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6 Tips for Overcoming Predictive Analytics Challenges

Like most software or tools, predictive analytics can cause challenges or problems for companies, which are often not recognized until the technology fails to deliver the expected results. These problems can sometimes arise from poor planning or unrealistic expectations, but there are some easy ways around ensuring that these issues do not occur.

However, these challenges are easily overcome or avoided. Read below the 6 most common predictive analytics challenges, and how these can be overcome. 

How to Overcome Predictive Analytics Challenges

1. Lack of Strategy 

Determining your goals and objectives will help you to decide which software is best suited to your business. This will align your objectives, and help to keep this goal in mind when working to ensure that efforts are put into ways to accomplish the strategy. Research should be undertaken, as well as pulling the correct data sources together. You should also run some informal tests (pilot studies) before the launch of the new technology, to get a feel for the technology and how it can be used in the real world business situations. 

2. Budget Restrictions

As risk management can often be a small department, it can be hard to get approval for significant purchases, such as data or predictive analytics software. To overcome this, risk managers should identify a budget for data analytics by measuring the ROI (Return on Investment) of a system, and make a strong business case for the benefits that can be achieved. They should also take into consideration the storage costs of keeping large amounts of data, and how best to overcome this. 

3. Poor Quality Data Inputted 

If inputted data is inaccurate, it will affect the output and make this unreliable. A key cause of this is manual errors made with data entry, that can lead to negative consequences if the analysis is used to influence decisions. To tackle this, ensure that there are automatic data inputs – such as drop-down fields – meaning less room for human error. 

4. Lack of Organisational Support 

Data analytics cannot be effective without organizational help and support. This is through all parts of the organization – from risk managers to those submitting data. Organizations should emphasize the risk management to all aspects of the organization, as well as education on the benefits of the system. 

5. Skill Shortage 

It is vital that people in the team understand the technologies capabilities, and how to exploit these. By helping to develop your team’s skills, they can broaden their roles. You can also ensure that analytical skills are present during the hiring process, and also have an analysis system that is easy to use. It also means that everyone can utilise the system, regardless of their skillset. 

6. Employee Fear 

Some employees may fear or be anxious about switching from traditional data analysis methods. Therefore, it is important that organizations define and state how predictive analytics will streamline the organization and individuals roles. 

 

Although predictive analytics can have some challenges, these can easily be solved with good planning and software that suits your business or industry, such as Financial Services or Healthcare. If your business is interested in Predictive Analytics but doesn’t know where to start, contact us now to see how we can help.