What is data quality?
Data quality measures how well data serves its intended purpose as well as its accuracy and relevancy. The goal of having high-quality data is to make empowered, informed, and data-driven decisions to improve your business.
How does quality data empower good business decisions?
Let's take a step back and review an example of how quality data can empower the best business decisions. Data Centric Inc. covers data quality in a short video, and we've included some of our own tips below.
You have data, but it's not usable yet.
At this point, you just have values in a database or an Excel sheet. This raw data doesn't have practical use. For instance, you have thousands of email addresses from your customers and their topics of interest in a CSV
You transform data into information.
You take that data to a tool where you can visualize it clearly in the right context. For example, an email list inside your marketing app. Now you can filter those email addresses according to their interests.
You obtain knowledge.
You analyze the information you've gathered and gain important insights from it. You might learn, for example, that 80% of your customers want to be contacted via email to get information about CRMs.
You make an informed decision.
With that knowledge, you can make a data-driven decision, such as deciding to create a newsletter with content about CRMs. When you have quality data, you have the necessary knowledge to make the right decisions for your business.
Characteristics of Data Quality
Since data comes in all shapes and sizes, it's not always easy to determine its quality. However, there are some characteristics typically attributed to high-quality data. Here are six examples of data quality characteristics to look for in your data.
Is your data correct? And does it reflect the context of the situation in which you're using the data?
No matter how much data you manage, if it isn't accurate, it won't be very helpful to your business. Inaccurate data can also challenge your data integrity which exposes your organization, employees, customers, and other stakeholders to unwanted consequences like decreased trust in your business.
To ensure the accuracy of your data, you'll want to employ a good data management strategy that is both sustainable and effective.
Is your data comprehensive? Incomplete information might be unusable.
Though it's not advisable to collect more than the strictly necessary, make sure your must-have values are mandatory when storing new entries in your database. Otherwise, you'll end up with first names without last names, or incomplete phone numbers you can't use.
Is this the data you need? Let's face it, not all the data you collect is going to be a game-changer. But if there's a reason why you are collecting data and the values you obtained can serve that purpose, then you have quality data.
For example, if you ask your customers what their birth year is when they're signing up for a trial with your product, but their age is not useful information to you, it's data without a purpose. Therefore, even if it's correct, that data isn't relevant to your business's needs. Having unnecessary data in your database can take away valuable time and resources you've dedicated to data security.
Does your data contradict other sources? High-quality data shouldn't contradict the data stored in other databases. Otherwise, you would have to assume one of them is wrong — but which one?
When there are inconsistencies between databases, it's a hassle to determine accuracy. Instead, ensure there's one source of truth when it comes to your data — whether that means getting everyone on the same data software or integrating your data tool with your CRM.
This way, everyone within your organization can access your data via a single tool, no matter where they are or when they need access.
Is the information accessible to the right people? Similar to the previous point we just covered, many companies interact with customers, prospects, partners, and employees via different applications.
As a result, data is scattered throughout different tools, and if there's no software integration in place, you have a data silos problem.
Data silos are among the main causes of poor data quality. Even with accurate, consistent, and relevant data, if the team who should be leveraging that information doesn't have access to it, it's not serving its purpose. To guarantee accessibility, integrate your business systems.
Is your data up-to-date? Data is constantly changing, and the problem with outdated data is that it may not be representative of the current situation. It's great to keep track of historical data, but with a clear sense of time.
Ensure you're keeping your data records but you'll also want real-time data and reports so you're aware of any changes as they're happening. This way you can either capitalize on those changes or work to mitigate any issues as needed.
Data Quality Analysis
Data quality analysis is how you ensure your data is... well, high quality.
In other words, it allows you to make sure your data is: accurate, relevant, up-to-date, and suited for its intended use and application. Data quality analysis is often part of the process of data quality management.
Data quality management is the process of ensuring your team has access to high-quality data — it entails pulling insights about the health of your data to improve upon that health. This leads to the application of accurate data and the creation of larger data sets.
Data Quality Metrics
Data quality metrics are how you determine the quality of your data — they're the unique measurements that you put in place to analyze your data.
Data quality metrics are what determine the level of your data's accuracy, relevancy, application, etc. — as a result, you'll know how high (or low) quality your data is.
Here are some of the most common data quality metrics to watch:
- Match Rate
- Confidence Level
- Key Field Population
- % of record completion or null values to be 'good'
- Verification Rate
- Number of Duplicate Values
- Email Address Validation including corporate v. webmail
Data Quality Management Best Practices
Here are some data quality management best practices to keep in mind while analyzing the quality of your data.
Determine your team's most important metrics.
You can use the metrics we've outlined above or create new ones. The key here is to choose and track only the metrics that will help you make decisions. For example, you wouldn't want to report on the total number of data entries if your team does not have a goal to attain more data entries.
The majority of Leadspace Customers are focused on high-quality, high-value metrics that support their respective Sales and Marketing motions to build pipelines and drive Demand Generation. With that said, the quality of key contact-level details such as Verification Statuses, Confidence Scoring and Match Rates are crucial to ensure your programs reach the right person, at the right time. As a reminder, if B2B data decays at a rate of 20-30% per year, on average—not including impacts from the pandemic—that means you’re potentially losing out on 30% of the associated pipeline due to that outdated, corroded data. Now, when you extrapolate that 30% across deal sizes and quantity of deals, you could be looking at millions of dollars of revenue lost simply because your data was out-of-date and not cared for.
Get data quality buy-in across your business so everyone understands its importance.
Data is one of those behind-the-scenes functions that often gets overlooked. If you're responsible for data quality and management at your organization, you'll have great success with your efforts when you get buy-in from stakeholders across the business. When everyone understands that their success depends on quality data, you'll be more likely to get the resources and support you need to strengthen your data management strategy.
As we mentioned earlier, if you have several places to store your data and each database has discrepancies, you'll have to choose one to be your source of truth. Deciding which one to choose shouldn't be an arbitrary decision. By performing a data quality audit, you can begin to understand which database is aligned with the data quality characteristics we outline at the beginning of this post.
Our Advice is as follows
- Map out your marketing and sales processes, motions, and channels
- Determine which data are necessary to enable those processes effectively
- Develop a rubric to measure data quality in key domains and define "what good looks like"
To learn more about buy-in and establishing alignment cross-functionally on a defined rubric for Data Quality, review our article on How to Fight Data Decay
Perform data quality audits regularly.
The best way to mitigate a problem is to prevent it. Performing regular data quality audits can help you spot potential issues before they get bigger. Your audit doesn't have to be complex, simply check on the metrics you outlined earlier in this post. You can do this on a weekly or monthly schedule, or even more often if you manage a lot of data.
Leadspace traditionally recommends running quarterly Database Refreshes which are supported by the automatic generation of a Data Health Report which offers unique, deep insights into the quality of the data in your database and allows you to enable organizational alignment on how to improve overall quality.
Dive into the reasons for any data quality failures or notable successes that your team experiences.
Did your team have a great win? Great! Find out why so you can repeat them. Do the same if your team encounters a roadblock. Optimizing your successes and troubleshooting your roadblocks can help your team become more effective at collecting and maintaining quality data.