If you can’t predict where your business is going you need to take a look at data analytics. Historically, the only available data analytics, organizations could use to monitor success were descriptive analytics. This information answers the question “what happened in the past with the business?” With the availability of big data we entered the new area of predictive analytics, which focuses on answering the question: “what is probably going to happen in the future?” The real advantage of analytics comes with the final stage of analytics: prescriptive analytics. This type of analytics tries to answer the question: “Now what?” or “so what?” It tries to give a recommendation for key decisions based on future outcomes. What’s the difference between these three ‘…tives’ and how do they affect your organization?
All three are necessary to obtain a complete overview of your organization.
1. Descriptive analytics is about the past. The past in this context can be from one minute ago to a few years back. Descriptive analytics help to understand the relationship between customers and products. Examples include management reports providing information regarding sales, customers, operations, finance and to find correlations between the various variables. Netflix for example uses descriptive analytics to find correlations among different movies that subscribers rent and to improve their recommendation engine they used historic sales and customer data.
2. Predictive analytics is about the future. Predictive analytics provides organizations with actionable insights based on data by estimating the likelihood of a future outcome. This data is generated from machine learning, data mining, modelling and game theory. Predictive analytics can for example help to identify any risks or opportunities in the future. Predictive analytics can be used for things like predicting customer behavior in sales and marketing, to forecasting demand for operations or determining risk profiles for finance. A very well-know application of predictive analytics is credit scoring used by financial services to determine the likelihood of customers making future credit payments on time. Determining such a risk profile requires a vast amount of data, including pubic and social data.
3. Prescriptive analytics provides advice based on predictions. Prescriptive analytics is the final stage in understanding your business, but it is still in its infancy. Prescriptive analytics not only foresees what will happen and when it will happen, but also why it will happen and provides recommendations how to act upon it in order to take advantage of the predictions. It uses a combination of many different techniques and tools such as mathematical sciences, business rule algorithms, machine learning and computational modelling techniques as well as many different data sets ranging from historical and transactional data to public and social data sets. Prescriptive analytics tries to see what the effect of future decisions will be in order to adjust the decisions before they are actually made. This will improve decision-making a lot as future outcomes are taken into consideration in the prediction.
As prescriptive analytics is so new, it is only around since 2003, and so complex there are very little best practices on the market. Only 3% of the companies use this technique, and still with a lot of errors in it. One of the best examples is the self-driving car of Google that makes decisions based on various predictions and future outcomes. These cars need to anticipate on what’s coming and what the effect of a possible decision will be before they make that decision in order to prevent an accident.
Prescriptive analytics could have a very large impact on business and how decisions are made and it can impact any industry and any organization and help them becoming more effective and efficient.
With these three types of analytics, understanding your business will become easier and better-informed decisions can be made that take into account future outcomes.
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Mark van Rijmenam is the founder of BigData-Startups.com, the number one big data knowledge platform. Mark is a strategist who advises organizations on how to develop their big data strategies. As such, he is a well sought after speaker on this topic. He is aware of the latest trends in the world. Next to blogging on BigData-Startups, he also blogs on SmartDataCollective.com, which is a platform with the world's best thinkers on big data. As such, he is a well sought after speaker on this topic. He is co-founder of 'Data Donderdag' a bi-monthly (networking) event in The Netherlands on big data to help organizations better understand big data. His book Think Bigger is a great essential resource for big data strategy.
What managers do is respond to daily crises, take on too much work, operate with continuous interruptions and make instant decisions. As a consequence, “fire prevention” doesn’t get the time and attention required.