How to gather internal allies for better data quality

Recognizing a data quality problem is easy. Getting your organization to invest the time and budget to fix it is harder. This article covers why data quality matters, what a Northwestern University professor told MIT Sloan Management Review about winning leadership support, and the practical steps for building your case.

Why data quality matters to your business

Better analytics and insights

Machine learning and other advanced analytics techniques depend on the quality of their input data. Clean, consistent records produce more accurate models, and insights you can actually act on.

Lower costs

Bad data causes real financial losses: incorrect billing, wasted marketing spend, inefficient operations. Fixing data quality problems before they cause damage costs less than cleaning up after them.

Better customer experience

Accurate customer data supports personalized outreach and reliable service. When contact details are wrong or duplicated, customers notice: mistyped names, duplicate outreach, missed appointments.

More efficient teams

When your data is clean and organized, your team spends less time double-checking and correcting it, and more time on the work that actually needs their judgment.

Regulatory compliance and risk mitigation

Regulations like GDPR require accurate records. Poor data quality creates compliance exposure, and makes it harder to catch risk before it turns into a fine or a legal problem.

Reliable decision-making

Decisions are only as good as the data behind them. Incomplete or inconsistent records produce misleading reports, and misleading reports lead to costly decisions.

Stronger reputation and trust

Organizations that consistently operate on reliable data build trust with customers and partners. Data errors erode that trust the same way service failures do.

How do you get management to invest in data quality?

Understanding why data quality matters is the easy part. Getting your manager, or your leadership team, to act on it is harder.

Joel Shapiro, Clinical Associate Professor at Northwestern University, addressed this directly in a webinar hosted by MIT Sloan Management Review. His answer starts with what not to do.

How do you free up budget for data quality?

This is the question MIT Sloan Management Review put to Shapiro directly during the webinar.

Shapiro: "Two answers here. Let me start by sort of talking about what you shouldn't do. Don't just go in and pitch: 'We need better data.' Because data is a vehicle. It is helpful only in certain contexts. So, if you go to your leadership and say, 'We really need better data,' it's going to probably fall short. It's going to seem a little bit empty. The best way to approach it is to tie it to a real and material business problem that you're facing. Walk in and say, 'This is a problem that we've got. This is how I believe data can be helpful to us. We need to invest in this and it's okay to start small.'"

Small pilots are fine as a first step. Shapiro's next point: measure the return as soon as you can.

Shapiro: "But I think one of the most important things, if you get people to agree that it's important to invest in data solutions for whatever problem, try your best to measure ROI as quickly as possible. Like if you put the burden on yourself to say, I did get some budget to address this problem and now I want to demonstrate impact. That can be a really nice way, because that tends to snowball: 'Oh, look at that. A nice return on that. Good idea. What if we do more of that.'"

His last piece of advice: find an executive sponsor.

Shapiro: "That is really important! Keep your eyes and ears open and try and find that one person, or maybe more than one person, who's really open to this, and get them in your corner. Get them to buy into that problem and the potential for data as a solution. That's my best advice."

How to convince your manager to prioritize data quality

Building support for a data quality initiative takes more than one good pitch. These are the tactics that work best once you're making the case.

Identify and quantify the impact

This is usually the most compelling argument. Look for concrete examples of how poor data quality is affecting your organization right now.

Can you identify:

• lost revenue due to incorrect billing or ineffective marketing?
• wasted resources spent on data cleaning or fixing errors?
• customer complaints or churn related to data inaccuracies?
• inefficiencies in specific processes caused by unreliable data?
• potential risks related to compliance or security from poor data management?

Gather specific numbers and present them clearly, for example: "our analysis shows that incorrect address data led to a $X loss in shipping costs last quarter." A concrete figure makes a stronger case than a general complaint. For more on what bad data actually costs, see the ten dangers of bad data quality in Salesforce.

Tie data to company goals

Place data quality initiatives in the context of your company's actual objectives. How will improving data quality help you hit a specific strategic goal, like customer satisfaction, operational efficiency, or revenue growth?

For example: "improving the accuracy of our sales data will enable more effective forecasting, directly supporting our goal of Y% revenue growth."

Present solutions, not just problems

Highlighting issues is the easy part. Propose concrete, actionable solutions instead:

• invest in data quality tools and technologies
• implement data governance policies and procedures
• train employees on data entry and management best practices
• establish clear roles and responsibilities for data quality
• run regular data quality audits and monitoring

Start small and show quick wins

Suggest a pilot project focused on one area where data quality issues are visible and the potential for improvement is high. A quick, tangible result builds momentum for a bigger initiative.

Highlight the long-term benefits

Data quality is an investment, not just a cost. Over time it pays back in better decisions and lower operational risk.

Collaborate and build support

Talk to colleagues in other departments who are also dealing with the pain of poor data quality. A united front strengthens your case.

Be patient and persistent

Convincing your manager can take time and more than one conversation. Be ready to answer questions, address concerns, and make your case again.