Predictive Analytics Challenges: How To Prevent Selection & Implementation Blunders

Today, many ERP providers design solutions with built-in predictive analytics capabilities. If you’re about to implement such a system (or any predictive analytics technology), it’s important to know how challenging these deployments can be.

Neglecting to follow best practices can set you up for software sorrow or ERP failure. Today, we’re sharing how to prepare for predictive analytics challenges and lead a successful implementation. 

What is Predictive Analytics?

Before we dive into the issues you can encounter, let’s briefly define what predictive analytics entails. In short, this is a type of data analysis that helps users predict future outcomes and make real-time decisions accordingly.

Users can glean these insights from a variety of sources, including:

• Historical data
• Machine learning
• Artificial intelligence (AI)

6 Predictive Analytics Challenges​

While analytics tools sound straightforward in theory, they can be difficult to implement. Let’s look at a few of the most common challenges that project teams face as they attempt to integrate predictive analytics into their organization. 

1. Finding the Right Expertise

Sometimes, deploying predictive analytics requires the knowledge and expertise of advanced data scientists. These are experts who have a deep understanding of statistical modeling, as well as programming languages, such as R and Python. 

It can be difficult to identify these professionals in your area, and hiring them can be cost-prohibitive for some small businesses.

Thankfully, as this field continues to evolve, more user-friendly platforms are emerging. These make it easier for teams to get up and running without the support of a dedicated, full-time data expert. 

2. Collecting the Right Data

Predictive models can help companies anticipate and prevent mission-critical issues, such as manufacturing delays and machine malfunctions. 

However, these models are only as valuable as the data that you put into them. If your teams are capturing and transmitting incorrect or incomplete information, then the outputs won’t be as beneficial to end-users and executives.

To prevent this issue, we recommend establishing robust procedures around data collection and quality assurance. The information you pull should be granular and precise enough to avoid ambiguity and enable accurate predictions. 

With the right protocols in place, you can ensure that data is clean and correct before it goes into the predictive analytics solution.

3. Developing a Clear Strategy

Organizations implement predictive analytics because they know it will benefit their business. However, they aren’t 100% sure how to leverage it in a way that does so.

Before you select a solution, think about the goals and objectives your company has. What do you want this technology to do, and which specific pain points do you expect it to relieve?

Then, run pilot studies and talk with vendors to ensure you’re choosing software that will support your initiatives.

4. Facilitating User Adoption

For many employees, predictive analytics is new and uncharted territory. They may question your decision to implement it and could refuse to adopt it altogether.

Many predictive analytics tools are designed to be used as standalone solutions, which means that users must switch from their primary business applications to the new solution. This disrupts their daily routine and can leave them feeling overwhelmed.

To help ease the transition, we recommend looking for predictive analytics tools that can integrate with your existing applications, such as your ERP platform. Alternatively, if you don’t have a robust ERP solution, you can look for a new ERP with advanced analytics already built in. This is true of many of the systems in on our top ERP systems list.

Regardless of your decision, you should prioritize organizational change management (OCM) to ensure the people side of your project doesn’t fall by the wayside as the technical side ramps up.

Learn more about change management in ERP.

5. Developing a Workable Budget

Risk management tends to be one of the smaller functions in an organization. Predictive analytics being a tool primarily used for risk management, it can be difficult to secure a large amount of funding for predictive analytics projects as they’re viewed as serving a lower-priority function. 

To gain executive-level support and financial approval, work with an ERP consultant to project the ROI of the system you’re considering. This can go a long way in convincing your C-suite that such software is necessary and will deliver a host of tangible benefits. 

6. Finding a User-Friendly Solution

Many data analytics programs require users to go through a litany of steps before they can move from Point A to Point B – from initial prep and data cleansing to final model deployment and real-time prediction.

This can be both time-consuming and exhausting for users. It also leaves the system more prone to errors because if one step is performed incorrectly it could affect the entire outcome.

To remedy this, look for systems that take automate at least a portion of these processes. Modern predictive analytics software is more intelligent and streamlined than ever before. This greatly reduces the number of actions that users must take on their own. 

Data Analytics is Rapidly Changing

With today’s tools and technology, users can make better sense of the information that passes through their company every day. By leveraging past insights and current trends, they can predict what will happen next and take the appropriate action. 

While these tools are powerful, it’s important to understand predictive analytics challenges so you can take preventative action and avoid having to use our ERP expert witness services. 

Our ERP consulting team can help you select a predictive analytics solution or an ERP system with predictive analytics capabilities. Contact us below for a free consultation.