Bad data leads to bad marketing, bad business decisions, and a lot of lost revenue. Use these data quality metrics to tell if flaws in your data are holding your business back.

Key Takeaways

  • Data quality is important to stay competitive
  • Data quality metrics will tell you if your data quality strategy is working
  • Only quality data is worthwhile, so it’s important to remove bad data from your system
  • Using these metrics will improve the quality of your data as well as your data strategy

Bad data doesn’t get you what you want. Clean, accurate, comprehensive data offers valuable insights that help you connect the people you want to reach, when you want to reach them, on the devices they prefer. Bad data undermines every one of those goals.

The goal is to have high-quality, error-free data, but that becomes more complicated as you widen your data search to include online sources and social media. The data you collect may not fit into those neat columns and rows of data collected elsewhere, which makes it difficult to clean up and its insights unclear. 

Data capture is only worthwhile if it adds value. So much data is being collected by organizations today that it can have a negative effect on data quality. In some situations, speed is of the essence as you try to gather useful information, and this may not allow time for processing and cleanup. 

However, to use any of that data effectively, it needs to be of a quality that supports good customer insights and decision-making. 

You likely have a process in place to improve data quality, but will it also measure the effectiveness of your data collection and quality strategy?

Why measuring  data quality is important

For businesses today, data quality can mean the difference between success, staying afloat, or shutting up shop. In fact, research says that companies believe they lose $15 million per year, on average, because of poor quality data. 

Accurate, quality data gives companies a better understanding of their customers and their needs, allowing them to capitalize on opportunities for targeted marketing that not only reaches their customers but builds relationships and leaves the competitors in the dust.

Without a plan to measure your data quality, you are essentially crossing your fingers, closing your eyes, and hoping for the best. 

7 data quality metrics you should be measuring

Measuring data quality means assessing the health of your data and prioritizing measurement criteria that supports how you want to use your data. Data quality can be determined by assessing the data itself, but real data accuracy requires assessing the data against the thing it represents or against an authoritative data set.

Before using these metrics to measure data quality, your organization must agree on the quality rules and business impacts you’ll design your measurement around. Then develop core quality metrics to support those goals.

1. Is your data complete?

This means deciding what “completeness” means to your organization. You will measure the absence of information in blank (null or empty string) values or the presence of non-blank values. This should be applied to critical data first.

2. Is your data unique?

Uniqueness in the data world means no entry is duplicated by other records in a data set, and the analysis must account for near-matches as well. The best way to do this is to compare your data with “real-world” data sources. For example, your database may contain John Smith and Johnny Smith at similar addresses, so you need an authoritative source to figure out if that’s the same person or different ones. This analysis might require third-party data for comparison using a value other than “name.” 

3. Is your data fresh?

Data can age quickly. Measuring your data against fresh, third-party lists increases its quality, whether you are using a physical mailing or email list. This should be a continuous process.

4. Is your data valid?

Valid data conforms to your definitional syntax. To measure data validity, compare data to the documentation for that data item’s rules, referring to:

  • allowable types: string, integer, floating point, etc.
  • Format: length, number of digits, etc.
  • Range: minimum, maximum, or contained as part of a set of allowed values

5. Is your data accurate?

Does your data correctly describe the real-world object or event? This real-world truth is ideally established by performing research specific to your industry, but the same measurements can be achieved by using trusted third-party data that uses the same chronology. You’ll be measuring the percentage of the data that mirrors real-world objects.

Accuracy depends on valid data. For data to be accurate, values must be valid, the right value, and in the correct form.

6. Is your data consistent?

Consistency means that when two records are compared in a particular data set, they conform to the same rules. Data can also be tested against a different data set that shares rules. This would be performed against multiple data sets, as well as assessing values or formats across single data items, records, data sets, and databases. 

7. Is your data providing good time-to-value?

Once again, your company has to decide on parameters. What does value mean? Data quality problems make it difficult to tease out the valuable insights. 

As they say, what you put into something is what you get out. Starting with accurate data is essential, and makes quality measurement easy. By measuring the current and ongoing quality of your data, you can continue to improve your quality strategy.

BDEX: Data that always measures up

The tools at BDEX help you get the quality data you need to meet your business goals and increase ROI.

BDEX is in a unique position as a Data Exchange Platform (DXP) to see data assets from over 100 sources and analyzes and filters this data through our Data Quality Assurance layer to make sure that all data going out the door can be authenticated as valid and accurate.

To improve your ROI, you have to get the most out of your data. Otherwise, you’re wasting time, money, and resources by marketing to the wrong audiences at the wrong time with the wrong messages. BDEX can empower you to better connect with the person behind each data signal.

Make real human connections with BDEX. Contact the team today to get started transforming the quality and accuracy of your data.

Established in 2014, BDEX is the first ever Data Exchange Platform (DXP). Combining hundreds of data sources in real-time into one unique data infrastructure, BDEX offers the most powerful, most accurate identity graph available in the U.S. market. BDEX empowers B2C companies to use the power of data to understand consumer behaviors and intents, helping them reach the right people at the right time.

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