With the current focus on acquiring data scientists to lead business analytics programs across all industries, midsize businesses are having difficulty competing against larger organizations with deeper pockets in the limited talent pool. As Fredric Paul notes on NetworkWorld, anyone with "data science" in their job title and a few years of experience is getting 100 recruiter emails per day. With both more money and perhaps more interesting problems to tackle, top data talent is regularly poached by top firms.

Rise of the Machines?

In an article on BetaNews, Lars Hård makes the case for adopting artificial intelligence (AI) in the form of machine learning to accelerate data analytics. In particular, he suggests that by applying learning algorithms to data sets "...to automate and optimize processes and predict outcomes and gain insights," they offer an advantage over human resources in both the speed and volume of data that can be analyzed.

It is not a one-time action of analysis that provides value, but the repeated analysis of data that allows the AI to learn, adjust and improve over time, analogous to humans attending school over a period of years, according to Hård. AI has the ability to discover connections within the data that can elude human analytical bias. As the intelligent systems adjust their behavior based on the continuous flow of data, they improve by becoming faster or finding deeper insights, more quickly than any standard hand-coded information system revision schedule could ever hope to achieve.

The application of machine learning to business data is showing promise in generating actionable business insights — the end goal for predictive data analysis. In application, AI can become adept at working with real-time data from the Internet of Things or customer social engagement to guide future business decisions. Intelligent machines also have the ability to scale effectively as data volumes increase in these business areas.

Source: midsizeinsider.com