Have
you ever heard of Six Sigma? Celebrating its 25th birthday, Six
Sigma is a business management strategy designed to improve product quality by
identifying and eliminating variability in production processes. Six Sigma was
the midwife to the idea of “continual process improvement,” (CPI) which seems
to be the mantra of every manufacturing company in the country (maybe the
world) and many service organizations as well.

The
essential ingredient in Six Sigma and CPI is high quality data. If the data is
flawed, inconsistent, outdated or incomplete, the entire quality-improvement
exercise is essentially pointless. In fact, high quality data is critical to
the improvement of a wide range of processes from manufacturing to marketing,
and from regulation to reputation. And the potential liabilities of low quality
data can be enormous. According to a survey
conducted of 162 CIOs by the consulting firm Accenture, billions of dollars are
lost annually because of faulty enterprise data.

While
the growth of CRM and business intelligence–and the need for companies to adopt
those technologies to remain competitive–have long fueled the need for better
data, new business drivers such a compliance have put additional focus on the
need for data quality improvement. Historically, problems with data quality
were measured in terms of how they affected customer-related issues.

If
names and addresses were incorrect for marketing initiatives, some customers
would never be contacted and some would be contacted repeatedly, sometimes to
the point of considerable annoyance. In general, faulty names and addresses
would generate a whole lot of measurable waste. With compliance, the stakes are
even higher.

And
those kinds of costs are just the tip of the iceberg. Poor quality data can
have a corrosive effect on any organization. Accurate information is critical
to every aspect of an organization’s performance including financial management
and supply chain management; where poor inventory data can lead to out-of-stock
situations and lost sales. And the success of almost any large-scale initiative
depends on high quality data. If the data is not accurate, the costs are easy
to identify–at least in retrospect.

Over
the past five years, companies have begun to realize that
they need to effectively manage their enterprise data holistically and need an
enterprise information management structure. Ironically, however, according to
the Accenture report, few companies have launched company-wide data quality and
data improvement programs. The need to do so is urgent.

 

 


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