Business intelligence (BI), once confined to the finance sector, has since evolved into its own industry and become best practice for organizations of every stripe. The Information Age and its attendant technologies have culminated in an abundance of data and a prevailing appetite for data-driven decision-making, prompting enterprises to launch BI projects, software providers to adopt BI solutions, and educational institutions to offer BI curricula. This guide will explore in detail all aspects of business intelligence, starting with a comprehensive definition.
Though there is no universally agreed-upon definition of business intelligence, the authors of this 2016 paper on BI reporting do an admirable job of synthesizing the most common definitions of BI into a single statement:
In this paper, we understand BI as a term which includes the strategies, processes, applications, data, products, technologies and technical architectures used to support the collection, analysis, presentation and dissemination of business information.
So while some might think of BI as the study of certain analytical practices, others might associate the term with a type of software application or with the data those applications are used to process. All of these fall under the BI umbrella. In simple terms, business intelligence can be used to describe anything related to the analysis of business data.
Business Intelligence Examples
Because BI can refer to a strategy, process, application, data set, technology, and/or technical architecture, exemplifying business intelligence isn’t as simple as naming a platform like Logi Analytics and calling it a day. Below is an example of each of these manifestations of BI.
A BI strategy is a comprehensive plan detailing a business’s data analysis goals and means of achieving them. What insights an organization wishes to surface will, for example, determine what data it collects.
The first known instance of business intelligence involved a banker named Sir Henry Furnese, who “profited from information by gathering and acting on it before his competition.” This he did by gaining early access to news stories he knew would affect the markets. Data acquisition is a key component of any BI strategy.
We might think of business intelligence processes as the tactical elements of an overarching BI strategy, and there are many of them. Data warehousing is one commonly adopted process that primes transactional data for analysis. Data quality management is another best practice by which organizations monitor and cleanse their data of errors and anomalies. Data discovery, data modeling, and bursting are also all BI processes!
Business intelligence applications aid in the process of data analysis, and they all do it a bit differently. Spreadsheet applications like Excel technically qualify as BI applications, but they of course work very differently from solutions like Logi. Logi makes it possible to analyze data housed in relational and non-relational databases without having to know code or querying languages. (See below for more on different types of BI applications.)
BI data is any data relevant to your organization and analyzed in service of your BI strategy. Logi clients typically source the data their SaaS customers enter and store within the application, but there are usually other supplementary data sources as well, such as reference data or free publicly available data.
They may also source data from other applications. Newport Credentialing’s cloud-based credentialing and provider enrollment platform CARE, for example, connects to a number of other medical systems, including HR, billing, privileging, and central verification platforms. Since all of this data helps CARE users manage their relationships with medical insurance providers, it all qualifies as BI data.
The business intelligence space is brimming with analytic technologies — data visualization tools, real-time intelligence, ad hoc reporting, and a great deal more.
But don’t let trendy tech trick you or into buying something you don’t need! This can lead to wasted funds and frustrated analysts. Our clients have shown us time and again that the most impactful tech is also often the most inconspicuous: the ability to collate reports a certain way, for example, or the power to create multiple subfolders in a report library, or the possibility of penning a custom function.
Yes, “business intelligence” can refer to an entire system architecture — everything from your data sources to your BI servers. This might include transactional databases for raw data storage, dedicated server resources used to run nightly ETL processes, a data warehouse or data marts, and/or a web farm or VM container system to handle BI application sessions.
Business Intelligence in Practice
Doing business intelligence is in large part a function of who’s doing it.
Eckerson divides BI users into two groups: casual users and power users. Although applied to self-service BI, this user breakdown is becoming increasingly common as BI solutions become more accessible to non-specialists.
Ninety percent of BI users fall in the casual category. These include healthcare providers pulling up office visit reports, directors monitoring department dashboards, marketing managers summarizing campaign performance — anyone whose core responsibilities fall outside data analysis but relies on it to do their daily work. They read, run, modify, and sometimes even build simple reports from scratch.
The remaining ten percent of BI users consists of individuals whose primary function is to analyze data and deliver insights.
BI Roles and Skills
Below are some of the most common titles for those specializing in data analysis. Keep in mind, however, that these roles can vary — and sometimes widely — from company to company. For this reason, we are going to stick to broad definitions.
A business analyst’s primary area of expertise is in the business function they support, be it sales, finance, operations, or another domain. They are often tasked with rooting out process inefficiencies and recommending performance improvements based on their findings. A solid foundation in data analysis and statistics is critical in helping analysts separate actionable signals from noise.
Unlike business analysts, data analysts (also known as BI analysts) aren’t required to have a deep semantic understanding of the business data they handle. Rather, they prepare and process the data into reports and visualizations, disseminating them to stakeholders throughout the company. In some organizations, data analysts may even prepare the reports business analysts use to make their recommendations.
Requirements for this role vary, but in addition to a solid foundation in statistics, it’s often valuable for these individuals to know some SQL, be familiar with data modeling principles, and have experience with one or more BI solutions.
If data analysts are primarily concerned with reporting on business as it stands, data scientists are interested in discovering what might be. Some are tasked with predictive what-if algorithmic querying while others are more devoted to pattern discovery in large data sets. Data scientists often have advanced degrees and backgrounds in such computer languages as Python and R to supplement their statistical knowledge.
So what does it take to start a career in BI? New programs in data analysis are popping up every day, but it’s also possible to break into the field without prior experience. Depending on the company, skill at the BI solution in question may be all you need to get started.
Types of BI Solutions
There are several types of business intelligence applications, each intended to satisfy a different use case. Below are a few prime examples:
As the name suggests, embedded business intelligence is designed to integrate with a host software application, delivering analytics capabilities to its users. Some embedded BI solutions even enable users to design reports and dashboards from within the host application.
The goal of self-service business intelligence is to empower non-analysts to surface their own insights without having to wait for assistance. Analysts and other IT personnel are nevertheless instrumental in facilitating self-service systems.
While nearly all BI solutions support charting, dashboarding, and data visualization, only some also offer tabular reporting. Reports are better than dashboards at surfacing and displaying detail data, which plays a significant role in daily operations.
Selecting a BI Solution
Selecting the best business intelligence tool for you always begins with a requirements assessment in following areas:
- Data integration
- Application integration
Application integration requirements are especially important if you’re looking for an embedded BI solution, but other types of BI tools may call for application integrations as well.
Once you have your requirements outlined, it’s time to register for product demonstrations. It’s not uncommon for teams to attend a dozen or more of these, but experts recommend keeping the number to about five to minimize confusion and prevent overwhelm.
Regardless of the type of BI solution you’re adopting, it’s critical that you go through a trial phase before purchase. Of course, the evaluation will only be as rigorous as your proof of concept, so put careful thought into your methods of testing the product fit. Ideally, you will be able to apply the same method to all solutions for a more controlled comparison.
The decision phase that follows should not hinge on price alone. Although price has emerged in recent years as a prominent deciding factor for enterprise BI buyers, fit must remain top priority if the project is to succeed. Organizations concerned with cost would do well to consider solutions offering flat-rate pricing options, as these will remain stable and predictable long term.
Analysts expect the business intelligence industry to continue to flourish, particularly as enterprises across sectors work to foster a data-driven culture among employees. Educational programs in analytics will continue to sprout up in response to the increased demand for data specialists. Business intelligence will likely remain a profitable sector for years, if not decades, to come.