Big data needs quality assurance

While the big data phenomenon is introducing a number of opportunities for companies around the world, it is also introducing new challenges, namely data quality. This is because the large volumes of information are often inconsistent and contain multiple variables, leading organizations to use false resources more often than they should.

A Wired report said big data researchers, individuals that are responsible for actually acquiring information, are able to succeed at the expense of their organization because they are not necessarily concerned with whether the resources they capture are accurate - all they want if the data. While organizations still try to implement quality assurance metrics to minimize inaccuracy, it can be hard to apply these parameters to such a broad range of resources.

Consulting firm Valerisys said data quality is crucial to effective decision-making processes, as it allows organizations to create and use a metric system that ensures the accuracy of all digital resources. This is especially important during the advent of big data, which is providing firms with a broader range of assets.

In the coming years, decision-makers need to consider placing quality assurance as a priority in their big data initiatives, as failing to do so will create inconsistencies throughout the organization.