Eight to Late

What is business intelligence?

I recently asked a few acquaintances to answer this question without referring to that great single point of truth in the cloud.  They duly came up with a variety of  responses ranging from data warehousing and the names of specific business intelligence tools to particular functions such as reporting or decision support.

After receiving their responses, I did what I asked my respondents not to: I googled the term.  Here are a few samples of what I found:

According to CIO magazine, Business intelligence is an umbrella term that refers to a variety of software applications used to analyze an organization’s raw data.

Wikipedia, on the other hand, tells us that BI is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes.

Finally, Webopedia, tell us that BI [refers to] the tools and systems that play a key role in the strategic planning process of the corporation.

What’s interesting about the above responses and definitions is that they focus largely on processes and methodologies or tools and techniques. Now, without downplaying the importance of either, I think that many of the problems of business intelligence practice come from taking a perspective that is overly focused on methodology and technique.  In this post, I attempt to broaden this perspective by making some potentially controversial statements –or heresies – that challenge this view. My aim is not so much to criticize current practice as to encourage – or provoke – business intelligence professionals to take a closer look at some of the assumptions underlie their practices.

The heresies

Without further ado, here are my six heresies for business intelligence practice (in no particular order).

A single point of truth is a mirage

Many organisations embark on ambitious programs to build enterprise data warehouses – unified data repositories that serve as a single source of truth for all business-relevant data.  Leaving aside the technical  and business issues associated with establishing definitive data sources and harmonizing data, there is the more fundamental question of what is meant by truth.

The most commonly accepted notion of truth is that information (or data in a particular context) is true if it describes something as it actually is. A major issue with this viewpoint is that data (or information) can never fully describe a real-world object or event. 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. Indeed, data represents only a fraction of the truth, however one chooses to define it – more on this below.

Some might say that it is naïve to expect our databases to capture all aspects of reality, and that what is needed is a broad consensus between all relevant stakeholders as to what constitutes the truth. The problem with this is that such a consensus is often achieved by means that are not 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 that is the way (many) organisations work. Another significant issue is that the notion of relevant stakeholders is itself problematic because it is often difficult to come up with clear criterion by which to define relevance.

There are other ways to approach the notion of truth: for example, one might say that a piece of data is true as long as it is practically useful to deem it so. Such a viewpoint, though common, is flawed because utility is in the eye of the beholder: a sales manager may think it useful to believe a particular KPI whereas a sales rep might disagree (particularly if the KPI portrays the rep in a bad light!).

These varied interpretations of what constitute a truth have implications for the notion of a single point of truth. For one, the various interpretations are incommensurate – they cannot be judged by the same standard.  Further, different people may interpret the same piece of data differently. This is something that BI professionals have likely come across – say when attempting to come up with a harmonized definition for a customer record.

In short: the notion of a single point of truth is problematic because there is a great deal of ambiguity about what constitutes a truth.

There is no such thing as raw data

In his book, Memory Practices in the Sciences, Geoffrey Bowker wrote, “Raw data is both an oxymoron and a bad idea; to the contrary, data should be cooked with care.”  I love this quote because it tells a great truth (!) about so-called “raw” data.

To elaborate: raw data is never unprocessed. Firstly, 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. Second, and perhaps more important, the method of collection affects the data. For example, responses to a survey depend on how the questions are framed and how the survey itself is carried out (anonymous, face-to-face etc.).   This is also true for more “objective” data such as costs and expenses. In both cases, the actual numbers depend on specific accounting practices used in the organization. So, raw data is an oxymoron because data is never raw, and as Bowker tells us, we need to ensure that the filters we apply and the methods of collection we use are such that the resulting data is “cooked with care.”

In short: data is never raw, it is always “cooked.”

There are no best practices for business intelligence, only appropriate ones

Many software shops and consultancies devise frameworks and methodologies for business intelligence which they claim are based on best or proven practices. However, those who swallow that line and attempt to implement the practices often find that the results obtained are far from best.

I have discussed the shortcomings of best practices in a general context in an earlier article, and (at greater length) in my book. A problem with best practice approaches is that they assume a universal yardstick of what is best.  As a corollary, this also suggest that practices can be transplanted from one organization to another in a wholesale manner, without extensive customisation. This overlooks the fact that organisations are unique, and what works in one may not work in another.

A deeper issue is that much of the knowledge pertaining to best practices is tacit – that is, it cannot be codified in written form. Indeed, what differentiates good business intelligence developers or architects from great ones is not what they learnt from a textbook (or in a training course), but how they actually practice their craft.  These consist of things that they do instinctively and would find hard to put into words.

So, instead of looking to import best practices from your favourite vendor, it is better to focus on understanding what goes on in your environment. A critical examination of your environment and processes will reveal opportunities for improvement. These incremental improvements will cumulatively add up to your very own, customized “best practices.”

In short: develop your own business intelligence best practices rather than copying those peddled by “experts.”

Business intelligence does not support strategic decision-making

One of the stated aims of business intelligence systems is to support better business decision making in organisations (see the Wikipedia article, for example). It is true that business intelligence systems are perfectly adequate – even indispensable – for certain decision-making situations. Examples of these include, financial reporting (when done right!) and other operational reporting (inventory, logistics etc).  These generally tend to be routine situations with clear cut decision criteria and well-defined processes – i.e. decisions that can be programmed.

In contrast, decisions pertaining to strategic matters cannot be programmed. Examples of such decisions include: dealing with an uncertain business environment, responding to a new competitor etc. The reason such decisions cannot be programmed is that they depend on a host of factors other than data and are generally made in situations that are ambiguous.  Typically people use deliberative methods – i.e. methods based on argumentation – to arrive at decisions on such matters.  The sad fact is that all the major business tools in the market lack support for deliberative decision-making. Check out this post for more on what can be done about this.

In short: business intelligence does not support strategic decision-making .

Big data is not the panacea it is trumpeted to be

One of the more recent trends in business intelligence is the move towards analyzing increasingly large, diverse, rapidly changing datasets – what goes under the umbrella term big data.  Analysing these datasets entails the use of new technologies (e.g. Hadoop and NoSQL)  as well as statistical techniques that are not familiar to many mainstream business intelligence professionals.

Much has been claimed for big data; in fact, one might say too much.  In this article Tim Harford (aka the Undercover Economist) summarises the four main claims of “big data cheerleaders” as follows (the four phrases below are quoted directly from the article):

1. Data analysis produces uncannily accurate results.
2. Every single data point can be captured, making old statistical sampling techniques obsolete.
3. It is passé to fret about what causes what, because statistical correlation tells us what we need to know.
4. Scientific or statistical models aren’t needed.

The problem, as Harford points out, is that all of these claims are incorrect.

Firstly, the accuracy of the results that come out of a big data analysis depend critically on how the analysis is formulated. However, even analyses based on well-founded assumptions can get it wrong, as is illustrated in this article about Google Flu Trends.

Secondly, it is pretty obvious that it is impossible to capture every single data point (also relevant here is the discussion on raw data above – i.e. how data is selected for inclusion).

The third claim is simply absurd. The fact is detecting a correlation is not the same as  understanding what is going on – a point made rather nicely by Dilbert.  Enough said, I think.

Fourthly, the claim that scientific or statistical models aren’t needed is simply ill-informed. As any big data practitioner will tell you, big data analysis relies on statistics. Moreover, as mentioned earlier, a correlation-based understanding is no understanding at all –  it cannot be reliably extrapolated to related situations without the help of hypotheses and (possibly tentative)  models of how the phenomenon under study works.

Finally, as Danah Boyd and Kate Crawford point out in this paper , big data changes the meaning of what it means to know something….and it is highly debatable as to whether these changes are for the better. See the paper for more on this point. (Acknowledgement: the title of this post is inspired by the title of the Boyd-Crawford paper).

In short:  business intelligence practitioners should not uncritically accept the pronouncements of big data evangelists and vendors.

Business intelligence has ethical implications

This heresy applies to much more than business intelligence: any human activity that affects other people has an ethical dimension. Many IT professionals tend to overlook this facet of their work because they are unaware of it – and sometimes prefer to remain so. Fact is, the decisions business intelligence professionals make with respect to usability, display, testing etc. have a potential impact on the people who use their applications. The impact may be as trivial as having to click a button or filter too many before they get their report, to something more significant, like a data error that leads to a poor business decision.

In short: business intelligence professionals ought to consider how their artefacts and applications affect their users.

In closing

This brings me to the end of my heresies for business intelligence. I suspect there will be a few practitioners who agree with me and (possibly many) others who don’t…and some of the latter may even find specific statements provocative. If so, I consider my job done, for my intent was to get business intelligence practitioners to question a few unquestioned tenets of their profession.