Business intelligence (BI) is descriptive, enabling better business decisions that are based on a foundation of current business data. Business analytics (BA) is then a subset of BI, with business analytics providing the prescriptive, forward-looking analysis. It is the umbrella of BI infrastructure that includes the tools for the identification and storage of the data for decision-making.

BI might tell an organization how many new customers were acquired last month and whether order size was up or down for the month. As opposed to this, business analytics might predict which strategies, based on that data, would most benefit the organization. For example: What happens if we increase advertising spending to give new customers a special offer?

BI platforms traditionally rely on data warehouses for their baseline information. The strength of a data warehouse is that it aggregates data from multiple data sources into one central system to support business data analytics and reporting. BI presents the results to the user in the form of reports, charts and maps, which might be displayed through a dashboard.

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

OLAP provides powerful technology for data discovery, facilitating BI, complex analytic calculations and predictive analytics. One of the main benefits of OLAP is the consistency of its calculations that can improve product quality, customer interactions and business process.

Data lakehouses are now also being used for BI. The benefit of a data lakehouse is that it seeks to resolve the core challenges across both data warehouses and data lakes to yield a more ideal data management solution for organizations. A lakehouse represents the next evolution of data management solutions.

The steps taken in BI usually flow in this order:

• Data sources: Identify the data to be reviewed and analyzed, such as from a data warehouse or data lake, cloud, Hadoop, industry statistics, supply chain, CRM, inventory, pricing, sales, marketing or social media.
  
  
• Data collection: Gather and clean data from various sources. This data preparation might be manually gathering information in a spreadsheet or an automatic extract, transform and load (ETL) program.
  
  
• Analysis: Look for trends or unexpected results in the data. This might use data mining, data discovery or data modeling tools.
  
  
• Visualization: Create data visualizations, graphs and dashboards that use business intelligence tools such as Tableau, Cognos Analytics, Microsoft Excel or SAP. Ideally this visualization includes drill-down, drill-through, drill-up features to enable users to investigate various data levels.
  
  
• Action plan: Develop actionable insights based on analysis of historical data versus key performance indicators (KPIs). Actions might include more efficient processes, changes in marketing, fixing supply chain issues or adapting customer experience issues.
  
  

Some newer BI products can extract and load raw data directly by using technology such as Hadoop, but data warehouses often remain the data source of choice.

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 before his competitors did. In 1958, an IBM computer scientist named Hans Peter Luhn explored the potential of using technology to gather BI. 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) began to store and organize the growing volumes of data. “Many historians suggest the modern version of BI evolved from the DSS database,” says the IT education site Dataversity. “An assortment of tools was developed during this time, to access and organize 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, BI 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, BI analysts and users needed extensive training to be able to successfully query and analyze their data.³

Business intelligence is as much a way of thinking as it is composed of hardware and software. By adopting a data-driven culture—based on a complete set of approaches, processes, digital technology and data analysis—an organization can find new insights to make better business decisions and gain new advantages. Installing a new BI software package alone does not bring about this culture shift.

Benefits of BI:

• Clearer reporting: BI gives organizations the ability to ask questions in plain language and get answers they can understand. Dashboards can prioritize the most important insights, saving time for both data experts and nontechnical team members.
  
  Instead of using best guesses, staff can base decisions on what their business data is telling them—whether it relates to production, supply chain, customers or market trends. The data can help answer an organization’s pressing questions: Why are sales dropping in this region? Where do we have excess inventory? What are customers saying on social media?
  
  
• Consolidated data: BI delivers business insights by pulling in and consolidating data from multiple sources—internal and external—for complete analysis. By providing an accurate picture of the business and market, BI provides an organization with the means to design a business strategy.
  
  
• Create new efficiencies: Organizations can monitor business operations against benchmarks and fix or make improvements on an ongoing basis—all fueled by data insights. Analytics can discover and help eliminate manufacturing or supply chain bottlenecks. Managers can monitor staff performance to help pinpoint where organizational changes can be made. Supply chain management can be improved by monitoring activity up and down the line and communicating results with partners and suppliers.
  
  
• Deeper data insights: BI helps organizations become more data-driven, to continually improve business performance, gain competitive advantage, and locate new customers and new opportunities. They can improve ROI by understanding their business and market, and intelligently allocating resources to meet strategic objectives. New data insights can reveal customer behavior, preferences and market trends. Those insights enable marketers to better target prospects or tailor products to changing market needs.
  
  
• Faster decision making: As progress is monitored and analyzed digitally, better informed decisions can be made more quickly for faster adjustments in the marketplace.
  
  
• Increase customer satisfaction: When customer service staff have access to customer data and insights, they can provide requested information and resolve issues more quickly.
  
  
• Increase employee satisfaction: Self-help access to important business data can optimize workflows so that staff can do their jobs faster, with fewer added or repetitive steps.

Challenges of BI

• Contradictory conclusions: Self-service BI empowers multiple teams to search for the insights they need, but can also lead to divergent conclusions, which can create more friction instead of a unified plan of action. This can be especially true if human bias creeps into the analysis.
  
  
• Skills shortfall: The need for data integration might be difficult, given a wide variety of sources, and integration might exceed current capabilities. Expertise in data science, engineering and architecture is required to help ensure that analysis yields insights that reflect reality.
  
  
• Up-front costs: The initial costs to develop a powerful, modern BI system might appear large—but the cost savings generated by analysis will offset the investment.

Data is the lifeblood of successful organizations. Beyond the traditional data roles—data engineers, data scientists, analysts and architects—decision-makers across an organization need flexible, self-service access to data-driven insights accelerated by artificial intelligence (AI). From marketing to HR, finance to supply chain and more, decision-makers can use these insights to improve decision-making and productivity enterprise-wide.

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. To deliver a BI system that can make all of this possible, organizations should:

• Set clear business objectives: Determining the most valuable and actionable information enables an organization to determine the data that needs to be collected or sourced and help select the BI system features needed to deliver that information.
  
  
• Comprehensive user training: The culture change to becoming a data-driven organization is most achievable when all users are given clear and compelling lessons on the new tools. Perfunctory training or self-guided hacking might discourage team buy-in or produce inaccurate results.
  
  
• Monitor data quality and relevance: Constant data monitoring is needed to help ensure that results are consistent and trustworthy. As market conditions change, new measures might need to be added or different reporting formats developed to add clarity. The input data sets must be sound and unbiased, and managed according to clear governance standards that ensure it is secure, private, accurate and usable. Any AI models that inform decision-making and forecasting must be explainable and transparent. And the BI system should connect to a wide variety of data systems across business functions and be usable by those who are not professional data analysts.
  
  
• Ensure data access to decision-makers:  Many businesses are behind. Essential data is not sufficiently captured or analyzed, according to an IDC report⁴ that estimates up to 68% of business data goes unleveraged. Companies with a modern data architecture and robust BI adoption enjoy competitive advantage: They are positioned to move even further ahead by adopting real-time decisioning practices and predictive analytics.
  

Business intelligence adds value across multiple functions in almost any industry. For example:

Customer service: With both customer information and product details available through a unified data source, customer service agents are able to quickly answer customer questions or begin to solve customer concerns.

Finance and banking: Financial firms can determine current organizational health and risks, and predict future success by viewing combined customer histories and market conditions. Data can be reviewed branch-by-branch with a single interface to identify opportunities for improvement or further investment.

Healthcare: Patients can quickly get answers to many pressing healthcare questions without asking time-consuming questions of staff or medical personnel. Internal operations, including inventories, are easier to track, minute-by-minute.

Retail: Retailers can boost 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.

Sales and marketing: By unifying data on promotions, pricing, sales, customer actions and market conditions, marketers and sales teams are better able to plan future promotions and campaigns. Detailed targeting or segmentation can help boost sales.

Security and compliance: Centralized data and a unified dashboard can improve accuracy and help determine the root causes of security problems. Compliance with regulations can be simplified with a single system to gather reporting data.

Statistical analytics:  Using descriptive analytics, organizations can review statistics to spot new trends and uncover why those trends are developing.

Supply chain: Worldwide data on a single pane of glass (SPOG) can speed the movement of goods and the identification of supply chain inefficiencies and bottlenecks.

Recent developments in business intelligence are focused on self-service BI applications that enable non-tech-savvy users to use automatic analysis and reporting. The IT team remains responsible for managing corporate data—including accuracy and security—but multiple teams can now have direct access to data and be responsible for their own analysis, rather than having the job wait in a queue for IT to run.

The ongoing advances in modern business intelligence and analytics systems are expected to integrate machine learning algorithms and AI to streamline complicated tasks. With the new emphasis on self-service, these capabilities can also accelerate the enterprise’s ability to analyze data and gain insights at a deeper level. AI-based systems can read from multiple sources automatically while grabbing the most relevant information to lead decision-making.

As an example, consider 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.

Modern BI solutions live on cloud-based platforms to extend the reach of BI worldwide. Consumer insights can be drawn from big data, producing information that ranges from descriptive to predictive. Many BI solutions now include real-time processing, enabling immediate decision-making.

Further advances in enterprise-grade BI systems include natural language queries, which are easier for users who are not SQL experts. Low-code or no-code development capabilities are available in some BI systems so users can create their own tools, apps and reporting interfaces to further speed the answers and time-to-market.