How Bad Data Sneaks into Financial Planning

This poor data quality translates into an average of $14 million per year in a ripple effect of financial loss, missed opportunities and high-risk decision-making. Also, it can impact your budgeting, financial planning and data management in a negative way.

That’s why we’re diving into different types of bad data, their outcomes and what you can do to prevent bad data from sneaking into your financial planning and analysis.

When two or more identical records appear, this is considered duplicate data. What’s the big 

deal? Surely it’s a case of just finding that error and removing it as quickly as possible, right? If only it were that simple.

When duplicate information appears for thousands of records, it’s not going to be an easy fix to delete a couple of errors in a spreadsheet by hand. And when 80% of spreadsheets typically contain errors, you’ve got your work cut out for you.

There’s more than just a time cost to duplicate data. The SiriusDecisions 1-10-100 Rule by W.Edwards Deming states that it costs $1 to verify a record as it’s entered, $10 to fix and $100 if nothing happens. Now multiply that rule by tens of thousands of duplicate data. 

Plus, you’re going to have misrepresentation of inventory counts and multiple lines of credit for the same organization. Not to mention unnecessary billing that’s going to confuse your finance and sales teams and potentially cause tension between them. The last thing you want to happen is for a ‘he said, she said’ argument to break out across your teams that will bring cash flow to a grinding halt. 

This kind of bad data involves the same records showing differing attributes. When this happens, you have ineffective budgeting across teams, not calculating the right amount of investment for projects and supplier & employee payments not being received because of inaccurate accounting.

A cautionary tale of conflicting data happened with Uber. Back in 2017, the taxi firm had to pay out tens of millions of dollars to New York drivers because of a data error. Since 2014, Uber had been taking a 25% cut from the net fare of a ride (i.e. the price a customer paid minus the sales tax and other fees).

But Uber realized it had been taking from the gross share of a ride (i.e. sales tax and worker compensation fees). This simple accounting error severely damaged Uber’s brand reputation and employee trust. 

Here we’re talking about missing attributes in records (i.e. customer contact email addresses or the last time they were contacted). There’s several issues that crop up with this. A risk of payroll not being processed and employees growing resentful. An inability to rank high/low-risk customers. Or extending credit to high-risk customers by mistake. These can all happen.

One place where incomplete information may crop up is in your CRM platform, especially if the responsibility for that data is shared among multiple people. Whether it’s a salesperson forgetting to input data directly after a sales call or finance not bothering to check if data has been updated, having too many (or no) cooks in the kitchen isn’t a good idea.

Returning to the 1-10-100 rule, it definitely applies to your CRM, as venture capitalist Tomasz Tunguz discovered. “If I’m lazy and don’t correct the error, later on that day, one of my colleagues might search our CRM for the company and comes across the erroneous record which he suspects is inaccurate. First, he will check his notes, then he will call me to verify and then he will change the record. The rigamarole has undermined his trust of my data and the 10 minutes he spent correcting my data entry are wasted. Worst of all is if I contact a startup to inquire about an upcoming fund raise with incorrect data. As a result, I could miss an opportunity to partner with a great company because of incorrect timing or lose credibility with the startup’s executive team. The cost to the firm could be in the tens of millions of dollars.”

Also, incomplete data will certainly have a detrimental effect on your marketing budget. Nearly a third of the average marketing team’s time is wasted on bad data, leading to 20 cents of every dollar draining away on marketing campaigns. 

Data validity is about information being relevant to the business metrics that are being described. Invalid data is anything that doesn’t conform to standardization (i.e. a 10-digit phone number being the standard metric and then someone decides to add 11 digits). The latter is invalid. 

The consequences of invalid data include skewed analysis, inaccurate financial figures and ethical violations. For example, a member of the sales team may put in the wrong product name and this erroneous information is then sent to the warehouse, adding to the total sales numbers for the day. The POS system is down, so it makes it difficult to get the sales numbers for the day, leading to incorrect sales figures for the month.

 At the end of the chain, the Sales Director decides to change an input and manipulate the total amount of sales for the month. The board sees success on paper, yet the invalid data will only create more problems later on.

Data synchronization is when information is constantly shared between one or more systems accurately. It’s of the utmost importance because it makes sure data is accurate. Plus, different teams can see multiple attributes at the same time and make the right business decisions.

Unsynchronized data creates team friction, a misalignment in goals and increases the risk of losing money in the long run. For instance, you might record a new sales deal in your CRM. But if it just sits there and doesn’t get integrated with your ERP system or other technologies, then how is finance meant to approve the deal or find out the credit history of the potential customer?

Now that you’ve seen the consequences of bad data, here are the surface-level benefits of cleaning your data and integrating it with your accounting systems.

• Streamlined data flow
• Less need for manual entry
• Fewer instances of data duplication
• More accurate budgets & forecasts
• Less wasted spend
• Higher project ROIs