Predictive analytics is an advanced form of statistical data analytics that seeks to make predictions about future events based on historical data.

Summary by The World of Work Project

Predictive Analytics

Predictive analytics is the process of making predictions about the future that are informed by historical data. Organizations do this by looking for relationships between different types of data in historic data sets and changes in historic data over time. These relationships are correlations are then using these to make predictions about the future. This, of course, assumes that past actions can be used to make future predictions, which is often the case.

At it’s simplest we can think of predictive analytics as having computers look for things that usually happened in the run up to a specific event, then saying if those run-up things happen again, the same outcome event will probably happen again.

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An Example to Bring it to Life

We could analyze the web searches of employees in an organization. If we did, we might find a relationship between the number of people who search for new external jobs, and the number of people that go on to resign from the organization.

As a result of our analysis, we might be calculate that for every 100 people that search for external jobs this month, 2 people will hand in their notice next month.

Predictive Analytics in the workplace can yield many insights
Maybe Samantha Black will leave our organization this month…

If this relationship holds true, we can use it to predict the future. For example if we notice this month that 600 people in our organization have searched for new external jobs, we can predict that 12 people will hand in their notice to leave next month.

This is, of course, a huge simplification of a process that usually considers a huge range of data points with multiple correlations and which may require huge volumes of data to be accurate.

Why do we care about Predictive Analytics?

As computing power becomes cheaper, as our software programs are increasingly able to learn and as our data sets grow, it becomes easier and easier for us to build and test the complex statistical models required to make accurate predictions about the future. And as it gets easier and cheaper, we find more and more uses for this type of technology.

Increasingly we are seeing predictive analytics playing a larger and larger role in the world of work. Many organizations use predictive analytics in relation to their production and supply cycles, to assess consumer demand, to manage their just in time deliveries, to assess their expected server-loads and so on.

In recent years we’ve also seen predictive analytics take on a bigger role in back office functions and HR. In these areas it’s now particularly popular for things like predicting employee turnover and retention. Organizations also use it to predict regulatory compliance breaches, fraud and future capability requirements.

Person performing predictive analysis in the workplace
Some financial services organizations are using predictive analytics to predict future fraud and regulatory non-compliance issues.

The World of Work Project View

Predictive analytics is just statistical analysis aided by computers. We’ve been doing it for years and years in many areas. Sow it’s cheaper and more effective and entering more areas of the world of work.

There’s not too much to say about it really. It’s neither good nor bad, it’s just what it is. Provided that we use learned insights in a good way it will help us, if we don’t it won’t.

Predictive analytics does lend itself well to the conversation about the automation and changing nature of work. With newer technologies come some suspicion and some loss of labor. That said, new technologies also create new opportunities. For example, many data analysts are now needed to build and assess all of our analytical models.

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The concepts behind this post is based on lectures we have attended, podcasts we’ve hosted and conversations and general reading around the subject. There are no specific references for it.

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