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A primer on business intelligence: Understanding the basics

June 10 2021
by Krishna Roy


Introduction


The origin of the expression 'business intelligence' (BI) is widely thought to date back to 1865, when it was used by US historian Richard Miller Devens in his book Cyclopædia of Commercial and Business Anecdotes to describe how banker Sir Henry Furnese profited from information by gathering and acting on it before his competition. This rationale, among others, remains a fundamental driver behind modern BI, which, owing to individuals such as IBM researcher Hands Peter Luhn, has become a technology-based discipline to support data-driven decisions.

Even though BI is increasingly adopted for data-driven decision-making, not every organization makes all strategic decisions using data, which means they are unlikely to have adopted BI because it essentially relies on data for insight to support these decisions. In 451 Research's survey, only 16% of respondents said that 'nearly all' strategic decisions are data-driven, while 7% said 'few' strategic decisions are data-driven. A clearer understanding of the potential benefits of BI, along with the products available to support it, could therefore help organizations become fully data-driven decision-making entities.

The 451 Take

BI continues to remain relevant. Even though cutting-edge analytics involving high levels of automation tends to occupy the limelight, more traditional forms of BI involving reports and dashboards are widely adopted, particularly for users in business roles (see below). That's critical because, in order to qualify as a data-driven organization, companies need to place data, as well as the analysis of it, center stage, which calls for widespread use of BI by personnel in myriad roles, not just data and business analysts and marketers, who are often its most fervent devotees. Furthermore, market education is still required on what BI is – and what it is not – to achieve this objective, which calls for practices beyond technology adoption to foster a data culture.

Why use BI?


The practice of gathering and using data for competitive advantage, as Devens illustrated, is time-honored, and remains important today. Furthermore, BI can offer additional benefits, such as helping users make better business judgements. However, it is important to note that improved decision-making is not simply a technology matter concerned with choosing the right BI products. It involves stringent data management practices to ensure that data-driven decisions are of a high quality and can be confidently utilized to make decisions because they are not based on 'dirty data,' as well as governance to ensure that decisions comply with corporate and regulatory mandates.

Furthermore, data culture and data literacy play key roles in ensuring BI supports better decisions, and data culture remains tied to product choices, according to our data. When asked which steps an organization has taken to improve its data culture, 44% of respondents said they had invested in new data management products and services, and 40% said they had invested in new analytics products and services, which were the two most popular steps, according to this survey.

Improved operational efficiency (through visibility and analysis), as well as better insight into customer activity, such as account churn rates, are additional benefits BI can bring. Financial analysis, as well as marketing analysis to support the likes of campaign management, also remain prevailing reasons why organizations implement BI.

What's a BI product?


The category of products used for BI is quite distinct. However, it can be confusing because 'BI' and 'analytics' are frequently used interchangeably. That's because BI is a form of analytics used to analyze data for insight. However, analytics is a broad church with many more pillars to it than BI.

For example, data science, which we explain here, is a form of analytics. Yet it is radically different from BI because it is essentially more complex and forward-looking, with a predictive aspect to it that BI doesn't have. Data science is concerned with predictive decision-making and, often, making those predictions actionable through recommendations.

BI products are concerned with presenting and analyzing the current state of play in an organization largely using historical data. For example, BI enables the examination and analysis of sales performance using historical data on sales from previous days, months or years.

BI reports and dashboards are prevalent but distinct BI products. However, the fact that they are both employed for analytical use cases using historical data can be perplexing. Moreover, their differences can be muddied by software vendors in their marketing of them, as dashboards often contain information in a report and are used to support enterprise reporting use cases.

Furthermore, the terms 'visualization' tool and 'BI' tool are sometimes used interchangeably, even though (strictly speaking) a visualization tool is a specific BI product with a particular use, not a generic BI product. Visualization tools are solely concerned with graphically depicting data in charts, graphs and other forms of visualization, while dashboards visualize data but can also provide insight in other formats. The key differences between reports and dashboards come down to the level of information they provide, as well as the way it is presented and able to be analyzed.

Pixel-perfect reports are one type of BI report. Pixel-perfect reports are formatted documents where every element has a fixed position. They are typically used to support operational reporting scenarios where static presentation of specific insight into corporate operations is paramount, such as for a company board meeting. Furthermore, they are most appropriate when reports need to be able to support multiple formats, such as PDFs, browsers and print.

Ad hoc reports are another form. They are used in situations where the generation of a one-off report to answer a specific question is required. Ad hoc reports are highly customized, narrow in scope and good for scenarios where detailed specifics on a small area of a company's business is needed, such as helping a business development team improve a revenue stream by analyzing the operational data necessary to spot a new trend.

Dashboards convey business metrics, visualizations and (increasingly) textual explanations to illuminate graphically depicted insight. They are most appropriate when organizations want an interactive on-screen window into business operations, which they can filter and drill down into for further details. Dashboards are frequently used to evaluate the success of specific corporate activities, such as sales targets, as well as to monitor other data points an organization, department, team or individual cares about – in one place.

The future of BI


The future of BI reports and dashboards looks bright – enterprises are increasingly looking to implement these types of offerings, particularly for senior and departmental/line-of-business decision-makers. The current market buzz may be all around the new breed of analysis tools that automate insight and push it to users, but classic BI approaches involving BI reports and dashboards continue to show strong signs of adoption, as the chart below illustrates.

Figure 1

Analytics Products Used by Senior and Departmental/LOB Decision-Makers 451 Research