Big Data

Big Data MBA: Course 101A – Unit III

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) April 11, 2012

This is the third in the series of four blogs that I’m writing on the “Big Data MBA.”  My first 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 them. I covered the types of questions they can ask and the types of decisions they can make.  My second blog, “Big Data MBA: Course 101A – Unit II,” introduced Michael Porter’s Five Forces Analysis as a way to help organizations identify where and how they could deploy big data for market or industry changing impact.  My third blog in the series will introduce Michael Porter’s more familiar Value Chain Analysis as another business valuation technique for identifying where and how big data can impact an organization’s internal operations.

Michael Porter’s Value Chain Analysis Definition

Taken from Wikipedia’s definition of Michael Porter’s Value Chain Analysis: “A value chain is a chain of activities for a firm operating in a specific industry.  The business unit is the appropriate level for construction of a value chain, not the divisional level or corporate level.  Products pass through all activities of the chain in order, and at each activity the product gains some value.  The chain of activities gives the products more added value than the sum of the independent activities’ values.”

See Figure 1 below.

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

The Value Chain Analysis covers two categories of activities – primary and support activities.  The primary activities are probably the most familiar, as it deals with the steps and processes necessary to take a product or service from its raw materials to final customer sale and support.

  • Inbound Logistics – covers the identification, sourcing, procurement, and supplier management of the “raw materials” that comprise the final product or service.
  • Operations – covers the engineering, inventory management, and manufacturing of the final product or service.  Note:  any technologies incorporated into the product or service are also included here.
  • Outbound Logistics – covers the logistics and distribution of the final product and service.
  • Marketing and Sales – covers the marketing, merchandising, promotions, advertising, sales, and channel management to get the completed product and service to the end customer.
  • Service – covers the support and maintenance of products and services after they are delivered to the customer.

The secondary activities are less familiar, but equally important in supporting product and service scalability.

  • Procurement – covers the procurement of supporting maintenance, repair and operations (MRO) materials and services
  • Technology Development – covers the supporting technologies, both information technologies as well as other technologies, important for keeping the lights on.  Technologies integrated into the product would be covered in the Operations stage.
  • Human Resource Management – covers the recruiting, hiring, development,and firing of personnel.
  • Infrastructure – covers the physical infrastructure (buildings, offices, warehouses)

Porter’s Value Chain Analysis Example

Let’s again say that you are in the on-line retail business and are looking at how we can leverage big data analytics to improve internal processes that power our “optimize merchandising effectiveness” business initiative.  Let’s use the Value Chain technique to understand how to apply big data analytics.

Figure 2:  Michael Porter’s Value Chain Analysis

In the area of Inbound Logistics, you could apply big data analytics to your sourcing and procurement activities to optimize your Merchandising Effectiveness initiative in the following way:

  • Use real-time POS data and an integrated data feed (API) to proactively notify suppliers of potential merchandising out-of-stock and over-stock situations prior to them becoming problems

In the area of Operations, you could apply big data analytics to your manufacturing and inventory activities to optimize your Merchandising Effectiveness initiative in the following way:

  • Use real-time POS and RFID data to predict merchandise demand, manage merchandise markdowns, and identify slow and no movers in order to optimize in-store/on-site inventory

In the area of Outbound Logistics, you could apply big data analytics to optimize your Merchandising Effectiveness initiative in the following ways:

  • Leverage social media and mobile data to uncover merchandising insights that could impact stock and inventory levels for in-flight campaigns
  • Use analytics sandbox to model event-driven logistics impacts (e.g., major league baseball game in the area, unplanned construction work on a major travel artery)

In the area of Marketing & Sales, you could apply big data analytics to your advertising, marketing, and sales activities to optimize your Merchandising Effectiveness initiative in the following way:

  • Use conversion attribution analysis across search, display, mobile, and social media to determine the driving factors in order to optimize ad placement, keyword bids, and messaging more quickly

In the area of Service, you could apply big data analytics to your service, support, and maintenance activities to optimize your Merchandising Effectiveness initiative in the following ways:

  • Combine social media data with your customer loyalty data to create more-frequent, higher-fidelity customer scores for retention, fraud, up-sell/cross-sell, and net promoter

You can see some further examples of applying big data analytics valuation to the Value Chain Support Activities in Figure 2.

Porter’s Value Chain Analysis Summary

The Porter Value Chain Analysis provides a business-centric approach to looking at how big data analytics could potentially impact your organization’s internal value chain, or its internal value creation process.  My next blog will wrap up this series by looking at the power of combining both the Value Chain Analysis and the Five Forces Analysis into the same business valuation exercise.  Until then, continue reading your Michael Porter books!!

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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