Predictive analytics becomes increasingly a more important tool for companies ready and willing to use all the data they collect in a better way. Machine learning might give an additional boost to a development of analytics, creating data resources that are more powerful and informative.
Data analysts increasingly apply methods of machine learning to predictive analytics because they “emulate statistical approaches of problem prediction”, says Thomas Dinsmore, author of the book Disruptive Analytics. “When organizations widely develop machine learning, they improve the efficiency of business processes,” he says. “Business gains depends upon how and where the organization uses a prediction.”
For example, advanced prediction through the machine learning process of insurance claims might detect a streak of questionable or fraudulent claims, lowering cost and increasing customer satisfaction. In the field of marketing, more advanced predictions could improve the effectiveness of an advertising campaign, through better identification of audience demographics and optimization of the offer. In the realm of retail merchandising operations, more advanced predictions of traffic flows in the stores would help to optimize merchandise placement and guide staffing numbers. “A list of potential fields of prediction applications in business operations is extensive”, Dinsmore said.
Machine learning makes it possible to do what would otherwise be impossible to do. For instance, machine learning allows assessing the damage made by a hurricane in accordance with images of buildings and communities, could detect cancerous cells, or identify a unique “signature” of a computer user.
Machine learning algorithms are highly scalable for large quantities of data, and they are better to convert into a grand-scale application. Machine learning approaches give predictions that are more precise, especially if an observed behavior occurs rarely, said Dinsmore: “They work better with dirty data, with a bevvy of database set (sets with a huge number of properties) and with untagged data.”
This year, Dinsmore expects to see an increasing number of machine learning systems manufacturers. “There is a crowded market of desktop versions of predictive analytics, available for business users. Some existing startups, probably, will be absorbed,” he said. “New startups, more than likely, will focus on tailored solutions for business in the area of marketing, financial services, public health services and security. We will see in a few years a wider application of machine learning because of the fact that enterprises bring into action a digital transformation,” said Dinsmore. “Transformation of analogue into digital causes huge volumes of new data and cheapness of storage means that we can store data, which you used to discard. Machine learning gives an opportunity to find templates and structures in this data.”
Machine learning engines with an open source code will gain ground, Dinsmore believes. “Manufacturers of commercial software respond to a problem in different ways. As a rule, they will focus efforts on the highest-level problem, usability feature, while problems of analytics, related to middle and basic level, will be solved with the help of open software.”
Whether it be in public, private or virtual bare-metal cloud, predictive analytics and machine learning are the natural addition of a model of elastic computing, to a far greater degree than, for example, data stores or business intelligence, said Dinsmore. “It means that manufacturers such as Microsoft, AWS, and Google will strive for machine learning along with traditional leaders in the area of analytics,” he said.
About the author: Melisa Marzett is a work-from-home writer. She enjoys frequent such, because it affords her opportunity for learning, and provides ideas for new articles. She works for PurEssay currently, and enjoys the experience almost as much as travel to a new destination.Share