How can businesses make better use of the data they’re gathering? Analysts and marketers alike have become an important asset for businesses who are looking to better utilize the data they’re constantly gathering. Now, these same companies are looking towards artificial intelligence to gather even more raw data.
Machine learning is the technology that makes it possible for computers to learn without being programmed to and is, therefore, capable of providing more of an impact on analytic data. Therefore this technology is now providing enterprises with more and more competent data resources. These analysts use machine learning techniques more frequently in order to obtain new predictive information because they often beat statistical methods at solving prediction problems. Thomas Dinsmore, an independent consultant and author of Disruptive Analytics says, “When organizations deploy machine learning abroad, they improve efficiency and effectiveness of business processes.” He continues, “Business gains depend on how and where the organization deploys predictions.”
During a marketing procedure, improved predictions increase successful ad targeting, audience selection, and optimization as well. When dealing with retail operations, efficient predictions of in-store traffic can better help the retailer when it comes to optimizing staffing. Put simply, the marketing opportunities machine learning presents are endless and promising. Machine education makes it more viable to perform tasks that would otherwise be impossible. For example, the education of machines makes it possible to estimate potential storm damage from simple images of different properties/regions, and effectively diagnose cancer, or locate the exclusive signature of a computer user. These techniques typically produce increased accuracy with predictions, mostly with behaviors that are rare, according to Dinsmore. They “tend to work better with dirty data and ‘wide’ data sets—sets with a very large number of features—as well as with unlabeled data.”
Algorithms that make machine learning possible scale sizable amounts of data, and can be easily accessed and merged with larger-scale apps. In fact, Dinsmore predicts that we will see an onslaught of machine learning vendors by the end of the year, all claiming to offer some increased measure of automation. “There is a crowded market for ‘desktop’ predicting information accessible to the business user; some existing startups will likely be acquired.” According to Dinsmore. “New startups will likely focus on targeted business solutions” within marketing, security, healthcare, and financial services.
Depending on whether you are in the public, private, virtual private cloud, or some other alternative, having the capabilities of predictive analytics and machine education offer an additional layer of versatility to current computing models, a change that will not only garner better data, but also one that evolves as the market evolves around them. Evidently, we can expect to see vendors like AWS, Google, and Microsoft undoubtedly make an impact on machine learning and traditional analytic leaders, ushering many industries into the world of technology and artificial intelligence.
