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EMC Journal Authors: William Schmarzo, Greg Schulz, Mat Mathews, Jeffrey Abbott, Mehdi Daoudi

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Embracing Conflict to Fuel Digital Innovation

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

 

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More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.