It was the article’s title that caught my attention – the “7 Habits of Highly Effective
Data Quality
” by William Sharp. I read Stephen Covey’s “The 7 Habits of
Highly Effective People” a while back, so I was curious to read Sharp’s take on
aligning Covey’s “true north” principles to data quality. Could it point the
way to success?

Here’s a look at a few of the habits and how they apply to data
quality management.


Be Proactive

Sharp’s point – be proactive and not reactive on data quality. I
couldn’t agree more with his analysis. “Proactive data quality means
identifying and remediating data quality issues before they become proliferated
throughout the enterprise,” Sharp states in his article.

This sounds a lot like the data quality firewall concept we’ve
been stressing for years. Think of bad data as a virus or malware trying to
infect your database. A data quality firewall will block bad data at the point
of entry.

It is much easier to correct data issues upfront before they enter
your database. Why? Just think of the 1-10-100 rule.

According to the 1-10-100 rule (promulgated by our own Bud Walker
in “The Real Cost of Bad Data: The 1-10-100 Rule’) – if data verification is
done as the contact information is submitted, it only costs $1.00. If you wait
to clean it in batch, the cost goes up to $10. And if you do nothing, the cost
is $100 – due to returned mail, lost opportunities, delayed shipments, etc.

The bottom line – it’s cheaper to be proactive on data quality at
the beginning of your data collection process, than much later.  


Seek First to
Understand, Then to Be Understood

Sharp states in his article, “You can’t be affective at solving a
problem without first knowing what it is.” That’s a great point. How can you
understand what your data quality issues are – if you don’t know where the problems
are in the first place?

That’s why we developed a free Data Quality Report that will
analyze a sample of your data and identify how much incorrect or outdated
contact information is in your database. Knowing your issues up front will help
you develop an effective data quality initiative.

Click here for more
info.


Conclusion

Sharp’s article brought up a lot of great points about how you
should align your data quality initiatives – just like you would your personal
life. By putting some guiding principles into place, you can effectively attain
your goals for success.

To read all 7 habits in Sharp’s article, click here.

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