Data Quality: How to Build, Maintain, and Own Your Data
For some, the difference between high-quality data and “bad” data seems simple. It’s either right or wrong, accurate or inaccurate. When you dig a little deeper though, you’ll find the quality of your data hinges on much more than accuracy alone. If you’ve got only half the relevant information, no matter how pristine, you don’t have high-quality data. Similarly, data that’s technically “correct” but isn’t timely can also influence its usefulness and overall integrity. So, how can you ensure data quality? Below, you’ll find a few tips and tricks to help you embrace your company’s data quality and – most importantly – use it to further your organization’s goals.
What is data integrity, and why is it important?
The word “integrity” conjures images of strength, honesty, and sincerity. You can trust a person with integrity. Similarly, you trust the integrity of a chair every time you sit on one – whether you realize it or not. You should be able to trust the integrity of your data in the same way, as it is the framework that supports your company’s business decisions. That’s why data quality is important – it enables you, your team, and your business to make educated and insightful decisions about the future.
Checking data quality starts with your business
Determining data quality is as tricky as defining it. On one hand, it seems straightforward; on the other, you have to keep a multitude of variables in mind to make an accurate assessment. The first step in gauging data quality actually starts before you collect it. Data quality is about helping your business or organization at its core – lead with that and avoid catering your data to what makes sense from a technological or IT standpoint. What should your data accomplish? Or, more importantly, what can your data help you accomplish?
Getting a grip on data collection
Onboarding data is the first opportunity for low-quality data to creep into reports. During data collection, you’ll encounter a variety of logistics and operations challenges, all of which come with unique opportunities for inaccuracies, duplicative information, etc.
A few things that make data collection a challenge:
- Gathering data from multiple sources, some of which may overlap or contradict each other.
- Mixing data from external sources and internal sources.
- Dealing with a massive amount of data. Even if your reporting looks simple, the data behind it can be extremely complex.
Sure, data collection is prone to mistakes, but that doesn’t mean you have to make them. During this critical phase, seek to understand your data at its most basic level. Don’t grab pieces of information and assume they are correct – double check to ensure they are. Comb through your sources to identify overlapping information, compare duplicates, and eliminate them.
Grab a label-maker and profile your data
Now that you’ve gathered the data, it’s time to profile and label it. During this step, you’ll need to keep your original “business-first” goals at in mind. By doing so, you’ll be able to see how different groups of data relate to each other.
- Review the data (again)
- Divide related sets into groups
- Ensure each group is thorough
- Ensure the summation of the groups represents the whole
The goal of data profiling and labeling is to make sure that, once you inject it into everyday reporting, it represents a holistic view of your business and data goals. By cross-referencing, comparing, and contrasting each data set and group, you may find weak spots or even correlations that you didn’t know existed before.
Read next: Data Integrity: Best Practices for Clean Data
How to maintain data quality
Data profiling and organizing isn’t a one-stop shop for data quality. You’ll also need to maintain your data moving forward. Here’s how:
- Build a data quality team. Data maintenance requires people. In order to get buy-in for the resources, you need to ensure and maintain data integrity. This will help upper management and stakeholders understand how integral data quality is to the success of the organization and their individual roles. Then, assign an appropriate number of resources to keep your data top-notch.
- Don’t cherry pick data. This is probably the simplest (and arguably the easiest) mistake to make. Cherry-picking happens when reports don’t show the whole picture. Instead, they’re stilted by elimination. More often than not, cherry picking happens because we tend to look for the results we want to see. When you find data indicating something positive, make sure you aren’t missing any factors that could provide a more realistic perspective. Once you’re sure of your success, your organization can use it as a springboard to create more success in the future.
- Understand the margin for error. Generally speaking, the more data you have, the larger your margin for error will be. While it’s difficult to accept the reality that data isn’t always perfect, knowing this will enable you to spot pitfalls, build on your success, and address problems quickly – even before they happen. In the end, knowing there’s a margin for error is the best way to continue improving data integrity instead of letting it stagnate.
- Accept change. Data is subject to change. Whether the goals of your organization changes or your data sources change, you need to be ready. Even without significant alterations in your business or data collection process, remember that perfecting your data is a journey. You can only keep moving forward if you’re committed to making improvements and refining your data structure.
- Sweat the small stuff. Data is like a vegetable garden. In order for good things to grow, you’ll need to keep a careful eye on the weeds. Even the smallest inaccuracies or duplicates data can throw your data off balance, so continually re-evaluate potential weaknesses and correct them. Whether it’s removing data that isn’t useful or filling in the gaps where data is sparse, fine-tuning your reports will keep your data quality initiative moving in the right direction.