“Information is the oil of the 21st century, and analytics is the combustion engine,” said Peter Sondergaard, former Senior Vice President at Gartner. To extract the value from this new oil and turn it into strategic steps to business success, data must be broken down and analyzed.
According to an IBM study, by 2020, 1.7MB of data will be generated every second per person. That is an enormous goldmine of information that potentially hides critical business insights.
The keyword is “hides.” Research by IDC shows that 90% of the data produced is unstructured. So, once you have a clear picture of a goal you want to reach and have prepared relevant data sets for the analysis, AI and ML algorithms will come into play.
By analyzing huge amounts of data, these algorithms will then start detecting patterns to support proactive business decisions—those that head off possible challenges before they arise.
As ML and AI evolve, predictive analytics is finding its way into various business use cases. Here is a look at just some of the uses it can be put to.
Predictive Analytics for More Efficient Onboarding
When welcoming a new member to the team, efficient onboarding is crucial because it defines the time an individual will need to reach full productivity and impacts their desire to stay or leave.
Keep in mind, there’s no established timeframe to determine successful completion of onboarding. We’re all different, so cognitive abilities, professional experience, educational background, and available skill sets influence the getting-up-to-speed period.
Predictive analytics can be used to deliver personalized recommendations for each user to guide them through a custom journey to success. By analyzing thousands of variables about each user and comparing them with years of accumulated data, a predictive analytics software offers recommendations for personalized activities tailored to every individual’s unique needs for the most effective onboarding.
Predictive Analytics for Performance Improvement
Usually, poor performance doesn’t mean that a person is lazy. In most cases, low workforce productivity results from three factors (the combination of several or one of them):
- The lack of what (objectives and guidance)
- The lack of how (skills and knowledge)
- The lack of why (engagement and motivation)
Again, predictive analytics can be helpful here. We can use it to determine personalized suggestions triggered for each user at the right time to tell them what and when to do something, guiding them through all tasks needed to complete a goal. This way, neither new recruits, nor your existing workforce feels lost or confused. As a result, performance management becomes much easier.
Predictive Analytics for Better Learning
To remain engaged, most employees benefit from being offered ongoing learning opportunities. No two people learn in the same way though, so if those learning opportunities can be personalized they are likely to be far more successful.
Such targeted learning is possible thanks to predictive analytics. It can identify patterns in data that tell the system about potential challenges other users have encountered while performing specific tasks and compare those to data gathered about the subject to create a learning program that is best suited to their personal learning style.