Читать книгу Data Management: a gentle introduction - Bas van Gils - Страница 12
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Synopsis - In this chapter, I will give an overview of why data is one of the key assets of an organization. To achieve this, I will first define the notions of data and asset. Then I will show what it means for data to be an asset. I will do this by stressing the relationship between processes (the “engine” of the organization), and data (the “fuel”) which are both needed to create value. I will illustrate the value of data through two short examples.
■ 2.1 DATA
So far, I have been using the word “data” colloquially without really defining it. Experience shows that people use the word differently so I will explore this concept first. On any such venture, the first step is to check a dictionary. The lemma for data from the Merriam-Webster Dictionary has three definitions:
1. Factual information (such as measurements or statistics) used as a basis for reasoning, discussion, or calculation.
2. Information in digital form that can be transmitted or processed.
3. Information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful.
These definitions are very similar to the way of thinking in the Design & Engineering Methodology for Organizations (DEMO) approach where a distinction is made between three levels of abstraction: forma - being all about documenting/ expressing facts and data; informa - being all about thought and reasoning; and finally performa - being all about using facts and data in the real-world, for example to decide on a course of action [RD99, Die06].
Citing earlier work from the mid 1980s by Appleton, Peter Aiken - one of the eminent writers about DM - positions the term data in relation to other concepts such as facts and information [App86, AG13]. Figure 2.1 summarizes this way of thinking. One of the things that can be learned from this diagram is that data is said to consist of facts which have a meaning. Another important aspect is that data can be used, which shows intelligence. Comparing this approach to the previously cited definition, the question arises whether it is possible, or even useful, to clearly and unambiguously distinguish between the concepts of data and information: the Merriam-Webster Dictionary definition for data heavily relies on the notion of information and vice versa.
Figure 2.1 Fact, data, information and intelligence
For purposes of this book, I will not make a hard distinction between the two concepts. I will use the term data as an umbrella term, meaning all three definitions from the Merriam-Webster Dictionary. Even more, I intend to use it both as the “raw ingredient” (data codified in systems) and how it is used in business processes (sometimes called “information” by other authors). I will expand on this discussion further in chapter 6. Example 2 clarifies this way of thinking further.
Example 2. Data management benefits
Suppose you are an avid runner, like me. Your coach has explained that your heart rate provides a good indicator of how your body is doing and that it should be used to guide your bi-weekly training sessions. After purchasing a heart rate monitor, you go out for your first run.
During your run, you can check your new gadget. It will measure how you are doing and individual data points are shown as you go along. Presumably, the gadget will also store this data, so that it can later be transferred to some online application for further processing. Together with your coach, you can use this data to analyze your fitness and training schedule for weeks to come.
■ 2.2 ASSET
As stated in the opening paragraph of chapter 1: it is often said that “data is an asset”. For example, the DMBOK states [Hen17]:
Data and information are not just assets in the sense that organizations invest in them in order to derive future value. Data and information are also vital to the day-to-day operations of most organizations. They have been called the “currency”, the “life blood” and even the “new oil” of the information economy. Whether or not an organization gets value from its analytics, it cannot even transact business without data.
The question that needs to be answered is: what is an asset? Relying once more on the dictionary, an asset can be defined as “an item of value that is owned or possessed”. Let’s explore that further through the cases listed in example 3.
Example 3. Examples of assets
Assume the asset is a car. It has different types of value to me: it gets me from A to B, but it also has monetary value. Now assume that the asset is money. Its value is in the security that I have some buying power to take care of myself. Finally assume that the asset is customer data. Its value is that I know who my customers are, where they live and what they have purchased in the past so that I can help them well in the future.
The examples show that assets can be tangible or intangible. They also show that assets have value. The latter point deserves further exploration. In previous research, I have shown that value is both personal (one person may see it differently than another person) and situational (in one situation it may be worth more than another) [Gil06]. Again, two small examples illustrate the point:
Example 4. Value of assets
The first example pertains to art. Let’s take a famous painting such as White on White1 by Kazimir Malevich. Some will claim it priceless, whereas others will claim it to be something so simple that a five-year-old can create it. Both observers, of course, are correct. This shows the personal nature of the valuation of assets.
The second example pertains to the value of water when compared to money. In most cases, I would value $10 over a small bottle of water. When standing in the middle of the desert, though, I may think differently. This shows the situational nature of valuation of assets.
1 https://en.wikipedia.org/wiki/White_on_White, last checked 2 June 2019.
The implications for data as an asset are clear: when we say that we consider data to be an important asset then we mean that we believe that the data in our systems has much value, either intrinsic (we have data that is worth money, for example if we sell it) or indirectly (which means we can use it in our processes to create value). This, finally, brings us to the relationship between data and business processes.
Before we dive into this relationship, there is one point that should be made. There is a big distinction between data assets and tangible assets: there is only one copy of a tangible asset but this doesn’t have to be the case for (intangible) data assets. To put it differently: you can make as many copies of data assets as you like without affecting the original. If this were the case for physical assets then we would all be as rich as Croesus for sure. This property of data is important in chapters to come when we talk about storing, using, transferring, and managing data.
■ 2.3 DATA AND PROCESS
This brings me to the final part of this chapter: the relationship between data and process. It is safe to say that data does not magically spring into existence. On the contrary: creating data takes effort by business professionals, for example by adding data into computer systems or by manipulating existing data to create new data.
The fact that we are not so (consciously) aware of this is not surprising. Years ago – before the computer era – a lot of our data sat in paper files and records. Creating data meant getting in there and updating the files. More data meant more paper. More paper meant more space required to store the data. This, eventually, lead to bigger and bigger libraries1. In the computer-age this is different: most data is now stored digitally and adding more bits and bytes requires very little extra physical space.
Producing data in business processes is useful in itself. Things become more interesting when we consider where else that data can be used/ where else data can be put to good use to create value. Example 5 illustrates this point.
Example 5. Data and processes
Suppose you work at a company that leases expensive medical equipment to hospitals. Each time the company closes a new deal with a hospital, its records are updated (new data is added to their systems). The value of this data is that it proves that the transaction took place and that the company is owed a certain fee each time.
The data is likely to be used in other parts of the company as well. For example, sales and marketing representatives are interested in the data to investigate whether they can cross-sell insurance products with the newly leased equipment, whilst management will be interested in monthly sales reports to see how well the company is doing.
This example illustrates a point that I cannot make enough: there is a strong relationship between business processes and data (see e.g. [BRS19] for a recent discussion of this topic, bridging the gap between research and practice). Data without use in processes has no value. Processes without data cannot happen: if processes are the value creation engine of the organization, then data is its fuel. As a corollary of this discussion, this book will also have much say about processes and not just about data.
Data can only be used if it is of the right quality and can be found. The former point is easily understood: just like poor materials will likely lead to the construction of a poor physical asset, so does poor data lead to poor process performance. The latter point requires a bit more explanation. The general thinking seems to be: our data is stored in our systems and we know which systems we have – so how hard can it be to find out data? Example 6 shows that in practice this may not be as easy as it seems.
Example 6. Finding data
Let’s go back to the library case that was mentioned previously. Libraries are structured in such a way that, by and large, it should be straightforward to find the books and articles that you need. In the old days this was done through extensive cataloguing, classification, and index systems. These days all of this is automated1. It is true that in most organizations all data is stored electronically in systems. In theory it should be easy to find. However, do you have any idea how many systems your organization has for storing data about customers or products? Chances are there are dozens! Finding the right information for use is one of the key challenges for many organizations.
1 If you want to know more about information retrieval, consider reading e.g. [Pai99] - which also has a good historical overview.
The point that this example tries to make is that data is often dispersed across many systems which makes it harder to locate the right data for the right person doing his/ her job at the right time. This, in turn, shows that the value of data depends on more than it being a correct representation of the real-world: being able to use it in processes in a timely manner might be just as important. If your data is “correct” but it can’t be found in time to be used in a process then, in fact, its value is very low, or even zero.
