Big Data

Disruption on the Doorstep: 3 Essential Components to Moving Forward with Digital Transformation for Large Enterprises

Joshua Siegel By Joshua Siegel Director, Big Data & IOT Consulting Dell EMC June 20, 2017

Established businesses – and indeed entire business models – are at risk of disruption due to digital transformation. New entrants, with new innovative business models, are upending industries such as transportation, manufacturing, financial services and communications, to name just a few – with many more on the horizon. Given the pace and impact of digital disruption, the term “Proven business model” is becoming an oxy-moron. Businesses cannot base decisions on what has worked in the past – they must re-evaluate everything in light of today’s digital, global economy. Everything: every customer touchpoint, sales cycle, business model and data source. In fact, some companies would be wise to consider how to START OVER.

Younger organizations have no legacy processes or infrastructure, so they don’t have to START OVER – they just have to START. Large enterprises may have deep pockets, but struggle to take advantage of digital transformation rather than become displaced by it. This requires assessing and re-evaluating  the potential value of one’s digital and data assets. Unfortunately, few understand the process to do that, and the consequences are happening all around us. More than half of the Fortune 500 have disappeared since year 2000!

Among the challenges large organizations face are: how to (1) measure and value the enterprise in light of digital transformation, (2) how to monetize that value and finally (3) how to minimize new and existing risks to that business.

  1. Measuring Value – GAAP does not and cannot currently accurately account for digital transformation. Assets by definition have utility.  But GAAP does not (in most cases) consider data, algorithms or models as assets – as opposed to more traditional assets such as physical plant or inventory. This model is inadequate because data has a unique property, in that, its value is increased every time it’s used – and, it doesn’t get consumed in the process. Companies need specific guidance and methodologies to both initially assess and keep this valuation up to date. My colleague Bill Schmarzo, CTO of Dell EMC’s Big Data Consulting practice, has a great perspective here: https://infocus.emc.com/william_schmarzo/economic-value-data-challenges/
  2. Maximizing Value – invariably, once the measuring is done, use cases are uncovered, and value calculated, new opportunities emerge. Data assets are often severely undervalued as it relates to potential value they can add (in terms of revenue, processes efficiency, cost reduction, risk management, etc.). Monetizing these data sources the key to unlocking the potential of the big data era. See @Schmarzo for excellent white paper on the economic value of data: https://infocus.emc.com/wp-content/uploads/2017/04/USF_The_Economics_of_Data_and_Analytics-Final3.pdf
  3. Minimizing Risk – Understanding the unlocked potential in data sources and models is critical to digital transformation. Once that value is fully understood, companies can and should reflect on how those digital assets are protected and insured. For example, a company may insure its inventory, physical assets or key executives’ lives, but many organizations do not internalize that data itself is an asset that must be secured and insured. 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. It has both present and future value – and only once that value is fully understood can the risk be mitigated.

Commercial enterprises that follow these three steps can accelerate their transformation journey. The threat of disruption can be your biggest fear, or your most powerful weapon. Your move.

Joshua Siegel

About Joshua Siegel


Director, Big Data & IOT Consulting Dell EMC

Josh Siegel has nearly 20 years experience in business strategy and process, application and data solutions design and delivery. He assists large enterprise clients with strategy and implementation around Big Data transformations. Solutions developed and implemented in Financial Services, Retail & Gaming, and Health Services among other industries. Previous to this role, Josh led Big Data Transformation for EMC’s Financial Services Industry Vertical and EMC Professional Services’ Cards & Payments Practice. Prior to joining EMC Consulting in 2010, he was Senior Manager for Financial Services at BearingPoint working with large Financial Services institutions on a variety of change initiatives. Josh was also senior project manager at IBM Business Consulting Services and a senior analyst at PricewaterhouseCoopers LLP. Josh attended the Yale School of Management where he earned a Master of Business Administration (MBA) degree and University of Pennsylvania, Wharton School of Finance Philadelphia, Pennsylvania where he received a Bachelor of Science in Economics.

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