Big Data

Big Data MBA: Course 101A

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) March 19, 2012

I recently had the opportunity to present at the Strata conference in Santa Clara.  I was part of the JumpStart program, which was positioned as an MBA track for Big Data Scientists.  The title of my session was “Do it Right – Proven Techniques for Exploiting Big Data Analytics” (a link to a pdf of my presentation can be found on the site).  My presentation focused on helping attendees identify what areas of the business to focus their big data analytics initiatives.  I received lots of positive feedback on the session, so I thought I’d share the contents and concepts of the session on my blog (over several blogs, actually).

Business Valuation Introduction

For decades, leading organizations have been exploiting new data sources, plus new technologies, for business differentiation and competitive advantage.  And for the most part, the questions that the business users are trying to ask, and answer, with these data sources and new technologies really haven’t changed:

  • Who are my most valuable customers?
  • What are my most important products?
  • What are my most successful campaigns?
  • What are my best performing channels?
  • What are my most effective employees?

What has changed are the nuances of terms such as “valuable,” “important,” and “successful” which fuel the insights that are the source of competitive advantage.  New big data sources, plus new advanced analytic capabilities, enable higher fidelity answers to these questions, and provide a more complete understanding of your customers, products and operations that can drive business impact across various business functions, such as:

  • Marketing to identify which marketing promotions and campaigns are the most effective in driving store or site traffic and sales
  • Sales to optimize prices for “perishable” goods such as groceries, airline seats, and fashion merchandise
  • Procurement to identify which suppliers are most cost-effective in delivering high-quality products on-time
  • Manufacturing to flag machinery and process variances that might be indicators of quality problems
  • Human Resources to identify the characteristics and behaviors of your most successful and effective employees

Business Valuation Techniques

This three part blog series (“The Big Data MBA”) will introduce three different business valuation techniques to help you identify where and how big data analytics can impact your business.

Each valuation technique will seek to drive collaboration with your Line of Business users to envision where and how big data analytics can impact the types of questions they can ask, and the types of decisions they can make.  These valuation techniques will guide the users in contemplating the “realm of the possible” with respect to how and where big data analytics could provide unique insights into their customers, products, markets and operations.

Business Valuation Technique #1: Big Data Worksheet

The first business valuation technique – the Big Data Worksheet – is ideal for organizations that already understand what business initiatives they want to target, and are trying to quantify how big data could impact those initiatives.

When you boil it down to its core, big data can impact your business initiatives in the following three ways (there may be more, but these are the three biggest value drivers):

  • More Detailed Structured Data.  Today, many organizations aggregate their structural transaction data in order to make it more manageable from a reporting and dashboard perspective.  Unfortunately, the act of aggregating the data wipes out many of the valuable nuances in the data that are the source of key business insights.  With big data, we can now provide access to the most detailed data (e.g., Point of Sales, Call Detail Records, call center, credit card transaction, shipments, payments, radio-frequency identification) to drive more granular analysis, insights and decisions
  • New Sources of Unstructured Data.  We also have access to an avalanche of new unstructured data sources, many of which sit outside the walls of your organization (e.g., social media, mobile, web logs, machine generated).  These new unstructured data sources can drive higher-fidelity analysis, insights and decisions
  • High-velocity Data.  Big data also includes high-velocity data that shrinks the latency between when the transactional or social event occurs, and when the data is available for analysis.  This low-latency data can enable more timely, more frequent data access, analysis, insights and decisions

The worksheet is structured to apply these big data “drivers” (row headers) against an organization’s key business initiatives (column headers).  You start by selecting your key business initiative (e.g., customer segmentation, cross-channel campaign effectiveness, product quality, inventory optimization) and then apply the big data value drivers of 1) more detailed, structured data, 2) unstructured data and 3) high-velocity data.  Let’s walk through an example to see how this works.

Let’s say that we’re in a Business-to-Consumer (B2C) industry like Retail, Telco, Healthcare or Banking.  Let’s also say that our organization has identified “Identifying and Growing our Most Valuable and Highest Potential Customer Segments” as their key business initiative.  Let’s now apply the three big data value drivers to see the potential impact on this business initiative (see chart below).

Applying the first big data value driver (More Detailed Structured Data) could yield the following benefits to our Customer Segmentation initiative:

  • Leverage more detailed POS and UPC data to create more granular, “tighter” customer micro-segments
  • Leverage un-sampled (versus aggregated) data to create richer, more accurate segmentation and clustering models and customer targeting scores.

Looking at the impact of the second value driver (Unstructured Data) could yield the following business benefits to our Customer Segmentation initiative:

  • Leverage social media and mobile data to create higher fidelity, more significant, material and actionable customer segments
  • Leverage more customer attributes (e.g., interests, affiliations, associations) to create richer segmentation and clustering models and scores

Finally, looking at the impact of the third value driver (Data Velocity) could yield the following business benefits to our Customer Segmentation initiative:

  • Fine-tune customer segmentation models and scores more frequently, especially after critical or noteworthy events (e.g., tornadoes, Euro crisis, celebrity death, economic news, Jeremy Lin-sanity)
  • Update customer acquisition models and scores daily, in-flight of customer acquisition campaigns

This type of exercise not only helps the business stakeholders think about where and how to start, but can also provide key guidance to help your IT and data science teams understand where best to target their efforts.  And filling out this worksheet in a facilitated, group-brainstorming session (something that EMC Consulting does as part of our Vision Workshop offering) can be both illuminating and fun.

Big Data MBA Next Steps

The next two blogs will cover the two other business valuation techniques.  Like the Big Data Worksheet, these business valuation techniques can help your business stakeholders – as well as IT and Data Science teams – to envision the “realm of the possible” with respect to where and how to leverage big data analytics for business value.

By the way, the next two blogs will come with homework assignments, so enjoy this blog while you can!


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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