For those new to business intelligence (BI), analytics is a broad topic, but a critical component of the BI space. What follows is a high-level introduction of the subject. I'll do my best to describe the fundamentals of what it is and what it does, at least in a way that relates to Dashboard Insight readers.
First off, the classic definition of analytics is "the method of logical analysis" or more basically, something related to analysis. Quality, facts-based analysis (hopefully) leads to the right business decisions, so the first question is: What gets analyzed? The rough answer is many things, but in our world, we're concerned with business data. And that can mean anything and everything from raw-materials and manufacturing information to industry regulation to sales and customer data. It's wide open - and it depends on what you want to see.
Thinking about killing an old product line? Are you questioning the efficiency of a particular production method? Contemplating barriers to entry in a new market? Got a sales rep. whose performance may not be up to snuff? Maybe you want to extend some credit? The extracted data will guide your decisions.
All data can be processed for analysis, whether it comes from spreadsheets, data warehouses, enterprise resource planning (ERP) systems or any other framework or application. In the graphic below ("the BI stack"), data is part of the bottom sections:
Most dashboard end users are more familiar with the other end, the presentation layer at the top. This
layer helps deliver data as useful, intelligible information via charts, scorecards, gauges, etc. For end users, the presentation layer is the most critical aspect of a BI system, since it broadly shapes their core understanding of the data displayed on their screen.
But where do those key performance indicators (KPIs), key risk indicators (KRIs) and other critical business numbers come from? Analytics is the middle section, the automated processing that measures, evaluates and compares bottom-layer data and presents it as top-layer viewer information.
More detail is beyond the scope of this piece, but it's important to know that the fallout from all this number crunching can land in two key areas: statistics and predictive analysis. Think about it this way, statistics is the past - it tells a tale of what happened. Predictive analysis is much spookier, it tries to give you an intelligent glimpse of the future, often in real time.
As the name suggests, predictive analysis tries to make forecasts of future events, trends and probabilities, based on data mining (looking for patterns in the data) and other complex event processing. You can easily see this kind of modeling in the insurance and credit industries, where someone's past actions may affect a future outlook. But nearly every industry engages in predictive analysis at some level, especially marketing departments, the security industry, the economics sector, among others.
So now that you have a basic understanding of business analytics, what's the next step? Well, this month's Dashboard Insight theme is "Business Analytics: Predictive and Beyond." Based on the past, I predict interesting and insightful articles are about to be posted. Let's see what turns up.
About the Author
Rob Hunter works as a software copywriter by day and as a Dashboard Insight editor by night (when he’s not playing his upright bass).
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