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 the DM 101 series. In the previous articles, we discussed the principles of the “Orange” model and its application areas, such as strategy development, implementation and optimization of data management function, and 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:
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.
Figure 1. The key components of the “Orange” model.
One of the key value propositions of data management is to deliver data to internal and external stakeholders at 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. Data management professionals perform these capabilities. Other capabilities belong to other domains like IT, security, and other business support functions.
A company should establish a formal data management function to implement a data management capability. The data management function will become operational by implementing four key components that enable data management capabilities, such as processes, roles, tools, and data (deliverable), as shown in Figure 2.
Figure 2. Business function realizes data management in practice
Performance management should demonstrate progress toward the intended results specified by the DM function. Performance management should meet the following criteria shown in Figure 3.
Figure 3. Key criteria to DM performance management.
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 through 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. It would be best to compare the planned and achieved matters resolved.
The third factor is the ability to demonstrate progress through time. For example, you can show changes in the number 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 evaluated differently by various 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 pretty 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.
Figure 4. Performance viewpoints.
The first one is effectiveness. Merriam-Webster’s dictionary specifies effectiveness as ‘the power to produce the desired result.’ Managers could mainly use such a performance criterion 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 evaluating 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 some data management sub-capabilities could be less or more critical in reaching business goals.
Let’s see if 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.
Figure 5. The 7-step approach for implementation of DM performance.
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 the 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 the business canvas. The modified format allows us 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.
Figure 6. An example of a heat map.
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 abstraction by detailing these dimensions. Figure 7 shows an example developed for one of the DQ sub-capabilities: data quality requirements management.
Figure 7. 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,’ and‘ operational.’ These statuses can be applied to each of the dimensions. For example, a process, a deliverable, a tool, or a role may be in ‘design’ status.
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.
A gap analysis between actual and desired results should be performed if the progress is not satisfactory. Then you can plan the actions required to close these gaps.
Irina is a data management practitioner with more than 10 years of experience. The key areas of her professional expertise are the implementation of data management frameworks and data lineage.
Throughout the years, she has worked for global institutions as well as large- and medium-sized organizations in different sectors, including but not limited to financial institutions, professional services, and IT companies.
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