Overcoming the Challenges of Predictive Analytics Solutions

Virtually every company wants to work smarter. Data and analytics are a crucial part of any corporate planning process. Data can hold the key for organizations to improve processes, predict trends, and set themselves up for future success.

Increasingly, companies are relying on predictive analytics to maintain a competitive advantage. Those that properly leverage past data to find key trends and determine what will happen in the future can set themselves up to be ahead of market trends.

Nearly every enterprise has access to this data, but not all are able to leverage the data successfully. The SAPinsider SAP Data Management Strategies Research Report found that about one quarter of companies are unable to support the type of analytics that their business demands.

To help organizations work smarter, the analytics experts at insightsoftware highlighted some of the most common roadblocks that companies run into when looking to utilize predictive analytics and the best ways for them to overcome those challenges.

Expertise

Predictive analytics are different from other types of analytics that companies may use. As such, they require different skill sets to understand and use correctly. Many solutions can only be deployed and used by data scientists.

Before settling on a predictive analytics solution, companies should ensure that their staff is equipped to maximize its value. Fortunately, there are a range of different solutions that have been released in recent years that do not require statistical modeling experience so that any user can take advantage of predictive analytics.

Adoption

Though predictive analytics are great in theory, they only help a company to the extent that their employees are willing to adopt a new analytics solution. Some users may be hesitant to use new technologies, while others may feel it adds another task onto their to do list.

To help adoption, insightsoftware recommends embedding predictive analytics directly into the applications that users already have experience with. This helps the practice feel more familiar and usable. Also, ensuring that users do not have to switch out of their primary application to a different interface can make analytics duties feel less cumbersome.

Empowering End Users

While predictive analytics can provide users with important insights into how to improve their business, they do not let users actually take action to implement those changes. Empowering end users can face the same issues that make adoption difficult – causing users to jump back and forth between applications is tiring.

Organizations should streamline the process of gaining insights and then putting them to use. They can do this by embedding intelligence into business applications. Allowing users to immediately apply the information they learned from analytics solutions can shorten workflows and make users feel more engaged.

Burdensome List Projects

Companies may be interested in launching a predictive analytics solution but choose not to implement it once they find out more about the process. Some projects can require over a dozen steps like preparing data, upskilling workers or hiring new ones, deploying the model, integrating the solution, and more.

It is important to note that not every predictive analytics solution is deployed the same. Many third-party options can streamline these processes by doing some of these steps themselves. This saves companies time and frustration.

Conclusion

Predictive analytics are a powerful tool. Every company can find some way to benefit from using past data to gain insights into the future. Leveraging predictive analytics to their fullest extent can give organizations an important boost in a competitive market.

To find out more about predictive analytics, users can learn more from insightsoftware. They released the Definitive Guide to Predictive Analytics, a white paper that analyzes how companies use predictive analytics, solving associated challenges, and how it can affect a company’s go-to-market strategy.