A few weeks ago, I discussed the results of a survey conducted by Oracle’s Insight team. The survey was based on the data management maturity model that the Oracle Insight team has developed over the years as they analyzed customer IT organizations to help them get more out of everything they already have. I thought you might like to learn more about the maturity model itself. It can help you figure out where you stand when it comes to getting your organizations data management act together.

The model covers maturity levels around five key areas: Profiling data sources; Defining a data strategy; Defining a data consolidation plan; Data maintenance; and Data utilization.

Profile data sources: Profiling data sources involves taking an inventory of all data sources from across your IT landscape. Then evaluate the quality of the data in each source system. This enables the scoping of what data to collect into an MDM hub and what rules are needed to insure data harmonization across systems.

Define data strategy: A data strategy requires an understanding of the data usage. Given data usage, various data governance requirements need to be developed. This includes data controls and security rules as well as data structure and usage policies.

Define data consolidation strategy: Consolidation requires defining your operational data model. How integration is to be accomplished. Cross referencing common data attributes from multiple systems is needed. Synchronization policies also need to be developed.

Data maintenance: The desired standardization needs to be defined, including what constitutes a ‘match’ once the data has been standardized. Cleansing rules are a part of this methodology. Data quality monitoring requirements also need to be defined.

Utilize the data: What data gets published, and who consumes the data must be determined. How to get the right data to the right place in the right format given its intended use must be understood. Validating the data and insuring security rules are in place and enforced are crucial aspects for full no-risk data utilization.

For each of the above data management areas, a maturity level needs to be assessed. Where your organization wants to be should also be identified using the same maturity levels. This results in a sound gap analysis your organization can use to create action plans to achieve the ultimate goals.

Marginal is the lowest level. It is characterized by manually maintaining trusted sources; lacking or inconsistent, silo’d structures with limited integration, and gaps in automation.

Stable is the next leg up the MDM maturity staircase. It is characterized by tactical MDM implementations that are limited in scope and target a specific division.  It includes limited data stewardship capabilities as well.

Best Practice is a serious MDM maturity level characterized by process automation improvements. The scope is enterprise wide. It is a business solution that provides a single version of the truth, with closed-loop data quality capabilities. It is typically driven by an enterprise architecture group with both business and IT representation.  

Transformational is the highest MDM maturity level. At this level, MDM is quantitatively managed. It is integrated with Business Intelligence, SOA, and BPM. MDM is leveraged in business process orchestration.

Take an inventory using this MDM Maturity Model and see where you are in your journey to full MDM maturity with all the business benefits that accrue to organizations who have mastered their data for the benefit of all operational applications, business processes, and analytical systems. To learn more, Trevor Naidoo and I have written the Oracle MDM Maturity Model whitepaper. It’s free, so go ahead and download it and use it as you see fit.