Data science and analytics remains a top priority for leaders in finance

April 22, 2021

Data represents a key element of success in the finance industry. Recent studies suggest that 60% of finance leaders consider the leveraging of data science and analytics for clearer insights and enhanced decision making as top priorities for this year.

The ability to use data analytics and big data to create a competitive advantage and to improve the operations and management of strategies plans are considered top factors for senior-level executives across the world.

Data analytics can be applied in three particular areas. In regards to planning, data analytics can be utilised for efficient risk management, data testing and statistical sampling. Data analytics can also improve the delivery of audits, generating rapid and efficient monitoring of particular controls, detecting possible cases of fraud and recognising any trends suggesting future risk.

Based on the findings from Protiviti’s latest finance trends survey, security, privacy and data analysis are considered top priorities. All three are connected with data, with data analytics regarded as very important by over 60% of CFOs surveyed.

Data is viewed as a very important area because it supports the generating of useful commercials insights, the ability to increase sales and improve the overall management and decision-making process. It also enhances the internal operations of the finance area, with over 50% of respondents regarding data analytics as a vital element of process improvement. Applying data analytics effectively, however, requires several factors to be put in place.

One key factor that senior finance leaders are struggling to cope with is the quality of data. As with most other industries, the analytical and reporting that is delivered by finance depends on the overall quality and completeness of the data used. Data governance is a vital part of this process. Finance leaders need to ensure they implement a strong data governance system and understand data ownership in the business.
The quality of data can also be improved by dedicated data management, an area that can require considerable time and expense. Once implemented and ready, however, finance leaders will be capable of gaining a much deeper understanding of various functions associated with profitability and risk. Without quality data, the results from any data and analytical processes will not necessarily hold as much reliability and value to the business. This is particularly true with the continued growth of AI and Machine Learning, meaning data quality will become even more important to businesses soon.

Data security

In a time of rising cybersecurity threats and new data legislation, businesses need to keep a close check on data safety. If any financial data is leaked, businesses could face considerable financial and reputational damages. This is why security and data in finance are regarded as a top priority by finance leaders. Over 70% of CFOs and VPs listed this as the most important factor.

For larger financial organisations, the stakes are much higher. The volume, complexity and sensitivity of their data reach another level and with more businesses moving to the cloud, the range of security risks continues to increase.

Other demands pointed out by finance leaders are the changing demands of customers, managing regulatory changes, the movement to the cloud and new tax requirements. One particular area that is often covered in the robotics process automation (RPA) market, yet many finance leaders are taking a fairly cautious approach to RPA. Only around 20% of CFOs and finance leaders regard RPA to be a top priority for the next year.

Tony Abel, the MD of Protiviti explains that many businesses are still collecting more information on how to leverage tools like RPA. Growing financial concerns and the demand for improving efficiency mean the use of RPA and other innovative technologies will increase over time.

One of the key benefits of RPA is that it can be deployed relatively easily and perform repetitive tasks across various systems. For many financial businesses, RPA could be effectively used in the accounts payable area, for processing invoices, payment verification and account reconciliation.

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