What does BDMM mean in UNCLASSIFIED

A Big Data maturity model is a framework that helps organizations assess their current level of ‘big data’ analytics capabilities and develop a strategy for achieving future capabilities. It allows businesses to identify which areas of big data need improvement in order to become more effective users of the technology.

The use of a Big Data maturity model provides structure and guidance when approaching an organization's big data strategies. By analyzing the current state of your data efforts, you can determine which areas need further development and create a roadmap for improving those areas. This allows for better use of resources and maximizes return on investment.

Each maturity model typically follows five components, which include Strategy & Governance, People & Organization, Technology, Processes, and Results. These components collectively provide an understanding of where your company stands in terms of big data analytics abilities and where improvements need to be made.

Implementing any kind of new framework within an organization can be difficult. Some challenges associated with using big data maturity models are determining how to prioritize projects in order to maximize return on investment; developing effective governance structures; finding or creating suitable talent; ensuring appropriate security measures; and integrating new technologies into existing infrastructure.

While every organization may not benefit from using this type of model, it is generally recommended that organizations take advantage of it if they plan on making serious investments into big data strategies or expanding their existing ones. It will help them evaluate their current state accurately as well as create effective plans for growth moving forward.

Generally speaking, it is suggested that organizations regularly monitor their progress against their defined goals for the model as part of their overall continuous improvement cycle. Depending on the scope and complexity of your particular setup, this may mean updating every quarter or even more frequently if necessary.

Typically speaking no specific tools are needed but having access to visual aids like charts and infographics can be beneficial in helping understand the various aspects that make up the model itself as well as helping visualize progress along the way. Additionally having access to analytical software can make certain processes much easier such as tracking KPIs or identifying trends in user behavior.