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The Importance of Data Quality in Finance

Poor Data is now the biggest issue that could wipe outboards and managements

Data quality has emerged to be one of the most pressing concerns for CFOs. In the IBM 2021 Global C-Suite Study, 70% of leading CFO’s say implementing enterprise-wide data standards is a top priority to help their organizations consolidate systems, cut costs, and scale rapidly. As companies become more reliant on data to drive success, the risks of misreporting due to poor data have become critical.

However, while most companies recognize the importance of quality data, many still don’t know how to use it effectively. This poses a significant challenge for many companies, as the costs of faulty data can be just as big, if not more so, than the benefits of using quality data.

No matter what the exact use of the data, data quality is important. From sending market materials to customers to maintaining a database of customer data, without high data quality, the data cannot fulfill its intended purpose.

Common Challenges Finance Leaders Face When Collecting Data

When it comes to analyzing financial data, CFO’s are frequently faced with the issue of either having an abundance of data and inadequate time to sort through it, or simply insufficient quality data. These scenarios can pose substantial problems for businesses in the long run, since they can degrade a company’s competitiveness in the industry and impact financial bottom lines.

Inconsistent forms of financial data collected without sufficient planning or purpose are one of the most common problems businesses face today. These problems tend to only be discovered after a significant amount of money and effort on data collection has been spent and when the company finally decides to use the data to make decisions. In this situation, to make the data useful, an immense data clean-up is required.

In a clean-up operation, data engineers are needed to assess the state of the data, identify flaws in the data collection pipeline, and provide recommendations on how to realign operations so that each data point established may be easily mined for the essential insights.

CFO’s should strive to acquire clear and purposeful data that will enable them to make sound business decisions and accurate financial projections for the company.

Disregarding Data - Not A Smart Move

If left unaddressed or overlooked, the repercussions of collecting faulty data or false data can lead to serious implications. Companies may be losing revenue due to non-performing or outdated business functions without even realizing it and company functional leaders may also make poor business decisions based on false assumptions about their consumers and what they want, resulting in wastage of resources.

Not investing the effort to correct bad data collection practices can also lead the company’s financial data to become highly biased or skewed. This will have an impact not only on the company’s business decision-making process but also on overall productivity since IT teams will be required to analyze and correct multiple data issues across affected departments.

Finance executives must be meticulous in their collection and interpretation of financial data. One erroneous assumption based on inaccurate data can lead to dire consequences.

High Data Quality is Essential

Increasing data quality is strongly necessary and brings in many positives. It enables strategic systems to integrate all related data to provide a complete view of the organization and the interrelationships within it.

Boards are starting to request more insights from sophisticated and real-time reporting and analytics. These insights are a challenge in themselves. They are risky and can be restricted if not based on consistent, reliable data which results in CFO’s constantly struggling to keep the raw data clean.

CEO at Metapraxis, Simon Bittlestone says that “As organizations become more data-driven, the effects of poor data decisions and the resulting performance will become much more serious”. Organizations of all sizes are tangled in countless systems that do not talk clearly to one another, which makes managing data a serious challenge.

Ensuring quality requires high efforts of manipulation, validation, recoiling data, and correcting errors. Thankfully today, cloud-based software offers more flexibility and integration tools that bring new insights from the beasts that are structured and unstructured data.



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