The Ultimate Guide to Data Cleansing for Finance Teams

Written by
Srividhya Gurumurthi
December 19, 2023

In the world of finance, data is king. It's what drives decision-making, helps identify trends, and ultimately impacts the bottom line. But what happens when that data is dirty? The outcomes become disastrous. In fact, according to Gartner, more than 50% of decision-makers say financial analysis does not portray the story of their business and its performance.

Somehow, I don’t find this surprising.

Anyone who has ever heard the phrase garbage in, garbage out knows the true essence of data management. The concept is straightforward: the quality of output is determined by the quality of the input.

Simply put, if you feed your system with unclean data, you're bound to get unreliable results. This aspect applies remarkably well in the world of finance, where the need for data cleansing is felt more intensely than ever before.

In this piece, you’ll learn,

- The impact of unclean financial data

- How awry data causes mishaps to finance teams?

- [Example] Data cleansing investments for improved bottom line

- Data management prophecies that best tackle unclean data

- Make finance data clean with Bluecopa

- Start building your data cleansing strategy   

Let’s explore.

The impact of unclean financial data

Financial data that's inaccurate, incomplete, or inconsistent can throw a wrench in the works. It can lead to costly mistakes such as poor decision-making, lost revenue, and reputation damage.

Imagine a scenario where your financial data is littered with discrepancies and redundancies. Your financial operations, the heartbeat of your organization, would be pushed into disarray.

From distorted financial reports to flawed decision-making processes, the repercussions of unclean data are far-reaching and potentially catastrophic. Finance data cleansing is a manual task borne from system flaws and human errors. It’s often a time-consuming endeavor that keeps your finance team stuck in a cycle of purely transactional activities.

How awry data causes mishaps to finance teams?

Now, consider two real-life scenarios.

You encounter a team member who stares at a report with furrowed brows and tries to make sense of the numbers that just don't add up. Adding to the rack, there’s a manager who is exasperated from constantly having to double-check figures.

These common-day problems have urged businesses to place a higher emphasis on data cleansing, which is the process of identifying and correcting errors in data sets to ensure they are uptight.

Arguably, data inconsistencies have a domino effect, such as:

- Introducing continual delays in reporting for finance teams 

- Causing distrust in internal systems and processes and hindering decision-making

- Inducing mid-management to devote valuable time to transactional activities

These data discrepancies lead to an ever-ending loop of unnecessary reviews and even worse, higher team attrition.

[Example] Data cleansing investments for improved bottom line

A leading eCommerce company faced grievous revenue leakages due to distorted financial data. To stem the tide, the team planned to invest in a thorough data cleansing initiative.

This exercise in turn improved data accuracy, facilitated better financial planning, and gave rise to a healthy bottom line.

This example supplements the Everest CFO survey finding, which reveals those growing investments in improving data quality yields >15% ROI.

Data management prophecies that best tackle unclean data

Below, we enlist two fundamental data management principles that have helped organizations manage the beast - soiled data. They are:

- System-managed checks during data entry and transfer: This eliminates erroneous data

- Continuous monitoring and exception management: This identifies outliers prudently

These data management processes enable teams to enforce appropriate and timely corrective actions.

Data cleansing rule of thumb

Although manual data massaging provides short-term benefits and immediately fixes data discrepancies, nailing that as a long-term strategy should be cautiously refrained. That’s because it is prone to a number of human errors.

Make finance data clean with Bluecopa

Bluecopa automates finance data cleansing completely. It is diligently designed to alleviate the pain points we've discussed so far.

Further, Bluecopa seamlessly integrates with your existing data systems, manages exceptions intelligently, and maintains stringent checks through automated workflows. The model frees up your finance team's time, allowing them to focus on strategic tasks rather than getting mired in transactional activities.

Let’s dive deeper into three key data cleansing capabilities of Bluecopa.

#1 Automated data cleansing modules to stop errors from creeping in 

Data cleansing through deduplication is a critical step in data management, ensuring accuracy and consistency. Duplication usually occurs due to human error, system glitches, or merging data from different sources. 

The AI-powered data cleaning module of Bluecopa identifies both exact deduplicates and records that are identical. This process not only improves data integrity but also aids in more precise data analysis and decision making.

Bluecopa leverages the power of automation to maintain stringent checks like data values and formats. Its model automatically checks data at every stage of the workflow, highlighting errors and enabling data correction seamlessly. It ultimately mitigates the risk of errors seeping into your financial reports. This feature can save countless hours otherwise spent on manual data checks.

#2 Real-time intelligent exception management ensuring clean data at all times

Identifying and validating exceptional transactions is a vital part of data quality assurance. Exceptional transactions refer to anomalies or outliers in the data, which could be due to errors, fraud, or other unusual circumstances. These anomalies can distort the overall view of data and can lead to misleading results in data analysis.

Bluecopa leverages statistical techniques, data mining algorithms, and machine learning models to spot deviations from typical patterns. Not only does Bluecopa identify exceptions, but also handle them smartly. 

The platform understands the context of exceptions both at the master data level and transactional level and takes appropriate action. This includes flagging the exception for manual review or correcting it based on predefined rules. With intelligent exception management, you can trust that your data is clean and reliable.

#3 Identifying missing data points and suggesting auto-fills

Missing data can be caused by a variety of factors, such as human error, system errors, or incomplete data entry. The absence of these data points can lead to biased or incorrect outcomes in data analysis. Bluecopa’s automated algorithms are employed to detect gaps in the data and propose suitable fill values based on patterns and relationships within the dataset. 

We use advanced techniques such as mean imputation, regression imputation, and advanced machine learning methods to solve the missing data problem. This auto-fill approach enhances data completeness, reducing potential inaccuracies in subsequent data analysis and decision-making processes.

Oftentimes, disparate systems pave the way to unclean data. Bluecopa's data cleansing model is designed to integrate flawlessly with your existing data systems. That means, it ensures a smooth flow of data across different systems, minimizing redundancy and discrepancy.

Bluecopa, a hub for clean financial data

Bluecopa's data cleansing model is a powerful tool that can transform the way you handle your data. It's efficient, reliable, and most importantly, it makes data cleansing a breeze. With Bluecopa, you can say goodbye to the woes of unclean data and look forward to a future of clean, reliable data.

By taking care of data cleansing, Bluecopa enables teams to:

- Perform value-added analysis

- Halt mundane and transactional activities

- Prepare for growth and profitability

Start building your data cleansing strategy

Unclean data is a silent menace that cripples your financial operations. The ultimate solution lies in recognizing the value of finance data cleansing and investing in the right tools and strategies.

And finally, clean data is the lifeblood of your organization and is crucial for a healthy business. So, if you’re wondering how uninfected data can unlock value for your business, reach out to us for a demo here.