The volume of data collected

Risk managers and other staff are frequently overwhelmed by the volume of data generated in today’s data-driven enterprises and the introduction of big data. An company may receive data on every occurrence and interaction that occurs on a daily basis, leaving analysts with thousands of interconnected data sets.
A data system that automatically collects and organises information is required. In today’s world, doing this process manually is far too time-consuming and needless. An automated system will allow employees to spend less time digesting data and more time acting on it.

Data collection that is meaningful and real-time

With so much data available, it can be challenging to sift through it all and get the insights that are most needed. Employees that are overburdened may not properly examine data or may just focus on measurements that are easier to obtain rather than those that genuinely bring value. Furthermore, if an employee must manually filter through data, real-time insights into what is actually happening may be impossible. Data that is out of date can have a substantial detrimental impact on decision-making.
This problem can be solved with a data system that collects, organises, and automatically notifies users to trends. Employees can enter their objectives and easily generate a report that addresses their most pressing questions. Decision-makers may be confident that any decisions they make are based on complete and accurate information thanks to real-time reports and notifications.

Data visual representation

Data is frequently displayed visually in graphs or charts to be understood and powerful. While these tools are extremely valuable, they are tough to create by hand. It is frustrating and time-consuming to gather information from many sources and enter it into a reporting tool. Strong data systems allow for report creation with the push of a button. Employees and decision-makers will have real-time access to the information they require in an appealing and informative format.

Data from several sources

The following difficulty involves attempting to interpret data from various, disparate sources. Different types of data are frequently held in separate systems. Employees may not always be aware of this, resulting in inadequate or incorrect analyses. Manually integrating data takes effort and can limit insights to what can be seen quickly. Employees will have access to all forms of information in one location with a comprehensive and consolidated system. This not only saves time spent accessing multiple sources, but it also allows for cross-comparisons and ensures data completeness.

Data that is inaccessible

Moving data into a centralised system has little impact if it is inaccessible to those who require it. Decision-makers and risk managers require access to all of an organization’s data in order to gain insights into what is going on at any given time, even if they are working off-site. Accessing information should be the most straightforward aspect of data analytics. Any concerns with accessibility will be eliminated by a well-designed database. Authorized personnel will be able to securely see or amend data from any location, displaying organisational changes and facilitating quick decision making.

Data of poor quality

Nothing is more damaging to data analytics than incorrect data. In the absence of good input, output will be unreliable. Manual errors occurred during data entry are a major source of erroneous data. If the analysis is utilised to influence decisions, this can have serious repercussions. Another issue is asymmetrical data, which occurs when information in one system does not reflect changes made in another, rendering it obsolete. These concerns are eliminated with a centralised system. With obligatory or drop-down fields, data can be entered automatically, leaving little possibility for human error. System integration ensures that a change in one area is immediately reflected throughout the system.

Top-down pressure

CFOs and other executives are demanding more results from risk managers as risk management becomes more prevalent in firms. They anticipate better returns and numerous reports on various types of data. Risk managers may go above and beyond expectations with a comprehensive analytic system and simply deliver any needed study. They will also have more time to act on insights and increase the department’s value to the organisation.

A lack of assistance

Data analytics cannot be effective without top-level and lower-level organisational support. Risk managers will be helpless in many efforts until leaders empower them to act. Other personnel are also important: if they do not contribute data for analysis or if their systems are inaccessible to the risk manager, it will be difficult to generate meaningful information. To overcome this obstacle, emphasise the importance of risk management and analysis in all sectors of the organisation. When other team members realise the benefits, they are more likely to cooperate. Change is tough to implement, but employing a centralised data analysis system enables risk managers to simply explain results and effectively gain buy-in from diverse stakeholders.

Confusion or worry

Even if they grasp the benefits of automation, users may be confused or frightened about migrating from traditional data processing methods. Nobody likes change, especially when they are accustomed to the way things are done. To overcome this HR issue, it is critical to demonstrate how analytics reforms can streamline the work and make it more meaningful and satisfying. Employees can use extensive data analytics to remove duplicate duties like data collecting and report creation and instead spend time acting on findings.


Budget is another issue that risk managers frequently encounter. Because risk is frequently a tiny department, it can be difficult to obtain approval for large acquisitions such as an analytics system. Risk managers can get funding for data analytics by calculating the system’s return on investment and developing a compelling business case for the benefits it will provide. Check out our blog post here for more information on garnering support for a risk management software system.

A scarcity of skills

Due to a shortage of talent, several firms struggle with analysis. This is especially true in organisations that lack structured risk departments. Employees may lack the knowledge or capability to do extensive data analysis. This difficulty is alleviated in two ways: by addressing analytical competency throughout the employment process and by providing an easy-to-use analysis system. The first option assures that talents are available, but the second simplifies the analytical process for everyone. This type of system is accessible to everybody, regardless of skill level.

Data Scaling Analysis

Finally, as an organisation and the amount of data it collects grows, analytics can be difficult to scale. Collecting data and producing reports is becoming more difficult. To address this challenge, a system that can expand with the organisation is essential.

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