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Interview with David Loshin: The Data Revolution

October 7, 2012


~by Paul Harris

Meet David Loshin, president of Knowledge Integrity, Inc. David is a recognized thought leader and expert consultant in the areas of data quality, master data management, and business intelligence (BI). David is a prolific author regarding BI best practices, via the expert channel at and numerous books and papers on data quality.

Recently, Global-Z had the unique opportunity to interview Loshin and discuss issues that are changing the way businesses think about data.

Global-Z – Everyone talks about the term “Big Data.” How would you define it from a modern perspective?

Loshin- The challenge for defining something like “big data” is to share thoughts that clarify the (now institutionalized) messaging that focuses on “volume, velocity, and variety.” My perspective is that big data reflects a capability for capturing many different large data sets for archival or analytical purposes using scalable technologies and techniques that are no longer beyond the capabilities of most enterprises. An example is the use of open source tools for harnessing commodity hardware to provide a scalable parallel computational resource for analyzing multiple large data sets for customer profiling. There are other aspects such as creating massive elastic data stores. There are lots of opportunities, and it will be interesting to see how more organizations take on big data within the enterprise.

Global-Z – What reoccurring mistakes do most organizations make when they begin a data quality (DQ) program?

Loshin- One of the biggest issues I see in initiating a data quality program is the perception that tools are going to solve data quality problems. In many cases, organizations evaluate and buy tools but neglect to put the right kinds of processes and standards in place, or provide the right training to staff members for using the tools.

Global-Z – Data stewards can become overwhelmed by all the continual hoops they have to jump through to make sure DQ initiatives are moving forward. What advise can you give to a team that feels like they are “stuck in the mud?”

Loshin- Having a clear vision of the expected outcomes for data governance can be a great motivating factor. Specific program objectives can be translated into milestones and deliverables, and at the same time you can define measures for monitoring observance of defined data policies. Making sure you have a plan that maps specific tasks to the deliverables and milestones will help keep you from getting stuck in the mud.

Global-Z – What common challenges do you see when organizations have to deal with international data (i.e., data from outside their domestic borders)?

Loshin- The biggest issue in the past was having access to any sources of information about international data and international data standards. This has changed dramatically in the recent years, although it still poses a challenge. Another critical governance challenge is tracking international privacy and data protection rules and making sure that the way data sets are handled does not violate any jurisdictional privacy regulations. And one more – being able to handle international data altogether can be challenging if you can’t manage Unicode character sets. That can be a really big problem.

Global-Z – Five years from now, what do you think will happen to the companies that continue to ignore DQ issues?

Loshin- More and more, companies are seeing how effective business processes are really dependent on information. And those companies that understand this dependence rightfully incorporate data governance and data quality as part of their system development lifecycle. Organizations that don’t “get it” are destined to become less competitive as they lag in rapidly taking advantage of emerging opportunities inherent in analyzing their data. I see data quality management as an enabling capability that must become a “dial tone” service.

Global-Z – Your book, “The Practitioners Guide to Data Quality Improvement” is regarded a practical resource for anyone in the DQ field. What other literature about DQ would you suggest to our readers?

Loshin- I have put together a collection of books that are useful data quality resources – see which is a portal listing different DQ books that I set up for people interested in data quality!

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