If you are in the role of managing data within your organisation, it is important to understand what capabilities your organisation has with data, the extent of that capability and where you are falling short. The best way of assessing this is to perform a Data Management Maturity Assessment (DMMA).
The primary goal of a data management capability assessment is to evaluate the current state of critical data management activities in order to plan for improvement.
Why do we need a Data Management Maturity Assessment?
Typically, Data Management programs develop in organizational silos. They rarely begin with an enterprise view of the data. A DMMA can equip the organization to develop an organisation wide vision that supports the organisations data strategy.
Such an assessment helps identify what is working well, what is not working well, and where an organization has gaps in their capability. Based on the findings, the organization can develop a road map to target:
- High-value improvement opportunities related to processes, methods, resources, and automation
- Capabilities that align with business strategy
- Governance processes for periodic evaluation of organizational progress based on characteristics in the mode
Business Drivers for Assessment
There are many business drivers which highlight the need for a maturity assessment. These include;
- Regulation: Regulatory oversight requires minimum levels of maturity in data management.
- Data Governance: The data governance function requires a maturity assessment for planning and compliance purposes.
- Organizational readiness for process improvement: An organization recognizes a need to improve its practices and begins by assessing its current state. For example, it makes a commitment to manage Master Data and needs to assess its readiness to deploy Master Data Management processes and tools.
- Organizational change: An organizational change, such as a merger, presents data management challenges. A DMMA provides input for planning to meet these challenges.
- New technology: Advancements in technology offers new ways to manage and use data. The organization wants to understand the likelihood of successful adoption.
- Data management issues: There is need to address data quality issues or other data management challenges and the organization wants to baseline its current state in order to make better decisions about how to implement change.
Data Management Maturity Assessment Process
Performing a DMMA is a five step process. These steps are;
- Define Scope & Approach – Decide on whether you are focusing organisation wide, which capabilities you will be assessing and which data management framework you will be using. If this is your first assessment, then the scope should include all capabilities, so you can build an organisation wide picture. Subsequent assessments can then target specific areas if required.
- Plan Assessment – Define how you will investigate the capabilities, who you need to talk too, and what tools you will use to gather evidence.
- Perform Assessment – Execute the assessment plan to gather the evidence. Score the capabilities based on the evidence found.
- Interpret Results
- Create Targeted Improvement Programme
Once the improvement programme has been actioned it is then important to reassess to see well the improvement plan has been executed.
Frameworks & Capabilities
There are multiple data management frameworks that can be used to measure your organisations data management capability.
- CMMIN CERT-RMM
- IBM Data Governance Council Maturity Model
- Stanford Data Governance Maturity Model
- Gartners Enterprise Information Management Maturity Model
- COBIT 4.1
Choosing DAMA-DMBOK2 for example, this lists the following capabilities and organisation should measure. These vary by framework.
- Storage & Operations
- Integration & Interoperability
- Document & Content Management
- Reference & Master Data
- Data Warehousing & BI
- Data Quality
Assessment Levels & Criteria
For each capability, an assessment level is determined using the following scale.
0 – No Capability
1 – Initial/Adhoc
2 – Repeatable
3 – Defined
4 – Managed
5 – Optimised
These levels are determined based on the following criteria;
- Activity: To what degree is the activity or process in place? Are criteria defined for effective and efficient execution? How well defined and executed is the activity? Are best practice outputs produced?
- Tools: To what degree is the activity automated and supported by a common set of tools? Is tool training provided within specific roles and responsibilities? Are tools available when and where needed? Are they configured optimally to provide the most effective and efficient results? To what extent is long-term technology planning in place to accommodate future state capabilities?
- Standards: To what degree is the activity supported by a common set of standards? How well documented are the standards? Are standards enforced and supported by governance and change management?
- People and resources: To what degree is the organization staffed to carry out the activity? What specific skills, training, and knowledge are necessary to execute the activity? How well are roles and responsibilities defined?
Outputs from the Assessment
- Assessment by Capability – A report showing how each capability has been scored to give its assessment level, including supporting evidence.
- Assessment Chart – A visualisation of the capabilities and assessment levels. This can include a desired state or previous assessment results to show progress or areas to focus on.
- Targeted Improvement Plan – a plan to improve those capabilities that are behind target.
(Figure reproduced from DAMA – Data Management Book of Knowledge)
Would you like to know more?
Would you like to know more on how your organisation can assess your own maturity in data management or take advantage of a modern cloud technology approach to solving your data problems? Contact us on the link below.