Accenture Enlists Oblong Industries to Drive Analytics Usage

Uncovering the Truth about Six Big Data Security Analytics Myths

Perhaps the biggest challenge associated with Big Data is not so much collecting it anymore, but rather figuring out what to do with it all. With that in mind, Accenture has enlisted Oblong Industries, a provider of telepresence systems, to make it easier to distribute visualizations of data created using the Accenture Analytics platform running on premise or in the cloud during a telepresence meeting.

Michael Bridges, managing director, information management and business intelligence for Accenture Analytics, says the most significant Big Data decision any organization can make is figuring out what use cases are actually worth the investment. IT organizations may wind up spending millions of dollars on Big Data analytics only to confirm either what the business already intuitively knows or provide an analytics result that is relevant to the business only a handful of times a year. In the latter case the organization might just as easily outsource that analytics process to a third-party provider versus investing in the infrastructure required to generate the result.

Bridges notes that industries that have complex processes usually associated with Internet of Things (IoT) applications tend to fare best when it comes to Big Data analytics. The reason for that is that access to 100 percent of the available data can have a material impact in the efficiency of that process. At the same time, however, how much additional analytic muscle a marketing application might get out of accessing 100 percent of the available data versus just 10 percent can vary considerably. In fact, they may find that being able to correlate that information with other data sources provides the most business value.

Investing in data science for its own sake is going to be poorly received by the business unless, of course, the cost of acquiring that intelligence is substantially less than the business value that the intelligence provides. For that reason alone, IT professionals don’t always make the best choice for deciding which Big Data analytics projects should go forward or where they should be hosted. Most IT organizations prefer to stand up their data warehouses, but the capital costs associated with building a Big Data warehouse running on premise can be fairly daunting.

In either case, Bridges says it’s a whole lot easier to get buy-in from business executives that are traditionally skeptical of any IT investment when they can more easily visualize the actionable intelligence that lies buried in all that data.