We picked up the sticker below at the Monte Carlo booth during the Snowflake Summit in June—and couldn’t help but appreciate their clever take on the classic “Spider-Man pointing at Spider-Man” meme. It humorously captures a familiar problem in modern data teams: when data quality issues arise, so does the finger-pointing.
In today’s data-driven organizations, many teams, from data science to marketing, interact with and rely on the data lake. But when something goes wrong, ambiguity around ownership often leads to confusion and blame. Everyone needs clean, reliable data, but too often, no one is clearly accountable for ensuring its quality.
Is it the responsibility of the data scientists? The data engineers? Marketing? BI analysts? This shared (and sometimes blurred) responsibility highlights ongoing challenges in data governance, cross-functional accountability, and team communication.
The truth is that data quality isn’t the job of one role or team. It’s a shared responsibility that requires alignment, communication, and processes across the organization. Let’s break down who plays what part, and why you need a collaborative approach to keep your data clean, consistent, and trusted.
Role in data quality: Data engineers build and maintain the pipelines that move and transform data from raw sources to usable formats. Their focus is on reliability, scalability, and automation.
Why they matter: If there are issues at the ingestion stage, i.e. duplicate records, missing values, inconsistent formatting, it’s likely a problem that started at the engineering level. Data engineers play a key role in ensuring sources are integrated correctly and that the underlying architecture supports clean, validated data from the start.
But: They can’t fix what they don’t know. Without input from downstream users, critical issues can go unnoticed.
Role in data quality: Analysts are often the first to spot anomalies or gaps in the data while building reports or dashboards. They are closest to business use cases and often catch quality issues that others miss.
Why they matter: They’re on the frontlines of turning data into insight, and they can tell when something “doesn’t look right.” Their feedback is essential for identifying inconsistencies, misaligned definitions, or flawed transformations.
But: They typically don’t have control over upstream data fixes, so their insights need to be looped back to the data engineering or governance teams.
Role in data quality: Data scientists rely on clean, labeled, and relevant data to build predictive models and run experiments. Bad data = bad outputs.
Why they matter: They push the data to its limits, and as a result, they are uniquely positioned to uncover deeper issues, such as bias, outliers, or missing data that skews machine learning results.
But: Their focus is often on deriving value from data, not cleaning or sourcing it. They need collaboration with engineers and analysts to make improvements upstream.
Role in data quality: Marketing teams rely on data for personalization, segmentation, attribution, and campaign performance. They also bring in external sources like customer data platforms (CDPs), intent data, and CRM platforms.
Why they matter: Business teams define what “quality” means in context. They know what questions need to be answered, which segments matter, and what outcomes drive ROI. If data quality is poor, they feel it first in campaign performance and customer experience.
But: They rarely own the technical process of cleaning or governing data and often lack visibility into how data flows through the stack.
Role in data quality:
Sales teams rely on accurate, timely data to prioritize leads, tailor outreach, and close deals. They are often the first to notice when contact information is outdated, company profiles are incorrect, or lead scores don’t reflect true intent.
Why they matter: Sales reps operate closest to the customer and can quickly validate whether data reflects reality. If a lead is marked as “hot” but doesn’t return calls, or if territory assignments are based on incorrect firmographics, revenue suffers. Sales teams provide essential, real-world feedback that can reveal flaws in CRM data, enrichment processes, and segmentation strategies.
But: Sales rarely has the bandwidth or access to investigate root causes or fix systemic issues. Their input needs to be captured and integrated into ongoing data quality efforts.
Data quality is a shared accountability across roles. Here’s a more productive way to think about it:
1. Establish a data quality framework: Define clear ownership, expectations, and escalation paths. 2. Invest in metadata and observability tools: You can’t manage what you can’t see. 3. Create feedback loops: Make it easy for analysts and marketers to flag issues and for engineers to resolve them. 4. Document everything: Data definitions, quality rules, transformation logic—it should all be accessible and consistent. 5. Treat data as a product: With owners, SLAs, and end users in mind.
Data quality isn’t one team’s job—it’s a cultural commitment. When everyone who touches data understands their role in maintaining its integrity, you stop pointing fingers and start building trust.
Clean data isn’t just a technical goal. It’s a business asset, and protecting it takes all of us.
With over 25 years of data industry experience, Lisa owns a deep knowledge and understanding of actionable data and predictive outcomes. She is passionate about architecting data-driven solutions that fit customer needs and helping them exceed their business goals. She owns avid listening skills, has an insatiable sense of curiosity, and loves to network with like-minded professionals.