Predictive analytics implementation -For the past few decades, business intelligence tools have been essential for companies wanting to stay ahead of the competition. Their use has become so widespread that a new approach was required and it came in the form of predictive analytics. The natural evolution of business intelligence, predictive analytics provides a deeper understanding of future trends, relying on historical data and statistical models. As with artificial intelligence, however, it is only as good as the input and the reasoning behind the algorithm used.
Implementing predictive analytics can help prevent fraud, predict customer churn, forecast for cash-flow and revenue, and improve marketing campaigns – but it must be properly executed.
01 Project definition
It is essential to be specific about what you hope to achieve by implementing predictive analytics methodology. Before starting, set out expected outcomes and clear deliverables, as well as the input which will be used. Establish that all data sources are available, up to date and in the expected format for the analysis.
02 Data collection
Since predictive analytics is all about using large volumes of data to get insights about trends and stay ahead of the game, the data collection phase is crucial for the success of the initiative. Most likely this will include information from multiple sources, so there needs to be a unitary approach to data. Sometimes information will be collated and cross-queried for a comprehensive picture of the underlying phenomenon.
Most of the time, data will be collected into a data lake – not to be confused with a data warehouse, which has some significant structural differences. A data lake contains information in a raw state. This means it can range from structured (tables) to semi-structured, like XML or unstructured (social media comments). For the success of the project, it is mandatory to understand the differences and employ the right tools.
03 Data analysis
Once you have all the data you need in place, it is time to dissect it. The investigation will hopefully reveal trends, help prevent fraud, reduce risks or optimize processes. Surprisingly, 80 per cent of this stage has to do with cleaning and structuring data, rather than modeling it. Once this is completed, results must be interpreted and actionable goals defined.
Statistics is just as important as big data when implementing predictive analytics, particularly when testing and validating assumptions. Very often, those in charge of the project will have a specific hypothesis about the behavior of consumers, conditions which indicate fraud and so on. By statistical methods, these are put to the test and decisions are made based on numbers, not hunches.