The same concept map of a business model demonstrates the role and key components of data management and digital transformation.
Figure 1: The concept map of a business model.
Data and information play a significant role in supporting the business value chain, as shown in Figure 1. All business stakeholders require business information to make relevant decisions. Customers, for example, need information about the company’s goods and services to decide on their purchase. Thus, in a business context, data management creates value by delivering information to relevant stakeholders, internal and external. Data and information value chains enable information delivery. They do it by transforming raw data into meaningful information. Business processes, people, tools, and other business resources enable the business value chain and the data chain.
Digital transformation driven by various business drivers start with changes in business processes caused by implementing new technology. Such changes impact data and information delivery, people, and other resources.
So, data management and digital transformation impact the same components of a business model: business processes, tools, people, and other resources. However, the order of making this impact differs.
The summary of the order of the impact, you can see in Figure 2.
Figure 2: The order of the impact from data management and digital transformation on business model/capability components.
Usually, a new data initiative starts with new information requirements (1). To deliver new information, a company should acquire or create new data (2). To create a new data chain, a company has to have resources, i.e., budget (3). Then, the company chooses technology for this data chain (4). The implementation of new tools impacts business processes (5). People that participate in these processes, may need new skills.
In digital transformation initiatives, everything starts with the requirements in changes in a specific business process (1). A company should have enough resources (2). The chosen IT technology and solutions enable changes in the processes (3). The new process defines the new outcomes, often in the form of information (4). To deliver new information, a company should acquire and/or create new data (5). People should upgrade their skills for the new process (6).
So, everything we have discussed above confirms that digital transformation and data management impact the same components of a business model/capability. Furthermore, a digital transformation initiative requires building new data and information value chains (data chains).
The implementation of a digital transformation initiative requires several data management capabilities.
As a result of a digital transformation initiative, a company must design and build new data chains. In doing so, the company should have in place several data management capabilities.
Figure 3: The core capabilities required to design, build, and exploit data chains.
To build a new data chain, a company should have capabilities that will allow designing, implementing, and exploiting this chain.
To design a data chain, a company needs the following data management capabilities: data governance, data modeling, enterprise architecture (data-, application-, technology ).
To implement and maintain the functioning of the data chain, IT capabilities such as data lifecycle and infrastructure management are required.
Supporting capabilities, including project- and change management, assist in realizing a digital transformation initiative.
Every company should define the set of data-related capabilities it needs for the digital transformation initiative. Every company has its own view on the definition of “data management.” Some companies consider data management as a part of IT. Others separate these two business areas. The point is that any digital transformation requires these capabilities to be in place.
A formal data management framework ensures the proper performance of various data management capabilities.
Every company deals with data. So, every company manages data. Some companies have formalized a data management capability. Other have not. What does a “formal data management framework” mean?
A data management (DM) framework is a collection of interrelated components that shapes data management into a business function. Data management function is embedded into the company’s organizational structure like any other business function, i.e., finance or marketing.
The DM framework consists of several components and serves various goals, as shown in Figure 4:
Figure 4: The definition of the data management framework.
A model and method are the key components of the data management framework. The data management framework serves four primary purposes regarding shaping the data management capability: design, implement, measure maturity, and measure and monitor performance.
The data management framework defines the way each of the discussed above data management and/or IT capabilities should function and includes the following:
A set of internal policies and standards that govern data management
The list of expected deliverables/outcomes
The set of data management processes that deliver the expected outcomes
The set of roles and their accountabilities
Tools needed to perform data management processes
The set of other resources required for data management functioning.
In simple words, the data management framework ensures functioning of data management as a business function.
I hope that by now I have convinced you in the fairness of the expression: Establishing a formal data management framework is an integral and mandatory part of digital transformation.
This article completes the series “Demystifying the clichés about data.”
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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