Big Data

Big Data Business Model Maturity Chart

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

Customers ask me:  How far can big data take us from a business perspective?  What could the ultimate end point look like?  How do I compare to others with respect to my organization’s adoption of big data as a business enabler?  How far can I push big data (“Virile Data”?) to power – even transform – my value creation processes?

To help address these types of questions, I’ve created the below “Big Data Business Model Maturity” chart.  Organizations can use this chart to get an idea as to where they sit with respect to exploiting big data and advanced analytics to power their value creation processes and business models.  It provides a benchmark against which organizations can measure themselves as they look at what big data-enabled opportunities may lay ahead.

Big Data Business Model Maturity Chart

Organizations are moving at different paces with respect to how they are adopting big data and advanced analytics to create competitive advantage.  Some organizations are moving very cautiously, as they are unclear as to where and how to start, and which of the bevy of new technology innovations they need to deploy in order to start their big data journeys.  Others are moving at a more aggressive pace to integrate big data analytics into their existing business processes in order to improve their organizational decision-making capabilities.

A select few are looking well beyond just improving their existing business processes with big data analytics.  These organizations are aggressively looking to identify and exploit net new data monetization opportunities; that is, seeking out business opportunities where they can either 1) sell their data (coupled with analytic insights) to others, 2) integrate advanced analytics into their products to create “intelligent” products, or 3) leverage the insights from big data analytics to transform their customer relationships and “customer experience.”

Let’s use the below “Big Data Business Model Maturity” chart as a framework against which we can not only measure where your organization sits today, but give you some ideas as to how far you can push the big data opportunity within your organization (see Figure 1 below).

Figure 1:  Big Data Business Model Maturity Chart

The Big Data Business Model Maturity Phases:

1. Business Monitoring.  This is the phase where companies are deploying Business Intelligence (BI) solutions to monitor on-going business performance.  Sometimes called Business Performance Management, Business Monitoring uses basic analytics to flag under- or over-performance areas of the business, and automates sending alerts with pertinent information to concerned parties whenever such a situation occurs.  The Business Monitoring phase leverages benchmarking (against previous periods, against previous campaigns, against industry benchmarks) and indices (brand development, customer satisfaction, product performance, financial) to identify under- and over-performing business areas.

2. Business Insights.  This phase takes Business Monitoring to the next step by using statistics, predictive analytics, and data mining to identify material, significant, and actionable business insights.  It then integrates those insights back into the existing business processes.  Think of it as “intelligent” reporting or “intelligent” dashboards, where instead of just presenting tables and charts of data, the application goes one step further to actually uncover material and relevant insights.  It then makes specific, actionable recommendations, calling out an observation on a particular area of the business where these specific actions can be taken to improve business performance.  One client called this phase the “Tell me what I need to know” phase.  Examples include:

  • In marketing, uncovering observations that certain in-flight campaign activities or marketing treatments are more effective than others, coupled with specific recommendations as to how much marketing spend to shift to the more effective activities.
  • In manufacturing, uncovering observations that certain production machines are operating outside of the bounds of their control charts (e.g., upper limits, lower limits), coupled with a prioritized maintenance schedule (with replacement part recommendations) for each problem machines.
  • In customer support, uncovering observations that select “Gold Card” members’ purchase and engagement activities have dropped below a certain threshold of normal activity, with a recommendation to email them a discount coupon.

3. Business Optimization.  This phase is the level of business maturity where organizations use embedded analytics to automatically optimize parts of their business operations.  To many organizations, this is the Holy Grail where they can turn over certain parts of business operations to analytic-powered applications that automatically optimize the selected business activities.  Business Optimization examples include:

  • Marketing spend allocation based upon in-flight campaign or promotion performance
  • Resource scheduling based upon purchase history, buying behaviors, and local weather and events
  • Distribution and inventory optimization given current and predicted buying patterns, coupled with local demographic, weather, and events data
  • Product pricing based upon current buying patterns, inventory levels, and product interest insights gleaned from social media data
  • Algorithmic trading in financial services

4. Data Monetization.  This is the level of business maturity where organizations are trying to #1) package their data (with analytic insights) for sale to other organizations, #2) integrate analytics directly into their products to create “intelligent” products and/or #3) leverage actionable insights and recommendations to upscale their customer relationships and dramatically rethink their “customer experience.”

An example of case #1 could be a smartphone app where data and insights about customer behaviors, product performance, and market trends are sold to marketers and manufacturers.  For example, MapMyRun.com could package the data from their smartphone application with audience and product insights for sale to sports apparel manufacturers, sporting goods retailers, insurance companies, and healthcare providers (see Figure 2 below).

Figure 2:  MapMyRun.com

An example of case #2 could be companies that leverage new big data sources (sensor data, user click/selection behaviors) with advanced analytics to create “intelligent” products, such as:

  • Cars that learn your driving patterns and behaviors, and adjust driver controls, seats, mirrors, brake pedals, dashboard displays, etc. to match your driving style
  • Televisions and DVRs that learn what types of shows and movies you like, and searches across the different cable channels to find and automatically record those shows for you
  • Ovens that learn how you like certain foods cooked and cooks them in that manner automatically, and also include recommendations as to other foods and cooking methods that “others like you” enjoy

An example of case #3 could be companies that leverage actionable insights and recommendations to “up-level” their customer relationships and dramatically rethink their customer’s experience, such as:

  • SMB merchant dashboards from online marketplaces that compare current and in-bound inventory levels with customer buying patterns to make merchandising and pricing recommendations
  • Investor dashboards that assess investment goals, current income levels, and current financial portfolio to make specific asset allocation recommendations.

5. Business Metamorphosis.  This phase is the ultimate goal for some organizations who want to leverage the insights that they are capturing about their customers’ usage patterns, product performance behaviors, and overall market trends to transform their business models into new services in new markets, such as:

  • Energy companies moving into the “Home Energy Optimization” business by recommending when to replace appliances (based upon predictive maintenance) and even recommending which brands to buy based upon the performance of different appliances as compared to your usage patterns, local weather, and environmental conditions such as local water conditions and energy costs
  • Retailers moving into the “Shopping Optimization” business by recommending specific products given a customer’s current buying patterns as compared with others like them, including recommendations for products that may not even reside within their stores (think “Miracle on 43rd Street”)
  • Airlines moving into the “Travel Delight” business of not only offering discounts on air travel based upon customers’ travel behaviors and preferences, but also proactively finding and recommending deals on hotels, rental cars, limo’s, sporting or musical events, and local sites, shows, and shopping in the areas that they are visiting

Begin With An End In Mind

The business possibilities seem almost endless with respect to where and how organizations can leverage big data and advanced analytics to drive their business models – from monitoring the business to data monetization to creating net new business models (see table below).

Companies need to start by understanding what they are trying to accomplish from a business perspective.  To quote Stephen Covey, you need to “begin [your big data journey] with an end in mind.”


 

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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0 thoughts on “Big Data Business Model Maturity Chart

  1. Thank you for writing this is in a clear-cut manner without all the extra fluff. The phases and cases bring the material to life.

  2. Thanks for the feedback Jennifer. I appreciate you taking the time to read it and give me the feedback. Makes it all worthwhile then!

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  5. Another insightful post. You and Jeremiah Owyang should meet, if you haven’t. What we have discovered here at Fohboh is the lifestyle data loop…where consumers live their lives and spend their money. We include travel, hospitality, restaurants, entertainment and retail in each profile. While we tend to leave non foodservice data and interpreted insights on the cutting room floor, we find it valuable in optimizing relationships and finding new ways to monetize.

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