Updated:  The “4 M’s” of Big Data
Cartoon courtesy of Timo Elliott @timoelliott

Updated: The “4 M’s” of Big Data

I’ve gotten lots of positive feedback on this old blog, so I thought I’d update it a bit. Plus a great cartoon at the end of the blog!

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).  See Figure 1.

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Figure 1: Big Data Business Model Maturity Index

Though I have written about the Big Data Business Maturity Index several times, 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 M’s” of Big Data but am going to make you wait until the end of the blog to unveil the “4 M’s”.  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 and operational processes (see Figure 2).  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.

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Figure 2: BDBMI: Internal Process Optimization

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 operational insights gleaned from the first three (internal) phases of the maturity index (see Figure 3).

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Figure 3: BDBMI: External Insights Monetization

This is the part of the big data journey that catches most organization’s attention; the opportunity to leverage the customer, product and operational insights gathered through the optimization of their internal business processes to create new revenue opportunities including new products, new services, new consumption models, new channels, new markets, etc..  And this is the focus area of the “4 M’s” of Big Data!!

Organizational Transformation

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

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Figure 4:  Organizational Changes

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 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 M’s of Big Data

So finally, what are these “4 M’s” of Big Data?  

“Make Me More Money”

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

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Figure 5: 4 M’s of Big Data:  Make Me More Money!

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

And as a bonus, check out read blog from Timo Elliott (@timoelliott) titled “From Understanding Big Data to MonetizingIt”. Love the cartoon!

Cartoon courtesy of Timo Elliott @timoelliott



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