In Big Data, Are You Using Refrigerators or Stoves?

EMC-Fridge-vs-Stove-PosterI am getting frustrated hearing vendors and sales teams over-simplify the big data discussion; buy this product or Hadoop distribution or tool or appliance and voila…you’ve got a big data solution!! Is it any wonder that so many organizations are failing to deliver on the business potential of big data? (1)

Too many “experts” (vendors, analysts, press, consultants) are over-emphasizing the big data technology aspects (“You need a Big Data Strategy engagement that reveals your big data technology needs”) and ignoring the really hard work – understanding what business opportunities exist and how the organization is trying to address them with data and analytics.

Last week, my University of San Francisco MBA class finished their Big Data MBA course. We used our trusty “thinking like a data scientist” process to teach our students how to identify a business opportunity, and then use the “thinking like a data scientist” process to drive cross-organizational collaboration to come up with ideas that they can turn into actions using data and analytics. My co-teacher, the ever talented and energetic Professor Mouwafac Sidaoui, and I asked our students: “What employer wouldn’t want an employee who can excel at doing that?”

Figure 1: “Train the Brain” Final Presentation

The results were stunning. We had 6 groups of 5 students take on a real-world consulting project (6 companies volunteered for this exercise). The teams delivered relevant insights and actionable recommendations backed by analytics to help each of these companies to address some of their key business initiatives. Professor Mouwafac and I could not be more proud of these students, their hard work and the final results. See Figure 1 for an example of the “portfolio” that was displayed as part of the final “Big Data Faire.” 

So, what is your excuse? What is your excuse for not identifying a business opportunity, and then using the “thinking like a data scientist” process to drive cross-organizational collaboration to come up with ideas that they can turn into actions using data and analytics? People are confused as to where to invest their time and effort. They think they are using a refrigerator, but in reality, they are using a stove. Let me explain the difference.

A refrigerator is used to keep food from spoiling, or in reality, delay when food spoils by providing a cold environment in which to store perishable goods. The keys to successfully “using a refrigerator” are:

  • Plug it in
  • Put food in

Yea, that’s right. Plug it in and go home. Sounds like how some vendors have tried to position their Hadoop distribution or their version of a data lake or their open source products or analytic tools.

However, a stove is used to cook something. The keys to successfully “using a stove” are:

  • Understand what dish or cuisine you want to make
  • Find a recipe
  • Buy the ingredients
  • Get the proper tools (including buying a stove if you don’t already have one)
  • Prepare the ingredients
  • Properly mix the ingredients
  • Properly cook the ingredients
  • Properly present the final product

Table 1: Using a Stove and Creating a Big Data Solution

Yea, it takes a lot more strategy, planning and technique to use a stove than to use a refrigerator. But the keys to successfully using a stove should look very familiar to the keys of successfully creating a big data business solution (Table 1).

Dang, that process looks a lot like the “thinking like a data scientist” approach. And as our MBA students proved (again), the process works. However the process requires a lot of work upfront before ever applying the technology and the multitude of “silver bullet” (2) tools and products appearing in the marketplace.

Summary: “Do Or Do Not, There Is No Try”

We know what’s required to be successful with big data. So it’s your choice: are you using refrigerators (plug in and walk away) or are you using a stove (which requires a substantial amount of pre-work before ever applying the “technology” to create something of value). It’s your choice.

1. “Only 29 Percent of Companies are Using Big Data to Make Predictions”

2. A “silver bullet” is a simple and seemingly magical solution to a complicated problem.

This post originally appeared on

Bill Schmarzo

Authored by:
Bill Schmarzo

The moniker “Dean of Big Data” may have been applied in a light-hearted spirit, but Bill’s expertise around data analytics is no joke. After being deeply immersed in the world of big data for over 20 years, he shows no signs of coming up for air. Bill speaks frequently on the use of big data, with an engaging style that has gained him many accolades. He’s presented most recently at STRATA, The Data Science Summit and TDWI, and has written several white papers and articles about the application of big data and advanced analytics to drive an organization’s key business initiatives. Prior to joining Consulting as part of EMC Global Services, Bill co-authored with Ralph Kimball a series of articles on analytic applications, and was on the faculty of TDWI teaching a course on designing analytic applications.

Bill created the EMC Big Data Vision Workshop methodology that links an organization’s strategic business initiatives with supporting data and analytic requirements, and thus helps organizations wrap their heads around this complex subject.

Bill sets the strategy and defines offerings and capabilities for the Enterprise Information Management and Analytics within EMC Consulting, Global Services. Prior to this, he was the Vice President of Advertiser Analytics at Yahoo at the dawn of the online Big Data revolution.

Bill is the author of “Big Data: Understanding How Data Powers Big Business” published by Wiley.

©Bill Schmarzo, 2015. Unauthorized use and/or duplication of this material without express and written permission from this site’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Bill Schmarzo and with appropriate and specific direction to the original content.

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