Big Data

Big Data MBA: Course 101A – Unit II

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) March 27, 2012

As part of my continuing series on the “Big Data MBA”, I’d like to introduce a second business valuation technique.  My last blog, “Big Data MBA: Course 101A,” covered the use of the “Big Data Worksheet” as a business valuation technique to help your line of business (LOB) users envision where and how big data analytics can impact the types of questions they can ask, and the types of decisions they can make.  My Big Data Worksheet is ideal for organizations that already understand what business initiative they want to target, and are trying to quantify how big data could impact that initiative.

This second business valuation technique assumes that have you not yet identified what business initiative you want to target.  This technique will help you explore the realm of the possible with respect to what key business initiatives your organization could target.

Big Data MBA Curriculum

No MBA course would be complete without some required reading from Michael Porter, the dean of business strategy.  Michael Porter wrote the definitive books on corporate strategies, “Competitive Strategy” (1980) and “Competitive Advantage” (1985).  The competitive insights and guidance provided in those books are as relevant today as they were in the 80’s, and provide a foundation upon which to envision the potential business functions, and related business benefits, that could be impacted by big data analytics.

Michael Porter’s Five Forces Analysis Definition

Taken from Wikipedia’s definition of Michael Porter’s Five Forces Analysis: “Porter’s five forces analysis is a framework for industry analysis and business strategy development formed by Michael E. Porter of Harvard Business School in 1979.  It draws upon industrial organization economics to derive five forces that determine the competitive intensity and therefore attractiveness of a market.  Attractiveness in this context refers to the overall industry profitability.  An “unattractive” industry is one in which the combination of these five forces acts to drive down overall profitability.  A very unattractive industry would be one approaching “pure competition”, in which available profits for all firms are driven to normal profit.”

The Five Forces analysis provides an industry perspective on competitive drivers across Competitive Rivalry, Supplier Power, Buyer Power, Product/Technology Development, and New Market Entrants (see Figure 1 below).  We will use this “outside-in” perspective to identify where and how big data analytics could impact an organization’s ability to change the dynamics of their market place.

Figure 1: Michael E. Porter “Competitive Strategy: Techniques for Analyzing Industries and Competitors”

Porter’s Five Forces Analysis Example

Let’s say that you are in the on-line retail business and are looking to “optimize your merchandising effectiveness” to foster industry change and derive competitive advantage.  We can use the Five Forces model to understand how big data analytics could be applied to merchandising optimization to impact your organization’s overall industry positioning and competitiveness.

In the area of Competitive Rivalry, you could apply big data analytics to your Merchandising Optimization initiative to derive competitive advantage in the following ways:

  • Use cross-media Conversion Attribution Analysis across search, display, social, and mobile advertising to outflank competition on cross-channel pricing, placement, and promotional effectiveness
  • Leverage A/B Testing to uncover merchandising messaging and placement insights that drive category market share growth and increased shopping occurrence profitability

In the area of Buyer Power, you could apply big data analytics to your Merchandising Optimization initiative to uncover unique market, product, and customer insights to counter the growing power of buyers and buying coalitions, including:

  • Leverage Sentiment Analysis from social media sites to identify and quantify micro-population merchandising trends and insights to improve customer segmentation, targeting, pricing, and packaging effectiveness
  • Leverage real-time customer sales and engagement data to optimize in-flight merchandise targeting to increase on-site customer monetization (e.g., increase conversion rates, increase up-sell and cross-sell effectiveness)
  • Leverage merchandising Recommendation Engines to improve the customer experience (e.g., net promoter scores, repeat purchases, loyalty), optimize merchandising margins and minimize merchandising markdowns

In the area of Supplier Power, you could apply big data analytics to your Merchandising Optimization initiative to glean unique market, product and customer insights to counter the growing power of suppliers, including:

  • Leverage detailed point-of-sale (POS) and RFID data to identify “hot” products more quickly than competitors in order to “lock in” supplier inventories and favorable terms and conditions
  • Leverage detailed POS and RFID data to cancel and/or return slow movers and no movers faster than competition in order to minimize merchandise markdown and inventory carrying costs

In the area of Product and Technology Innovation, you could apply big data analytics to your Merchandising Optimization initiative to identify areas where products and/or technology can be used to drive buyer or supplier lock-in, or create barriers of entry for new market entrants, including:

  • Provide a software-as-a service dashboard and predictive analytics platform that leverages merchandising data and insights to help suppliers minimize their procurement, inventory and distribution costs
  • Couple merchandising data and insights with predictive analytics capabilities that recommends in-flight supply chain and inventory adjustments to your key channel and distribution partners

In the area of New Market Entrants, you could use big data analytics to identify and pre-empt market opportunities before new market entrants can gain a foothold, including:

  • Constantly monitor social media and mobile data for merchandising trending insights that can be used to pre-empt new market entrants

Porter’s Five Forces Analysis and the Big Data MBA

One of the biggest big data analytics challenges is the possibility of your big data analytics initiative morphing into a science project.  That’s when a small priesthood of analytics experts plays with the data and technology capabilities, but the business benefits and the industry changing impact are never realized by the business uses and executives.

The Porter Five Forces Analysis provides a business-centric approach to looking at the potential of your big data analytics initiative from the framework of how it could impact the forces and players that define your marketplace.  This “outside-in” business valuation technique facilitates collaboration with your LOB stakeholders to help them to envision the realm of the possible, and gain early buy-in to support the organization’s big data analytics initiative.

My next blog will cover a more traditional internal business valuation framework – Value Chain Analysis.  In the meantime, dig out those old Michael Porter books and let’s get started with your Big Data MBA!

 

If you are at the Gartner BI Summit in Los Angeles, feel free to stop by the EMC booth or join me at my speaking session on April 3rd at 3pm.  I’ll discuss how companies we work with are leveraging big data and give you some tips on how to do it in your organization.

 

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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