Need to pitch your startup to a potential investor? You’ll need quality data.
Want to identify your budget for the next quarter? You’ll need quality data.
Need to be sure you’ll hit your target revenue? You guessed it. You’ll need quality data.
However, it’s not unusual for startups to deal with data that has old, inaccurate, or invalid information. Also known as dirty data, this inaccuracy can put your company in a bind.
Identifying dirty data and cleaning it up is one of the most important tasks of a growing startup. Here's what to know about the technical tools and expert skills you can rely on to get the most out of your data and keep it squeaky clean.
What Is Dirty Data?
Dirty data is information in your database or customer relationship management (CRM) program that is incorrect, inaccurate, or overly skewed.
Data that is considered "dirty" is not helpful and will make getting accurate results difficult. Unclean data is a result of technical issues, such as duplicate imports, or human error, such as swapping numbers around.
The Importance Of Data Quality In The Financial Industry
It's no exaggeration that data quality is the lifeblood of the financial industry. Whether it's the numbers in a financial account or the Social Security number of the account holder, having the most accurate data can improve your decision-making and financial outcomes.
It also stops you from investing in dead-end leads or paying a hefty financial penalty for dealing with dirty data. Tech consulting firm, Gartner, estimates that dirty data issues costs an average of $12.9 million per year.
Strategies For Cleaning Your Data
An essential strategy for keeping your financial data usable is cleaning it. But what does cleaning data mean?
When you clean financial data, you remove any potentially incorrect, duplicated, or anomalous figures. Consider the following strategies to help identify and fix dirty data.
Remove Duplicate Entries And Address Missing Data
It's important to spot and consolidate duplicate entries, such as two profiles listed for the same client or an invoice entered twice.
These duplicate entries may be exactly the same or created at different times with varied information.
Detect And Handle Outliers
Outliers may be some of the simplest dirty data to spot because their values are so far outside the typical range.
When you come across unusually variable values, investigate their cause and impact, and decide whether to correct or remove them based on the context.
Standardize Data Formatting And Implement Data Quality Monitoring
To ensure that you're collecting consistent data, consider standardizing data formatting. This means that all information comes in through the same collection form or document input method.
For example, dates are invariably written in the same format, such as MM/DD/YYYY, and financial values always have two decimal places.
Rectify Inaccurate Data Entries And Cross-Reference
Though time-consuming, combing through data to verify accuracy is one of the best steps to take to improve its quality.
One way to do this is to cross-reference entries from original sources or hard copies. When you have a verified source of data, you can increase the precision of your overall dataset.
Examples Of Low-Quality Data
Poor-quality data can come from several sources, many of which may be accidental. Learning to spot examples of dirty data and limit this low-quality content from your CRM or other databases and dashboards can prevent errors from turning into bigger problems.
Look for the following dirty data examples when performing a cleanup.
Incomplete, Inaccurate, And Invalid Data
You may have inaccurate or invalid data in your financial records, such as:
- Spreadsheet errors: If there were errors in a spreadsheet formula used for calculating sales tax, those calculations are now completely unusable.
Double-checking the formula is the best way to catch these mistakes.
- Customer profiles: Some of the customer entries in your database may be incomplete, inaccurate, or invalid.
Many of these issues may result from inconsistent self-reporting habits from customers. Perhaps you have a customer's email address but not a last name, or you have their old physical address, but they have since moved without updating you.
- Invalid financial data: Invalid data may have been mistakenly entered by a team member.
Mistyping numbers or skipping lines can cause entire financial statements to be incorrect.
Inconsistent Data Formatting And Duplicate Entries
Issues with data accuracy may also arise due to formatting and duplicate entries.
- Formatting: If your numbers aren't all right-aligned, the human eye may have difficulty calculating sums or observing comparisons.
Mistakes are more likely when formatting is untidy or inconsistent in a statement.
- Duplicate entries: Businesses may encounter duplicate entry issues when they keep digital and paper records.
At month's end, if a team member attempts to record bills using both paper receipts and online bank transactions, they may end up entering duplicate charges. This provides an inaccurate picture of expenses and cash flow.
Outliers, Anomalies, And Bias
Values that fall significantly outside the rest of your data may be outliers, anomalies, or the result of bias.
- Record errors: If your invoices have been duplicated, inadvertently inflating your totals to an outlying value, take the time to re-enter this information correctly.
- Anomalous values: If you have a quarter where sales appear to be significantly higher, verify that it is not the result of an error.
- Bias: You may experience something called "availability bias," where you have incomplete data on sales, making it seem as though their averages are all higher than reality, which can negatively impact business decisions.
Don't Let Poor Data Quality Affect Your Financial Decision-Making
Give your team the best opportunities to make sound financial decisions with high-quality data. A small amount of time spent cleaning up your data now can lead to better decisions and, ultimately, more profit.
A unified financial platform that organizes all of your data in one place can serve as an important tool for managing your data-cleaning process. The simplicity of organizing from multiple sources in a singular location means there is less friction in your cleaning efforts and better tools to maintain this data.
Use data platforms to improve your cleaning process and get the most from your numbers.