What makes data high quality or poor quality? Check off this list of quality characteristics when you’re evaluating marketing data.
Digital marketers now have access to loads and loads of consumer data. While big data makes it easier to effectively market to the right people and through the right channels, unfortunately, a lot of data means a lot of bad data.
Poor-quality data is inaccurate, out of date, or incomplete. Using this data can mean that the time and money you’re putting into a marketing campaign will see no ROI, since the messages won’t reach their targets.
So, how do you define data quality? And what are the key characteristics of high-quality data? In this post, we’ll break down the answers to those questions and give you a checklist of attributes to make sure the data you use is top quality.
What is data quality?
Data quality reflects the accuracy and usefulness of the consumer information in your campaigns. If the data directly and positively impacts your business, effectively informing decisions and producing desired outcomes, data quality is considered good or high.
However, when data causes issues, then it’s not accomplishing its intended purpose and probably creating roadblocks that limit campaign effectiveness and reach. This data quality may be considered poor — anytime you are led astray by inaccurate or outdated data, it’s not good.
Because data quality is so important for organizations of every shape and size, data quality management (DQM) has emerged as a major business function. DQM ensures that team members dedicate their time and energy to ensuring data being used is of high quality.
6 Characteristics of data quality
- Accuracy. First and most important is data accuracy. If information is full of errors and false material, it’s really no use at all. For example, if you have the wrong email address for a lead, your message won’t reach the right potential customer — which could be a disaster if it’s personalized— and it may not reach anyone at all if it’s a defunct address.
- Completeness. Often when you’re using consumer data, it’s not quite complete. Unfortunately, if information is missing, or if there are a lot of gaps that you have to try to pull together yourself, data is not considered high quality.
- Reliability. Another factor in determining data quality is whether it’s reliable or not. But what does this mean exactly? A lot of data contradicts other data you may have, and it’s hard to know which information is correct. This is not reliable data, and that makes your job more confusing and ambiguous.
- Timeliness. There’s a lot of data out there that hasn’t been updated. If a lead moves, their old address may be useless to you. If a consumer was shopping for a specific item weeks ago, your messages won’t land as they could have in the moment. Outdated or obsolete information is poor-quality data.
- Relevance. It’s important to note that even if data is accurate, it may not be applicable to what you’re trying to do. When you gather a lot of irrelevant information about consumers, you’re wasting time and energy on data that you don’t really need, and that can impact potential ROI.
- Availability. Finally, data must be available in order to be useful and high quality. People within the organization must be able to access the data they need to do their jobs. Information should be stored in a place where everyone who requires it can see it in a manageable format, like a simple dashboard that includes summaries, analysis, and graphs.
How to ensure data quality
Check off the above list of data quality characteristics when you’re trying to clean things up. But you also need to make sure you fully vet your data sources. Ask these questions when identifying where your data comes from:
- How many sources are you using?
- Where is data being stored from each source?
- Do you have a lot of redundant data?
- Is your data siloed?
- Who is the creator of the data?
- Have any revisions been made to the data?
- Has the data already been used? Where?
Data should be located in one place so your organization has only one source of truth. Otherwise, you may be using outdated or duplicate data.
Another best practice is to use a data management tool that timestamps information, so you always know how old it is and which data point is most relevant at a given time.
Using BDEX ensures high-quality data
The BDEX team recently conducted research that shows30% of data in the ecosystem is invalid. We recognize just how big of an issue this is for marketers and organizations as a whole. That’s why we’re dedicated to only providing you the most relevant, accurate, and complete data in our BDEX Data Exchange Platform (DXP). We also provide a BDEX ID Check to ensure that the data in our identity graph is high quality.
To learn more about how we ensure our data is clean, get in touch with our team of data experts.