How Business Intelligence Drives Smart Decision-Making

What is Business Intelligence?

As organizations collect more and more data, they need a process that turns raw data into meaningful strategies and operations. Business Intelligence (BI) refers to the set of infrastructure, tools, applications and best practices that organizations leverage to help them drive their strategic decision-making. While traditional BI has focused on collecting, integrating and analyzing historical data to support better decision‑making, modern BI increasingly incorporates advanced business analytics, including predictive insights, to help organizations drive growth.

The term “business intelligence” can encompass a combination of data warehousing, business analytics, data visualization and reporting tools. However, the BI lifecycle begins with data extraction via ETL (extract, transform and load), continues with data warehousing and culminates in dashboards, predictive analytics and reporting systems. A robust BI implementation should also feature data governance, master data management (MDM) and strong access control.

In this blog, we’ll explore how BI tools work, the kinds of insights business leaders can gain from BI and how Databricks is building the next generation of analytics with its AI-powered business platform.

Historical Development of BI

The origins of BI can be traced back to the 1960s with decision support systems, which provided interactive software-based solutions to assist in decision-making. Over the next decade organizations used computers to gain insights from data, but were limited by siloed data systems and an overall lack of centralized data.

By the 1970s, IBM and others introduced next-generation relational databases that laid the groundwork for data warehouses in the 1980s. These data warehouses aggregated large amounts of data from diverse sources – in both structured and unstructured formats – while also allowing users to cross-reference the sources to provide deeper insights.

The data warehouse model matured across the 1990s as new tools, such as ETL and online analytical processing (OLAP) – as well as spreadsheets like Microsoft Excel – gave users the ability to query datasets in faster and more efficient ways.

Today, however, the sheer amount and velocity of data that an organization might collect requires a business intelligence model that can keep pace with that speed of data and also slice and dice the right data and insights for any particular query.

The Best Business Intelligence Tools and Technologies

BI tools are software platforms that help organizations transform data into readable, accessible and actionable insights. Some of the leading BI tools on the market include:

• AI/BI (Databricks): Databricks AI/BI is a native business intelligence solution that combines dashboards, natural language querying with Genie and AI-powered analysis tools to help users explore, explain and act on data directly within the Databricks Data Intelligence Platform.
• Power BI (Microsoft): This tool integrates deeply with Microsoft 365 and Azure services, and supports real-time dashboards and strong query capabilities.
• Tableau (Salesforce): Known for high-performance visual analytics and interactive dashboards. Tableau is regarded as a top choice for those looking for data exploration and storytelling.
• Looker (Google Cloud): Built with LookML, it allows scalable data modeling and integrates tightly with BigQuery.
• Qlik Sense: Features an associative engine that enables users to explore data freely without being confined to predefined queries.

Today, artificial intelligence (AI) and machine learning (ML) are pushing BI forward by introducing capabilities such as:

• Predictive Analytics: Leveraging historical data with supervised models (e.g., regression, decision trees) to forecast future trends.
• Natural Language Processing (NLP): Users can query BI platforms with plain language.
• Anomaly Detection: Algorithms flag outliers in data streams without manual thresholds.
• Recommendation Systems: ML models propose next actions or suggest metrics worth tracking.

Databricks is building the next generation of business intelligence with AI/BI. This tool is complementary to traditional BI tools, and with the help of AI, powered by data intelligence, learns your data over time to give users tailored insights based on natural language questions.

AI/BI is native to Databricks and unified with Unity Catalog, which means all of your data is natively integrated into the Databricks Platform and there are no separate licenses to procure or additional data warehouses to manage.

How Business Intelligence Works

How an organization builds its business intelligence pipeline will depend on its specific KPIs and outcomes. However, they tend to follow the same general path:

Data Ingestion: Business intelligence begins by gathering data from either structured sources – such as SQL databases, ERP systems or flat files in cloud storage – or from unstructured sources, such as text documents, emails and web pages. Increasingly, data is in an unstructured format, making the cleaning and transformation process vital.

Data Cleaning and Transformation: This is a critical step where raw data is refined. It involves identifying and correcting errors, handling missing values, standardizing formats and transforming data into a structure suitable for analysis.

Data Storage: The processed data is typically stored in a data warehouse or data lake. A data warehouse is a centralized repository of integrated data from one or more disparate sources, designed for reporting and data analysis. Data lakes, on the other hand, can store raw, unformatted data, and offer more flexibility for various analytical workloads.

These storage options have powered business intelligence for decades, but they each face some real limitations for BI. The Databricks Lakehouse architecture combines the best elements of data lakes and data warehouses into a unified data platform. This architecture simplifies data management by eliminating silos and providing a single platform for integration, storage, processing, governance, sharing, analytics and AI. It offers low query latency and high reliability for BI, as well as advanced analytics to gain the freshest insights.

From Data Analysis to Insight Generation

Once data has been collected, cleaned and organized, BI platforms then generate actionable insights. These often include the following types of analytics:

• Descriptive Analytics: This view summarizes historical data to show trends, comparisons, and performance over time. Descriptive analytics present a view of past events based on metrics like totals, averages or year-over-year comparisons.
• Diagnostic Analytics: Diagnostic analytics explores the causes and contributing factors of your data. For example, if an organization experiences a decline in customer conversions, diagnostic analysis might show the region and rationale for that decline. Analysts can use SQL queries, statistical methods or built-in drill-down features in BI tools to isolate correlations or patterns that explain the observed outcomes.
• Predictive Analytics: Predictive analytics uses statistical models and machine learning to forecast future outcomes based on historical patterns. This helps businesses anticipate problems or opportunities before they fully materialize.
• Prescriptive Analytics: This insight offers specific actions based on the data and predictions. Prescriptive analytics use optimization algorithms, simulation models or reinforcement learning to test different scenarios and recommend the best course of action.

Applications and Benefits of Business Intelligence

BI helps organizations transform billions of rows of data into granular KPIs, customer segmentation models, and operational alerts. By ingesting real-time or near-real-time data, organizations can stream data into a BI pipeline with incredibly low latency to offer near-immediate insights.

Databricks’ AI/BI Dashboards and Genie is empowering customers with faster data queries to help them deliver on the very mission and vision of their organizations.

Premier Inc. is a technology-driven healthcare improvement company that serves two-thirds of all U.S. healthcare providers. By adopting the Databricks Platform and the AI/BI Genie, Premier has been able to eliminate fragmented data and enable natural language queries, and it has led to 10x faster SQL creation and seamless integration of data across systems.

By deploying Genie, Premier can organize data with clear metadata and governance rules, while Unity Catalog ensures that Genie delivers accurate and secure results.

Enhancing Strategic Decision-Making

An organization’s strategic decisions, such as whether to expand into a new market, pivot a product line or allocate marketing budget, must be increasingly data-driven. This requires a tool that can provide the right data at the right time. For Premier, this means exploring new use cases beyond clinical operations. By leveraging Genie’s flexibility, Premier aims to assist their healthcare customers with addressing operational challenges, such as resource allocation and supply chain optimization, further supporting their mission to improve care delivery.

Best Practices for Implementing Business Intelligence

The success of an organization depends on its ability to identify, collect and transform the right kind of data for their operations. Implementing Business Intelligence that leads to actionable insights requires organizations commit to adopting some adopting best practices.

• Integrate: Effective BI implementation must be integrated into daily business operations. Users can embed analytics directly into key systems like Salesforce or SAP, or internal tools to support decision-making in context. Event-driven automation – or data-driven triggers like email alerts when KPIs dip below a threshold – can help users and systems respond in real-time.
• Iterate: Adopting BI practices is an iterative process. Organizations should start small and create a limited set of questions to answer. By limiting the KPIs and reporting logic in short sprints, users can give feedback and integrate changes to more accurately assess user needs. From there, organizations can build out additional questions and queries to begin scaling your solution.
• Build a Data-Driven Culture: Providing data literacy training across an organization can emphasize the importance of data-driven decisions. Non-technical users should have the opportunity to interpret visualizations and metrics confidently, while self-service BI capabilities can help business users explore data without constant engineering support.
• Measure Outcomes: Finally, track how often reports are used and by whom, and ensure that any BI goals are aligned with measurable business outcomes such as revenue growth, cost savings, product development timelines.

BI is essential for organizations to compete in today’s data-driven environments. Implementing BI successfully requires committing to integrating analytics into everyday workflows, iterating through continuous feedback and fostering a culture where data literacy and self-service capabilities are widespread. With business intelligence platforms and solutions like Databricks AI/BI, users can make faster, smarter and more confident decisions.