Predictive Analytics: Making sense of big data with machine learning

AB: Predictive analytics involves using data, statistical algorithms and machine learning techniques to predict the likelihood of future trends and business results, based on past trends and data.

It brings together a number of different advanced analytics disciplines and technologies, from statistical analysis and deep data mining through to predictive modelling and machine learning, predicting the most likely future outcomes and answering valuable questions for organisations such as, “what will happen?” or “how are customers likely to react to this?”

Predictive analytics is about producing valuable business insights and accurate foresights or forecasts from large sets of data, to help users decide on the next-best course of action and provide better customer experiences.

The increasing volumes of data available, combined with faster, cheaper computing power, and the development of more user-friendly analytics and AI software; like Salesforce Einstein, have enticed an increasing number of companies to leverage predictive analytics to achieve their business goals and gain a competitive advantage.

The value of predictive analytics is clear. It makes sense of big data, it helps to inform decision-makers and it gives them “actionable insights”.

According to Bluewolf’s most recent State of Salesforce report, based on a survey of more than 1,700 Salesforce customers, 75% of companies increasing investment in analytics can cite revenue gains as a measurable business outcome.

Most interestingly, the report revealed that 81% of Salesforce customers cited increasing the use of predictive analytics as the most important initiative for their sales strategies.

So what is predictive analytics? Put as simply as possible, there are four levels of analytics, from descriptive analytics, which describes the real world in concrete data, to diagnostic analytics, which looks at historical data to provide insights as to why things happened as they did in the past, through to predictive and prescriptive analytics.

It is these latter two areas of advanced analytics, where processes are increasingly automated, that are of immense value to businesses looking to make sense of big data.

Organisations can confidently look forward and anticipate different business outcomes based on predictive analytics, and not merely on a hunch or a gut-feeling.