Business Intelligence or Business Analytics?

Business Intelligence or Business Analytics?


Business Intelligence and Business Analytics vendors have their definitions. Consultants have theirs. Senior management has yet another understanding. You just want to get answers to the problems you face and make better decisions. Business Intelligence and Business Analytics, or BI and BA as they are commonly abbreviated, have been around for some time. The term Business Intelligence has been with us since 1865 when Richard Millar Devens wrote his definition in the Cyclopedia of Commercial and Business Anecdotes. Business Analytics has early roots in the late 1800s but only became something we would recognize today in the 1960s in the form of computer-based Decision Support Systems.

To be sure, the terms have morphed over time. Traditional Business Intelligence vendors, like SAP, Oracle, and IBM, have extended their BI platforms to include predictive analytics features that were formerly the sole purview of Business Analytics tools, further blurring the lines. Let’s explore the terms a bit more, find the overlap and differences, and, hopefully, help you understand how these tools can inform and improve your business decisions.




Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business analysis purposes. BI can handle enormous amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities. BI allows for the easy interpretation of volumes of data. Identifying new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability.
BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.


Computer-based techniques used in spotting, digging-out, and analyzing ‘hard’ business data, such as sales revenue by products or departments or associated costs and incomes. Objectives of a BI exercise include (1) understanding of a firm’s internal and external strengths and weaknesses, (2) understanding of the relationship between different data for better decision making, (3) detection of opportunities for innovation, and (4) cost reduction and optimal deployment of resources. See also competitive intelligence.



Business analytics (BA) refers to the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning, which is also based on data and statistical methods.

Business analytics makes extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision making. It is therefore closely related to management science. Analytics may be used as input for human decisions or may drive fully automated decisions. Business intelligence is querying, reporting, online analytical processing (OLAP), and “alerts.”

In other words, querying, reporting, OLAP, and alert tools can answer questions such as what happened, how many, how often, where the problem is, and what actions are needed. Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (that is, predict), what is the best that can happen (that is, optimize).


Process of determining and understanding the effectiveness of various organizational operations. Business analytics can be either focused on internal or external processes. Different specializations exist, encompassing most major aspects of business, including risk analysis, market analysis, and supply chain analysis.



Business Intelligence is a process for turning raw data into meaningful and useful information for business analysis purposes. It reports on what happened and informs process optimization and evolutionary change. It aggregates raw data into reports and dashboards based on pre-defined key metrics, also known as Key Performance Indicators or KPIs. BI dashboards can include alerting functions based on historical normal operating ranges. BI reports and dashboards are commonly used to meet compliance requirements.

A big downside of BI is that it relies on aggregated data and can miss interesting signals contained in the raw data. It depends on the person looking at the results to find relationships and patterns and anticipate future outcomes.


Business Analytics is a process for finding relationships based on past data that model business behavior and predicts likely future outcomes. That is, it extends Business Intelligence’s data collection and reporting through traditional statistics and modern machine learning techniques to detect relationships and patterns and anticipate future outcomes. Business Analytics can lead to revolutionary change and has the potential to create competitive advantages.

Business Analytics is not all skittles and beer, though. Predictions can go terribly wrong if they are divorced from common sense and domain knowledge. One needs to look no further than the subprime mortgage crisis to see painful evidence of this malady. Another pitfall is false predictors. The “Redskins Rule” of predicting presidential election results based on the Washington Redskins last home game before the election, while highly correlated, has nothing to do with the election, itself. It depends on using a particular time window (sample data) and falls apart when the window is extended. Additionally, BI’s tools can fall victim to over-fitting. That is, the resulting model fits the training data very well (perhaps too well…) but falls apart with data outside the training range. One should look for a model that is generally accurate, not precisely wrong.


Business Intelligence and Business Analytics both depend on historical data. Without data, neither process can function. BI vendors have begun to add BA functionality to their offerings, further blurring the distinction between the two.


Business Intelligence is, traditionally, a rear-view mirror looking at what has happened, whereas Business Analytics uses the view from the past to analytically, rather than intuitively, predict likely future scenarios. BI aggregates raw data into pre-defined Key Performance Indicators and reports. Business Analytics seeks to find underlying relationships and build predictive models and emphasizes exploration over “this is what the report says”. Business Analytics seeks to share learning rather than confine insights to the one guy who exported BI data into an Excel workbook on his laptop.


Call it what you will, BI and BA functionality can improve your ability to make informed business decisions. Both are rooted in W. Edward Deming’s concepts of managing by fact. Given the data and computing power available today, BI and BA are within reach of most companies, though, perhaps, not without outside help as they can be complex and tedious.

BI, to a lesser extent, and BA require domain-specific knowledge to be used effectively. BI and BA techniques that are appropriate for the finance department are different than those needed by manufacturing which are different still than the needs of marketing. This has led BI/BA vendors to realize that they need to move from general purpose to domain-specific tools. The transition to domain-specific tools and techniques will help deliver intelligent and relevant applications.

While BI/BA tools have been available for many years, their adoption has been limited within organizations. As a recent Gartner report says, “”They were never really fully embraced by the business analyst masses, primarily because they are perceived by most as being too difficult to use.” Compounding the steep tool learning curve, the analytic skills required to effectively use and make sense of the information these tools deliver are not common. This may change over time, much as spreadsheet and word processor skills increased through the late 1980s and into the 1990s. If “Where’s the VisiCalc guy?” sounds familiar, you know what I’m saying.

While there are barriers to using BI/BA, the competitive advantages offered by both are worth the investment, even if you need to find outside help to make it happen. Think big, but start small and deliver rapidly. Find a partner who understands your particular domain. And make sure to share the learning. While “being the genius down the hall” may have its appeal, it offers less value to the business than sharing the knowledge.

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