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Nine Laws of Data Mining, part 1


Tom Khabaza, one of the authors of the Clementine data mining workbench and of CRISP-DM methodology for data mining process, proposes and explains 9 laws of data mining.



By Tom Khabaza.

This content was created during the first quarter of 2010 to publish the “Nine Laws of Data Mining”, which explain the reasons underlying the data mining process.  If you prefer brevity, see my tweets: @tomkhabaza.  Also, see the “9 Laws of Data Mining” subgroup of the CRISP-DM LinkedIn group for a discussion forum.  The 9 Laws are also expressed as haikus here. Tom is also a founding chairman of the Society of Data Miners.

Data mining is the creation of new knowledge in natural or artificial form, by using business knowledge to discover and interpret patterns in data.

In its current form, data mining as a field of practise came into existence in the 1990s, aided by the emergence of data mining algorithms packaged within workbenches so as to be suitable for business analysts.  Perhaps because of its origins in practice rather than in theory, relatively little attention has been paid to understanding the nature of the data mining process.  The development of the CRISP-DM methodology in the late 1990s was a substantial step towards a standardised description of the process that had already been found successful and was (and is) followed by most practising data miners.

CRISP-DM
Although CRISP-DM describes how data mining is performed, it does not explain what data mining is or why the process has the properties that it does.  In this paper I propose nine maxims or “laws” of data mining (most of which are well-known to practitioners), together with explanations where known.  This provides the start of a theory to explain (and not merely describe) the data mining process.

It is not my purpose to criticise CRISP-DM; many of the concepts introduced by CRISP-DM are crucial to the understanding of data mining outlined here, and I also depend on CRISP-DM’s common terminology.  This is merely the next step in the process that started with CRISP-DM.

1st Law of Data Mining – “Business Goals Law”:

         Business objectives are the origin of every data mining solution 

This defines the field of data mining: data mining is concerned with solving business problems and achieving business goals.  Data mining is not primarily a technology; it is a process, which has one or more business objectives at its heart.  Without a business objective (whether or not this is articulated), there is no data mining.

Hence the maxim: “Data Mining is a Business Process”.

2nd Law of Data Mining – “Business Knowledge Law”:

Business knowledge is central to every step of the data mining process

This defines a crucial characteristic of the data mining process.  A naive reading of CRISP-DM would see business knowledge used at the start of the process in defining goals, and at the end of the process in guiding deployment of results.  This would be to miss a key property of the data mining process, that business knowledge has a central role in every step.

For convenience I use the CRISP-DM phases to illustrate:

  • Business understanding must be based on business knowledge, and so must the mapping of business objectives to data mining goals. (This mapping is also based on data knowledge data mining knowledge).
  • Data understanding uses business knowledge to understand which data is related to the business problem, and how it is related.
  • Data preparation means using business knowledge to shape the data so that the required business questions can be asked and answered.  (For further detail see the 3rd Law – the Data Preparation law).
  • Modelling means using data mining algorithms to create predictive models and interpreting both the models and their behaviour in business terms – that is, understanding their business relevance.
  • Evaluation means understanding the business impact of using the models.
  • Deployment means putting the data mining results to work in a business process.

In summary, without business knowledge, not a single step of the data mining process can be effective; there are no “purely technical” steps.  Business knowledge guides the process towards useful results, and enables the recognition of those results that are useful.  Data mining is an iterative process, with business knowledge at its core, driving continual improvement of results.

The reason behind this can be explained in terms of the “chasm of representation” (an idea used by Alan Montgomery in data mining presentations of the 1990s).  Montgomery pointed out that the business goals in data mining refer to the reality of the business, whereas investigation takes place at the level of data which is only a representation of that reality; there is a gap (or “chasm”) between what is represented in the data and what takes place in the real world.  In data mining, business knowledge is used to bridge this gap; whatever is found in the data has significance only when interpreted using business knowledge, and anything missing from the data must be provided through business knowledge.  Only business knowledge can bridge the gap, which is why it is central to every step of the data mining process.

3rd Law of Data Mining – “Data Preparation Law”:

 Data preparation is more than half of every data mining process

 It is a well-known maxim of data mining that most of the effort in a data mining project is spent in data acquisition and preparation.  Informal estimates vary from 50 to 80 percent.  Naive explanations might be summarised as “data is difficult”, and moves to automate various parts of data acquisition, data cleaning, data transformation and data preparation are often viewed as attempts to mitigate this “problem”.  While automation can be beneficial, there is a risk that proponents of this technology will believe that it can remove the large proportion of effort which goes into data preparation.  This would be to misunderstand the reasons why data preparation is required in data mining.

 The purpose of data preparation is to put the data into a form in which the data mining question can be asked, and to make it easier for the analytical techniques (such as data mining algorithms) to answer it.  Every change to the data of any sort (including cleaning, large and small transformations, and augmentation) means a change to the problem space which the analysis must explore.  The reason that data preparation is important, and forms such a large proportion of data mining effort, is that the data miner is deliberately manipulating the problem space to make it easier for their analytical techniques to find a solution.

 There are two aspects to this “problem space shaping”.  The first is putting the data into a form in which it can be analysed at all – for example, most data mining algorithms require data in a single table, with one record per example.  The data miner knows this as a general parameter of what the algorithm can do, and therefore puts the data into a suitable format.  The second aspect is making the data more informative with respect to the business problem – for example, certain derived fields or aggregates may be relevant to the data mining question; the data miner knows this through business knowledge and data knowledge.  By including these fields in the data, the data miner manipulates the search space to make it possible or easier for their preferred techniques to find a solution.

 It is therefore essential that data preparation is informed in detail by business knowledge, data knowledge and data mining knowledge.  These aspects of data preparation cannot be automated in any simple way.

This law also explains the otherwise paradoxical observation that even after all the data acquisition, cleaning and organisation that goes into creating a data warehouse, data preparation is still crucial to, and more than half of, the data mining process.  Furthermore, even after a major data preparation stage, further data preparation is often required during the iterative process of building useful models, as shown in the CRISP-DM diagram.