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5 min read
Predictive analytics - Looking into the future through data
"Data is the new oil in the 21st century!" A quote by British mathematician Clive Humby coined in 2006 has become a veritable buzzword in recent years. However, we should add that, like oil, data only has real value if we can make good use of the amount of information we gather. One of these uses is forecasting, a methodology known collectively as predictive analytics. In this article, we review what is worth knowing about predictive analytics and why it is important for companies.
"The power of prediction"
Predictive analytics is the umbrella term for the analytical tools that can be used to predict anticipated, future events based on past patterns and behaviour. Forecasting methods include data mining algorithms, statistical models, and artificial intelligence-based analytical tools that, when integrated into everyday operations, allow us to make decisions before - potentially undesirable - events occur.
By using historical data, we can, for instance, predict our expected end-of-month profits, and estimate the expected sales of our products or customer churn.
By looking ahead in time, we lend ourselves the opportunity for early intervention and to make decisions in time to improve our expected performance, increase sales, or retain customers. Then, with the right backtesting and evaluation methods, we can measure and verify the actual business impact of our actions, i.e. our data-driven decisions.
Structured and unstructured Data- and text mining
Structured and unstructured data Data- and text mining, among other methods of predictive analytics, combined with statistical models enable companies to efficiently reveal previously hidden relationships between both structured and unstructured data.
Structured data are defined as information that is ready for analysis because it is stored in a predefined format. Examples include demographic data and data on shopping habits. Unstructured data are found in a native format, i.e. not organised in a predefined way. This includes video, audio or image files, as well as log files, sensors, or social media posts. The specificity of predictive analytical methods lies in the fact that they use historical data available at the time of forecasting.
Thus, when collecting data, particular attention must be paid to the temporal structure of data generation.
One special case of predictive analytics is time series analysis, where observations are available at the same points of time in the form of a time sequence (e.g. product sales data).
In other cases, we make a forecast based on events that precede the event we want to predict (e.g. if a customer in a loyalty program unexpectedly spends his/her entire balance, this may predict attrition).
In summary, predictive analytics is a data-driven decision support tool that enables companies to directly improve their business performance and increase their competitive edge.
Predictive analytics is used by companies in a wide range of areas. In the sequel to this article, we will review these spheres of activity and describe in detail the benefits of analytical methods.
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