Applications providers, like SAP, emphasize that their work-flow solution is configurable and ready for analytics integration and business intelligence . When database vendor Oracle extends R capabilities, Greenplum, another cool database vendor, is driving the future of big data analytics by integrating Base SAS libraries at database server level. When we hear partnership with SAS and R, the names long associated with reliable statistical modeling and analysis, it’s quite convincingly implied that they are providing analytics. A world of reporting tools, like MicroStrategy, SAP Business Objects or Hyperion provide data mining, business intelligence and analytics that leverage on multi-dimensional databases and brings out insights to the management in a drill down-roll up fashion.
Once you peddle through the jargons, they, Google analytics included, are still talking about reporting that just got extravagant with all the technological advancements. It is hard is to define the term analytics without offending a lot of people. A lot of people already claimed their stake in this ‘next big’ thing. Not related, but an MIS division head I know recently changed his title to ‘ Data Scientist’. When I checked what changed, he said they hired a consultant to do Hadoop and Microstrategy for them.
Coming back to the main topic: what’s core analytics means? Yes I added ‘Core’ to emphasize. Probing deeper, it sounds like they mean predictive modeling or predictive analytics. A set of old fashioned test-control- validation exercise using various statistical techniques like logistics regression, survival analysis, classification trees or even machine learning. Wiki does a better job in explaining this. People who do such work are often called modelers and they want to differentiate themselves from a set of IT or near IT guys who primarily deal with reporting systems. The seemingly simple issues these people deal with are not solved by the smartest visualization software – Like what is a statistically sound substitution to use for missing values in sample data? How to derive some performance for the customer we never had? – There are hundreds of articles published in journals on these topics and hundreds were awarded PhDs. But still there is no agreement. Since there is no one rule and generalization is not a possibility, software cannot hide it under a layer. From data side, such analytics are often supplemented with data that’s not available in corporate Hadoop Big Data mine. So they don’t believe an off-the-shelf application sitting on that mine is going to get the things done. There is a difference in deliverables too. The so called generic analytics applications provide reports, tracking dashboards or warning systems. Core analytics deliverables are sets of rules that sit in an application and acts like an expert. Say a scoring model that replaces an underwriter. When humans learn from the new environments and their own mistakes, an expert system pretends that what it had gleaned from the past is still sound. Moody’s AAA rating of junk bonds in 2008, is an example. (I know genetic algorithms and artificial intelligences counter argue, but they cannot detect human lie).
The paragraph above attests how drawn-out and jargonized this topic is. Liberals have no place in such discussions.
Then there is an overlap of these two worlds. Google Analytics for example, it’s possible to setup a test- control strategy to see what works best in real life (A/B Testing). Many analytics applications can be configured to work dynamically, for example: Amazon recommendations or fraud detection, are trained on the fly but rules behind them still lying in the disputed land.
I counted at least a dozen times the word analytics used in our meetings last week. It almost always meant some numbers to support an idea or argument. A report. With that statement, this topic is open for discussion.
















