[BreachExchange] Why CXOs ignore data quality problems

[Audrey McNeil]

http://www.techrepublic.com/article/why-cxos-ignore-data-quality-problems/

There is virtually no business without a data quality problem somewhere. It
might be an incomplete record of a service call, an erroneous name or
address spelling of a customer, or even an errant billing. When data
quality issues like these arise, they are usually the result of human error
in systems of record that have operated for many years. C-level executives
and line managers tolerate them, and in most cases, the data quality
problems are simply worked around and forgotten.

Now with the influx of vast piles of unstructured big data, these
historical data quality problems have multiplied exponentially—but the
C-level response of relative indifference remains. Should it?

As a big data industry analyst and commentator, the logical answer for me
is no. However, there are there are actually myriad reasons why CXOs feel
justified ignoring data quality issues.

To start, there are the ways in which CXOs see big data. Typically their
view is through dashboard eyeshots of how their business operations and/or
sales are going. It's also through the milestones achieved in new big data
initiatives undertaken by their staff, that CXOs must report to their
boards.

In a nutshell, these C-level execs are looking for project completion
milestones and results. They aren't focusing on whether their data analysts
are spending 75 percent of their time cleaning up data to prepare it for
analytics. And a new generation of CXOs who have grown up with Internet and
rapid app prototyping are more than comfortable if an app doesn't work
correctly 100 percent of the time—as long as it basically works.

Another one of the reasons data quality gets ignored is because it lacks
urgency. CXOs are asked to keep the company profitable, to build the
company's brand, and to return benefits to stakeholders. CXO performance
evaluations, compensation and boardroom conversations revolve around this.
Along with these obligations come all of the daily responsibilities that
CXOs must address. If equipment breaks on the manufacturing floor, or if
safety conditions at a company facility are challenged by regulators, CXOs
must take charge. If a major customer is disappointed at a botched order,
the CEO might have to get on a plane and personally visit the customer to
get the situation back on track. It is hard for CXOs to worry about data
quality when these major daily exigencies must come first.

What happens is that big data (and other data) initiatives move forward
until the flawed data prevents migration to a new system, or something
catastrophic happens, like a data breach or an errant forecast produced by
poor quality data. An example of this was the faulty Google flu forecast,
which was based on errant data and assumptions.

When data miscues like this happen (and Google is not alone in this),
that's what grabs the attention of C-level executives because their boards
and their stakeholders are there demanding answers.

In a recent visit I had with Dan Ortega, vice president of marketing for
data intelligence and accuracy solutions company Blazent, Ortega said that
improvement in company data initiatives must "come from the top down, from
the level of the CEO or the general manager."

He's right. The best thing that CXOs can do is to avoid getting into these
dire situations. They need to acknowledge that their already hectic
schedules cannot absorb additional attention to issues like data quality so
they can find ways to avoid embarrassing situations that can compromise
corporate revenues, performance, or brands.

One proactive step that can be taken is to assign data quality to the
corporate risk management function that already shocks loan portfolios in
financial services firms (to check them for stability), weighs the
possibilities of supply chain disruptions for shippers and retailers, or
evaluates the results of potential economic downturns and/or regulatory and
product failures. Risk management is a logical place to add data
quality—and it is likely to become even more important if data one day
becomes a tangible asset that is valued on corporate balance sheets.

Will elevating the stature of data quality by placing it within the
corporate risk management function solve every data quality issue? Of
course not. But by making data quality a new risk management category, it
will assist CXOs and those who work for them in developing a methodology
where data quality can be kept under the scope in the same way that
earnings, revenue generation, operational costs and brand performance are.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.riskbasedsecurity.com/pipermail/breachexchange/attachments/20160225/529a5624/attachment.html>