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Forecasting sales is a skill that all businesses would like to have, but not all of them do. It is easy to understand the importance of being able to produce effective sales forecasts, as this means being able to adjust production or product stock accordingly, avoiding wastage (in the case of overproduction) on one hand, and lost sales (in the case of insufficient production or not meeting real demand) on the other. In a nutshell, knowing how to forecast sales leads to greater business efficiency.
Predictive Business Intelligence (BI), powered by artificial intelligence, enables your company to predict sales with great accuracy. Business Intelligence systems have become a commodity in many companies and provide descriptive and diagnostic support for decisions within an organisation. Research by Polimi’s Business Intelligence Observatory shows how this technique provides a strategic position on the ICT priority scale of companies. The share of expenditure dedicated to Business Intelligence systems, compared to the total ICT budget, has in fact grown in recent years alongside the advancement of predictive tools.
According to data from Polimi’s Artificial Intelligence Observatory , the Intelligent Data Processing market – which also includes Business Intelligence – is one of the fastest growing markets in 2021 (+32%) and has the largest market share (35%). The majority of SMEs (62%) make use of predictive analytics.
We have already noted that Business Intelligence is the main, and most effective, technique used for predictive analysis today. What does it consist of? BI is based on structuring business data from different sources and defining a semantic metadata model, in which business logic and business context rules are applied, so that raw data is transformed into valuable information. It is now clear that mastering data is the key to success for companies, small or large: having large amounts of data is not enough if the data is not analysed with a methodology that can encompass the process of collection, validation, analysis and extraction, fundamental in the support of strategic decision-making.
Sales dynamics are one area that your company can best understand with Business Intelligence used as a predictive tool.
The fundamental difference between traditional and predictive Business Intelligence can be found in the questions that these two techniques answer: for business intelligence – “What is happening now?“, while for predictive analytics – “What will happen in the future?”.
The basis of predictive BI techniques is predictive modelling, which has considerable potential and is therefore used in various application areas, such as quality analysis applied to production processes; warehouse management and logistics; marketing and risk reduction. Predictive capability is a typical quality of predictive modelling and makes use of both supervised and unsupervised learning, both machine learning techniques.
From a technological point of view, some predictive analysis tools are extensions offered by business analysis and reporting providers, integrated with AI algorithms; others are synchronised with a specific data storage product, work with generic formats – such as csv – and offer the best performance with proprietary databases of the company that developed the predictive capabilities. Python libraries – for data analysis and visualisation – and Kubernetes are used for predictive BI systems; languages based on Apache Spark, Delta Lake, TensorFlow and ML Flow (four types of popular open source software); Iterative machine learning algorithms capable of acquiring training data and transforming them into models.