Predictive Algorithms CAN Be Easy for Business Users. Here’s How!
When a business or a manager is trying to illustrate the value of augmented analytics to its business users, there is nothing more helpful than providing examples. Sample business use cases that relate to a team member role or responsibility, or an illustration of an analytical technique can allay user fears about the complexity of augmented analytics or about the real value of integrating an augmented analytics solution into the day-to-day workflow.
Gartner predicts that, ‘90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.’
‘Share this information with your users to help them understand how these various techniques can be used to identify the root cause of problems, to clearly understand challenges and opportunities and to strategize and share data.’
If your enterprise is planning to implement an augmented analytics strategy to enable data democratization and improve data literacy and the accuracy of decisions and strategies within the organization, it should support and encourage user adoption by illustrating the value of analytics for business users and how predictive analytics can help team members to complete tasks and make confident decisions.
In this article, we provide a list with links that will detail some of the many analytical techniques your business users will employ and provide examples of how these techniques can be used to solve problems and identify opportunities with clear, easy techniques and results. Business users are often daunted by the titles and perceived complexity of the predictive analytics algorithms. But it is crucial to understand that these sophisticated techniques are simplified in use within the augmented analytics solution, so users do not have to be a data scientist, an IT professional, or a business analyst. Self-serve assisted predictive modeling provides recommendations and suggestions based on data volume, type and other parameters, so users will be directed to the appropriate analytical techniques and receive clear, concise results that are easy to understand and apply for confident decisions.
We invite you to explore the explanations and examples contained within each of these links and to share these with your users to help them understand how these various techniques can be used to identify the root cause of problems, to clearly understand challenges and opportunities and to strategize and share data.
‘Allay user fears about the complexity of augmented analytics and the real value of integrating an augmented analytics solution into the day-to-day workflow.’