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Home > Strategy > Competing on Analytics: Part 2

Competing on Analytics: Part 2


We have seen in my earlier post on Analytics that how and which companies are competing on the basis of analytics. The next questions that a person is bound to ask is that what type of companies or companies in which industry sector make the best use of Analytics?

The answer is ANY. Yes, any company in any sector can compete on the basis of analytics. Analytics does not mean that one has to have hoards of data and numbers. Analytics can also be use on transactions that involve verbatim details. Companies even make use of the analytics in analyzing words, phrases, the sentiments etc.

So now we know that any company can implement analytics. But, to make use of the analytics framework and to derive the desired results, the human component is as important as the mathematical model and IT component driving it.

There are a few more questions that arise in the implementation of analytics:

What data to capture?

This question has no straightforward answer. The executives driving the implementation should decide on what data will enable them to achieve the insights required. Some executives try capturing every bit of data that they can lay their hands on. It is good to capture as much data as one can because you never know what will be required at what point in time. But al J.L. Distinguished Prof. of Kellogg University puts it “It may lead to data obesity and knowledge starvation”. There no measure to determine the “optimum” amount of data or the “optimum” parameters on which to collect data. Thus, it is a pure Human component of the implementation that decides what to capture (unless one is implementing a package which requires mandatory fields to be captured). The capture of data also depends upon the requirements of the “downstream” systems and channels. For example in the CRM system may not require all the data for itself, but has to mandatorily capture it for the downstream systems such as Billing and Finance.

Correct: The data captured should be correct in all respects. Incorrect data will spoil the quality of results and may even lead one to wrong results.

Complete: The data to be captured should complete. There should be no missing fields. The missing fields may be considered as “zero” or “null” by the analytical tool. This may again lead to incorrect results.

Current: The data should be consistent with the time line. The data being captured at this moment should be current. Any data that is not current should specify the timeline when it was captured.

Consistent: The data should be consistent along the time line. There should not be huge deviations and fluctuations in the quantity and quality of data. This discounts the cyclicity of the data.

Context: The data being captured should be in line with the context. The context is again defined by the parameters which define the scope and scale of the framework.

Controlled: The data should be controlled and manageable. The control on the data makes sure that the data under analysis is the sample that one wants to analyse. The control on the data also ensures the management of sudden spikes and troughs.

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  1. December 13, 2009 at 2:55 am

    Harshie
    I have few questions (not sure if it will be answered in next posts .. however )
    1.What is the quality of data that is acceptable for analytics. The more you refine the data , the more its costs. If you take a firm, which is small and cannot spend more on analytics – At which point it can implement analytics.
    2.Generally we do analytics using historical data only. Current data means.. how much current it should be for getting better results.. where to draw a line !
    3.Is the parameters are going to be same for all the kind of tools like SAP Business Objects, IBM Cognos Versus Open Source tools : Is there any refining of data once it pass through DWH to analytics space !

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