Data quality is a key component of your business’s long-term success, especially in the data-driven business world we live in. High quality data can drive better customer experiences, increasing retention and driving higher top-line revenue.
What Is Poor Data Quality?
Poor data quality, meanwhile, leads to analytics problems and insights that don’t accurately reflect customers, misaligns moments of engagement, and creates negative brand experiences. All of these consequences can lead to missed revenue and extensive challenges in an increasingly competitive world.
Given the impact of poor data quality then it’s a surprise that 54% of companies still experience data quality and completeness as a major challenge, according to Dun & Bradstreet. A well-maintained database can drive 66% higher conversion rates—a fact that should place ensuring data quality high on the list of every organization, no matter the size.
Resolving Data Quality Problems
If you’re struggling with typical data quality issues, as many of you likely are, there are a number of ways that you can address the problem. Here are four options to solve data issues:
- Fix data in the source system. Often, data quality issues can be solved by cleaning up the original source. The saying “garbage in, garbage out” applies in this context, because if there is incorrect or incomplete source data, then the database will get corrupted and produce low quality results. Fixing data in the source system is often the best way to ensure effective customer experiences and analysis on the other end of the process.
- Fix the source system to correct data issues. This may sound like the first method, but in reality, it functions differently. The source system that collects data can be set up to automatically cleanse data before it enters the database. It’s preferable to set up your source system, whether that’s a website or some other source, to automatically normalize issues with data. This isn’t entirely a “set-it and forget-it” situation, but it comes close.
- Accept bad source data and fix issues during the ETL phase. Before customer data can be analyzed, it’s frequently put through an extract, transform, and load (ETL) process. If you’re able to fix data in this stage, before it enters the database, you can solve a number of data quality errors.
- Apply precision identity/entity resolution. This is likely the most difficult method of fixing data quality issues, but simultaneously the most powerful. One of the most significant issues with many customer databases is that they have multiple records for the same customer/household, and no way to tell that these pieces of information are interrelated. Applying precision identity/entity resolution can identify a customer/household in all its variations, which allows for more targeted and efficient marketing because you’re not sending the same offer to multiple people in a household and not duplicating offers to the same customer.
The business world has become increasingly data-driven over the past decade, and this trend is unlikely to abate any time soon. Because of this, you have to ensure a high level of data quality and adopt the right data quality tools. Otherwise, you risk sending the wrong message to the wrong people and missing an opportunity to drive powerful customer experiences.
