Out damn’d SPOT: an essay on data, information and truth in organisations

Introduction

Jack: My report tells me that we are on track to make budget this year.

Jill: That’s strange, my report tells me otherwise

Jack: That can’t be. Have you used the right filters?

Jill: Yes – the one’s you sent me yesterday.

Jack: There must be something else…my figures must be right, they come from the ERP system.

Jill: Oh, that must be it then…mine are from the reporting system.

Conversations such as the one above occur quite often in organisation-land.  It is one of the reasons why organisations chase the holy grail of a single point of truth (SPOT): an organisation-wide repository that holds the officially endorsed true version of data, regardless of where it originates from. Such a repository is often known as an Enterprise Data Warehouse (EDW).

Like all holy grails, however, the EDW, is a mythical object that exists in only in the pages of textbooks (and vendor brochures…). It is at best an ideal to strive towards. But, like chasing the end of a rainbow it is an exercise that may prove exhausting and ultimately, futile.

Regardless of whether or not organisations can get to that mythical end of the rainbow – and there are those who claim to have got there – there is a deeper issue with the standard view of data and information that hold sway in organisation-land.   In this post I examine these standard conceptions of data and information and truth, drawing largely on this paper by Bernd Carsten Stahl and a number of secondary sources.

Some truths about data and information

As Stahl observes in his introduction:

Many assume that information is central to managerial decision making and that more and higher quality information will lead to better outcomes. This assumption persists even though Russell Ackoff argued over 40 years ago that it is misleading…

The reason for the remarkable persistence of this incorrect assumption is that there is a lack of clarity as to what data and information actually are.

To begin with let’s take a look at what these terms mean in the sense in which they are commonly used in organisations. Data typically refers to raw, unprocessed facts or the results of measurements. Information is data that is imbued with meaning and relevance because it is referred to in a context of interest. For example, a piece of numerical data by itself has no meaning – it is just a number. However, its meaning becomes clear once we are provided a context – for example, that the number is the price of a particular product.

The above seems straightforward enough and embodies the standard view of data and information in organisations. However, a closer look reveals some serious problems. For example, what we call raw data is not unprocessed – the data collector always makes a choice as to what data will be collected and what will not. So in this sense, data already has meaning imposed on it. Further, there is no guarantee that what has been excluded is irrelevant. As another example, decision makers will often use data (relevant or not) just because it is available. This is a particularly common practice when defining business KPIs – people often use data that can be obtained easily rather than attempting to measure metrics that are relevant.

Four perspectives on truth

One of the tacit assumptions that managers make about the information available to them is that it is true.  But what exactly does this mean?  Let’s answer this question by taking a whirlwind tour of some theories of truth.

The most commonly accepted notion of truth is that of correspondence, that a statement is true if it describes something as it actually is.  This is pretty much how truth is perceived in business intelligence: data/information is true or valid if it describes something – a customer, an order or whatever – as it actually is.

More generally, the term correspondence theory of truth refers to a family of theories that trace their origins back to antiquity. According to Wikipedia:

Correspondence theories claim that true beliefs and true statements correspond to the actual state of affairs. This type of theory attempts to posit a relationship between thoughts or statements on one hand, and things or facts on the other. It is a traditional model which goes back at least to some of the classical Greek philosophers such as Socrates, Plato, and Aristotle. This class of theories holds that the truth or the falsity of a representation is determined solely by how it relates to a reality; that is, by whether it accurately describes that reality.

One of the problems with correspondence theories is that they require the existence of an objective reality that can be perceived in the same way by everyone. This assumption is clearly problematic, especially for issues that have a social dimension. Such issues are perceived differently by different stakeholders, and each of these will legitimately seek data that supports their point of view. The problem is that there is often no way to determine which data is “objectively right.” More to the point, in such situations the very notion of “objective rightness” can be legitimately questioned.

Another issue with correspondence theories is that a piece of data can at best be an abstraction of a real-world object or event.  This is a serious issue with correspondence theories in the context of data in organisations. For example, when a sales rep records a customer call, he or she notes down only what is required by the customer management system. Other data that may well be more important is not captured or is relegated to a “Notes” or “Comments” field that is rarely if ever searched or accessed.

Another perspective is offered by the so called consensus theories of truth which assert that true statements are those that are agreed to by the relevant group of people. This is often the way truth is established in organisations. For example, managers may choose to calculate Key Performance Indicators (KPIs )using certain pieces of data that are deemed to be true.  The problem with this is that consensus can be achieved by means that are not necessarily democratic. For example, a KPI definition chosen by a manager may be hotly contested by an employee.  Nevertheless, the employee has to accept it because organisations are typically not democratic. A more significant issue is that  the notion of “relevant group” is problematic because there is no clear criterion by which to define relevance.

Pragmatic theories of truth assert that truth is a function of utility – i.e. a statement is true if it is useful to believe it is so. In other words, the truth of a statement is to be judged by the payoff obtained by believing it to be true.  One of the problems with these theories is that it may be useful for some people to believe in a particular statement while is useful for others to disbelieve it. A good example of such a statement is: there is an objective reality. Scientists may find it useful to believe this whereas social constructionists may not. Closer home, it may be useful for a manager to believe that a particular customer is a good prospect (based on market intelligence, say), but a sales rep who knows the customer is unlikely to switch brands may think it useful to believe otherwise.

Finally, coherence theories of truth tell us that statements that are true must be consistent with a wider set of beliefs. In organisational terms, a piece of information or data that is true only if it does not contradict things that others in the organisation believe to be true. Coherence theories emphasise that the truth of statements cannot be established in isolation but must be evaluated as part of a larger system of statements (or beliefs). For example, managers may believe certain KPIs to be true because they fit in with other things they know about their business.

…And so to conclude

The truth is a slippery beast: what is true and what is not depends on what exactly one means by the truth and, as we have seen, there are several different conceptions of truth.

One may well ask if this matters from a practical point of view.  To put it plainly: should executives, middle managers and frontline employees (not to mention business intelligence analysts and data warehouse designers) worry about philosophical theories of truth?  My contention is that they should, if only to understand that the criteria they use for determining the validity of their data and information are little more than conventions that are easily overturned by taking other, equally legitimate, points of view.