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

By now, a lot of companies have been running data management initiatives for already several years.

What you often see in practice, is that some data management capabilities have been developed better than others. I will share with you some striking insights that I have received as a result of the data management maturity assessment review 2019. Companies put a lot of effort into developing data management documentation and the implementation of tools. And at the same time, they often do not reach expected results in the implementation of data management related processes and the delivery of required artifacts. It gives us a good understanding of the need to optimize some of the already existing data management functions.

In this article, let’s discuss the following questions:

  1. What are the commonly used data management capabilities?
  2. Why should companies optimize already implemented capabilities?
  3. How to optimize a particular data management capability to get quick wins?

Commonly used data management capabilities

Each company chooses its metamodel of data management. Still, some capabilities are commonly used in any company. The “Orange” model of data management includes several such capabilities. One of the key “Orange” model schemes can be seen in Figure 1.

Figure 1. The key components of the “Orange” model.

The “Orange” model describes data management as a set of business capabilities that enable data value chains. One of the key value propositions of data management is to deliver data of required quality to internal and external stakeholders for different purposes. Data management sets up data value chains that transform raw data into meaningful information. Different data management capabilities should enable these 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. There are also other capabilities that belong to other domains like IT, security, and other business support functions.

The key reasons to optimize a particular data management capability

The key reasons have been listed in Figure 2:

Figure 2. Key reasons to optimize a particular data management capability.

  1. We need to improve particular capabilities to be able to deliver practical results. Quick wins are one of the key means to show the effectiveness of data management to top-management and key stakeholders.
  2. A capability can be more or less critical in delivery of value to stakeholders. So, the most critical capabilities should get the most attention.
  3. The performance and effectiveness of each of the data management capabilities should be assessed and improved.
  4. You might also want to optimize the ratio between revenue/cost contribution.

Now, how can we do this?

7-step practical approach to optimize a particular data management capability

One of the models to answer this question is a 7-step approach shown in Figure 3. As an example, I will use data quality capability.

Figure 3. The 7-step approach for optimisation of a data quality capability.

Step 1. Specify key stakeholders and their concerns.

First of all, you should identify the key stakeholders and identify their concerns.

Data quality has different groups of stakeholders, internal and external. Each group has different concerns and needs regarding the quality of data. I will share with you one practical example. Once, I delivered a workshop to a group of 20 professionals that formed a virtual working group. The group consisted of business, IT, data management and architecture professionals. They performed several data quality projects for different stakeholder groups and all of their concerns and needs were completely different.

Step 2. Identify the business value proposition per stakeholder group.

Business value proposition means products or services or benefits associated with them. In our case, we decided to perform the assessment of the value proposition for each stakeholder group. For that, we used the technique called Business canvas. This is a well-known technique that is usually used for business modeling of an entire business. I have explained this method for a particular business function in one of my previous articles.

Step 3. Design data quality capability dimensions.

Four dimensions enable data quality capability. These are processes, tools, roles, and data. You can find the description of these dimensions in detail, in the first article of this series.

The “Orange” model offers two different options to design a business capability.

Option 1. You describe each dimension as shown in Figure 4 one by one.

Figure 4. Option 1 to design a data capability dimensions.

You could start, for example, with the key outcomes of the capability. Examples are lists and definitions of data quality dimensions, data quality requirements, etc. In order to deliver these artifacts, corresponding business processes should be operational. For example, a process to gather and deliver data quality requirements. Roles will be involved in performing the processes and will belong to each category of data stewards. Tools should enable the function of business processes.

Option 2. You first decompose the data quality capability into sub-capabilities at a lower level and then describe each sub-capability using four components. This option is shown in Figure 5.

Figure 5. Option 2 to design data quality dimensions.

The final result should be similar to the result of option 1.

Step 4. Map business capabilities and value chains.

One of the high-level values of DQ capability is the delivery of data of required quality. To produce this value, a company should perform a set of actions that form a value chain as shown in Figure 6. Examples of such actions are: to identify DQ requirements, profile data, etc. To perform these steps, a company should possess certain capabilities such as data quality checks and controls management, DQ monitoring and reporting, data modeling, data lineage, stakeholder management, etc.

Figure 6. Mapping between value chains and business capabilities.

Step 5. Assess the current and desired level of maturity

The fifth step is the assessment of either maturity or performance of the chosen business capability. The “Orange” model offers you two methodologies. The first one is to perform a maturity assessment. I have explained this in-depth in the third article of this series. There are also plenty of free materials on our website about this subject. Another option is to apply techniques that allow assessing the performance of business functions. This topic I will cover in the seventh, and last, article of this series. Returning to our case. If you recall, I mentioned three different data quality projects. Stakeholders and value delivered varied for each of the three projects. We have used this methodology to measure the maturity level for each of the data quality projects. To our surprise, the level of maturity of each component varied. For one project, for example, processes were perfectly developed and implemented and got the ‘green’ status. While the same processes of other projects were assessed as ‘red’.

After assessing the current and desired levels of maturity, it is time to move on to make GAP analysis.

Step 6. Perform GAP analysis.

The differences between the current and desired levels of data management form gaps that need to be identified.

Step 7. Develop roadmap and plans, and implement them.

Once you are done with the analysis, you can proceed with the roadmap and its implementation.

In the next article, I will share with you my experience with designing the set of data management/governance roles.