A practical and pragmatic approach to the 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 on 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 develop a new model and practical method to implement and optimize the data management function. 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 to implement or optimize the data management function
The first time I faced the challenge of implementing data management was eight years ago. Over the years, I have developed a methodology for the practical implementation of data management that is universal for all types of companies. I have called it “the data management star.” You can see it in Figure 1.
Figure 1. “The Data Management Star” by Data Crossroads.
This methodology is based on my collection of hands-on knowledge and experience. In 2019 I published the book The Data Management Toolkit, where I present this methodology in detail. This book contains templates on implementing data management in a practical, feasible, and ‘to-the-point’ way and can be used as a step-by-step guide.
In this article, we will discuss and explore the following three questions:
What are the data management capability and function, and what are the differences between these two concepts?
Why does a company need to implement data management?
How can you implement data management functions pragmatically and effectively?
What are the differences between data management capability and function?
The “Orange” model considers data management a business capability. What does it mean? A business capability is the ability of a company to reach goals and deliver outcomes.
The “Orange” model of data management is based on five key data management sub-capabilities, as shown in Figure 2: data governance, data modeling, data and application architecture, data quality, and data chain. Data management professionals perform these capabilities (marked orange). The remaining capabilities (marked grey) belong to IT and security domains.
Figure 2. Critical data management capabilities in the “Orange” model.
The key question is how to move from the theoretical model of data management capability into practical implementation. You do it by establishing a formal data management function within your company. And you do that by implementing the key dimensions of the data management capability – processes, roles, and tools, that will enable the delivery of the specified outcomes.
Why would a company formalize and implement a data management function?
For a company to survive in the long term, it should create and elaborate its competitive advantages. Data management enables the company to deliver value to different stakeholder groups, including its customers. Its value proposition is the supported decision-making, as shown in Figure 3. Each group of stakeholders will require particular information to make some decisions. Customers will need the information to make decisions about purchasing products or services. The company’s management at different levels will require information to make decisions about the strategy or operational functioning of the company and so on.
Figure 3. Data value creation cycle.
The formalized data management function should be in place to deliver the value correctly. The key challenge is to design and implement the data management function that fits the company’s needs and resources.
How to implement data management functions pragmatically and effectively?
In 2018, Data Crossroads developed a practical and universal 5-step methodology for the implementation of data management. This approach allows you to implement data management from scratch or optimize an existing function.
There are two distinctive features of the model:
First, you should scope your data management initiative to a feasible minimum by defining the key 1-2 business drivers.
The model is iterative. There are different iterations that you can make:
An iteration of all steps when another business driver is focused on.
An iteration between different steps. For example, in step 2, you identify roles and related tasks. But then, in step 3, you discover you missed a few tasks. So, it would be best to go back to step 2.
An iteration is within each step.
Scope your data management initiative.
The implementation of data management starts with the identification of key business drivers. I believe a company will never implement data management just for fun. There is always one or multiple business drivers that drive the company to do that. For data management, a business driver is the ultimate reason or the need for its implementation. It assists in realizing the company’s business strategy at a particular moment. In the context of business, we always speak about the external and internal environment. Specific factors from both environments could serve as drivers for data management.
Furthermore, these factors can also complement each other. For example, strengthening competitive advantages will demand improvement in the efficiency and speed of decision-making. And these are good examples of business drivers for data management.
Step 1. Define data needs and requirements
A particular group of stakeholders can have concerns regarding the specified driver. These concerns will be expressed in information needs and requirements. Assume that one of your key drivers is, for example, compliance with regulations (e.g., the well-known General data protection regulation related to personal data (GDPR)). Customers will have concerns about the protection of their data and will require information. Regulators should control your company, and they will also have particular information requirements. For all groups of stakeholders, data management should specify information needs and requirements. To satisfy these needs, a data management framework should be designed.
Step 2. Design the data management framework and divide tasks and accountabilities
To implement the data management function, you should specify who will do it and what. Finally, it would be best to design your data management framework, a set of rules and roles. The implementation of data management can be planned at different levels: strategic, tactical, and operational. Therefore, the list of rules and roles’ accountabilities will differ per level. One of the follow-up articles will be devoted to the topic of designing data management roles.
Step 3. Build the data management framework
The third step is the building of the data management framework. This is the most challenging and time- and resource-consuming task. You should start developing data management artifacts and bringing data management practices to your company at this step. My practical experience brought me to the following conclusion: implementation of data management follows the logic of the documentation of data lineage—the key steps in implementing the data management framework you find in Figure 4.
Figure 4. Key steps in the implementation of the data management framework.
Step 4. Assess your results and make a gap analysis
When you are done with the first implementation of the data management framework, you can compare the expected and achieved results. You can do it in two ways:
You can perform the data management maturity assessment. I have described this approach in the previous article of this series.
The second way is to assess data management performance. I will highlight the techniques in one of the follow-up articles.
Step 5. Define new goals and design a plan for further actions
Assume you have achieved your goals and met business drivers’ requirements. But life continues, and you can always start a new cycle of expanding the data management function.
In the following article of the series, we will discuss the optimization of one of your already existing data management capabilities.
For more insights, visit the Data Crossroads Academy site: //academy.datacrossroads.nl.
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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