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Strategic Analytics: Tools vs Insights, Part 1

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Strategic Analytics History, Part 1: Tool vs Insights

Strategic Analytics usually need a good statistical and data mining tool.

Tools are developed for all kinds of purposes, but unfortunately, no single tool may address the needs of all the sophisticated users that Strategic Analytics are. Hence, as needs methodologies, techniques and technologies evolved a choice has been between tools that cover broad needs, and those we may call: "niched."

Usually, focused users like AroniSoft tend to be less interested in the quantity of features, than the quality of the insight garnered from using the tool.  In fact, for us a good strategic analytics tool needs to meet at least four criteria:

1) Does the tool allow us to get from data from multiple sources to relevant information

2) Does the have the statistical and/or data mining functions needed

4) Is the tool flexible in its proposed functions?

3) Is the tool easy or intuitive to use?

Other criteria may be added, but we will focus on these.

Users at AroniSoft have decades of collective experience using several statistical and data mining tools. Some tools have come and gone, others have withstood the test of time. Yet others have either trimmed down their ambitions or used in marginal areas of applications.

We remember tools like Silicon Graphics DMINER that failed to catch up despite initial great promises, or those that were able to carve out their niche, such as MINITAB and EXCEL.

Other tools have established their names as the advanced analytics tools. These include SAS (including SAS Enterprise Miner,SAS JMP), S-Plus, R, SPSS,  and STATA.

There are a whole lot of number of tools used in specialized domains, usually not in the public domain,  but those are out of the scope of our analysis.

Without a doubt, SAS has become the powerhouse of analytics. It has dominated the industry for more than 30 years.

Critics have tried to pick holes here and there but could not tame  its momentum.  For some of us, the first use of SAS goes back to when we had it for DOS and Mainframe. Back then, it was a whole lot experience that the new generation of strategic analysts see only in movies.

Since its introduction SAS ride the wave of challenges, most positive we may add:

1) The availability of cheap and fast computing powers

2) The explosion of technology innovation

3) The advances in statistical methodologies and data mining techniques

Initially, SAS choose to focus on incremental growth: adding existing techniques, a venue made possible by the new computing powers.

This gave an edge to SAS, but at the same time allowed new tools to find their ways in:

1) SPSS came in with easier to use tools with better graphics

2) STATA came at the heels of SAS with more focus on highly advanced analytics and less on data crunching

3) Later S-Plus explored the new territories of cutting edge statistical techniques, with the flexibility of an advanced programming language

The database marketing in the financial, packaging, telecommunications industry made all possible, or at least  a great deal of it. Database marketing managers, mostly composed on statisticians and operations researchers pushed the enveloped and forced the tools to implement advanced statistical methods, Neural Networks, Artificial Intelligence, Decision Trees (CHAID/CART),  and Genetic Algorithms.

The strategic analytics industry adapted to the never ending needs and demands and went to the extremes of selling the tools under the marketing scheme: "No Statistician or Operations Researcher needed." It backfired.  Because of two problems, maybe more: the blackbox effect and the lack of expertise. It is  one thing to trust fortunetellers it is another to trust an uneducated advisor.

The ease of use concept ignored the understanding of statistics and operations research part necessary for gaining insights and supporting an informed decision making.

What was forgotten is that: Strategic Analytics is not about the numbers. It is about the insights and the decision made about them to impact the bottom line.

Hence, most of the tools that sold on the ease of use failed to catch up. Silicon Graphics DMiner may be classified as one of them.

Another selling point was the integration with other existing tools. That was a good move given the leverage of computing technologies. Nevertheless, once again integration was secondary to insights generation.

Finally, better integration,  multiple techniques within the tool, and beauty over substance made the tools sometimes  have a prohibitive cost, far outweighing their intrinsic value. SAS Enterprise Miner, a wonderful tool has failed to catch up, as it was for SPSS Clementine and other general purpose data mining tools.

Speaking of general purpose, most of the tools makers forgot the basic law of economics: expertise.

The lack of focus killed some tools. As William J. O'Neil  put it in his National Bestseller book "How to Make Money in Stocks. The McGraw Hill Company, 2009." who want to be operated by a doctor who is a part timer?

SAS, S+ and SPSS, the major players in the area of Strategic Analytics may have learned the lesson:

1) Focus of you areas of strength

2) Sell the bottom value not the "nice to have": it is all about the strategic insights

3) Flexibility: Innovate consistently

Tools that will last will be those that meet the three criterias.

We will look at how each tool has addressed these challenges, focusing on those that have been successful (SAS, SPSS, S+), those that have found their niche (MINITAB, Excel, STATA), and those that died a slow death such as Silicon DMINER.

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