Big Data

The “4 Ms” of Big Data

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) May 1, 2013

By far, the below Big Data Business Model Maturity Index generates the most discussion whenever I talk to customers about where and how to start their big data journeys.  This chart gets the creative juices flowing, not only about how organizations can enhance their current business intelligence and data warehouse investments, but also about the business potential of big data. To be successful with big data, organizations need to “begin with an end in mind” (to quote Stephen Covey) otherwise “if you don’t know where you are going, you might end up someplace else”(to quote Yogi Berra).



Though I have written about the Big Data Business Maturity Index previously, there have been some interesting observations that have come out of those discussions that I want to share.  In particular, I want to share with you the “4 Ms” of Big Data, but am going to make you wait until the end of the blog to unveil the “4 Ms.  So hang tight!


Initial Big Data Focus:  Optimize Internal Business Process

The first observation is that the first three phases of the Big Data Business Model Maturity Index are focused on optimizing existing internal business processes (see figure below).  These first three phases leverage an organization’s existing business intelligence and data warehouse investments, which is focused on identifying the metrics, key performance indicators, and data sources necessary to monitor the organization’s key business process.  This includes the operational reporting and management dashboards to monitor the performance of those business processes.


In particular, there are three big data capabilities that organizations can leverage to expand their business intelligence and data warehouse investments to optimize (versus just monitor) their existing internal processes and move up the maturity index:

  • Integrate unstructured data with detailed structured (transactional) data to provide new metrics and new dimensions against which to monitor and optimize key business processes.
  • Deploy predictive analytics to uncover insights buried in the massive volumes of detailed structured and unstructured data (note:  having business users slice-and-dice the data to uncover insights worked fine when dealing with gigabytes of data, but doesn’t work very well when dealing with terabytes and petabytes of data).
  • Leverage real-time (or low-latency) data feeds to accelerate the organization’s ability to identify and act upon business and market opportunities in a timely manner.


Ultimate Big Data Opportunity:  Monetize External Customer Insights

The last two phases of the Big Data Business Model Maturity Index are focused on creating new monetization or revenue opportunities based upon the customer, product, and market insights gleaned from the first three phases of the maturity index (see figure below).



This is the part of the big data journey that catches most organization’s attention; the opportunity to leverage the insights gathered through the optimization of their internal business processes to create new corporate assets – data, analytics, and insights – that can be used to create new sources of revenue for organizations.  And this is the focus area of the “4 Ms” of Big Data!!


Organizational Transformation

But before we get to the “4 Ms”, let’s cover one other key observation.  Subtle organizational transformations occur as organizations advance up the maturity index (see figure below).



As organizations advance along the maturity index, three organizational transformations, or attitudinal changes, will take place:

  1. Organizations start to treat data as an asset, not a cost of business.  And the organizations will develop an insatiable appetite for more and more data – even if they are not sure how they will use that data today.
  2. Organizations will put into place formal processes and procedures to capture, inventory, refine, and legally protect their analytics (analytic models, processes, and insights) as intellectual property (IP).
  3. Organizations’ confidence in making decisions based upon the data and analytics will grow.  Organizations will advance to the place where they are confident to make business decisions based upon what the data tells them, instead of defaulting to the “Highest Paid Person’s Opinion” as the decision criteria.  The organization’s investments in data, analytics, people, processes, and technology will be for naught if the organization isn’t prepared to make decisions based upon what the data and the analytics tell them.


4 Ms of Big Data

So finally, what are these “4 Ms” of Big Data?

“Make Me Mo’ Money”

Big data is really interesting to the business stakeholders when it translates into new revenue opportunities – opportunities to leverage those customer, product, operational, and market insights to “Make Me Mo’ Money” (see figure below).



This is the exciting part of the big data story – envisioning how an organization can leverage structured and unstructured data sources in real-time to uncover new customer, product, operational, and market insights that can power new monetization opportunities.  The 4 Ms of Big Data:  “Make Me Mo’ Money!!”


Finally, I will be at EMC World next week, so if you happen to also be there, please look me up.  I’m leading a Bird of a Feather discussion on Tuesday, 1:00 – 2:00 on the subject:  “Best Practices for Adding Big Data Technologies to an Enterprise Data Warehouse.”  Hope to see you there!




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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