Home
 > Research and Reports > TEC Blog > Data Governance: Controlling Your Organization’s Mission-...

Data Governance: Controlling Your Organization’s Mission-critical Information

Written By: Lyndsay Wise
Published On: December 4 2007

If you think data governance is something dry and remote, think again.

When it comes to compliance, data governance can help your company avoid hefty fines and even jail terms for its senior executives. In terms of customer service, data governance can reduce customer churn while giving your company a decisive competitive edge. And as for supply chain management, data governance can help your company understand what products are selling, what products are in stock, and what products are on the way from suppliers.

And that's just the tip of the iceberg. To find out more about data governance and how it can help your organization excel, listen to Data Governance: Controlling Your Organization's Mission-critical Information. In this informative TEC podcast, analyst Lyndsay Wise chats with Daniel Teachey, DataFlux director of corporate communications, about the benefits, business drivers, and implementation challenges of data governance.

Click here to download this podcast now.

This podcast examines the following questions:

  • What is data governance, and why is it so important to your organization?
  • How can data governance help your organization meet compliance standards while improving organizational performance?
  • What are the special challenges of implementing a data governance solution?
  • What makes DataFlux's data governance solution unique in the marketplace?


Listen to the entire podcast
by downloading the file, or save for later playback.


Transcript

Hi, and welcome to TEC radio. My name is Lyndsay Wise, and I'm the senior research analyst for business intelligence [BI] here at Technology Evaluation Centers. Today I am speaking with Daniel Teachey, the director of corporate communications at DataFlux. DataFlux's main focus is data quality. However, based on their implementation experience, they have developed a data governance maturity model. Today we will be discussing the importance of data governance, the benefits and challenges associated with its implementation, and how the model was developed.

Lyndsay Wise: Hi Daniel, thank you for being with us today.

Daniel Teachey: Thank you.

LW: Why is data governance important?

DT: We see a number of different factors playing for the drive of data governance. One of the first things is the amount of data that companies have is simply increasing—terabytes, gigabytes—all the data that they have in is just increasing exponentially every year. So, just the amount of raw material data that they're having to manage is going up.

Another factor that we see driving a lot of this is regulatory compliance. Companies are starting to understand [that] in order to do things like Sarbanes Oxley, they had to have a good understanding of their underlying data before their CFO [chief financial officer] or CEO [chief executive officer] can sign off some of these reports. If the data underlying all these reports is not valid, then potentially, someone can go to jail, there's some fines [that] can be levied against your organization—there's a lot of negative consequences to not doing a more governed approach to data management. And I think those are two of the key drivers we see, especially within our business groups that are working out there today.

LW: And Daniel, what are some of the business drivers for data governance?

DT: On the business side, we see companies coming to us saying that they have problems either on the customer side, or sometimes on the product or supply chain side. On the customer side, it's things about customer churn—losing customers that they had and have had for years. And as a result, you'll see a lot of data governance companies—at least those at the forefront—are in industries like financial services or telecommunications.

Where there are a lot of different vendors out there, there are a lot of different choices for you in the marketplace as a consumer. [These vendors] understand that in order to interact more successfully with the customer, they [have] to provide better offers, provide better service. The best way to do that is to understand the data that you have about your customers and help make a holistic view of that customer. The only way to do that is to have principles in place to help govern the control data that you have over time.

On the product or supply chain side, it's a similar thing, where companies don't understand what products they have, or what products are actually selling, what products are coming in from suppliers, what inventory they have ... There's a host of problems out there on the product side and the supply chain side. So [companies] are trying to optimize their supply chain, but without a simple pool of usable, actionable information, they find that's very difficult.

Data governance provides the backbone of that principle, the policies that you can put in place to take control and manage that information better and drive better discussions down the supply chain.

LW: And what are the unique challenges of implementing data governance?

DT: There's really two ways of looking at this one. Basically, we see companies going at data governance from a top-down approach or a bottom-up approach.

Top-down is where an executive starts to understand that his company's reputation, or even his free time if jail's involved, is somehow in play by the amount and the quality of the data that he has on customers or products out there. So if an executive comes to the management team and says, "We need to do data governance or some similar program," how do you actually turn that into a reality? The top-down approach requires managers to seek out data stewardship teams and teams that are actually in charge of implementing data governance throughout the organization, and then down the line the technology you have to put in place to add business rules to your IT environment, and force them over time, and make sure you have a repository of business policies that sort of direct what your organization used to be good data regardless of the originating application or data source.

The bottom-up approach is a lot more difficult because you don't go into it with that executive sponsorship. The bottom-up approach requires you to say, “OK, I'm going to build from, maybe the management or from the data stewardship level, a business case for data governance. I have to help executives understand why this is important and try to bubble that up—[more of the] grassroots approach.” But I think the unique challenge there is [that] there is a number of different strategies to it. No one really kind of goes into it ... having people that are involved, the policies already in place, and technology ... already under the hood. They may have a component or two that is aggregating people, policies, and technology, and the one sort of hub—one sort of solution—within your organization. That's causing a lot of problems right now.

LW: And why did DataFlux feel the need to develop a data governance maturity model in the beginning, and how's is it different than what is already in the market?

DT: It was a customer-driven effort. It was a matter of listening to what our customers were doing with our technology, and understanding how they were using it. DataFlux, for the last 10 years, has been about data quality, about standardizing and normalizing data the company has about products and customers and suppliers, and things like that. As such, it's very adaptable. Some people were using it for fraud detection, using matching technology to match a customer record to maybe a list of transactions. Some are ... doing transitional CRM [customer relationship management] or ERP [enterprise resource planning] implementations, and using data quality there.

We found that there was really no rhyme or reason to how the data quality technology was being used other than people had a definite pain. And when you boil that pain down ..., it was that there was no single [governance] policy within organizations for data quality. They had policies in place for HR [human resources] practices and for financial practices and travel policies, but they didn't have policies in place to help understand, “How do you govern what a customer needs? Is it a parent company or any subsidiary companies along with that?” And every company usually has these sitting in some well or some place within the organization. It could be in each business unit; every division can have their own view of it.

But what we started to understand was that the real pain here was that there was no pervasive logic across the organization about what good data means. So we started building this data and the data maturity model based on the types of data governance we saw out there. And where we think is different is that data governance is the high growth area for our organization, and I think it resonates very well with the companies that have seen the model and started implementing components of it. They understand that you can do things like master data management and customer relationship management, and all the little acronyms that are out there in the IT organization. But data governance ... if you do that right, you are much more likely to be successful—and wildly successful—because any application, any piece of infrastructure you put in place, will be based on [a] usable set of data that can drive better business decisions down the road.

LW: Well, thank you Daniel.

DT: Thank you.

For more information about business intelligence, Technology Evaluation Centers, or the topics discussed in this podcast, please visit http://www.technologyevaluation.com/.

For more information and to start your own custom solution comparison, please visit

TEC's Business Intelligence Evaluation Center

 

 
comments powered by Disqus

Recent Searches
Others A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

©2014 Technology Evaluation Centers Inc. All rights reserved.