Good data is the fuel for any efficient sales department. It provides vital information about your customers and their needs, allowing your reps to deliver the perfect offer every time.
However, not all data is useful. Even if you have a bulging CRM, it might be that some of the information you have collected is completely inaccurate.
The solution? Measure your data quality.
There are numerous different ways to analyze your data sets and unearth data quality issues. In this post, we’re going to look at some of the most effective techniques.
Why Data Quality Measurement Is Super Important
In theory, collecting a ton of data should help your sales team to unlock deals that would be otherwise missed.
Unfortunately, many companies have databases that are riddled with duplicates, out-of-date information, typos, and other flaws. In turn, this poor data quality can have a real impact on your sales performance.
If you believe that a potential client has a budget of $1 million to spend, but they actually have $10 million in the bank, you could be missing out on a massive contract.
Likewise, sending emails to unchecked accounts and calling prospects by the wrong name are both great ways to lose business.
In other words: making sure your data is up to scratch is really, really important.
What Are the Factors That Can Affect Data Quality?
Obviously, any mistake in your data is a problem. However, there are many different ways in which information can be wrong.
Over time, experts in data quality management have settled on standard labels for the various errors you may encounter within a database.
Here’s a list of the key factors:
- Accuracy — Perhaps the most obvious, this factor relates to whether the data you collect provides an accurate representation of the real world
- Completeness — Even if your data is accurate, you might be missing some key information
- Relevance — If your CRM is full of irrelevant information, it may be difficult for your sales reps to find the most important insights
- Validity — Data is only useful if it’s collected and stored in the right format, and acquired from appropriate sources
- Timeliness — Depending on the type of data you hold, it might need to be updated once a year or every day
- Consistency — If you keep multiple versions of the same data points, all the versions should be identical
Checking for these factors allows you to verify the quality of your data, and identify any problems that might exist. For example, poor overall accuracy suggests that you need to shift your attention to the most trustworthy sources.
It’s also a good idea to run tests on information that you acquire from B2B data providers. While some vendors provide highly accurate data, others sell access to databases that are riddled with error. Data validation tests can help you avoid the cowboys.
How to Measure Data Quality: 9 Key Tests and Metrics
Keen to start checking your data? While there are many different ways to measure data quality, some methods are more insightful than others.
To help you create the perfect data quality assessment, here’s a checklist of essential metrics:
1) Data/Errors Ratio
As a starting point for your data assessment, it’s a good idea to set yourself a benchmark with a data/errors ratio.
This metric uses a simple equation: the number of errors in your data, divided by the total number of data points.
While the data/error ratio won’t tell you a lot without context, it’s an important test for measuring changes over time.
2) NULL Values Test
Another important benchmark is the number of data sets that have missing information. You can find this figure through the NULL values test.
A large number here is a warning light that indicates either that some part of the data gathering process is badly flawed, or data is being lost in transit.
A real-world example of this would be opening multiple profiles in your CRM, only to find they all have missing contact information.
3) Uniqueness Tests
Almost every database contains some duplicate data. The purpose of uniqueness tests is to sniff it out.
A little bit of duplication isn’t a problem; it’s a natural consequence of rushed sales reps trying to manage a database.
But you don’t want to let it get out of hand. Quite apart from unnecessary extra storage costs, you can end up contacting the same prospects twice and accidentally sending two identical messages to the same end user.
4) Numeric Distribution
Sometimes, you collect some data that looks fine at first glance. But look more closely, and you may realize that some of the stored values are clearly way off the mark.
Numeric distribution is a metric that helps you gauge whether specific data sets are falling within a reasonable range. It’s a very useful test for checking data accuracy at scale.
5) Data Transformation Error Rates
What happens when you attempt to convert your data from one format to another?
If the process causes few problems, this is a sign of good data quality. On the flip side, high data transformation error rates can indicate issues with formatting that you may want to address.
6) Freshness Tests
As we mentioned earlier, freshness is an important dimension of data quality. Although some data entries will stand the test of time, it’s much more common for information to have a use-by date.
Freshness checks are designed to highlight when data is getting old due to a lack of updates. Most are based around latency — you simply create rules for how often your data should be updated, and allow the test to work in the background.
7) Email Bounce Rate
This one might seem a little out of place. But actually, email bounce rate is a really good data quality metric.
If a high number of messages are bouncing, it’s a clear indicator that your customer data is either out of date, or simply inaccurate.
The same goes for unanswered calls; it shows that the phone numbers you have on record need to be updated.
8) Dark Data Tests
Most of the metrics and tests mentioned above focus on the specific factors of data quality.
But if your goal is data quality improvement, it’s helpful to get a higher level view. This is what dark data tests can provide.
Dark data is any information that is unusable, no matter what the reason might be. By tracking this low-quality data over time, you can get a clear picture of how much of your database is actually helping you with business intelligence.
9) Data Time-to-Value Test
To understand the cost of bad data, the final test we would recommend is data time-to-value.
This measures the average amount of time it takes for staff at your firm to access the information they need.
Time-to-value can be a very insightful metric, because it takes into account not only the quality of the data, but how accessible it is.
A big number here indicates that your team is wasting time on everyday business processes.
How to Improve Your Data Quality Going Forward
Measuring the quality of the information you have acquired and setting up data quality rules are both worthwhile tasks. But what if you discover that you’re sitting on a mountain of poor-quality data?
Here are a few steps you can take to improve the situation:
- Train your staff in data management — Most workers across your company probably have little experience with data management. By providing training, you can guide them towards better habits.
- Start using defined values — Mistakes will always creep into your data when you allow people to enter data manually. By creating predefined values for each field, you can easily cut out the typos.
- Encourage a data-driven culture — Set certain expectations about data governance and data integrity at a company-wide level, and you’re likely to see an improvement in data quality.
- Put someone in charge of data quality — To ensure that your new expectations are being met, consider assigning someone to the role of data steward.
- Use better data sources — Ultimately, you will only come up with the right data if you choose good sources. Always go to the original source where possible, and make sure to vet data providers before partnering up.
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