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. Today we discuss how to develop a data strategy.
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 for the 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 a data strategy
Nowadays, a lot of companies run data management, but few of them have a formal data (management) strategy in place. There could be different reasons for that. One that comes to mind is that it is often unclear what the content of a strategy should be. Here’s an example. On Intranet, I found a very good-looking example of a data strategy proposed by a prominent solution vendor. While reading, I found that 20% of the content was about data and 80% about application and technology architecture. In my opinion, data always come first, application and technology architecture then follow the needs of data. In this article, we will talk about the common topics to be worked out in the strategy and how the “Orange” model of data management assists in the development of a data (management) strategy. So, the question remains: “How to develop a data management strategy?”
The definition and content of a data strategy.
I took the definition of data strategy from DAMA-DMBOK2: “a strategy is a set of choices and decisions that together chart a high-level course of action to achieve high-level goals” (DAMA-DMBOK2,p.31). Data strategy should cover the following questions: “what data the organization needs, how it will get the data, how it will manage it and ensure its reliability over time, and how it will utilize it” (DAMA-DMBOK2, p.32). Reading these questions, you immediately understand that all these questions relate to the set-up of the data management framework. The components of the data strategy proposed by DAMA-DMBOK2 (DAMA-DMBOK2, p.32), I have assembled in three groups as shown in Figure 1:
Why a company needs a data (management) strategy
What topics the strategy should cover
How to develop and implement a data strategy
Figure 1. Key components of a data (management) strategy.
Let’s talk about these three groups of the data strategy one by one.
Why a company needs a data (management) strategy
The data strategy vision should follow and support the overall business strategy. Data is one of the resources required for achieving business goals. I am sure that no company will implement data management just for fun. This is a cost-, time-, and resource-intensive process. A company needs to have strong business drivers to do it. One of the ways to align data strategy with the overall business strategy is to find the crucial business drivers. These drivers are the ultimate reasons to implement data management. In the context of business, we always speak about the external and internal environment as shown in Figure 2. Specific factors from both these environments could serve as drivers for data management. Furthermore, these factors can also complement each other. For example, the strengthening of competitive advantages will demand improvement in the efficiency and speed of decision making.
Figure 2. Internal and external drivers specify the needs of data management
While preparing a data management strategy, you need to think about the value propositions of data management. For different stakeholders, internal and external ones, data management will deliver different value propositions. I recommend analyzing all groups of stakeholders and corresponding value propositions. For example, the key value from data management for top management will be enhancing decision making. For an external customer, an example of a value proposition could be: providing correct and trustful information about the company’s products and safeguarding the privacy of personal data.
So, business drivers and the analysis of the key value propositions substantiate the necessity of data management.
Now it is time to discuss what are the key data (management) topics that the data strategy should cover.
What topics the strategy should cover
The first topic is the data (management) principles. Principles are the rules that each company should follow in taking all their decisions around data. you should recognize a distinction between data principles and data management principles. Data principles focus on defining the role and value of data in the company’s culture. Data management principles concentrate on the way the data will be handled. The defined principles should correspond to the chosen data management drivers as shown in Figure 3. Assume, compliance with personal data regulations is one of the business drivers/ Then the principles you choose should concentrate on data sharing and data security. Implementation of each of the principles will simultaneously bring some benefits to business and cause some implications to be resolved.
Figure 3. The relationship between business drivers and data (management) principles
The second topic is the scope of data management. In the first article of the series, I have explained the “Orange” model approach to the scope of data management and its key components. Below, in Figure 4, you see the key data management capabilities that constitute data management. There are some other factors to be taken into account while specifying the scope of data management. These are company culture, resources available, and the company’s size, the number of data chains, and the complexity of the information systems landscape. These factors will size your data management imitative and bring it to the feasible minimum.
Figure 4. The scope of data management
The third topic is the structure and size of the data management framework that includes the organizational structure of data management function, the set of required rules (policies, processes, etc.) and roles. The complexity of the data management framework mainly depends on the size of the organization. Usually, data management will be performed at three organizational levels: strategic, operational, and tactical as shown in Figure 5. The development of data strategy, policies, standards, models, processes, rules, and roles belong to the strategic level. These tasks will be mainly performed by data management professionals with some involvement of business stakeholders. The implementation of all of these documents belong to the tactical level and will be performed by both business and data management professionals. At the operational level, the data related tasks will be mainly performed by the business.
Figure 5. The structure of the data management framework
Now we will discuss what documents will be required to plan the implementation of the data strategy.
How to implement the data strategy
The first step is determining a period for the strategy. It might differ per company. Within the specified period, a company should set up its SMART objectives. It is advisable to link these objectives to business drivers. And then translate them into objectives per data management capability and data chains as shown in Fig.6
Fig.6 Objectives should be linked to particular data management capabilities
A realistic roadmap will conclude the content of the data strategy.
In Figure 7 you see the short summary of all steps you should cover to develop a data strategy.
Figure 7. Key steps in the development of data management strategy.
Maturity assessment of your current data management capabilities and projection of the required level of maturity will assist you to specify the realistic and feasible Data strategy. The next article of this series will cover this topic.
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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