In Part 1 of the article, I discussed three topics regarding digital transformation:
- What does “digital transformation mean?
- Why does a company need it?
- How to implement this concept into practice?
Part 2 focuses on the relationships between digital transformation and data management.
Below is my conclusion to this article:
Establishing a formal data management framework is an integral and mandatory part of digital transformation.
The following reasons support this statement:
- Digital transformation and data management impact the same components of a business model/capability.
- The implementation of a digital transformation initiative requires several data management capabilities.
- A formal data management framework ensures the proper performance of various data management capabilities.
Let’s consider these statements one by one.
Digital transformation and data management impact the same components of a business model/capability.
Let’s start with the definitions of data management and digital transformation:
Data management is a business capability that safeguards and manages data resources and delivers business value from them.
Digital transformation is a change in the business model led by technological changes.
The same concept map of a business model demonstrates the role and key components of data management and digital transformation.
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 starts 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.
You can see the summary of the order of the impact in Figure 2.
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 for 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 several data management capabilities in place.
The “Orange” data management framework defines the key data management and IT capabilities for this task, shown in Figure 3.
To build a new data chain, a company should have the capabilities to design, implement, and exploit this chain.
To design a data chain, a company needs the following data management capabilities: data governance, data modeling, and enterprise architecture (data-, application-, and technology ).
IT capabilities such as data lifecycle and infrastructure management are required to implement and maintain the functioning of the data chain.
Supporting capabilities, including project- and change management, assist in implementing 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 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. Others have not. What does a “formal data management framework” mean?
The 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:
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 desired 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.
Simply put, the data management framework ensures the functioning of data management as a business function.
I hope I have convinced you by now of 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.”
For more insights, visit the Data Crossroads Academy site: //academy.datacrossroads.nl.
Leave A Comment