You may have noticed many vendors transitioning away from the term business intelligence and instead using business analytics. I recently wrote about industry trends and the longer term, ongoing market shift from business intelligence towards business analytics in a July 2014 SQL Server Pro article. In that article I shared that analytics is more of a forward looking approach using data to gain business insights. It means interacting with information at the speed of business, continuous iterative data discovery, exploration, and fact-based decision making using statistical and quantitative analysis, explanatory, predictive and prescriptive modeling. Let me take this opportunity to connect and clarify a few of these concepts.

Analytics is closely related to operational management science. Where traditional business intelligence was historically focused on measuring past performance with querying, reporting, OLAP, and dashboards answering what happened, how many, how often and where type questions. Analytics strives to answer why, and forward looking questions such as what if these trends continue, what will happen next, and what is the best that can happen? The movement towards analytics also reflects reporting maturity. Most organizations have basic historical reports. The next logical step is to get proactive insights.

Analytics covers a broad array of areas. According to Gartner’s first ever Magic Quadrant for Advanced Analytics in 2014, they defined advanced analytics as, “the analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) — such as query and reporting — are unlikely to discover.” I also like the definition and structure that INFORMS presents for analytics.

Descriptive Analytics

  • Prepares and analyzes historical data
  • Identifies patterns from samples for reporting of trends

Predictive Analytics

  • Predicts future probabilities and trends
  • Finds relationships in data that may not be readily apparent with descriptive analysis

Prescriptive Analytics

  • Evaluates and determines new ways to operate
  • Targets business objectives
  • Balances all constraints
Source: INFORMS

If I take these categories relate them to commonly used tools and technologies, analytics might be a bit more relatable. Although this list is not complete by any means, I am going to guess that you will recognize and probably have even used a few of these tools in your past projects.

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

I do feel that understanding how predictive analytics, optimization, simulation and other prescriptive techniques work is becoming more relevant for business intelligence professionals to architect and refer appropriate analytical solutions. There are increasingly more automated, “easy button” predictive solutions in the market that can return the wrong results because the users may not provide accurate data to represent the population being examined. The computational engines under those easy buttons still need decent data sets, prepared appropriately for the algorithms being used, to provide reliable results.  Think garbage in, garbage out – don’t be fooled by “slickery” vendor demos.

For Further Learning

If analytics is an area that you’d like to learn more about, there are tons of free and low cost resources for ramping up. Coursera and Microsoft MVA’s free courses include beginning through advanced analytics topics. Personally I am a huge INFORMS fan and enjoy their monthly Analytics Magazine. I also treasure ACM’s included Books 24×7 and Safari Books Online collections with their reasonable $99/year membership.  PASS BA Conference is an event for individuals looking to learn advanced data analysis skills.  Last but not least, most of the analytics vendors do provide excellent, free YouTube videos and tool specific training.  In future articles, we will dive into different areas of analytics with real-world examples and explanations on when to choose one analytical problem-solving technique over another.

It should be enjoyable for business intelligence professionals to get cozy with the meaning of the data that we have been extracting, transforming, storing and querying throughout our entire careers.  Don’t fret over this market shift or label trends.  It is a great time to be a data pro.  In reality, we are all still in decision support.  Who remembers that trendy term for business intelligence from the 90s?