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One of the major barriers to  improving the maturity of Data Quality in enterprises around the globe is the mistaken belief, by far too many DQ practitioners, that good data creates a good enterprise.

The reality is quite the reverse of this. It is a good enterprise that creates good data. Good data is the output of the effective execution of the Business Functions of the enterprise.  Good data is a major indicator of the health of an enterprise – it is not a driver of it!!! 

Data Dialysis Insanity

Putting healthy data into an unhealthy enterprise in an effort to make it healthy - insanity?

The current approach to Data Quality of finding and correcting data defects in an effort to make an enterprise ‘healthier’, is about as effective as trying to turn an unfit and unhealthy body into a fit, healthy one by removing its unhealthy blood,  running it through a dialysis machine and returning it to the body.  In the medical world, this would be seen as an expensive (and insane) waste of  time, blood and money that would in no way improve the fitness of the body and, ultimately, result in the degradation and loss of blood and, not long after that, the death of the body.

Data dialysis, is slowing killing those enterprises that practice it.

Quality Assurance

The world of manufacturing many years ago moved away from the archaic practice of Quality Control. Manufacturers at last realised how insane it was to spend time and money creating defective products and then more time and money trying to find and remove these defects. However, for some bizarre reason, the world of Data Quality has remained locked in this outmoded and totally ineffective Quality Control time warp, doomed to perpetually fail to deliver any real long-term benefits to the enterprises that practice it.

Nothing changes without change.  Until those involved in Data Quality come to realise that the only way to achieve genuine, sustainable Data Quality is to practice Data Quality Assurance, then nothing will change. Data Quality Assurance is all about getting it right first time every time. It is all about executing all Business Functions in such a manner that  all data created and transformed by their execution is done so correctly first time, every time.

Zero Is Just a Number

The world of manufacturing was not always as enlightened as it is now.  The concept of zero defects, of manufacturing things in such a way as to get them right first time, every time, seemed impossible to many enterprises. History shows that in some enterprises the conversations had to begin with phrases like, “Suppose we could show you ways to manufacture a car with just  fifteen defects, would you be interested?” When these enterprises realised that fifteen was just a number, they then realised that they could reduce it all the way down to zero.

This realisation has yet to come about in the world of Data Quality. Zero is just a number. At the moment creating 1000 defects a day might be acceptable in an enterprise. That enterprise might well see a reduction of this number of defects to 500 a day as being desirable and achievable, while at the same time seeing a reduction to zero as being impossible. However, when they  begin using Quality Assurance techniques and get to 500 defects a day, they will also begin to see 250 defects a day as both desirable and achievable. They are one step closer to seeing the number zero as being both desirable and achievable.

It can be done. But not until DQ practitioners change from practising Quality Control to  practising Quality Assurance.

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One Response to “Is Data Dialysis Killing Your Enterprises?”

  1. Milan Kucera

    I agree with this article. In my country most of the data quality projects is based on implementation of massive inspections and automate data correction during distribution data across the enterprise. This approach is incorrect and against the principle of quality management ( the 3rd Deming quality point). I think it is as well the reason why data quality is understand as cost activity increasing costs of business process, decreasing effectiveness of those and etc.

    Monitoring and measuring of a real business benefits from this type of project is missing. Who measures the costs of poor quality?

    It looks like data quality does not accept sound principles of quality management and things that these are not valid for this area.

    “Zero deffect”? It is the same or similar story. Reaching “zero defect” is quite complicated and from my view impossible. It is nice to have “zero defect” data but at this moment we can ask: is the valid record and accurate record? I know many examples where it was not true. And the second question is: What is the benefits of valid but inaccure records to business? I think – it is a null.

    We shoudl return to sound principles of quality management and correctly apply these on data.

    To a high IQ! (the slogan of Larry P. English)


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