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12 Challenges of Data Analytics and How to Fix Them

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Data analytics are extremely important for risk managers. They improve decision-making, increase accountability, benefit financial health, and help employees predict losses and monitor performance. Not convinced? Check out two of our blog posts on the topic: Why All Risk Managers Should Use Data Analytics and 6 Reasons Data is Key for Risk Management.

However, achieving these benefits is easier said than done. There are several challenges that can impede risk managers’ ability to collect and use analytics. Fortunately, there’s a solution:

12 Challenges of Data Analytics and How to Fix Them

1. The Amount of Data Being Collected

With today’s data-driven organizations and the introduction of big data, risk managers and other employees are often overwhelmed with the amount of data that is collected. An organization may receive information on every incident and interaction that takes place on a daily basis, leaving analysts with thousands of interlocking data sets.

There is a need for a data system that automatically collects and organizes information. Manually performing this process is far too time-consuming and unnecessary in today’s environment. An automated system will allow employees to use the time spent processing data to act on it instead.

2. Collecting Meaningful and Real-Time Data

With so much data available, it’s difficult to dig down and access the insights that are needed most. When employees are overwhelmed, they may not fully analyze data or only focus on the measures that are easiest to collect instead of those that truly add value. In addition, if an employee has to manually sift through data, it can be impossible to gain real-time insights on what is currently happening. Outdated data can have significant negative impacts on decision-making.

A data system that collects, organizes and automatically alerts users of trends will help solve this issue. Employees can input their goals and easily create a report that provides the answers to their most important questions. With real-time reports and alerts, decision-makers can be confident they are basing any choices on complete and accurate information.

3. Visual Representation of Data

To be understood and impactful, data often needs to be visually presented in graphs or charts. While these tools are incredibly useful, it’s difficult to build them manually. Taking the time to pull information from multiple areas and put it into a reporting tool is frustrating and time-consuming.

Strong data systems enable report building at the click of a button. Employees and decision-makers will have access to the real-time information they need in an appealing and educational format.

4. Data from Multiple Sources

The next issue is trying to analyze data across multiple, disjointed sources. Different pieces of data are often housed in different systems. Employees may not always realize this, leading to incomplete or inaccurate analysis. Manually combining data is time-consuming and can limit insights to what is easily viewed.

With a comprehensive and centralized system, employees will have access to all types of information in one location. Not only does this free up time spent accessing multiple sources, it allows cross-comparisons and ensures data is complete.

5. Inaccessible Data

Moving data into one centralized system has little impact if it is not easily accessible to the people that need it. Decision-makers and risk managers need access to all of an organization’s data for insights on what is happening at any given moment, even if they are working off-site. Accessing information should be the easiest part of data analytics.

An effective database will eliminate any accessibility issues. Authorized employees will be able to securely view or edit data from anywhere, illustrating organizational changes and enabling high-speed decision making.

6. Poor Quality Data

Nothing is more harmful to data analytics than inaccurate data. Without good input, output will be unreliable. A key cause of inaccurate data is manual errors made during data entry. This can lead to significant negative consequences if the analysis is used to influence decisions. Another issue is asymmetrical data: when information in one system does not reflect the changes made in another system, leaving it outdated.

A centralized system eliminates these issues. Data can be input automatically with mandatory or drop-down fields, leaving little room for human error. System integrations ensure that a change in one area is instantly reflected across the board.

7. Pressure From the Top

As risk management becomes more popular in organizations, CFOs and other executives demand more results from risk managers. They expect higher returns and a large number of reports on all kinds of data.

With a comprehensive analysis system, risk managers can go above and beyond expectations and easily deliver any desired analysis. They’ll also have more time to act on insights and further the value of the department to the organization.

8. Lack of Support

Data analytics can’t be effective without organizational support, both from the top and lower-level employees. Risk managers will be powerless in many pursuits if executives don’t give them the ability to act. Other employees play a key role as well: if they do not submit data for analysis or their systems are inaccessible to the risk manager, it will be hard to create any actionable information.

Emphasize the value of risk management and analysis to all aspects of the organization to get past this challenge. Once other members of the team understand the benefits, they’re more likely to cooperate. Implementing change can be difficult, but using a centralized data analysis system allows risk managers to easily communicate results and effectively achieve buy-in from multiple stakeholders.

9. Confusion or Anxiety

Users may feel confused or anxious about switching from traditional data analysis methods, even if they understand the benefits of automation. Nobody likes change, especially when they are comfortable and familiar with the way things are done.

To overcome this HR problem, it’s important to illustrate how changes to analytics will actually streamline the role and make it more meaningful and fulfilling. With comprehensive data analytics, employees can eliminate redundant tasks like data collection and report building and spend time acting on insights instead.

10. Budget

Another challenge risk managers regularly face is budget. Risk is often a small department, so it can be difficult to get approval for significant purchases such as an analytics system.

Risk managers can secure budget for data analytics by measuring the return on investment of a system and making a strong business case for the benefits it will achieve. For more information on gaining support for a risk management software system, check out our blog post here.

11. Shortage of Skills

Some organizations struggle with analysis due to a lack of talent. This is especially true in those without formal risk departments. Employees may not have the knowledge or capability to run in-depth data analysis.

This challenge is mitigated in two ways: by addressing analytical competency in the hiring process and having an analysis system that is easy to use. The first solution ensures skills are on hand, while the second will simplify the analysis process for everyone. Everyone can utilize this type of system, regardless of skill level.

12. Scaling Data Analysis

Finally, analytics can be hard to scale as an organization and the amount of data it collects grows. Collecting information and creating reports becomes increasingly complex. A system that can grow with the organization is crucial to manage this issue.

While overcoming these challenges may take some time, the benefits of data analysis are well worth the effort. Improve your organization today and consider investing in a data analytics system.

ClearRisk’s cloud-based Claims, Incident, and Risk Management System features automatic data submission and endless report options. Management will be impressed with the analytics you start turning out! 

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Topics: data analysis risk management data analytics for risk management challenges in data analytics