How can an enterprise achieve analytic agility with big data?

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All sorts of businesses are jumping on the big data bandwagon, but some are having much better results than others. Where do some enterprises go so wrong, and where do others go so right?

Achieving good results with big data starts with sufficient system capacity. When leaders engineer the right kinds of solutions for a big data environment, the hardware can easily process its workloads, and people don’t have to run around trying to solve network capacity problems. This means allocating enough CPU cores or processing power to central servers, addressing needs for dynamic memory, and providing adequate storage solutions, along with monitoring how data will flow through the system and identifying and eliminating any bottlenecks.

Another big part of "agile big data" has to do with people. A company has to have the right training and the right resources for implementation. Having adequate talent on board is vital, and where there are any gaps, quick and effective training and cultivation of in-house people is key. Companies can rely on consultants for many things, but at the end of the day, there needs to be enough savvy about these big data systems for the business to handle them confidently.

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Yet another fundamental area of using big data correctly comes in when businesses start to actually use the data they've collected. Adequate hardware systems can perform data operations well, and talented people can maintain and use them correctly, but there's still a good deal of difference in the results that companies get, based on how the system builds reports, culls data, and presents just the right analytics results in just the right ways. A lot of this has to do with sorting through structured and unstructured data sets conceptually, not going into the system and head-counting data, but instead, having a philosophy of data that focuses on just the most vital data sets and discards irrelevant and indigestible data.

All of these strategies will lead an enterprise to eventual success with big data systems. Companies need to look critically at implementation in terms of practicality, so as not to disrupt existing operations. They need to look at how new and modern tools will sit on top of legacy systems or how big data will be migrated through a new IT architecture. With careful research and analysis, leadership teams can circumnavigate the pitfalls of big data and get winning results for an enterprise.

Techopedia Staff

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