Predictive Analysis in Business
Decision-making in business is often based on assumptions about the future. Many companies aspire to develop and deploy an effective process for understanding trends and relationships in their activity in order to gain forward-looking insight to drive business decisions and actions. This is called predictive analytics. We can define predictive analytics as a process that uses data and a set of sophisticated analytic tools to develop models and estimations of an environment's behavior in the future.
In predictive analysis, the first step is to collect data. Depending on your target, varied sources are using, such as web archives, transaction data, CRM data, customer service data, digital marketing and advertising data, demographic data, machine-generated data (for example, telemetric data or data from sensors), and geographical data, among other options. It is important to have accurate and up to date information. Most of the time, you will have information from multiple sources and, quite often, it will be in a raw state. Some of it will be structured in tables, while the rest will be semi-structured or even unstructured, like social media comments.
The next important step is to clean and organize the data - this is called data preprocessing. Preprocessing usually takes up 80% of the time and effort involved in all analysis. After this stage, we produce a model using already existing tools for predictive analytics. It is important to note that we use collected data to validate the model. Such an approach is based on the main assumption of predictive analytics, which claims that patterns in the future will be similar to the ones in the past.
You must ensure that your model makes business sense and deploy the analytics results into your production system, software programs or devices, web apps, and so on. The model can only be valid for a certain time period, since reality is not static and an environment can change significantly. For example, the preferences of customers may change so fast that previous expectations become outdated. So, it is important to monitor a model periodically.
There are plenty of applications for business based on predictive analytics. To conclude this article, we will briefly consider some of them.
