Want Answers to your Analytic Question Space? Don’t Be Afraid to Ask

In my last blog, I talked about Analytics Question Space. In this one I want to expand upon this concept and look at how to make your Analytics Question Space part of the business analytics process.

One big reason that Big Data hasn’t been adopted more quickly and effectively is that business people have been taught not to ask questions.  We’re used to our corporate data being under lock and key in the Enterprise Data Warehouse.  We’ve been told repeatedly “No, you can’t get that information.”  Meanwhile, IT organizations lament that “The business doesn’t know what it wants.”

Even though the variety and volume of data are more available all the time, a large part of the business community believes that Big Data doesn’t apply to them or “it’s just hype.”

So here is the point:

You can get almost everything you want, but you have to ask.

Ok, maybe its not that simple, you may not get everything you want, but you certainly won’t get it if you don’t ask.  And as my mother always told me, “It’s important how you ask.”

Defining the Analytics Question Space is a good first step in developing a sustainable analytics program. This implies that I need to understand not just a single question but the entire set of questions that will result from a asking and getting an answer to the first question.  Instead of limiting what we want to know, be prepared for one question to set off a chain reaction of new questions.  I illustrated how this works using Sentiment Analysis example in a recent article in Information Week..

How do you ask?

There are lots of ways to ask for something. Let’s focus on one that can be productive. The Dialog

The Dialog

The best way to go about asking questions to engage in a dialog. In this dialog there are the business people (hopefully, business leaders) asking the questions and the analyst.  The analyst’s job is twofold:

  1. Listen to the question and ask one these questions:
    1. Why do you want that?
    2. What will you do with it
    3. What else would you do?
  2. Structure the answers so that data and analytics professionals can utilize the results.

The analyst should keep asking until there are no more questions.  With this information the analyst should be able to go off and provide a structure for this exchange.  The structured output is given to the business leaders to validate and expand upon.

The Dialog Can Be Cross-Functional

We start with the perspective of one part of the business to create the question space and then include different perspectives from other functions.

Working with a manufacturing company, I was told that this process would be difficult because everyone wanted something different.  That’s partly true.  Sales and Manufacturing needed different information.   But we also found that about 40% of what they wanted was the same. Sales wanted access to manufacturing plans while manufacturing wanted sales projections. Knowing the 40% overlap allowed us to leverage the data and decrease both the cost and time to produce results.

There is one question we know we can’t answer:  the one that isn’t asked.   With more data sources and advances in techniques to assemble and analyze data, you can ask all your questions and discover ways to have them answered.

Do you want to know more about Analytics Question Space and the Analytics Framework?  Email us at info@avalonconsult.com or contact me directly at applebaumw@avalonconsult.com.



Wayne Applebaum About Wayne Applebaum

VP of analytics and Data Science for Avalon Consulting, LLC.

I have over 30 years of experience in data analytics and enterprise consulting. It is my belief that, Great Analytics can only be enabled by Great Data.

I hold a doctorate in statistics and have spent 30 years working for companies like SAP, Oracle, Business Objects, and EDS to guided Fortune 500 executives in aligning analytics with business needs.

Many companies are only using a fraction of the data they need. When information is lacking, analytics projects fail. Events over the past 10 or so years have provided us with a unique opportunity to help companies leverage both structured and unstructured data to create a foundation that drives innovation and measurable business results.

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