Customer experience strategy and innovation expert Woody Bendle is back today taking another swipe at big data with help in thinking through how to monetize it (vs. just parking it in the cloud and praying for rain). Here’s Woody!

Strategic Insight – Monetization Is the Real Big Data Dilemma - Woody Bendle

Unless you’ve been hiding under a rock for several years, you’re aware Big Data is a big topic! Just look at this Google Trends graph depicting search volume for “Big Data” the past five years.


That’s one incredible upward trajectory wouldn’t you say!

But have you ever wondered why this topic is so hot right now?

In my opinion, we’re hearing so much about Big Data because of several related factors:

However, I feel the primary reason we continue to hear more about Big Data is due to very few companies actually realizing the purported Big Data promise – or what I call the Big Data monetization dilemma.

If you were to listen all of the sensational Big Data spiel out there, you’d have to believe that by simply having Big Data, your organization would automatically (almost magically) be smarter, faster (agile), more competitive, and ultimately, more profitable. And that’s just not the case.

What many organizations are quickly realizing is not all data are created equal.  Having a lot of this digital Big Data stuff being captured and stored doesn’t mean you can readily access it, analyze it, or provide useful answers to meaningful questions.

Unfortunately, this is the reality for most Big Data out there in the cloud today - much of it simply is not configured in a manner that allows for analysis. And, there really are no magical short cuts; there is a tried and true (but not necessarily easy) approach that will help you to realize its promise, however.

Three Strategic Questions for Monetizing Big Data

Just because storing your Big Data is relatively inexpensive doesn’t mean your Big Data strategy should be “Fire, Ready, Aim!” Have you heard anyone say something like this? “Let’s keep pumping all of our Big Data into the cloud and we’ll figure it out as we go.” If this is your approach, you will find monetizing your Big Data to be very costly!

If you expect to monetize your Big Data asset, there are three fundamental questions to continually ask, answer and address:

  1. What questions do I want/need my Big Data to answer?
  2. What types of analysis will be needed to answer our questions?
  3. How do our data need to be structured in order to perform the required analysis?

These questions might feel like a blinding flash of the obvious, but you’d be surprised by how few organizations actually start here.

By first defining the questions you want your Big Data to answer, it will be easier to determine the most appropriate type(s) of analysis your organization will need to perform – and there is a wide range of analytical complexity that can be employed (see below).


Once you know the types of analyses you need (or want) to perform, it will be easier to define how best to structure your Big Data.

When performing statistical analysis in particular, your data need to be (or need to become) numbers that represent meaning or measure (structure).  This frankly is one of the biggest challenges with Big Data – most of it is typically unstructured (e.g., text comments, videos, website browsing streams, etc.).  While nearly all unstructured data can be transformed into structured data (numbers), it is really important to understand that not all numbers are created equal either (see below).


Numbers can have very different meaning depending upon the level of measure they represent. Different types of measures are also better suited for different types of analyses. Given this, you can see why it is important that your Big Data are transformed (structured) in a very thoughtful and purposeful manner.

Will you monetize your big data?

My intention with this discussion was not to provide a detailed playbook for monetizing your Big Data. Rather it is to acknowledge the real and increasing challenges many are currently dealing with and offer insight for addressing some of the more fundamental problems.

As you start/revamp/update/overhaul your Big Data strategy, remember to ask, answer and address these foundational questions:

  1. What questions do I want/need my Big Data to answer?
  2. What types of analysis will be needed to answer our questions?
  3. How do our data need to be structured in order to perform the required analysis?

If you do, you can be sure that you moving your Big Data strategy in the right direction.  If you don’t, just keep in mind what happens when you try to stand on quicksand! -  Woody Bendle


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