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Energy partnership drills down on data quality issues

Breitburn Energy Partners employed data quality tools to address the business pain of bad data, using the software to give end users the means to fix data quality issues themselves.

It's one thing to clean up data quality issues, and another to keep data clean on an ongoing basis. That is a takeaway...

from Patrick Seals, who has helped direct data quality efforts at oil and gas producer Breitburn Energy Partners.

To accomplish the latter, data quality procedures should be part of everyday business operations in organizations, he advised.

"Data quality is a day-to-day effort. You need tools that clean up data, while also ensuring that business processes are followed [on data entry]," said Seals, vice president and CIO at Breitburn, which is based in Los Angeles.

As part of work to combine diverse systems acquired via mergers, Breitburn implemented an ERP system from Enertia Software for general ledger, electronic invoicing and other operational needs. The system also connects to a data warehouse to support business intelligence and analytics applications.

Seals took over management of the ERP implementation upon joining the company in 2014. It was his job, he said with some bemusement, "to finish up the project and not screw anything up."

After the big bang

After the major "big bang" -- Seals' term for the work of launching a comprehensive ERP system at one time -- was wrapped up in April 2015, Breitburn set about to ensure that data standards defined during the implementation were continuously met by its business users.

We needed a tool that identified problems with data and ... ensured internal folks adhered to the standard.
Patrick Sealsvice president and CIO, Breitburn Energy

There was some give and take in the effort in order to ease the burden on users, Seals said. As part of that process, for example, the Breitburn data team reduced the number of primary attributes required to successfully fill out some business documents.

The data effort was, in part, enabled through the use of Naveego Inc.'s software, which includes data quality and master data management (MDM) tools.

"We needed a tool that identified problems with data and that, as we added new data, ensured internal folks adhered to the standard," Seals said. Naveego's cloud-based technology lets Breitburn's data analysts check for data quality problems in the ERP system and flags errors as data is entered, he noted.

Cross-system comparisons

Naveego last week updated its data quality software, with a release that includes cross-system data comparison capabilities designed to make bad data more visible across diverse systems. Seals said such comparisons are a useful addition to Breiturn's data quality toolbox.

"It easily shows the interrelationships of data and how terms are used in our different systems," he said. Connections to Naveego's MDM software, he added, help data managers at Breitburn highlight the downstream effect that data quality issues can have on business processes.

Cross-system comparison is an important addition for data quality tools like Naveego's, according to Michael Osterman, an independent researcher and consultant. "Now, if you have similar elements in a CRM [customer relationship management] system and in an ERP system, you can quickly compare the data across them," he said.

Osterman added that the functionality Naveego and other vendors provide for building data quality checks into business processes also puts some responsibility for quality at the front end of operations, where he thinks it should be. "It's not just the person working on master data management that is the focus of their efforts. Instead, the software is meant to help the end users out in the field," he said. "Each user has to be empowered to manage the data quality."

Such tools help streamline the process of uncovering data quality issues. Particularly for a company like Breitburn that has been heavily focused on growing via acquisitions, software that ensures employees correctly "fill in the blanks" on data as it's entered into a system is becoming a critical data management component.

That's even more so because Breitburn has encountered some financial challenges in pursuit of its strategy of acquiring, developing and operating oil and gas production properties, a business that has seen its share of ups and downs in recent years. The company, set up as a master limited partnership, currently is restructuring under Chapter 11 in U.S. bankruptcy court.

Next Steps

Explore how data governance helps ensure data integrity

Learn how users can apply data quality rules

Read our guide to data quality assurance

Dig Deeper on Data quality management software

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