Machine learning can be used to develop analytics that are predictive, prescriptive, and proactive — harness the power of ML to improve your business’s analytics today.
Join the DZone community and get the full member experience. „Many organizations claim that their business decisions are data-driven. But they often use the term „data-driven“ to mean reporting key performance metrics based on historical data — and using analysis of these metrics to support and justify business decisions that will, hopefully, lead to desired business outcomes. While this is a good start, it is no longer enough“. The traditional role of data and analytics has always been in supporting decision-making. Now, they are applied where they have never been before. Today data and analytics are not only used for describing, diagnosing, predicting, or even recommending the best actions but also triggering those actions automatically. The motivation behind this new area of application is the goal of many businesses to reduce task performance time and the volume of human labor. To be effective, a business needs to transform the data it possesses into effective decisions. That’s where analytics comes to the rescue and provides the scientific process for this. „Analytics – the scientific process of transforming data into insight for making better decisions“. Let’s see how analytics has evolved over time. In the beginning, having any kind of data at all was an accomplishment. From that data, we drew some basic analysis and described the situation based on that. Descriptive analytics answered the question: what happened? Then analytic techniques and tools made some progress and began explaining not just what happened, but also why. For example: why did traffic to the website go up 50% yesterday? Analytics became diagnostic. Diagnostic analytics answered the question: why did it happen? This generation is predictive in its nature, helping us to not only understand what happened in the past, but what could happen in the future. Predictive analytics answers the question: what will happen? After prediction, there is the next step of analytics — prescriptive analytics that tell us what to do. Prescriptive analytics answer the question: what should we do? Reacting to a situation or to acting proactively by creating or controlling a situation can help us cause something to happen, rather than responding to it after it has happened. The last generation of analytics is the proactive generation, in which the machines don’t need a human to act. They will simply act proactively and do the work. There are several ways to implement some form of proactivity in real-life business. One of them is a simple rule-based empirical approach, which is when you manually define and hard-code the rules for a specific situation (e.g. if the price is lower than 100$, then sell). Then some software programs can make predefined actions.
