Clean data versus more data

Garbage InA SearchCIO article asks “When does more data trump clean data?

The article highlights a common misconception used by the “dirty data” argument, starting with the line “The days of scrubbing data until it’s squeaky clean are quickly becoming a luxury

Data quality is not, and never has been, about data being squeaky clean Tweet this

Data quality is about ensuring that data is fit for purpose

In his response to the question Greg Pfluger, SearchCIO’s expert in this case, illustrates this well with three examples. In one case, data can be of relatively poor quality (high level insights), in the other two information quality is important to ensure that business goals are met.

Adding more data that is not fit for purpose adds complexity, not value. Tweet this

Adding more, poor quality data is good for the storage and analytics vendors but it may not be good for your bottom line.

Pfluger’s conclusion – there is no standard answer for what level of quality is good enough for your business needs, but some level of quality is necessary for most business purposes.

A sound data governance strategy will provide you with the framework to categorise different data according to its use and importance. You can then plan to put the correct levels of data quality in place, based on what is right for you. Data governance has been shown to have a positive impact on big data  analytics success as discussed in Big data, Quality matters

Ensuring successful BI projects is not the only reason to govern big data projects.Tweet this

As discussed in Does poor data governance make you a Target the inappropriate use of big data can have devastating reputational and financial consequences.

The debate is not about More data vs Clean data. It is about the appropriate use of data.

Data governance and data quality are what ensure that your use of data is appropriate

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