We’ve previously discussed data quality, including the cost of bad data. Despite a basic understanding of data quality, many people are unsure what the term “quality” means.
Is it possible, for example, to quantify that quality, and if so, how? In this article, we’ll try to answer those questions and more.
Exposing Myths About Data Quality
One of the most common misconceptions about data quality is that it must be completely error-free. With so much data collected by websites and other campaigns, getting zero errors is nearly impossible. Instead, the data must only adhere to the standards that have been established for it. To understand what “quality” is, we must first understand three concepts:
- Who develops these specifications?
- How are the specifications developed?
- What kind of leeway do we have in meeting those requirements?
Many businesses have a single “data steward” who understands and sets these requirements, as well as determining error tolerance levels. If there is no data steward, IT frequently plays a role in ensuring that those in charge of the data understand any flaws that may affect it.
Everything from gathering the data to tailoring it to the needs of the company exposes it to potential errors. Having 100% complete and accurate data is not only prohibitively expensive, but also time consuming and barely moves the ROI needle.
With so much information coming in, decisions must be made quickly. As a result, juggling and judging accuracy and completeness is a delicate balancing act when it comes to data quality. If this seems like a daunting task, you’ll be relieved to know that there is a method to the madness, and the first step is data profiling.
What exactly is Data Profiling?
Data profiling entails examining all of the information in your database to determine whether it is accurate and/or complete, and determining what to do with entries that are not. It’s relatively simple to import a database of products manufactured by your company and ensure that all of the information is correct, but it’s a different story when you’re importing details about competitors’ products or other related details.
Data profiling takes into account the accuracy of the data. Is it 1916 or 2016 in the system if you launched on July 1, 2016? In combing through the information you’ve obtained, you may even discover duplicates and other issues. Profiling the data in this way provides us with a starting point – a springboard to ensure that the information we use is of the highest possible quality.
Data Quality Assessment
So, now that we have a starting point for determining whether our information is complete and accurate, the next question is, what do we do when we discover errors or issues? Typically, you have four options:
- Accept the Error – If the error is within an acceptable range (for example, Main Street instead of Main St), you can choose to accept it and proceed to the next entry.
- Reject the Error – Sometimes, especially with data imports, the information is so badly damaged or incorrect that it is better to delete the entry entirely rather than attempt to correct it.
- Correct the Error – Misspellings of customer names are a common mistake that is easily remedied. If a name has multiple variations, you can designate one as the “Master” to keep data consolidated and correct across all databases.
- Create a Default Value – If you don’t know the value, it’s better to have something (unknown or n/a) than nothing.
Bringing the Data Together
When you have the same data in multiple databases, the possibility of errors and duplicates increases. The first step toward successful integration is determining where the data is and then combining it in a consistent manner. Investing in proven data quality and accuracy tools to help coordinate and sync information across databases can be extremely beneficial in this situation.
Checklist for Data Quality
Finally, because you’re dealing with so much data in so many different areas, having a checklist to ensure that you’re working with the highest quality data possible is beneficial. DAMA UK has produced an excellent guide on “data dimensions” that can be used to gain a better understanding of how data quality is determined.
Their data quality dimensions are as follows:
- Completeness – The percentage of data that contains one or more values. It is critical that critical data (such as customer names, phone numbers, email addresses, and so on) be completed first because non-critical data is unaffected by completeness.
- Uniqueness – There is only one entry of its kind when compared to other data sets.
- Timeliness – What effect do date and time have on the data? This could be previous sales, product launches, or any information that has been relied on for a long time to be correct.
- Validity – Does the data meet the standards that have been established for it?
- Accuracy – How well does the data reflect the real-world person or thing that it is supposed to identify?
- Consistency – How well does the data match a predetermined pattern? Birth dates have a common consistency issue, because in the United States, the standard is MM/DD/YYYY, whereas in Europe and other areas, the standard is DD/MM/YYYY.
Quality Data Solutions
We’ve established the significance of data quality at this point. To maximize your return on investment (ROI) and ensure that you are targeting the right audience, your marketing strategies and decisions rely heavily on accurate data.
Managing data quality can be difficult, particularly for smaller teams. A single digital marketer may be required to manage multiple data sources and complex data ecosystems while adhering to a strict standard. This is where you should look into data quality solutions like ObservePoint.
ObservePoint is an effective tool for helping digital marketers ensure the quality and accuracy of their data. Use it to easily monitor your digital properties and ensure that the data collected is complete, accurate, and reliable.
But how does ObservePoint help with data quality?
Here’s how it works:
- Validation of Data Collection: ObservePoint can ensure that data is collected correctly and consistently across all of your digital properties. This ensures that your data is complete and accurate, allowing you to make informed decisions.
- Tag Management: ObservePoint can assist you in managing your tags and ensuring that they are firing correctly, giving you more confidence in the data you are collecting.
- Data Governance: ObservePoint offers a centralized data governance platform. Manage all of your data sources, data types, and data access from one platform.
- Automation: ObservePoint can automate audits of your digital properties, saving you time and resources while ensuring the accuracy and reliability of your data.
- Reporting: ObservePoint provides detailed data quality reports, allowing you to easily identify areas for improvement and make informed decisions.
In addition, features such as a user-friendly interface and customer support make ObservePoint an excellent tool for removing some of the stress from maintaining data quality. More importantly, it increases your confidence that your decisions will lead to success.
The Big Picture Regarding Data Quality
As you can see, there is no “one-size-fits-all” solution for maintaining accuracy and completeness on all types of data for every business. And, as big data’s appetite for information grows by the day, it is more important than ever to address data quality issues head on. Although it may appear overwhelming, using data hygiene tools allows computers to do what they do best: crunch numbers.
The most important step you can take is to simply begin. Because data will always grow as more prospects join and new markets are discovered, there will never be a “best time” to address data quality issues. Taking the time now to define what data quality means to your company or organization can result in improved customer service, a better customer experience, a higher conversion rate, and longer customer retention – and those are the kinds of returns on investment that any business will gladly accept!
