What is business intelligence?

Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing.

According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.”(1)

Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities.

Business intelligence, data warehouses and OLAP

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.

Evolution of business intelligence

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.

Why business intelligence is important

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 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.

Key features of effective business intelligence

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: “Companies now collect and analyze massive quantities of customer data every day. In doing so, many businesses find themselves able to solve many of the pain points that strategy teams once grappled with for months before reaching a conclusion — and with better results.

“Machine learning and AI, by automatically reading all sources of information to augment decisions, is further expanding the frontier of analytics-based decision making.”

Case studies

Featured offerings

IBM Planning Analytics

Automate planning, budgeting, forecasting and analysis processes. Go beyond spreadsheets to create efficiency and remove manual steps. 

"We're delighted with IBM Planning Analytics on Cloud; it’s become the one-stop shop for all of our finance and accounting needs."   

- Mick Ferguson, Finance Manager, Hunter Industries

IBM Cognos Analytics

Take advantage of this single analytics solution across your entire organization to confidently monitor, explore and share insights from data. 

"We're much more confident in our metrics — in fact, there’s now an attitude in the business that 'it doesn’t count if it doesn’t come from Cognos'." 

- Stefanie Nicholson, Head of Operations, Go Health Clubs

IBM SPSS Modeler

Use predictive analytics to help you uncover data patterns, gain accurate insights and improve decision making. 

"Deep analytics. Just add data." 

- Mark Lack, Strategy Analytics & Business Intelligence Manager, Mueller, Inc.

Resources

Sources

  1. https://www.cio.com/article/2439504/business-intelligence-definition-and-solutions.html   
  2. https://www.dataversity.net/brief-history-business-intelligence/
  3. https://www.betterbuys.com/bi/history-of-business-intelligence/