5 Approaches to Balance Data Quality with Practicality in Your Organization
Balancing data quality with day-to-day realities is a common challenge for modern teams. This article shares practical, field-tested approaches backed by insights from seasoned experts. Expect clear steps on tiered standards, automated checks, upstream ownership, CRM hygiene, and 80/20 stewardship that can be put to work immediately.
Adopt Tiered Standards By Business Impact
One approach that has worked consistently for us is implementing tiered data quality standards instead of holding every dataset to the same level of precision. Critical data—like financial, customer, or regulatory information—must meet strict validation rules and automated checks, while lower-impact datasets follow lighter controls focused on usability rather than perfection. This let us raise overall quality without slowing the business down.
The balance came from aligning quality requirements with actual downstream impact. We asked each team to define what "good enough" meant for their workflows, then automated the checks that mattered most. By not chasing 100 percent accuracy everywhere, we freed up resources to focus on the data that truly drives decisions. The result was a practical, maintainable system where quality improved meaningfully without creating bottlenecks or unnecessary process friction.
Embed Automated Checks Into CI Pipelines
An effective approach to data quality across the organisation is to embed automated data quality testing into continuous integration pipelines. With this approach, you can automate the validation of data quality anytime when code changes occur. It helps to catch issues early before they cause downstream processes or users.
The balancing of practicality with perfection is done by focusing on automation to deal with large volumes of complex data, that would be impractical to monitor manually. While focusing on excellence across all data quality dimensions, the approach identifies that 100% perfection is due to evolving data sources and business needs. Otherthan focusing on proactive detection of errors and quick corrective action using tools like data diffs, column level lineage and shifting left in the development process. In this way, the organisation maintains high data integrity and minimises disruptions to avoid trying to get unattainable perfection.
Shift Ownership Upstream And Enforce Contracts
Early in my career, I treated data quality as a downstream cleanup job. I thought if I hired enough smart engineers to build elaborate filtering pipelines, we could scrub our way to the truth. That turned out to be a losing battle. The most effective approach I have used is shifting the responsibility upstream. We stopped treating data teams as digital janitors and started treating application developers as data owners. If a team built a feature that generated data, they became responsible for that data's integrity until it landed in the warehouse.
This requires a balance between rigid standards and speed. You cannot enforce perfection on every single log line without grinding innovation to a halt. We compromised by distinguishing between tier-one metrics that drove financial decisions and tier-two data used for exploration. We established strict contracts for the critical few and allowed flexibility for the rest. This taught the organization that quality is not about having zero errors. It is about preserving trust in the numbers that actually steer the ship.
I remember a specific instance where a minor update to a mobile app inadvertently changed a timestamp format, silently breaking our churn prediction model for weeks. The immediate reaction was to write complex validation code to catch it next time. Instead, I simply asked the mobile engineering lead to sit in on our weekly insights review. Once she saw how her raw logs directly influenced executive strategy, she became our fiercest advocate for consistency. We did not need better software. We just needed her to see that she was part of the story we were telling.
Maintain CRM Hygiene With Regular Verification
We use a CRM as the heart of our sales and marketing operations and make sure that the information is accurate and up to date. Emails are verified every quarter to make sure that when we reach out, we actually get a response. All contact data has to be filled out manually or with data enrichment tools. All of this helps us have clean records for sales, marketing and customer support.
Establish 80/20 Stewardship Across Functions
To be honest, the most effective approach I've used to ensure data quality was implementing a "80/20 Data Stewardship Model"—a mix of light governance and embedded ownership. I really think it should be the default for any organization that wants reliable data without drowning in bureaucracy.
Here's what I did: instead of creating a massive central data policing team, we appointed data stewards inside each function—marketing, finance, HR—people who already lived inside the data. Their job was simple: run monthly quality checks using a shared rule set, flag anomalies early, and own fixes within their lanes. Central analytics only stepped in when patterns repeated.
I still remember one early win. Our talent team kept complaining about inconsistent job level data messing up dashboards. Once an HR business partner became the steward, she caught a recurring sync issue between our ATS and HRIS within a week. A problem we'd tolerated for months disappeared overnight.
What you and I believe doesn't matter, the fact is that perfect data is a myth—but predictably clean data is achievable. The 80/20 model gave us discipline without rigidity, accuracy without obsession, and a culture where data quality felt like everyone's responsibility, not just IT's.
