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SEP 7, 2012 4:25pm ET

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Big Data Quality: Persistence vs. Disposability

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We last spoke about how to reboot our thinking on master data to provide a more flexible and useful structure when working with big data. In the structured data world, having a model to work from provides comfort.

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Comments (3)
Nice article Michele. With the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it's computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quick and simple. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery. More info at http://hpccsystems.com
Posted by HAANA M | Tuesday, September 11 2012 at 6:16PM ET
Bravo Michelle! One of the best articles I have seen on this topic. Instead of just saying 'Big Data is different' and stopping there, like many of the articles and papers I have read, you actually explain why and offer suggestions on how to adapt governance to it. When we look at the value of an organization's Big Data and how it meshes with existing corporate information, coupled with sophisticated profiling tools, we can make useful decisions on the value and usage of the data, instead of wasting time, energy, and resources on cleaning it. Does anyone go out and clean the leaves off the forest floor? They look just fine there and they serve a useful purpose. At the same time, an artist might walk that same forest and collect only the red leaves for use in a leaf painting. It's all about perception of value and risk. On the other hand, data cleansing/standardization could be key in trend analysis, householding, consumer sentiment, etc, some of the many use cases we've seen with Big Data. Tools must also evolve to meet the scalability requirements that Big Data presents, as well as how to incorporate unstructured data- whether it's data profiling, integration, cleansing, metadata management, building a business glossary, or security. That means as vendors, we need to do more than opportunistically just slap 'Big Data Enabled' in front of our tools offerings. It's time to "Walk the Walk"... or run the risk of being lost in the woods, aimlessly picking up leaves.
Posted by Cindy C | Wednesday, September 12 2012 at 2:33PM ET
Cindy,

I agree that tools need to meet scalability requirements. We also need to take the perspective of what information actually needs to go through processing. I think there is still a balance to be struck on when you bring out the big engines and when the engine is good enough. I also think there is "big" hype, and to your point the tools that win are those that are truly scalable and added to this, those that are flexible to meet various data data structures and environments.

-M

Posted by Michele G | Wednesday, September 12 2012 at 9:51PM ET
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