BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps.

Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. For example: What are sales for our eastern region versus our western region this year, compared to last year?

“OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.”

Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.

The term business intelligence was first used in 1865 by author Richard Millar Devens, when he cited a banker who collected intelligence on the market ahead of his competitors. In 1958, an IBM computer scientist named Hans Peter Luhn explored the potential of using technology to gather business intelligence. His research helped establish methods for creating some of IBM’s early analytics platforms.

In the 1960s and 70s, the first data management systems and decision support systems (DSS) were developed to store and organize growing volumes of data.

“Many historians suggest the modern version of business intelligence evolved from the DSS database,” says the IT education site Dataversity. “An assortment of tools was developed during this time, with the goal of accessing and organizing data in simpler ways. OLAP, executive information systems and data warehouses were some of the tools developed to work with DSS. ²

By the 1990s, business intelligence grew increasingly popular, but the technology was still complex. It usually required IT support — which often led to backlogs and delayed reports. Even without IT, business intelligence analysts and users needed extensive training to be able to successfully query and analyze their data. ³

More recent development has focused on self-service BI applications, allowing non-expert users to benefit from their own reporting and analysis. Modern cloud-based platforms have also extended the reach of BI across geographies. Many solutions now handle big data and include real-time processing, enabling decision-making processes based on up-to-date information.

Business intelligence gives organizations the ability to ask questions in plain language and get answers they can understand. Instead of using best guesses, they can base decisions on what their business data is telling them — whether it relates to production, supply chain, customers or market trends.

Why are sales dropping in this region? Where do we have excess inventory? What are customers saying on social media? BI helps answer these critical questions.

“Business intelligence provides past and current insights into the business,” says Maamar Ferkoun in his IBM cloud computing and business intelligence blog. “This is achieved through an array of technologies and practices, from analytics and reporting to data mining and predictive analytics. By providing an accurate picture of the business at a specific point in time, BI provides an organization with the means to design a business strategy based on factual data.”

Business intelligence helps organizations become data-driven enterprises, improve performance and gain competitive advantage. They can:

• Improve ROI by understanding the business and intelligently allocating resources to meet strategic objectives.
• Unravel customer behavior, preferences and trends, and use the insights to better target prospects or tailor products to changing market needs.
• Monitor business operations and fix or make improvements on an ongoing basis, fueled by data insights.
• Improve supply chain management by monitoring activity up and down the line and communicating results with partners and suppliers.

Retailers, for example, can increase cost savings by comparing performance and benchmarks across stores, channels and regions. And, with visibility into the claims process, insurers can see where they are missing service targets and use that information to improve outcomes.

Organizations benefit when they can fully assess operations and processes, understand their customers, gauge the market, and drive improvement. They need the right tools to aggregate business information from anywhere, analyze it, discover patterns and find solutions.

The best BI software supports this decision-making process by:

• Connecting to a wide variety of different data systems and data sets including databases and spreadsheets.
• Providing deep analysis, helping users uncover hidden relationships and patterns in their data.
• Presenting answers in informative and compelling data visualizations like reports, maps, charts and graphs.
• Enabling side-by-side comparisons of data under different scenarios.
• Providing drill-down, drill-up and drill-through features, enabling users to investigate different levels of data.

Advanced BI and analytics systems may also integrate artificial intelligence (AI) and machine learning to automate and streamline complex tasks. These capabilities further accelerate the ability of enterprises to analyze their data and gain insights at a deep level.

Consider, for example, how IBM Cognos Analytics brings together data analysis and visual tools to support map creation for reports. The system uses AI to automatically identify geographical information. It can then refine visualizations by adding geospatial mapping of the entire globe, an individual neighborhood or anything in between.

According to a report on digital reinvention by the IBM Institute for Business Value: "Looking five years out, 58 percent of 1,100 executives we surveyed in the Digital Reinvention Study expect new technologies to reduce barriers to entry and 69 percent expect more cross-industry competition."

"Advanced analytics enable deeper business intelligence and consumer insight to be drawn from big data, producing information that ranges from descriptive to predictive."