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Reference.org
Big data maturity model
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<p>Big data maturity models (BDMM) are the artifacts used to measure big data maturity. These models help organizations to create structure around their big data capabilities and to identify where to start. They provide tools that assist organizations to define goals around their big data program and to communicate their big data vision to the entire organization. BDMMs also provide a methodology to measure and monitor the state of a company's big data capability, the effort required to complete their current stage or phase of maturity and to progress to the next stage. Additionally, BDMMs measure and manage the speed of both the progress and adoption of big data programs in the organization. </p><p>The goals of BDMMs are: </p> <ul><li>To provide a capability assessment tool that generates specific focus on big data in key organizational areas</li> <li>To help guide development milestones</li> <li>To avoid pitfalls in establishing and building big data capabilities</li></ul> <p>Key organizational areas refer to "people, process and technology" and the subcomponents include alignment, architecture, data, <a href="/facts/Data_governance/nt0J5rgc">data governance</a>, delivery, development, measurement, program governance, scope, skills, sponsorship, <a href="/facts/Statistical_model/jKkT5ftm">statistical modelling</a>, technology, value and visualization. </p><p>The stages or phases in BDMMs depict the various ways in which data can be used in an organization and is one of the key tools to set direction and monitor the health of an organization's big data programs. </p><p>An underlying assumption is that a high level of big data maturity correlates with an increase in revenue and reduction in operational expense. However, reaching the highest level of maturity involves major investments over many years. Only a few companies are considered to be at a "mature" stage of big data and analytics. These include internet-based companies (such as <a href="/facts/LinkedIn/Iq2IcNMt">LinkedIn</a>, <a href="/facts/Facebook/0IhXFR6u">Facebook</a>, and <a href="/facts/Amazon.com/YzUTUVE8">Amazon</a>) and other non-Internet-based companies, including financial institutions (fraud analysis, real-time customer messaging and behavioral modeling) and retail organizations (<a href="/facts/Clickstream/D9hs15tH">click-stream</a> analytics together with self-service analytics for teams). </p>