ifferent data sources, different forms, heterogeneous data management systems – when it comes to collecting and managing data, the devil is in the well-known detail. And since we’re accumulating more and more data every day, there’s a risk that inaccuracies will multiply. But a functioning MarTech stack depends on clean data. We’ll show you five steps to improve data quality so that your tech stack can work more effectively for you.
In modern, data-driven, technology-based marketing, data is at the center of all operations. At the same time, every company has incomplete data, typos, and duplicates. Often, these aren’t even acknowledged as a problem, with the result that marketers have to work with suboptimal figures. These data quality issues can affect the entire company and, in some cases, lead to very high losses.
So: the data must be correct and therefore reliable! For companies, it’s high time to focus on clean data. Clean data refers to usable, complete data sets without errors such as incorrect email addresses, missing ZIP codes, or typos. As soon as errors exist, they creep into all subsequent operations. To clean up the data, updates and integrations are necessary.
This process isn’t straightforward, partly because the marketing team doesn’t always have a say in data collection. Close, cross-departmental collaboration—for example between Marketing and IT—is needed here.
Improved data quality and clean data can be introduced in any company. What it takes is a bold, patient, and creative MarTech team that works together with other departments to improve data management.
We recommend these steps to improve data quality in marketing and across the entire company:
Continuously keep yourself informed about current and potential future data-quality issues
These often include challenges such as typos, incorrect names, data-entry errors, a lack of standards, too many formats, different data sources, and duplicate data.
Put a focus on high-quality data
Often, only 20 percent of the data collected is usable at all. To avoid that, you should prioritize quality over quantity. Optimize data collection, for example by making forms simpler.
Find the right MarTech solution for your company
The best software solution doesn’t have to be expensive, but it should meet the team’s requirements. Ideally, it can identify data, find errors, automatically clean up, and contribute to more clean data thanks to data matching. A Customer Data Platform (CDP) has such functions. Read more about CDPs here.
Work with executives
Many bosses (still) don’t understand that data is not only an IT problem, but also a marketing problem—and therefore affects the entire company. Get support from as many sides as possible to improve data management efficiently and with greater financial power.
Identify the source of problems
Sometimes bad data comes from inefficient processes, incomplete landing pages, or a lack of data checks. By implementing better processes in the future, data quality in the MarTech stack can be improved in the long term and sustainably.
Good data is collected correctly right from the start. As a marketing expert, it is therefore important to put yourself in your customers’ shoes. What exactly do the individual funnels look like? Are they easy to use? Are there fields that are prone to errors, or important fields that are not mandatory? Ask yourself what happens at each step of data collection. This will give you a basic understanding of the data and help you avoid errors right at the source. Your MarTech stack will thank you!
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Till Zier is CPO at Matchilla and, as a MarTech expert, reports in the MatchZINE on news and trends in marketing, automation, analytics, and data.
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