Is Your Financial Data Integration Strategy Hurting the Business?

Business agility isn’t possible without the finance team and the work you do.

But it’s incredibly difficult to make sure you get access to the right data at the right time. And it gets harder still as data sources multiply. In this article, I’ll explore what financial data integration entails, how it benefits businesses, the different types of data integration and the potential outcomes.

Data integrations combine data from different sources to create a single, unified view. This helps businesses operate more smoothly, make better decisions, and work more efficiently by keeping all data accessible and consistent across different platforms. Financial data integrations specifically link financial information from various systems into one cohesive system. This makes it simpler to track expenses, check business credit reports, manage budgets, and produce financial reports, giving a clear and complete picture of a company's financial health.

At its most basic, financial data analysis is looking at historical information to look at the overall financial health of a company. This is financial data alongside sales, marketing and procurement information that builds up the big picture. The problem arises when all this data is stored in multiple, disparate internal and external locations. Parsing through all that data and making sense of it all can be incredibly difficult, especially when you’re trying to gather it from multiple locations. This is why data integration is so valuable, according to Leighton Weston, Global Account Director at Creditsafe.

“The main benefits of financial data integration include efficiencies in business processes and not having as many windows open to execute those processes. This saves time and resources, while also removing the need to do monotonous data entry tasks. 

One example of a use case is when customers are focused on having data in the right areas of their business to avoid extra clicks or steps. So, setting up automatic accounts with clean data makes things a lot easier.”

Another use case is taking specific financial KPIs and breaking them down into a profit and loss statement, cash flow statements, etc. These metrics could include:

• Operating expenses ratio (OER): This lets you track operating expenses by comparing the cost against total revenue. Any changes to the OER show if you need to increase sales without increasing operating expenses.

• Gross profit margin: This indicates how much profit you’re making on every dollar of revenue after all direct costs. The higher the gross profit margin, the more income you keep. 

• Current ratio: This shows whether you’re able to pay short-term debts/obligations over a year. The higher this number, the better your financial health. 

• Accounts payable turnover ratio: This shows how quickly you pay suppliers. The higher the ratio is, the faster you’re able to pay suppliers. It’s useful for negotiating payment terms in the future. A lower number may be preferable in certain situations because you’ll want to be managing your working capital effectively. 

Modern financial data ecosystems tend to be a blend of legacy systems, custom tools and disparate sources. So, figuring out the right kind of data integration should be assessed based on company goals, cash flow and credit decisions. To make it easy for you, I’ve outlined some of the most common methods and whether they’re good or bad for your business.. To make it easy for you, I’ve outlined some of the most common methods and whether they’re good or bad for your business.

Manual

Financial data is inputted by hand into spreadsheets, either by one person or multiple people. Or it means data integration is handled using custom-written code, without any automation. This is typically used for integrating a small set of data sources.

The assumed benefit of manual integration is that it’ll cost less because you’re doing everything yourself. But all it takes is a few duplicate or incorrect sets of data to cost you potentially millions of dollars to fix those mistakes. Also, there are all the hours wasted on manual data input and the difficulty of scaling financial projects.  

Middleware

This kind of financial data management involves software that connects applications and moves data between databases. Think of middleware solutions as the bridge between clunky legacy systems and data applications.

Middleware integration provides automation, removing the need for manual entry. But limitations come in when you consider that middleware solutions sometimes only work with specific data systems. Plus, they need to be maintained by a developer with technical knowledge and that can be an extra expense from a money and time perspective. The developer may need to spend time training other departments on how to use the software.

Application integration 

Software applications do all the heavy lifting for you with financial data integration. The benefits include simpler information exchange between departments and a uniform process where the application does everything automatically.

Similar to middleware solutions, this method relies on specialist technical knowledge and maintenance. Application integration can also be limited to end-to-end point connections. In other words, it can only deliver messages between the systems that it’s connected to.

Data warehousing 

Another approach is data warehousing, also known as common storage integration. This is when financial information is presented uniformly and that data is copied and stored within a digital warehouse.  

With this method there’s better data version management control i.e. data is accessed from one source instead of multiple places. There’s also the opportunity to conduct deep analytics into the quality of the data, without having to worry about compromising that quality. As with other integration methods, there are drawbacks. Creating data copies leads to increased storage and maintenance costs. 

I’m not here to tell you what the best financial data integration strategy is. I’ve simply presented options to explore and to provide a balanced view. But, according to Leighton Weston, Account Director for Creditsafe, data warehousing and middleware platforms can be advantageous.

“Pre-built, no-code integrations are very influential as they demand little to no resources from the client. It places the data you need in the right areas of your business processes. Salesforce and Netsuite have to be the most effective currently due to the ease of implementation.”

Salesforce offers plenty of customization from a financial data integration perspective. There’s financial analytics feature that can be connected with your CRM data to let sales, marketing and finance teams build a full picture of customers and prospects. 

There’s also the impact of fraud to think about with financial data. The finance sector is the second most vulnerable industry in America when it comes to data breaches. And the average cost of one data breach in an organization is $4 billion. 

Keeping this in mind, you’ll want to build in risk assessment protocols into your data integration strategy. Here are some tips:

• Set up risk scores to calculate the likelihood of fraudulent activity or a fraudulent business. These scores can be assigned based on DBT (Days Beyond Terms), legal filings, payment history and credit scores.

• Consider the size of the dataset that is being integrated with a fraud detection system. Because the bigger the dataset, the more it’ll impact how everything is processed. For example, a machine-learning-based system can make accurate predictions with a lot of data, but there should be optimization in place to make sure there’s no delay. 

• Think about what banks are doing and blend AI with fraud detection. AI will prevent false positives from being detected, meaning legitimate data and transactions can be flagged mistakenly. The knock-on effect is more accuracy and higher customer satisfaction.

The key takeaway here is that there’s so much data available. But if finance teams don’t have access to the right data at the right time, it can throw off the quality of the analysis and lead to decisions that hurt the business. 

It’s worth thinking about what financial data integration will look like in the future. Leighton Weston, Global Account Director for Creditsafe, shares his thoughts. “With data becoming much cleaner in North America and the ideology of a universal identification system, the focus will be on cleaning up non-trading entities within the U.S. and Canada. This will allow companies to finally have a reliable source of truth outside of a privately owned database and identification system.”