Big Data

Embracing Conflict to Fuel Digital Innovation

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) July 10, 2017

When talking to clients about their business goals, most business executives are pretty clear as to what they want to accomplish, such as reducing customer churn or reducing inventory costs or improving quality of care or improving product line profitability. But these “one dimensional” business initiatives really don’t push the organization’s innovative thinking. For example, I can easily reduce marketing costs if I significantly reduce advertising and promotional spending. Or I can easily improve product line profitability by cutting all marketing and advertising spending and laying off anyone not directly related to manufacturing and sell products.

Where organizations need to get innovative is when the business initiative has more than one dimension or condition, for example:

  • Reduce customer churn while reducing customer service costs, or
  • Reduce inventory costs while improving on-time product delivery, or
  • Improve quality of care while reducing operational costs

These conflicting conditions force organizations to think out of the box; to embrace an optimization mentality – where optimization is making the most effective decision in a situation of conflicting conditions – which requires careful and thoughtful balance of addressing the conflicting conditions. Organizations should embrace these conflicting conditions and the need to optimize across two or more conditions because these conflicts are the fuel for driving innovation.

Automobile Industry Example

CarAh, the muscle car. I had a 1968 Plymouth Fury III in high school. Not a true muscle car, but that 318 engine could certainly pump out the horsepower. But it came a high cost of fuel efficiency (probably around 8 miles per gallon, and that was just when it was going downhill!). And that was typical of the muscle cars in the late 1960’s and early 1970’s – you could get some serious horsepower but it came at a cost of beer and pizza money.

Today, the automobile industry is again seeing a huge resurgence in “muscle cars.” As you can see from Figure 1, horsepower has been on a steady rise ever since the 1979 Energy Crisis.

Figure 1:  The Insatiable Appetite for More Power

Figure 1:  The Insatiable Appetite for More Power

What is really shocking is that these massive increases in horsepower have come as the mileage per car has also improved dramatically (see Figure 2).

Figure 2:  Automobile Horsepower versus Fuel Efficiency[1]

Figure 2:  Automobile Horsepower versus Fuel Efficiency [1]

If you had challenged car manufacturers in 1979 to increase the horsepower per car while also increasing the mileage per car, the automobile executives would have told you that you were crazy. However, that is exactly what happened.

The market impetus that forced automobile manufacturers to innovate their way through this dilemma was when the U.S. Government mandated higher fuel mileage in 1975 and again in 2007. And instead of going out of business, car manufacturers (or at least some of them – I’m looking at you Hummer) embraced the dilemma and ended up both increasing fuel mileage and horsepower through a number of product design, development and manufacturing innovations including:

  • Use of lighter weight alloys to reduce weight
  • Use of turbo-charging and super-charging
  • Use of small displacement high-compression engines
  • Advancements in diesel engines
  • More valves per cylinder
  • Cylinder deactivation
  • Improved aerodynamics

Conflict, Digital Innovation and the Economic Value of Data

When organizations try to determine the economic value of their data (EvD), there arises a nature conflict between 1) keeping all the data because of its potential monetization value versus 2) the potential storage and data management costs, not to mention potential fines and liabilities associated with data security and privacy breeches of that data. Josh Siegel recently discussed this EvD challenge in his provocative blog. In particular, Josh highlighted the following conflicts:

  • Maximizing Value – Data assets have considerable potential economic or financial value they can add in terms of new revenue opportunities, process efficiencies, cost reductions, risk mitigation, etc. Monetizing these data sources the key to unlocking the potential in the big data era.
  • Minimizing Risk – Many organizations do not fully quantify the costs and risks associated with the corporate data. Denial of access to data such as we recently saw with the global wannacry cyberattack, is just one example of the risk inherent in underappreciating reliance on data. Data has both present and future value – and only once that value is fully understood can the risk be mitigated.

The careful and thoughtful balancing of Maximize Value versus Minimize Risk is where innovation is going to happen with respect to data and digital transformation. Organizations will miss out on innovation opportunities if they only embrace one condition or perspective. Leading organizations understand that the key to digital transformation success are those initiatives that seek to optimize across two or more conflicting conditions – just as the automotive industry has done.

Because like the Michelob beer commercials from the 1980’s, we can have it all!  Do not settle for less.

[1] Source:  “America’s Cars Are Suddenly Getting Faster and More Efficient” https://www.bloomberg.com/news/features/2017-05-17/america-s-cars-are-all-fast-and-furious-these-days

 

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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2 thoughts on “Embracing Conflict to Fuel Digital Innovation

  1. Very well said! Though the capabilities do finally exist to analyze and apply data in meaningful ways to meet multi-dimensional and even conflicting objectives, I find that very few enterprises are really capable of harnessing the knowledge they have to act on it. Even services companies whose only relevant mean’s of differentiation is their client experience, struggle to harness the significant data they have to personalize preferences across touch points to make that a more elegant interaction and optimally cared for customer. Until leaders are held accountable for and measured on meeting multi-mentional objectives, and the right practitioners are in place to help them apply it, we will likely continue to struggle to use the assets we have in our “big data” to innovate and instead, continue to see spending on data warehouses full of data or analytic tools that are never utilized to their potential, if at all.

    • Wow Alysen, very well said! I couldn’t agree more. As is typically with something as game-changing as big data/data science/machine learning, it’s the organizational challenges that impede progress, not the technology issues. And given the potential organizations changes need to exploit data and analytics to financially exploit these areas of “conflict,” we are probably going to have to wait for the next generation of business leaders who will be more comfortable with data and analytics as business disciplines and monetization tools.

      Thanks again for your insightful comment!