Business Intelligence (BI) can mean many things to many people, but generally BI is associated with business reports. When you fold business analytics (BA), especially advanced analytics that are predictive or prescriptive, under the BI umbrella you inherently dilute the value proposition that analytics can provide to an organization.

Why is this important? Because everyone knows analytics is hot, so everyone today is selling some kind of analytics. When we allow business analytics to be synonymous with BI, we allow everyone's claims that they can "do analytics" appear to ring true.

Let's look at some typical BI terms and measurements:

• Key performance indicators (KPIs) are associated with performance management and consist of "a set of quantifiable measures that a company or industry uses to gauge or compare performance in terms of meeting their strategic and operational goals" as defined by Investopedia . KPIs also go hand in hand with scorecards and provide value. However, typical KPIs and scorecards are measuring progress toward a goal which means evaluating events that have happened already and reporting on the results. In other words, this information source allows decision makers to react to what has happened instead giving them information on how they might influence or adjust course to avoid or encourage something to occur.
• BI reports tend to be used across all organizations simply because they do provide information on how operations are either performing right now or in the past. As mentioned above, typical BI reports do not necessarily include analytics which allows insight into what may happen in the future with a certain degree of confidence.
• Descriptive statistics allow analysts to better group like events, assets, products, customers, employees, etc... and helps to better "describe" entities which allows better historical information to be included in BI reports or the ability to provide different views (hierarchies or drill paths) to look at how groups or individual entities have interacted across different dimensions.

Notice how all the grammar used to describe KPIs, BI reports, and descriptive statistics use the past and sometimes present tense. This is the key differentiator that separates predictive analytics or what is shortened to analytics most of the time. When discussing and describing predictive analytics the grammar tense should shift from past and present tense into future tense. If someone is trying to show you reports with a claim that they include analytics and nothing in the information provides you insight into what may happen in the future, then guess what? They don't really have predictive analytics.

Drum roll please. According to Techopedia: "Predictive analytics describes a range of analytical and statistical techniques used for developing models that may be used to PREDICT future events or behaviors.   There are different forms of predictive models" (not just regressions, which are typically the first used and most basic form of predictive models), "which vary based on what event or behavior or data that is involved in the prediction."

I bet -- or should I say -- predict most people reading this blog think it is done (given a 75 percent confidence level :). However, I have not defined analytics, just a sub-category of analytics known as predictive analytics.   According to BusinessDictionary: "The field of data analysis: Analytics often involves studying past historical data to research potential trends, to analyze the effects of certain decisions or events, or to evaluate the performance of a given tool or scenario.  The goal of analytics is to improve the business by gaining knowledge which can be used to make improvements or changes."

I've always broken "analytics" into its 3 main sub-categories: predictive analytics (aka data mining), forecasting, and optimization.  Bottom-line: advanced analytics allow you to be proactive in taking action, while all the other forms of reporting only allow you to be reactive in taking action.

For more information on this topic, I recommend reading books by Thomas Davenport and Evan Stubbs, or obviously contact us here at SAS.