Big Data Maturity Assessment

In order to systematically improve the capabilities of their Big Data organization, enterprises can conduct regular (typically annually) Big Data maturity assessments. Big Data maturity models 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.

A Big Data maturity assessment provides tools that assist organizations to define goals around their Big Data program and to communicate their Big Data vision to the entire organization. The underlying maturity models 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, the Big Data maturity assessment measures and manages the speed of both the progress and adoption of Big Data programs in the organization.

Big Data Framework Maturity Assessment

The Big Data Framework maturity assessment is an example of such a maturity assessment and in based on the 6 dimensions of the Big Data framework. It measures the maturity of Enterprise Big Data over each of these components based on the five point Capability Maturity Model (CMM) scale developed by the Carnegie Mellon Software Engineering Institute. The CMM offers guidelines for organizations to determine their current process maturity and develop a strategy for improving software quality and processes. It consists of the following five stages:

Figure 1: The levels of the Capability Maturity Model (CMM)

The five levels of Big Data maturity are based on the five-scale CMM-levels:

1. Analytically Impaired (chaotic and ad hoc activity) – Minimal analytics activities and infrastructure across the enterprise, with ambiguous data and analytics strategy.
2. Localized Analytics (initial activity) – Pockets of analytics across the enterprise, however functioning in silos and no overarching data or analytics strategy.
3. Analytical Operation (repeatable activity) – Expanding siloed functional analytics to shared operational level analytics with support and commitment from the C-suite.
4. Analytical Enterprise (managed activity) – Data and analytics are viewed as an enterprise priority. The organization is developing enterprise wide analytics capabilities across all domains to create meaningful content and ideas.
5. Data Driven Enterprise (optimized activity) – Trusted insight created by enterprises with analytics that support strategic decision making. The enterprise is reaping the benefits and is focused on optimization of analytics.

Every area of the Big Data Framework is subsequently assessed to determine the level of capability.  The outcome of the Big Data Framework maturity assessment is depicted in the figure below and provides valuable information on the potential improvement areas for the organization.

Figure 2: Big Data Framework Maturity Assessment

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