Читать книгу Data Management: a gentle introduction - Bas van Gils - Страница 13

Synopsis - This chapter picks up where the previous chapter left off: if data is an important asset, then it should be managed as such. In this chapter, I will briefly introduce the Data Management Body of Knowledge (DMBOK) reference work on data management upon which part I of this book is based. I will use this as a backdrop to discuss some of its key challenges for data management. The challenges are illustrated with small examples.

■ 3.1 A DEFINITION OF DATA MANAGEMENT

In the previous chapter, I have discussed the concept of data as an asset to signify the importance of data for an organization. We pick up the discussion with a claim: if data is such an important asset to the organization, then it should be managed as such. This is the realm of data management.

Simply put, DM is the capability that is concerned with managing data as an asset. This definition is still somewhat vague and requires further clarification. In [AB13], Peter Aiken points out that “any holistic examination of the information technology field will reveal that it is largely about technology – not about information”. We begin by stating that data management is largely about putting the “I” back in “IT”. This observation shows that DM is not solely an IT capability.

Sidebar 2. Interview with Marc van den Berg (summer 2019)

Many organizations are currently experiencing challenges with data due to past decisions and are paying the price because of the investment they have to make to fix their data after the fact. At the same time, these organizations want to make a quantum leap forward and reap the benefits from new technologies such as big data and artificial intelligence. This will not work, as first you must have your house in order. In my view this means: make sure you have shared goals about what you want to achieve with data, and subsequently align business and IT to attain those goals.

Marc van den Berg is managing director of IT and Innovation at PGGM, a Dutch pension provider.

It appears that in most organizations there is no longer a real, meaningful difference between “the business side of the organization” and “the IT side of the organization”, at least not in the classic sense of business/ IT alignment literature from the 1980s and 1990s [PB89, HV93]. With the rise of process automation, digital/ digitalization we see that the two perspectives are now intertwined to such a degree that the distinction is fading rapidly (see e.g. [RBM19, Gue12] in which a distinction is made between digitalization of existing processes, or by a more radical departure and creating digital, information-enriched value propositions). In this context, it feels safe to say that DM is an important capability for the organization, regardless of whether it leans towards business, IT, or both.

The DMBOK definition of DM is as follows [Hen17]:

Data management is the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their lifecycle.

The interesting aspect that can be learned from this definition is that data management encompasses many activities that together enable the organization to use data effectively. For now, this exploration of the definition of DM will have to suffice. A more detailed discussion will follow in chapter 7.

The DMBOK also states that these activities are likely to be cross-functional and that “the primary driver for data management is to enable organizations to get value from their data assets, just as effective management of financial and physical assets enables organizations to get value from those assets”. The value of DM is discussed further in the next section.

■ 3.2 VALUE OF DM

The key point of DM is to manage data as an asset which helps the organization to derive value from its data assets. As such, it has no direct business value. Its value is more indirect; it enables the organization to achieve goals through data. This means that organizations should think carefully about which goals they want to achieve through the use of data and what would be required to realize these goals.

In a recent article about data strategy, this was compared to the world of sports [DD17] such as soccer or ice hockey. In these sports, you’ll never win the game if you only do defense: it will be hard for the opponent to score goals, but you’ll never get to score goals yourself either. The inverse is also true: you’ll never win the game if you only do offense: you’ll probably score a few goals, but it will be super easy for the opponent to score goals since there is no one to defend your own goal.

The trick to being successful is to balance between offense and defense and to make sure that the two stay connected. Example 7 illustrates this point.

Example 7. Balancing data management offense and defense

This example stems from the early 2000s when I did a consultancy assignment with a large Dutch governmental organization. Roughly speaking, the organization had several units which served citizens as well as businesses. The organization was structured along the lines of a classic front-office, mid-office, and back-office pattern. At the front-office level the units operated independently. At the mid-office and back-office level, this organization was attempting to standardize several processes and systems. This included the launch of a data delivery platform which served both analytics and reporting functions.

From a business perspective it was very clear what the value of data was and how it could be used to fuel their business processes (data management offense). From an IT perspective it was – after some searching – clear what data was available in which system and how it should be transported to the data delivery platform in a timely manner while retaining high levels of data quality (data management defense).

Unfortunately, communication between the two groups was less than optimal – to say the least. The effect was that it took years before their supply of data on this platform was well suited to meet the demands of business stakeholders, and a lot of the data that had been loaded on the platform early on was never actually used. This endeavor was not only costly, it also gave data/ DM a bad reputation at this organization.

The same line of thinking also applies to DM. Here, defense pertains to “grip on data”, meaning the activities through which the organization knows what data assets they have, where and when they were created, what their quality is, etc. This is what traditionally was seen as DM. In this context, offense pertains to generating value through the use of data, meaning the activities related to using data in business processes. This can be in various shapes and forms such as selling the data itself, handling business transactions, using big data analyses to detect fraud patterns or to use traditional business intelligence reports to manage some business unit.

■ 3.3 KEY CHALLENGES FOR DM

The final topic for this chapter deals with two questions: what are the key challenges that DM attempts to solve and what are key challenge to overcome when getting started with DM?

The first challenge you have to tackle is for the organization (or at least key stakeholders in the organization) to recognize that DM is really a “thing” they should worry about. As stated previously, many people seem to think along these lines: data is stored in our systems, we know which systems we have, so what’s the big deal? The thinking has to change to: processes are the value creation engine of the organization and we change systems all the time so we should really take good care of our data to help us to be successful. This transition is usually the biggest challenge. Sidebar 3 illustrates this point.

Sidebar 3. Interview with Marco van der Winden (Summer 2019)

We are now realizing that data is the link between business(-operations) and systems. It is the universal language between business and IT. We have to understand that it will make our lives easier instead of more complex by focusing on data and not on systems or our own operation. My experience is that people only think that focusing on data is about more rules, more work, and being more accountable. I think (and hope) that we’ll understand we have to spend less time on acquiring data and changing our operations in favor of the more exciting things we can do with our data.

Marco van der Winden is manager of the corporate data management office at PGGM, a Dutch pension provider.

It is often the case that discussions about DM lead to the question of a business case, possibly with the exception of situations where regulators simply demand that an organization has a strong DM capability. Making a business case is the second challenge and it is a topic that we will address in greater detail in chapter 23. This challenge ties in with the previous one: if people confuse data for systems, then it is hard to argue that the organization should invest in managing its data. One aspect that I would like to mention is this: rather than boiling the ocean¹ it often makes more sense to identify a small area that needs improvement, solve it, and use the “win” as a catalyst to set up the next improvement iteration.

The third challenge is related to building a DM capability that is “just right” for the needs of the organization. In many cases we see that this capability is over-engineered or too focused on implementing tools that will act like a silver bullet and make all the problems go away. The purpose of part II of this book is to show how specific topics in this category can be solved by building the DM capability one step at a time.

■ 3.4 VISUAL SUMMARY

¹ The phrase “boil the ocean” is a colloquialism that refers to taking on an overly large and potentially impossible task given the reality of your resource.