taken to achieve desired outcomes.
    • Tools. Relies heavily on machine learning models and rules engines that connect patterns in data to trigger decisions and offer recommendations.
    • Examples. Prescriptive intelligence provides actionable insights that help businesses such as supply chain management and financial services firms make quick decisions based on data-driven recommendations and formulate plans to respond to changing conditions.
  • Operational intelligence: With its focus on real-time monitoring and operations management, operational intelligence can provide insights into the current state of a business, allowing for better decision-making and problem-solving.
    • Purpose. Provides the real-time business analytics people need to make quick decisions in daily business operations.
    • Tools. Operational intelligence relies on software that guides a business to take immediate action based on a flow of real-time data and analytical insights.
    • Examples. Can be used in manufacturing to detect slight anomalies that point to product quality issues or equipment failures so that businesses can take immediate action. In finance, operational intelligence systems can monitor transactions in real time to detect and prevent fraud.
  • Strategic intelligence: By analyzing information from various sources to identify emerging trends, potential threats, and opportunities, strategic intelligence systems focus on providing insights to support high-level decision-making.
    • Purpose. Concerned with broader, more long-term objectives that shape the future of an organization.
    • Tools. Software that collects and analyzes data at a level that executives feel comfortable with is key to helping them formulate strategies to guide the overall direction of an organization. AI systems are increasingly playing a role here.
    • Examples. Use cases include forecasting and scenario planning, long-term risk management, and competitive intelligence. Strategic intelligence is often used by senior leaders to allocate resources and develop long-term plans.
  • Cognitive intelligence: This is a broad term that encompasses a variety of abilities related to thinking, learning, and problem-solving. It is often associated with human intelligence but can also be applied to advanced software.
    • Purpose. Cognitive intelligence seeks to mimic human thought processes in the analysis, interpretation, and comprehension of complex data sets, particularly while handling large volumes of data.
    • Tools. Makes use of natural language processing, machine learning, and deep learning systems.
    • Examples. Cognitive intelligence might incorporate emotional intelligence capabilities to help interpret and respond to human emotions, which is useful in customer-facing applications. Think of a chatbot able to recognize when a caller is becoming frustrated.

Data Intelligence Use Cases

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.

  1. Customer service teams find success in analyzing customer data, such as past purchases, support messages, and returns, to gain a clear understanding of customer behavior, such as why people purchased a product, how they use it, what they like about it, or common problems that may result in dissatisfaction. This is powerful information for spotting trends and opportunities, fixing quality problems, and honing customer support processes.
  2. Education uses also abound. Colleges, universities, and school districts are using data intelligence to help students succeed. For example, AI algorithms analyze classroom performance and other student data to help identify learning gaps, adapt teaching strategies, and provide targeted interventions that support each student's progress.
  3. Finance firms are heavy users of data intelligence. For example, they use machine learning models to analyze transaction records and spending patterns against a customer’s financial aspirations and mix those insights with market data and economic indicators to suggest investment opportunities or identify potential areas of concern, such as a potential loan default.
  4. Healthcare firms find that data intelligence, especially when combined with artificial intelligence, can help create predictive models around health risks in an individual and then facilitate preventive intervention from providers. Data intelligence can also enable medical image analysis with systems that can read and interpret X-rays, MRIs, or mammograms to help identify patterns and detect diseases. The combination of compute power and machine learning algorithms are helping to more accurately predict whether a drug will work on an individual based on past medical history, gene markers, and recent health data.
  5. Human resources teams, like other business units, often use data intelligence to improve decision-making and make operations more efficient. These efforts might include using AI to analyze data from employee feedback and project outcomes to help managers identify high-performing workers. Or HR might analyze data on turnover rates and employee satisfaction surveys to identify trends that can guide future strategic decisions on things such as recruitment and workforce development.
  6. Operations teams benefit from data intelligence in myriad ways. For example, real-time data intelligence helps the business make decisions on the fly about shipping logistics and automated customer support. Longer-term data analysis can help them make better predictions and strategic decisions in those same operations.
  7. Retail makes heavy use of data intelligence, factoring it into many decisions. Large retailers, for example, find that clean data and AI can help them analyze competitor prices, local demographics, and the performance of promotions to determine the best price to charge for an item. Retailers also combine historical sales data and customer demand patterns to predict future sales and manage inventory levels. Real-time analytics uses advanced algorithms to analyze transaction data, customer behaviors, and patterns to identify potentially fraudulent activities.
  8. Sales and marketing teams use data intelligence systems to more clearly understand their current or potential buyers’ interests and online behaviors. That insight lets them both develop communication plans for current customers and find the right prospects and know how to connect with them.

Accelerate Decision-Making with 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.

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Data Intelligence FAQs

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:

  • Descriptive intelligence. Summarizes historical data so businesses understand what happened in the past.
  • Diagnostic intelligence. Seeks to delve into historical data and determine why specific events occurred.
  • Prescriptive intelligence. Suggests future actions based on historical data and can include statistical models that seek to identify patterns and trends.
  • Operational intelligence. Focuses on operations data to provide insights into a business’s current state.
  • Strategic intelligence. Analyzes information from various sources to identify trends, threats, and opportunities.
  • Cognitive intelligence. This category focuses on human thought processes and advanced AI and is useful in customer-facing applications.

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.