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Big Data Maturity Model


Big Data Maturity Model (BDMM) is a qualitative method to show the growth and increasing impact of big data capabilities in an IT environment from both business and technology perspectives. It comprises  a set of criteria, parameters and factors that can be used to describe and measure the effectiveness of the big data adoption and implementation.

5 levels of maturity are defined: Advanced, Dynamic, Optimized, Primitive, and Tentative (ADOPT). The definitions of all these levels are listed below:
  1. Primitive: initial stage of disconnected activities in an unorganized fashion
  2. Tentative: ad-hoc experiments of trial and error with some level of organized data management
  3. Advanced: comprehensive framework and lifecycle for effective execution
  4. Dynamic: consistent operationalization by means of reference architecture and best-practice patterns
  5. Optimized: converged platform with a repeatable process and policy-driven codification
The Big Data Maturity Model is illustrated as follows.


BDMM facilitates an in-depth assessment for an organization, which is typically conducted by seasoned professionals. It is followed by the strategy formulation for incremental adoption and iterative evolution of big data.

By the way, there is an advanced version of BDMM, which describes fine-grained aspects such as value creation, risk management, compliance, competency, architecture, policy, security, organization, audit, etc. You may email the author for additional details and/or advisory.

For more information, please contact Tony Shan (TonyShan@live.com).


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Tony Shan works as a senior consultant, advisor at a global applications and infrastructure solutions firm helping clients realize the greatest value from their IT. Shan is a renowned thought leader and technology visionary with a number of years of field experience and guru-level expertise on cloud computing, Big Data, Hadoop, NoSQL, social, mobile, SOA, BI, technology strategy, IT roadmapping, systems design, architecture engineering, portfolio rationalization, product development, asset management, strategic planning, process standardization, and Web 2.0. He has directed the lifecycle R&D and buildout of large-scale award-winning distributed systems on diverse platforms in Fortune 100 companies and public sector like IBM, Bank of America, Wells Fargo, Cisco, Honeywell, Abbott, etc.

Shan is an inventive expert with a proven track record of influential innovations such as Cloud Engineering. He has authored dozens of top-notch technical papers on next-generation technologies and over ten books that won multiple awards. He is a frequent keynote speaker and Chair/Panel/Advisor/Judge/Organizing Committee in prominent conferences/workshops, an editor/editorial advisory board member of IT research journals/books, and a founder of several user groups, forums, and centers of excellence (CoE).