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It appears that the distinction between data and system is difficult to grasp for many stakeholders. The stakeholders in example 8 conflated data and systems and had formed the mental image that “data cannot be to blame”: any error in the data must be caused by poor systems design, so that is where the problem must lie. There is something to be said for this line of reasoning. It makes sense from a systems perspective but not so much from a DM perspective.

Example 8. Data and systems

In the early days of a recent project, I ran into the following situation. We were trying to get an answer to the question “do our business stakeholders believe there is a data quality management problem in the organization?”

We held a series of interviews with groups of stakeholders to explore this topic. In one of the meetings, a stakeholder commented as follows: “Data quality problems? No, I don’t think we have those. We do have a lot of system problems though. People fill in all kinds of nonsensical data, data from different systems is hard to integrate, and our management reports are always late. So, perhaps we should stop talking about data management and start fixing these problems?”

IT management is, like business process management, an important capability that has received much attention over the last few decades, both in academic and business discourse. Loosely defined, IT management is about managing IT resources (applications, infrastructure) according to an organization’s priorities and needs. There are several major frameworks in this area, including ITIL and COBIT [Per16, ISA12].

Taking a slightly broader perspective, it can be argued that software/ system development and its associated methodologies should also be considered. This would also put frameworks such as Scrum in scope of this discussion [Rub12], as would system development philosophies such as domain driven design [Eva04] or architecture approaches such as micro services [New15].

The point that I am trying to make is that IT management is a broad capability which includes a number of aspects, many of which have a link to data and data management. Simply put: data is stored in systems and flows between systems. When done well, the DM and IT management can reinforce/ strengthen each other. Let me offer two examples to illustrate:

■ One of the key processes in IT management is incident management. Through this process, organizations attempt to ensure that IT services are restored as soon as possible after an incident. This process is very similar to data quality issue management (see chapter 16). Given how closely related data and systems are, it may make sense to align these two processes.

■ One of the key considerations in systems development is user interface design. This discipline is traditionally largely focused on making sure user interfaces are easy to use and the interaction between user and system to ensure work can be performed effectively. From a data management perspective, this would include such aspects as consistent use of language, intuitive use/ ergonomics, and ensuring that there are “guard rails” in place that will prevent users from entering an incorrect input that the system will not be able to process correctly.

Here, too, it is safe to conclude that, in practice, both disciplines are important and tightly linked.