Risk Management

How Can Risk Managers Conquer Data Analytics Challenges?

Though data analytics is a primary vehicle of support for risk managers, they often find it difficult to handle. To leverage the advantages of analysis, address the challenges. In this article at ClearRisk, Rebecca Webb discusses 12 ways that risk managers can tackle data analytics challenge.

Challenges to Conquer for Data Analytics

Data analytics comes with a lot of benefits, but how to get those when you are rigged with problems? Following are 12 ways risk managers can conquer data analytics challenges:

  1. Voluminous Data Collection: As several devices are sending a huge amount of data, risk managers are overwhelmed while collecting those. Automating data analytics can reduce the burden of manually parsing through all that data.
  2. Risk of Missing Out on Relevant Data: As the volume is huge, employees tend to go for the data that is easy to collect and analyze. So, relevant data can be easily missed. Automated data alerts can reduce this hiccup.
  3. The Ordeal of Translating Data to Display: Though representing data in visible charts is good, it is tough to get appropriate data. To make data analytics work in your favor, leverage systems that can filter out all the unnecessary data first.
  4. Multi-Source Data Confusion: If it is difficult to manage data from one source for data analytics, imagine parsing through data from several sources. Reroute data into one place to remove confusion.
  5. Problem Accessing Information: With all the centralization and security systems, your data is useless if nobody can use it for data analytics. Set up authorization protocols to give relevant access.
  6. Inefficient Data Collection: After collecting data from various devices, if its quality is unsatisfactory, it is a wasted effort. With a centralized database, you can leverage mandatory or drop-down fields to reduce human errors.
  7. Senior Management Dominance: With risk management becoming more crucial for business success, risk managers are directly answerable to the higher management. If you have upgraded the data analytics system, you can provide reports regularly without much latency.
  8. The Gap in Organizational Support: For data analytics, you need to get support across hierarchies and business units. To get that, risk managers should insist on better risk management policies and analysis.
  9. Resistance to New Tools: It is common for people to resist automation as they fear job loss. The HR department should enable dialogs to mellow down such concerns and improve data analytics.
  10. Budgetary Meltdown: Limited funds are always a problem for risk managers. Measure the ROIs that can be expected from the new systems to achieve executive buy-ins.
  11. Scarcity of Skilled Resources: Employees in several organizations still do not have the required skills for data analytics. Hire capabilities or retrain existing resources for adequate resource pipeline.
  12. Updating Systems: Old systems cannot scale as fast as the organization or size of data. Invest in updated systems and regularly upgrade those to stay relevant.

To view the original article in full, visit the following link: https://www.clearrisk.com/risk-management-blog/challenges-of-data-analytics

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