How to gather internal allies for better data quality

7 facts about the importance of data quality in your company

Better analytics and insights

Advanced analytics techniques like machine learning rely heavily on the quality of the input data. High-quality data yields more accurate models and more meaningful insights, driving innovation and competitive advantage.

Cost reduction

Errors in data can lead to significant financial losses, whether through incorrect billing, flawed marketing campaigns, or inefficient operations. Investing in data quality proactively can prevent these costly mistakes.

Enhanced customer experience

Accurate customer data enables personalized interactions, targeted marketing, and efficient service delivery. Poor data quality can lead to frustrating experiences for customers, such as incorrect contact information, irrelevant offers, or delays in service.

Improved efficiency and productivity

When data is clean and well-organized, employees spend less time cleaning, correcting, and verifying information. This frees up valuable time and resources, allowing them to focus on more strategic tasks.

Regulatory compliance and risk mitigation

Many industries are subject to strict data regulations (e.d. GDPR). High-quality data is essential for compliance and avoiding hefty fines and legal repercussions. Furthermore., accurate data helps in identifying and mitigating potential risks.

Reliable decision making

High-quality data ensures that analyses and reports are accurate, leading to informed and confident decision-making. If the data is riddled with errors, inconsistencies, or incompleteness, the resulting insights will be misleading, potentially leading to costly mistakes.

Stronger reputation and trust

When an organization consistently operates based on reliable data, it builds trust with customers, partners and stakeholders. Conversely, errors and inconsistencies can damage its reputation.

"Tie your problem to a material business problem. Then go to management"

Now that you're all up to speed regarding the importance of data quality, let's focus on actually addressing the issues of data quality in a working environment. Like, how do you get your manager involved. Recently, Joel Shapiro, Clinical Associate Professor at Northwestern University, discussed the importance of data quality during a webinar hosted by MIT Sloan Management Review. He had some interesting things to say about getting upper management engaged. He also shared some key frameworks to strengthen effective data leadership within your organization.

MIT: How do I get leadership to free up budget to actually do something about the problems we’re facing?

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.’”

Shapiro says it's fine to run little pilot tests first, take little steps. He continues: “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.”

The other piece of advice that Shapiro offers is trying to find an executive sponsor. “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 and, you know, get them to buy into that problem and the potential for data as a solution. That's my best advice.”

Onwards and upwards

Now that we have established the importance of data quality in your company, and you've been given some real-life examples from an expert, how can you convince your manager to act on data quality? Here's a couple of pointers you might want to consider.

Identify and quantify the impact

This is often the most compelling argument. Look for concrete examples of how poor data quality is currently affecting the organization. If you want to know more about the dangers of bad data quality, have a look at this article.

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 due to 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.' This should make a strong case.

Tie data to company goals

Place data quality initiatives in the context of the company's overarching objectives. So, how will improving data quality help achieve key strategic goals, such as increasing customer satisfaction, improving operational efficiency, or driving revenue growth? Have a look at this example. 'Improving the accuracy of our sales data will enable more effective forecasting, directly supporting our goal of achieving Y% revenue growth.'

Present solutions, not just problems

Don't just highlight the issues, that's the easy route. Instead, propose concrete and actionable solutions, such as:

• invest in data quality tools and technologies;
• implement data governance policies and procedures;
• provide training to employees on data entry and management best practices;
• establish clear roles and responsibilities for data quality;
• implement regular data quality audits and monitoring.

Start small and show quick wins

Suggest a pilot project focused on a specific area where data quality issues are prominent and the potential for improvement is high. Demonstrating tangible results in a short timeframe can build momentum and support for broader initiatives.

Highlight the long-term benefits

Emphasize that investing in data quality is not just a cost but a strategic investment that will yield significant long-term benefits, such as improved decision-making, reduced risks, and a stronger competitive advantage.

Collaborate and build support

Talk to colleagues in other department who are also experiencing the pain points of poor data quality. A united front can strengthen your case.

Be patient and persistent

Convincing your manager may take time and multiple conversations. Be prepared to answer questions, address concerns, and reiterate the importance of data quality.

We know that poor data quality can have a devastating impact on your business, from lost revenue and operational inefficiencies to increased customer churn and compliance risks

The hidden costs are significant, but they can be avoided with the right strategies. Yes, upper management, we're looking at you. By implementing data entry best practices, regular data cleansing, integrating your systems, and establishing continuous monitoring frameworks, you can dramatically improve the quality of your CRM data. By combining a clear understanding of the importance of data quality with a well-articulated and data-driven argument, you can effectively convince upper management to act and prioritize this critical aspect of organizational success.

We know that poor data quality can have a devastating impact on your business, from lost revenue and operational inefficiencies to increased customer churn and compliance risks. The hidden costs are significant, but they can be avoided with the right strategies. Yes, upper management, we're looking at you.

By implementing data entry best practices, regular data cleansing, integrating your systems, and establishing continuous monitoring frameworks, you can dramatically improve the quality of your CRM data. By combining a clear understanding of the importance of data quality with a well-articulated and data-driven argument, you can effectively convince upper management to act and prioritize this critical aspect of organizational success.