Scott Koegler

Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

Scale Back Your Use Of Big Data In Favor Of Accuracy For Better Results

Big data is now a topic of discussion in many boardrooms where most organizations believe that if they collect all data from different streams into their companies, they can apply analytics and they will, without doubt, gain significant value from it. However, although big data can have a massive impact on your business if data is gathered and used appropriately, it can also present a challenge if too much data is collected without regard to verifying accuracy. This article highlights reasons why you need to scale back your big data in favor of accuracy to realize better results.

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Is Big Data a Threat to Legal Confidentiality?

Many positive things have been said about big data. The increasing adoption of big data among organizations has however shown the downsides of this concept and technologies associated with it. Legal confidentiality is one of the areas that is under threat from big data. In the rush to embrace big data and opportunities that come along with it, we must never forget where we have come from. For instance, back in the early days of the dotcom boom, many internet businesses failed to address the most basic of things. For example, many forgot to secure their assets by following the correct legal procedures and intellectual property rights. As such, many lost their innovations.

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Big Data Is Changing How Businesses Operate

From one-person business to multinationals, big data is changing the way companies operate for the better. With the increase in the amount of data originating from different sources due to the advancement of technology, companies are now deploying different innovative approaches for analyzing and extracting different useful data sets. The ability of businesses to access valuable and organized data offers them an edge and ensures long term returns. It makes it easy to make decisions which would otherwise be hard using conventional methods.

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