A practical and pragmatic approach to implementation of data management that delivers quick wins is one of the key challenges of any data management professional. Sooner or later, you will deal with this at one point in your career.
In the series of presentations Practical implementation or optimization of data management with the “Orange” model, I share with you my practical experience of the past 10 years. This experience has led me to developing a new model and practical method for implementation and optimization of data management. This method is a collection of techniques and templates that can be used for performing various tasks related to the development and optimization of data management in your company.
Using the “Orange” model for developing and performing a data management maturity assessment
This is the final article of the “Orange” Model of DM 101 series. In the previous articles, we have discussed the principles of the “Orange” model and the areas of its application such as strategy development, implementation and/or optimization of data management function, maturity assessment. When your data management (DM) function becomes operational, the finishing touch is to implement DM performance management.
In this article, we will discuss the following topics related to data management performance:
- Performance viewpoints
- 7-step implementation methodology.
Requirements for DM performance management
The key principles of the “Orange” model have been described in the first article of this series. The “Orange” model describes data management as a set of business capabilities that enable data value chains as shown in Figure 1.
One of the key value propositions of data management is to deliver data to internal and external stakeholders in the required quality for different purposes. Data management sets up data value chains that turn raw data into meaningful information. Different data management capabilities should enable data value chains. The core data management capabilities taken into the “Orange” model are data modeling, information systems architecture, data quality, and data governance. In Figure 1, they are marked orange. These capabilities are performed by data management professionals. Other capabilities belong to other domains like IT, security, and other business support functions.
To implement a data management capability, a company should establish a formal data management function. The data management function will become operational by implementing four key components that enable data management capability such as processes, roles, tools, and data (deliverable) as shown in Figure 2.
Performance management should demonstrate progress towards intended results specified by the DM function. Performance management should meet the following criteria shown in Figure 3.
- First, performance assessment should deliver objective evidence of the expected progress. It is not enough to say that ‘we make progress’. You should prove it in the form of implemented processes, systems, or delivered artifacts.
- To make the evidence objective, it should be measurable. This is the second criterion. For example, you can prove your progress by demonstrating the number of data quality issues resolved within a specified period. You should also compare the planned and achieved resolved issues.
- The third factor is the ability to demonstrate progress through time. For example, you can show changes in the numbers of resolved issues in the current and previous months.
- The last criterion is that performance should be assessed from the viewpoints of different stakeholders. The progress in data quality can be assessed differently by different stakeholders that have a concern about it. For example, the application owner will be satisfied with the number of built data quality checks and controls. But if these checks and controls do not solve issues with data, the assessment of the data quality performance made by a data user might be quite different.
Data management has different stakeholders. Their concerns and needs regarding data management vary. Therefore, performance management should reflect these differences by providing different viewpoints on data management performance.
In Figure 4, there are several examples of different performance viewpoints.
- The first one is effectiveness. Merriam Webster’s dictionary specifies effectiveness as ‘the power to produce the desired result’. Such a performance criterion could be mainly used by managers to assess the progress in achieving the desired goals or delivering intended outcomes.
- The second criterion is revenue/cost contribution. Finance management should be interested in the evaluation of the level of direct or indirect revenue and associated incurred costs generated by the data management function.
- The third criterion is coverage. Coverage is the degree to which a business capability is used by more than one business unit or other business capabilities.
- The final example is business criticality. It means that some data management sub-capabilities could be less or more critical in reaching some business goals.
Let’s see you can design and implement DM performance management.
7-step implementation methodology
Data management (performance) can be measured at different levels of abstraction:
- A set of data management capabilities
- A particular data management capability and its dimensions.
We offer the 7-step approach as shown in Figure 5.
- Specify key stakeholders and their concerns.
Data management capability has different stakeholders. Their concerns regarding data and their viewpoints on the success of data management vary.
- Identify data management value propositions.
For each group of stakeholders, data management will deliver different value propositions. A value proposition is either a product or service and benefits associated with it. It might even happen, that one data management capability such as data quality, for example, will deliver different values to different stakeholders. To identify business values, the business canvas methodology can be used. Data Crossroads modified the initial format of business canvas. The modified format allows to simultaneously structure business capabilities into different levels, and map them to value propositions and value chains.
- Assess the viewpoint per stakeholder group.
Effectiveness, revenue/cost contribution, coverage, and business criticality reflect the viewpoints of different stakeholders. We have discussed these viewpoints earlier in the article.
- Apply the heat-map methodology.
The methodology is promoted by The Open Group and supports the measurement of different performance types. In figure 6, you can see an example of performance measurement of the data quality capability.
The level of performance is represented in different colors. The basic colors shown here are green, yellow, and red. For each level of performance, colors will have different meanings. Each company should design its own set of maturity levels and corresponding colors.
- Design and implement KPIs.
The “Orange” model offers a clear and straightforward method to design and measure KPIs. Four dimensions enable data management capability. These dimensions are processes, roles, data, and tools. Data management capability can be described at different levels of abstractions by detailing these dimensions. In Figure 7, you can see an example developed for one of the DQ sub-capabilities: data quality requirements management.
Processes, deliverables, tools, and roles that enable this capability have been listed. Some of these items can be chosen as KPIs. Five statuses represent the maturity level of each KPI. These statuses are ‘does not exist’, ‘informal’, ‘in design’, ‘in implementation’, ‘operational’. These statuses can be applied to each of the dimensions. A process, a deliverable, a tool, or a role may be in ‘design’ status, for example.
- Perform monitoring and GAP analysis
When the system of KPIs is developed, the last step is to set up the system of monitoring and measuring KPIs.
- Develop a Roadmap and/or implementation plans.
Should the progress not be satisfactory, a gap analysis between actual and desired results should be performed. Then you can plan the actions required to close these gaps.