Predictive analytics uses big data, statistics and modeling, machine learning and AI to identify future risks and opportunities based on available information
Making predictions about unknown future events no longer requires a crystal ball and tarot cards. Today, there is a better and more precise tool known as predictive analytics.
Predictive analytics is a practice that uses big data, statistics, and modeling, as well as machine learning and artificial intelligence to identify future risks and opportunities based on currently available information.
Predictive analytics has been around for quite some time, but humans used to do much of the heavy lifting. Insurance actuaries are one of the most common examples of the use of predictive analytics in its most basic form. Actuaries are people who collect and analyze data to better determine risk for selling life and health insurance policies. But even the insurance industry is using modern tools available for more robust predictive analytics.
Common uses today of predictive analytics are direct marketing, health care (predicting risk of developing certain illnesses, and medical decision making), cross-selling (recommending products based on previous purchases), fraud detection, and risk management (portfolio mixes).
We are using predictive analytics across our entire company in everything from HR (talent acquisition) to operations (the design and implementation of predictive maintenance, monitoring, and planning systems and technical assistance, as well as the distribution and installation of specialized measuring equipment).
Our services line has a new approach to managing waste collection services based on data and dynamic decision-making adapted to the needs of a city. Using data collected by sensors, together with information related to weather, traffic, and special events, allows us to determine optimal waste collection routes.
We help water management authorities and engineers predict water demand (for example in coastal locations, where there are significant fluctuations between the high and low tourist seasons). Using predictive analytics, water authorities can make improvements in and boost the efficiency of the water supply network, all done in real-time.
We are also able to instantly detect potential I&I issues, identify exact locations where repairs need to be made, and reduce the amount of water that is wasted, among others.