Bad data leads to bad marketing, bad business decisions, and lost revenue. Use these data quality metrics to tell if flaws in your data are holding your business back.
Key Takeaways about Data Quality:
- It is important to stay competitive
- Its metrics will tell you if your strategy is working
- 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
What is it and why is it important?
Clean, accurate, comprehensive, quality 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.
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.
What’s the goal?
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.
However, to use any of that data effectively, it needs to be of a quality that supports privacy, 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, 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 accuracy requires assessing the data against the thing it represents or against an authoritative data set.
Before using these metrics to measure, your organization must agree on the quality rules and 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. Apply this to your data first.
2. Is your data unique?
Unique data is free of duplicates. The analysis must account for matches. 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.
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? One way achieve this measurement is 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?
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.
Get Data to Help You Enhance your Data Strategy
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. Don’t waste 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.