Why modern data optimization requires a unique approach

Why modern data optimization requires a unique approach

Why modern data optimization requires a unique approach

Too many IT operations are trying to cope with today's data using yesterday's methods, says Christian Beedgen, CTO of machine data analytics company Sumo Logic. In an interview with The Enterprisers Project, he explains what approaches work now.

The Enterprisers Project (TEP): What must companies do to meet the challenges of today's data?

Beedgen: We already know that current data tends to be voluminous, or big. But we also need to focus on data arriving in streams to process in real time with minimal latency. This process can be hard to do and it differs quite a bit from traditional data management and the ad-hoc query approach common in the database world.

And in contrast to the clean structures found in relational data in traditional databases, today's data comes from all sorts of places. It's usually not overly clean and is almost always poly-structured. Modern data strategies need to aim for integrated solutions that cover streaming acquisition, cleansing, and transformation of data flowing into real-time analytics engines that are required to produce insight with very little latency. If your competitor can do it better and faster, you will be out of business.

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TEP: M2M (machine-to-machine) data is an increasingly significant part of any organization's data set. What are the best strategies for using this data?

Beedgen: Most M2M, or machine data, is created by the applications that support and often enable business. There is a maturity model here.  

In short, it starts with using machine data to enable efficient operations of the applications that run the business. With machine data, ideally, the strategy would be to not only maintain the applications but also enable agile development on top of them.

 



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