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.
The goals of BDMMs are: To provide a capability assessment tool that generates specific focus on big data in key organizational areas To help guide development milestones To avoid pitfalls in establishing and building big data capabilities Key organizational areas refer to "people, process and technology" and the subcomponents include alignment, architecture, data, data governance, delivery, development, measurement, program governance, scope, skills, sponsorship, statistical modelling, technology, value and visualization.
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.
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 LinkedIn, Facebook, and Amazon) and other non-Internet-based companies, including financial institutions (fraud analysis, real-time customer messaging and behavioral modeling) and retail organizations (click-stream analytics together with self-service analytics for teams).
Categories
Big data maturity models can be broken down into three broad categories namely: Descriptive Comparative Prescriptive
Descriptive
Descriptive models assess the current firm maturity through qualitative positioning of the firm in various stages or phases. The model does not provide any recommendations as to how a firm would improve their big data maturity.
Big data and analytics maturity model (IBM model) This descriptive model aims to assess the value generated from big data investments towards supporting strategic business initiatives.
Maturity levels
The model consists of the following maturity levels: Ad-hoc Foundational Competitive differentiating Break away Assessment areas
Maturity levels also cover areas in matrix format focusing on: business strategy, information, analytics, culture and execution, architecture and governance.
Knowledgent big data maturity assessment
Consisting of an assessment survey, this big data maturity model assesses an organization's readiness to execute big data initiatives. Furthermore, the model aims to identify the steps and appropriate technologies that will lead an organization towards big data maturity.
Comparative
Comparative big data maturity models aim to benchmark an organization in relation to its industry peers and normally consist of a survey containing quantitative and qualitative information.
CSC big data maturity tool
The CSC big data maturity tool acts as a comparative tool to benchmark an organization's big data maturity. A survey is undertaken and the results are then compared to other organizations within a specific industry and within the wider market.
TDWI big data maturity model
The TDWI big data maturity model is a model in the current big data maturity area and therefore consists of a significant body of knowledge.
Maturity stages
The different stages of maturity in the TDWI BDMM can be summarized as follows:
Stage 1: Nascent
The nascent stage as a pre–big data environment. During this stage: The organization has a low awareness of big data or its value There is little to no executive support for the effort and only some people in the organization are interested in potential value of big data The organization understand the benefits of analytics and may have a data warehouse An organization's governance strategy is typically more IT-centric rather than being integrative business-and-IT centric Stage 2: Pre-adoption