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Deriving value through data quality analysis and big data governance

Stuart Brown discusses the opportunity that lies in data quality analysis and in defining what big data governance mean to your organization.

In this Q&A with, Stuart Brown, senior consultant and data and information governance lead in the business intelligence and information management practice at RXP Services Ltd., shares his perspective on the wealth of opportunity that lies in data quality analysis and big data governance, and the importance of defining what quality data means to your organization for maximum value and seamless process integration.

Is the push-pull between an emphasis on data quantity versus data quality something new, or is this cyclic?

Stuart Brown: It is all very cyclic. Data quality has reached a level of maturity with most countries and companies where it is being built into business processes. Organizations are recognizing the need and adjusting accordingly, or paying the price and figuring it out on the next time around. Vendors are pushing through some notions that big data requires less data quality; however, it really depends on what data quality means to you.

If you are of the school of thought that metadata is integral to data quality, or at least data governance, then it's an imperative to include metadata definitions into your big data program. Trying to find a needle in a haystack is made much easier by big data technology, but not if you have defined a different type of needle [than] the one you are looking for. Industry models and preconfigurations certainly make life easier, but they are by no means the one-size-fits-all solution.

I have often heard that the rewards of the cloud, such as cost savings, or the rewards of BYOD [bring your own device] in terms of productivity outweigh the risks of using such services and devices, and the potential for data exposure, regulatory violations and security threats in general. Where do you stand on the 'rewards outweigh the risks' debate?

By understanding the environment, constraints and what you are searching for, you create a much better chance of success.

Brown: Here is a slightly different perspective. I was recently in the South Pacific doing some volunteer work for an international NGO [nongovernmental organization]. The NGO provided infrastructure support and services to 22 member nations of the Pacific community -- services they would otherwise not be able to provide themselves. In this case, the cloud was very much a platform where the benefit outweighed the risk. These countries are so exposed to the elements that climate change is making their islands disappear in front of them and one tsunami could wipe out a data center.

As an Australian, data sovereignty is a very big issue. As with most countries in the world, there is a legal responsibility to not send data overseas, particularly when it is related to the government. However, in this situation, the Pacific governments have little choice unless they want to risk it all to a natural disaster. They still have to ensure that their privacy is maintained between the nations, as any country does.

Part of the work there was to try and help map a way forward to forming a data-sharing agreement that could be used as a governance mechanism to establish the trust and outline responsibilities between the NGO and member nations. Certainly, cloud for them is not so much about cost savings, BYOD, nor productivity. It's merely about survival and sustainability.

Are corporations developing big data analysis or big data governance strategies? Why or why not?

Brown: I think a lot of them are not, to be honest. I recently spoke at a healthcare big data conference, and there was an overwhelming buzz of excitement. I felt as though my talk, as the data governance guy, was a massive wet blanket. There seemed to be very few holistic approaches, and the big data analysis and big data analytics initiatives were largely being performed by researchers and projects off to the side. There was no consistency in approach and no common models of classification or metadata formed for the organization.

There were some brilliant things happening, don't get me wrong. However, there was no cohesive enterprise approach or corporate direction. Big data presents such a wealth of opportunity that people often forget that governance is there to guide and help derive value. Without a concerted focus on building some foundations of governance, there will be a lot of missed opportunity along the way.

Any advice on how to shape a big data governance or analysis strategy for employees? What are the initial steps?

More on big data strategies

Brown: It's very simple: Define what you are looking for. Big data can be used as a fishing expedition, so let's talk about that as if data were fish. If anyone out there is familiar with fishing, you'll know that there is actually quite a lot more to it than just throwing a worm on the hook and throwing it forth into the briny deep!

If you are in a particular area, you know what species to target. You will have needed to get a license and understand the regulations for how much you are allowed to catch, what sizes you must throw back and so forth. You can then understand the species, its habitat, its feeding habits and what type of food it likes to eat. Now, understanding the quality of the waterways you have fished from, you can understand whether that fish is something you want to eat, much like whether you can trust the data sources. By understanding the environment, constraints and what you are searching for, you create a much better chance of success.

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