Predictive Analytics are quickly evolving from a buzzword into a tool required for consultants to succeed.
Due to the growing interest by companies in using predictive analytics, many opportunities are developing for skilled analysts in this field. In fact, Econsultancy.com reported that 40% of companies interviewed are actively using or planned on using predictive analytics. Lack of expertise on how to implement predictive analytics, however, is a key problem that the same companies acknowledge. A whopping 99% of the survey’s respondents believe that predictive analytics will be highly important to enterprises in the future. Below is a spotlight on how some analysts and data scientists are approaching the needs of clients’ predictive analytics goals.
How Are Analysts Interacting with Predictive Analytics?
A Harvard Business Review article stated that predictive analytics activities are permeating throughout many industries. Categories of businesses that can yield benefits from predictive analytics span from large to small sizes and include all segments, including healthcare, manufacturing and telecommunications. With the correct solutions, executives may be able to predict customer demand, enhance pricing accuracy, anticipate operational maintenance schedules and discover new, improved processes/applications. Examples of such activities being aided by predictive analytics include data scientists from Microsoft using vast amounts of datasets to predict sales of automobiles. Additionally, insights from e-commerce website visitors can also help to gauge the reasons and time frames regarding why people are visiting certain web pages. Internet of things devices such as sensors can now also help analysts to predict when equipment may need repairs and upgrades.
An analysis on LinkedIn reviews the typical work of a consultant involved in the predictive analytics field. Quite often the interaction between a client and predictive analytics consultant is based on basic business questions that may be able to be answered by analyzing the customer’s own data. For instance, questions about increasing sales may very well be able to be answered by data from the firm’s own payment transactions history. With the combination of an analyst’s skills, solid metrics, precise algorithms, and the proper data, vast insights can be gleaned and insightful answers can be delivered to clients.
Search Data Management examined the concept that analysts may be spending more time than needed in predictive analytics. Building predictive models and algorithms can be time consuming activities, indeed. Independent consultant Dave Wells stated “…Now we have data warehouses, data lakes and data scientists’ sandboxes,” in regards to all the various places in which to obtain information. Specifically, analysts may need to become creative when analyzing because, at times, established algorithms and data locations do not provide the necessary insights. Accordingly, an analyst may need to develop unique solutions to customer queries.
Tools for the Future
A myriad of tools like machine learning, artificial intelligence and data cleansing steps can now make data analysis even more scalable, speedier and more powerful. Consultants and other professionals are using these advanced technologies to tackle the petabytes of awaiting data. With the right technology and strategies, even previously untapped silos of data can bring unexpected and valuable intelligence to clients, including answers to questions they did not even know to ask.