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More isn’t always better. How can organizations reduce the noise in their data to achieve targeted, accurate analytics?


More isn’t always better. How can organizations reduce the noise in their data to achieve targeted, accurate analytics?

With the recent emergence of big data systems, one of the big questions for companies is how to keep these projects well-targeted and efficient. Many of the tools and resources built for big data are built to suction up vast amounts of information in a wide net. They're not always as attentive toward refining that data, and keeping it simple. However, there are some best practices emerging in the industry in order to create more targeted and useful big data projects.
One pillar of a targeted big data approach is to use the right software tools and resources. Not all analytics and big data systems are the same. Some can more effectively filter out excessive or irrelevant data, and allow businesses to just focus on the essential facts that will determine their core processes and operations.

Another major part of this involves people. Prior to getting involved in a big data project, and while sourcing vendor software, pursuing implementation and training others, a central group of people needs to be in charge of the process, and delegating research and brainstorming tasks well. This can make a big data approach into a precise, surgical method that will enhance the business without becoming too top-heavy and disrupting day-to-day operations.

For example, task forces or other core groups can sit down and look in detail at the ways in which implementation will be done, how the business will start to evaluate the data sets, how they will cross-index accounts, what kind of paper or digital presentations they will use to disseminate that information, how they will build useful reports, etc. These details will protect the business from big data bloating.

Also, as companies start to acquire more vendor services, do more big data crunching and make IT architectures more complex, they've learned to separate out the most sensitive data from everything else.

One way to do this is to create a tiered system. For example, a core data set of customer IDs and histories can be kept in a specially maintained database under a particular cloud security contract, or on-site. Other sets of data can reside in less specialized data environments, either because they are less sensitive in terms of data breaches, or because they’re less directly relevant to the analytics that the business is doing. Tiered or multi-level systems allow for cost-effective big data implementation.

These are some of the ways that businesses are getting smart about getting big data the right way. Rather than just vacuuming up any data they can grab, they treat certain data sets as most critical to get the most business intelligence with the least effort.

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