Data quality improvement projects require dollars and business sense

Getting executive approval and financing for data quality improvement projects can be a long road. Just ask the folks at iJET Intelligent Risk Systems.

Getting executive approval and financing for data quality improvement projects can be a long and difficult process.

In this section of the Data Quality Software Buyer's Guide, readers will get tips on selling investments in data quality technology to business executives and learn how one IT organization was able to win the approval needed to embark on a data quality initiative.


Table of Contents

Buyer’s Guide: Choosing data quality tools and software
Gartner: Open source data quality software focuses on data profiling
Data quality improvement projects require dollars and business sense
Execute data quality improvement projects with senior-level thinking
Gartner Magic Quadrant ranks data quality tools vendors


The IT workers at Annapolis, Md.-based iJET Intelligent Risk Systems Inc. understand how tough it can be to get executive backing for a major data quality improvement initiative.

A provider of travel risk management services, iJET’s core business involves alerting business travelers to any threats they might face when visiting countries around the globe. If the water is contaminated in Moscow, if there is a terrorist threat in Mumbai or if a tornado is about to touch down in Kansas City, iJET’s job is to let its clients’ business travelers know about it.

The task requires iJET, which was founded in 1999, to collect loads of global threat data and compare it to the travel plans of its clients’ employees, said Richard Murnane, iJET’s enterprise data operations manager. Any pertinent information about potential threats is then automatically related to travelers' itineraries via iJET’s Worldcue Travel Risk Management tool. 

“For a small shop of about 100 people, we have a lot of data,” Murnane explained. “We ingest all of the travel data [as well as] other types of asset data like some supply chain routes, warehouse [data] and all kinds of information that is important to our clients.”

With so much information coming in from so many places, it was clear to Murnane upon joining the company nearly five years ago that a host of minor data quality and duplication issues could eventually mushroom into a bigger problem.

But it would take three summers of product research, testing and convincing -- not to mention a few complaints from customers about duplicate files -- before company executives agreed to finance a sweeping new data quality improvement project.

In the end, Murnane won approval for the initiative in part by explaining to senior management how much time a particular employee was taking to clean and merge duplicate files.

“I was doing metrics for my senior management and [explaining that] I have a senior data analyst who is making whatever dollars per year, and she is spending months cleaning up a thousand records,” Murnane recalled. “But according to the dog and pony shows from [data quality tools vendors], she could be spending a day instead.”

Looking back at that time, Murnane said he understands why senior management was reluctant to sign off on the data quality improvement project for so long.

“We were really focusing on technology issues,” he said. “We were saying: ‘Well, this report shows a person twice.’ And a senior manager said: ‘Well, why do I need to spend a lot of money to fix that?’”

Making the business case for data quality improvement
Murnane’s experiences in trying to get executive buy-in for data quality improvements are not uncommon, according to Rob Karel, a data governance and data quality analyst with Cambridge, Mass.-based Forrester Research Inc. But there are some tips to keep in mind that can ultimately make the road to executive buy-in a less bumpy trip.

The first thing to do is stop evangelizing data to company leaders because they’re generally not interested in data, Karel said. They are, however, interested in making decisions that will make the business a success.

The best way to justify a data quality initiative, he continued, is to figure out how data quality is positively or negatively impacting the ability of the business to increase revenue, improve operational efficiencies, reduce risks and differentiate itself from the competition.

Karel said data management pros can begin this process by asking some key questions, including: What business processes are most important to the organization? What information is used to support those processes? What people, systems and processes create, capture and update that information? What is the level of confidence in using that information?

It’s also a good idea to find the business leader who is most affected by data quality and get them on board first.

“There are plenty of business stakeholders that are screaming about processes [that are broken] because of poor quality data,” Karel said. “Work with them to build a business case instead of trying to convince others that they have a problem when they might not see it.”

Tying data quality improvement to business processes
After the lengthy evaluation process, Murnane’s team narrowed down its data quality vendor options to DataFlux and Harte-Hanks Trillium Software. Trillium had a slight edge on ease of use, but DataFlux offered a better price point and Web service enablement capabilities, according to Murnane.

The company ultimately chose the DataFlux’s Enterprise Integration Server and DataFlux’s profiling, monitoring and data quality desktop toolset. IJET went live with the DataFlux tools about two years ago and Murnane said he is highly pleased with the results.

“We’re really changing the way that iJET manages data,” Murnane said. “And it’s not just the technology; we’ve actually changed business processes as well.”

One of the business processes that iJET was able to improve thanks to DataFlux has to do with how it deals with clients who join forces in a corporate merger. Prior to DataFlux, the process of bringing two clients together in iJET’s database involved paying “a high-priced Java developer,” and it could take up to six months. Nowadays, a data analyst can merge the two companies’ information in a much simpler and less expensive fashion.

“I can actually have a data analyst plug in the data, affect the data in production and move things around very quickly,” Murnane said. “We’re also monitoring for data issues everyday, which is something we weren’t really doing very well [before], and this leads to [fewer] client calls and trouble tickets.”


Table of Contents

Buyer’s Guide: Choosing data quality tools and software
Gartner: Open source data quality software focuses on data profiling
Data quality improvement projects require dollars and business sense
Execute data quality improvement projects with senior-level thinking
Gartner Magic Quadrant ranks data quality tools vendors


 

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