For the past few decades, business intelligence tools have been the norm for companies wanting to stay ahead of the competition. It has become so widespread that a new approach was required. Predictive analytics is the natural evolution and the next step towards a deeper understanding of future trends. It relies on historical data and statistical models. However, it’s only as good as the input and the reasoning behind the algorithm used. Since this is a tool and it is always part of a project, we will discuss the things you need to consider at each step of this process. Project definition phase Be specific about what you hope to achieve by implementing the predictive analytics methodology. Draft a document which includes the expected outcomes, clear deliverables as well as the input which will be used. Before starting out, make sure that all data sources are available, up to date and in the expected format for the analysis. The following are problems which can be successfully tackled by implementing predictive analytics according to : InData Labs’ article ● Customer churn prediction ● Marketing campaign improvements (customer segmentation, optimization, customer recommendations) ● Cash-flow and revenue forecasting as well as dynamic pricing ● Credit scoring ● Fraud prevention 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. The data need to have a unitary approach. Sometimes information will be collated and cross-queried for a comprehensive picture of the underlying phenomenon. Most of the times the data is collected into a data lake. This is not a synonym for data warehouse since there are significant structural differences which should be acknowledged. 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. 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. You might be surprised, but 80% of the study has to do with cleaning and structuring data and not modeling. This is even more obvious in the case of data lakes, which, as previously mentioned have no predefined structure, they just host data. Only when it is needed is the retrieved information processed precisely for the intended purpose. Of course, the most critical aspect of the analysis is interpreting the results and defining actionable goals for the next period. Statistics Even if predictive analysis relies more on Big Data, statistics is still part of the game. It’s used to test and validate assumptions. Most of the times management has 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. Be ready to have your ideas challenged by data and accept that sometimes the obvious logical outcomes are not supported by reality. Keep an open mind and trust computations. Modeling Most of the time researchers use existing tools. There are countless libraries, built on open-source programming languages like Python and R. There is no time to reinvent the wheel. It’s more important to know the available options and choose the best one for the job. The goal is to democratize modeling and make it available to business analysts, as well as data scientists. Deployment Once data has gone through statistical analysis and the model has been calibrated, results need to be interpreted and integrated into daily routines. As suggested, once the model is created and deemed sufficient, it should be used to dictate the daily choices and govern the processes in the organization. It’s not enough to have numbers which show what would be best for the company unless that translates to actionable steps and measurable results. Monitoring Reality is not static; neither is data. A model can be valid for a certain period while the external conditions don’t change significantly. It’s good practice to revisit the models periodically and test them with new data to make sure they have not lost their significance. This is especially important for those models used for marketing campaigns. The preferences of the customers and trends in consumer market sometimes change so fast that previous expectations quickly become yesterday’s news. Conclusions There is a transition taking place from relying on reports and past data towards looking at the future and preparing for it. The extremely competitive marketplace is pushing companies to find new ways to get ahead of their peers and to rely more on data than on hunches or just performing business and usual. They must understand opportunities before these arise and be ready when it happens. The most important things to consider when employing predictive analytics for your business include: 1. Be clear about the scope of your project and define from the beginning the expected results and outcomes. Don’t just go with the flow. 2. Make sure you have the right data. Store it in a data lake to be able to use it repetitively for different purposes, in different environments. 3. Perform the analysis on clean, organized data and try to interpret the results in actionable ways. 4. Trust the numbers, not the hunches. Perform in-depth statistical analysis. 5. Don’t strive to reinvent the wheel. Scan through existing free tools. 6. Create fool-proof processes based on the results of the investigation. 7. Revisit your models frequently enough.
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