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Your Data Pond Needs Ripples

In his recent post, The Stone Wars of Root Cause Analysis, Jim Harris discussed how using root cause analysis to remove data quality issues is like trying to stop people throwing stones in a pond by taking an endless series of defensive actions, such as blocking paths to the pond, building a high stone-deflecting wall around the pond, removing all of the stones, etc. This approach, no matter how much time, money or energy is expended on it is, ultimately, doomed to failure. A beautiful ripple in a 'data pond'

A radically different and much more successful approach to take towards stone throwers is to welcome them with open arms! The fact is that your data pond needs stones to be thrown.  It is by throwing stones into the data pond that you create the energy to drive the appropriate data into and through the enterprise in order to generate the outcomes that make the enterprise successful.

Every enterprise should welcome stone throwers.  It should warmly invite them and provide them with easy access to the pond and an abundant supply of stones, all of the correct weight and shape.  It should also provide them with an intuitive, easy-to-use and highly efficient means of getting the stone into the pond at the correct speed and angle and show them how, by following all instructions and delivering the stone in the correct way, they will be able to create the most amazing and beautiful wave in the pond every time they do so.

They can also be assured that if they do not follow the instructions, then no harm will be done.  The ripples caused by their misdirected stone will be ignored, as it is only the correct stones delivered in the correct manner that generate ripples at the specific frequency required to bring about the promised, amazing effects.

Pond Architecture

In order to be able to provide such a mutually beneficial experience for the enterprise and those parties with whom it is doing business you will first need to define the architecture for your ‘data pond’.  To do this you will need to know:

  1. What it is that the enterprise ought to be doing.
  2. What information it needs in order to be able to do this.
  3. The elements and structure of the data needed to provide this information.

In order to effectively define 1 above you will need to build a Business Function Model (BFM) for the enterprise. Once you have the BFM, you can then build the Logical Detritus in Data PondData Model (LDM), which will enable you to do 2 and 3.

Data Detritus

If your enterprise does not have both a Business Function Model and a Logical Data Model then you will not need to worry about people creating uncontrolled ripples in you data pond for very long as, without these two essential architectural tools,it will soon be full to the brim with vast amounts of data detritus – a form of invasive ‘Big Data’ perhaps?

Conclusion

By first having a clear and unambiguous picture of what ought to happen in an enterprise, you can ensure that all interactions with the enterprise are fully facilitated and channelled in order to bring about the desired results correctly and efficiently, first time, every time.

This makes Data Quality not just easier to achieve but also far more enjoyable an experience for all concerned.

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