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QueBIT Blog Post-Putting Thought Behind the Numbers: Analytics Strategy Part I

Posted by Gary Corrigan

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Jun 18, 2014 1:05:34 PM

Is data the source to find right answers, or is it used to weed out the obvious wrong conclusions? If you believe in the latter point, you are probably on to something. And data and statistics guru Nate Silver would agree. In many instances, there is no magic right answer to a business problem or scenario. There can be many answers that can be construed as being right or justifiable. The goal of any CFO or CIO is to eliminate as many incorrect or unusable data points as possible. For them, it’s like taking an exam with five different answer options, eliminating the clear wrongs, and then potentially deducing the best rights from two or three very viable choices.

Getting back to a basic (yet highly important) point we made in the blog Big Data Perception vs. Reality: Is it Value or Noise?, predictive models and advanced analytics deliver probabilities that can cut down on the frequency of irrelevant data points popping up. Probabilities exist to enhance the chances that a good decision can be made under a certain set of circumstances; not that it will be made. Once decision-makers start blurring the lines between probabilities and certainties, they’re caught flat-footed.

To maximize the effectiveness of these models, Silver suggests to:

  1. Think “probabilistically”
  2. Lean on numbers to guide but not solve business questions
  3. Stick to the data model when analyzing the data and don’t deviate too far

Now, for the model to work properly, you have to start with accurate data. As InformationWeek pointed out in their article, 6 Lies About Big Data, there are plenty of organizations that don’t even have complete data to begin the analysis phase. This means dealing with data sets that are inaccurate, incomplete, and misaligned. 19% of companies that took the Information Week 2013 Big Data Survey revealed that they pull their data from smart devices and website visits. These aren’t always the most reliable sources to track success factors or gauge performance.

Leveraging data models and adhering to probabilities are just a few of the critical steps CIOs and CFOs need to take before they make decisions. In our next blog, we run through another important piece of the analytics puzzle: relying on data findings and taking action.

   

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