Big Data Analytics Challenges in Enterprise

The adoption of Big Data Analytics in the Enterprise can deliver huge benefits but also presents equally important challenges. Adopting Big Data Analytics is both an opportunity and a concern. Several examples are in order:

  • Inability to share/correlate knowledge (data and algorithms) across organizational boundaries impacts the bottom line. Two or more business units may be working on a similar set of challenges. With no leveraged knowledge, each business unit will duplicate efforts only to discover similar solutions. Sharing the power of Big Data, more precisely the ability to build and improve on it, underpins substantial productivity gains and accelerates innovation.
  • Data is locked in many disparate data marts. This is not necessarily a new challenge. We’ve been dealing with it since the early days of enterprise databases when two or more departments could not come to terms on a common set of requirements, went their own ways instead and built separate data stores. The advent of Big Data exacerbates the age-old dispute—the sheer volume of data requires even more data marts to be created. Big Data mitigates this challenge by leveraging technologies that are built from the ground up to be scalable and schema-agnostic.
  • Traditional enterprise IT processes (i.e. user authentication and authorization) don’t scale with Big Data. Not being able to enforce and audit access controls against huge quantities of data leaves the enterprise open to unauthorized access and theft of the intellectual property.