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Strategic Finance

Dirty Financial Data Can Crush a Business. Here’s How to Keep It Clean and Organized

Published on December 21, 2021, Last Updated on February 1, 2024
Brad Mundell

Finance Manager in Customer Success

Turn CRM Chaos Into Clean Revenue Data

The foundation of any strategic finance function is clean, organized data. But dirty financial data is the kind of silent killer that you don't recognize until it causes a major issue. Don't sit back and wait for that moment. Learn how to commit to maintaining clean, organized financial data as early as possible in your organization.

Whether you’re building out a board deck, going through a due diligence list for a new funding round, or just starting at a new company and trying to get a handle on the numbers, there’s one thing that can grind your work to a halt—dirty financial data.

If you’re dealing with dirty data, the best-case scenario is that it slows down your financial analytics and reporting processes. But the worst case is that you report your numbers to the board and key stakeholders only to realize there was a significant error. Even though you can go back and fix the mistake, it still hurts stakeholder trust in the numbers.

Dirty financial data can be a silent business killer. You don’t realize it’s a problem until something breaks. But there are steps you can (and should) take as early as possible to ensure you’re creating a foundation for clean, organized financial data. It’ll help your team move faster and play a more strategic role in driving business growth.

1. Make Clean Data Part of Your Company’s DNA

Having clean data is crucial for reporting. Typos, inaccuracies, missing information, or other errors can dirty up your data and negatively impact the accuracy of reports you create with that data.

Finance sits in a solid position to champion data integrity across the organization. But it shouldn’t fall entirely on finance’s shoulders. Clean, organized data has to become a fundamental part of the company culture if it’s going to remain clean and organized at scale.

There are three pillars of clean data to address in your organization—processes, tools, and people.

Build the Right Processes

Sometimes simple tweaks to existing processes can make massive improvements to data integrity in your organization. Evaluate the data flows across your business and consider whether or not they could use improvements.

Ask yourself:

  • Are there guardrails we can put in place to make data entry more accurate and complete?
  • Do the teams responsible for providing data to the finance team know what you actually need and when you need it?
  • Are there technical challenges that prevent business users from maximizing data integrity?
  • Where is there friction in your processes that leads to frustration for business users and errors in data entry?

There are so many different areas of your business where process inefficiencies can hurt data integrity. But data workflows around the CRM are often the best example.

Because the CRM is so customizable, it’s easy for workflows on the sales side to lead to disorganized financial data and poor CRM hygiene. You might have duplicate records for opportunities. Or, you might have multiple closed deals with missing data because you don’t have enough required fields. If sales doesn’t know what’s needed to maintain financial data integrity, you’ll end up with problems down the line.

Working with sales ops to establish the right processes for CRM data entry will help you establish a culture of data integrity and keep your numbers clean.

Implement New Tools

There’s so much new technology available to finance teams that can help maintain data integrity. If you find yourself sinking time into the same tedious process of cleaning up data from certain systems month after month, it might be time to consider an automated solution so you can focus on more strategic tasks.

Some finance teams have trouble justifying spending money on themselves. Ask yourself these questions to decide whether an investment might be worth it:

  • How many hours per month is the team spending on the task you’re thinking about automating?
  • Are manual errors causing data integrity issues that waste time for your team?
  • How much time are department stakeholders spending on manual data management to keep financial data clean?

New tools don’t always have to be massive end-to-end platforms. You can find many specialized tools to clean up small (but painful) data integrity issues. For example, on a recent webinar, Spiff Director of Finance Meir Rotenberg talked about how he implemented one simple, automated tool to clean up duplicate CRM entries, so he didn’t have to worry about doing it manually every month.

Build the Finance Team as Needed

Optimizing processes and implementing new automated tools can significantly improve data integrity. But eventually, you’ll need to bring more people onto your finance team to keep up with business needs.

The best finance teams proactively hire new people rather than waiting for something to break. That’s what keeps a foundation of data integrity strong as a company scales. Some warning signs that it might be time to hire include:

  • Being stuck in a reactive reporting cycle as you respond to ad hoc requests for data from sales, marketing, or the CEO.
  • Knowing that a major tech stack shift like migrating from QuickBooks to NetSuite is on the horizon, and data complexity will increase.
  • Seeing that increased data volume pushes your tools and systems to their limits and, small optimizations won’t be enough to stay ahead.

Maybe it’s time to bring an accounting hire in-house to improve data collection. Or, maybe you need a financial analyst to help rethink data workflows for planning and reporting. Ensuring you have enough people on the finance team to manage the workload will help maximize data integrity at scale.

2. Be More Proactive with Reporting

You may not be able to anticipate all of the verticals and metrics you’ll need for a report that you create six months or a year from now, but you also don’t want to think to yourself, “Why didn’t I include that when we set up our CRM?” If you can spend some time thinking ahead, you may save yourself from having to go back and add tags or additional details to a year’s worth of data.

To facilitate this process, consider the needs of those viewing the reports and how complex those needs might be. Perhaps you’re the CFO at a small company, and the only people reviewing the reports will be you and the CEO. In that case, it might be pretty simple to sit down together and work out what you want to be able to get out of your financial data reporting.

However, if you’re at a larger company, you may need to create reports for an entire C-suite team, senior vice presidents, vice presidents, a board of directors, and so on, who all have different needs and concerns to account for. There’s an expectation of more financial rigor that you need to meet without getting weighed down by too many ad hoc analysis requests.

No matter the size of your leadership team, the possibility of overcorrecting—going too deep and including more verticals and metrics than you need—can introduce its own set of problems. If you include too much information, it can take much more time than you have available to close the books at the end of the month or create the reports you need. You need to master the balancing act of accounting for everything the team needs without creating an unmanageable workload for finance.

3. Make Sure Everyone Understands the Part They Play

When it comes to the process of pulling the data together to compile and analyze for reporting purposes, the size of the task will be highly dependent on a company’s size, age, and complexity. Many CFOs will say that one of their top priorities is optimizing this process to reduce the amount of time spent on this activity and dedicate more time to other key tasks, like forecasting.

APQC’s Open Standards Benchmarking database has been surveying companies for years to see how long it takes them to complete period-end reports. Its most recent report said that companies that close the month, quarter, or year best can do it in seven days or less, while companies that perform the worst take 16 days or longer.

This is where all the work you’ve done to prepare should come together. Everyone involved in financial data reporting should know what role they play in collecting data, what the timing is for each of their individual responsibilities, and what the timeline is for the process overall. Those providing data should know how long it takes them to get the data needed for reporting and ensure they can deliver it on time. The finance team should know how many data sources they will have and when they can expect to receive that data.

By adding transparency to the process of financial data reporting, you can also help your team understand why each small part matters to the whole. For example, if one person is behind on providing their department’s financial data, they may not realize how much of the process gets put on hold. Something as simple as a shared calendar that includes the key dates and deadlines for reporting can provide employees with better awareness of your reporting process.

Spend More Time Making Decisions that Drive Business and Less Time on Cleaning and Collecting Financial Data

Optimizing the way you handle financial data at every point in the process—from emphasizing clean data as early as possible to thinking ahead about reporting metrics and verticals—allows financial teams to create more accurate reports faster so they can spend more time on strategic planning and decision-making.

If you and your team are spending more time than you want on cleaning, organizing, and collecting data, consider adopting a Strategic Finance Platform. Mosaic integrates data from your most critical source systems to create a framework for financial planning and analysis. It’s the connective tissue that keeps data clean and organized under the surface while giving teams the real-time insights needed to drive business growth.

Request a personalized demo to learn more about how to create a foundation of data integrity with Mosaic.

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