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Your non-technical colleagues are resistant to data quality standards. How can you get them on board?

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Understanding the importance of data quality standards can be challenging for non-technical colleagues, but their commitment is essential for effective data governance. Data quality standards ensure that the information your organization relies on is accurate, consistent, and reliable. Without these standards, decision-making processes can become compromised, leading to potential errors and inefficiencies that could affect the entire organization. To get your non-technical colleagues on board, it's important to communicate the benefits of data quality in a way that resonates with their roles and responsibilities.

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1 Educate Gently

When introducing data quality standards, start by gently educating your colleagues about what data governance is and why it matters. Explain that data governance involves overseeing the availability, usability, integrity, and security of data used in an organization. Highlight how quality data can streamline their work, reduce errors, and support their decision-making processes. Use relatable examples and analogies to demystify technical aspects, making the concept more accessible and less intimidating for those without a data background.

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G. Taylor B.
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Users are often the first to encounter problems and challenges associated with improper data governance. By highlighting how user challenges are a result of the lack of governance processes, we can help users see the value of Data Quality. Data Quality cannot be driven solely by end users though. It requires thinking through the overall structure of data at the enterprise and what that data is supposed to represent.

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2 Highlight Benefits

Focus on the direct benefits that high-quality data can bring to your colleagues' daily tasks. Emphasize that adhering to data quality standards can lead to more efficient workflows, time savings, and a reduction in frustration caused by incorrect or incomplete data. Make it clear that while the process might require some initial effort to learn and integrate into their routines, the long-term advantages will significantly outweigh any short-term inconveniences.

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Brandie Green, M.S.,CSMP

Digital Marketing Expert + Data Analytics Evangelist + Google Analytics and GTM Expert + Speaker + Marketing Coach + Web Analyst
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It’s important to step away from language that is too technical and focus on the language that will resonate with the stakeholder. If you can show how this work impacts theirs, you will bridge the knowledge gap. Most times, technical jargon is difficult for non-tech people to grasp. Provide relatable examples and how this work will benefit what they do.

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Yagazie A.

Founder
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Explain that high-quality data leads to more accurate and reliable insights, enabling better business decisions and strategies. When decisions are based on clean, consistent data, the outcomes are more predictable and successful. Also, Show how high-quality data can enhance customer experiences. For instance, accurate customer data helps in personalizing interactions and services, leading to higher customer satisfaction and loyalty and share success stories or case studies from other companies that have benefited from implementing data quality standards.

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3 Simplify Processes

To alleviate resistance, simplify the processes related to maintaining data quality standards as much as possible. Provide clear guidelines and user-friendly tools that make compliance straightforward and intuitive. Offer training sessions that are tailored to different roles within the organization, ensuring that each colleague understands how to apply the standards within their specific context. The easier it is for your colleagues to follow these standards, the more likely they are to adopt them.

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G. Taylor B.
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Automation is so important to get user buy-in for DQ. Many methods exist to accomplish this including, using machine learning to identify potential rules. Automation can also be achieved by the data catalog tool or a standalone DQ application such as Soda or Great Expectations. Finally workshops are a great way to help users think about how to define data quality standards. Work with them to identify and focus initial efforts on developing rules and standards for the datasets and other assets that have high criticality and thus high data quality needs. Trusting data requires some hands-on work initially, but when it starts to works well, it is barely noticed.

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4 Share Success Stories

Sharing success stories from other departments or organizations can be a powerful motivator. Provide examples of how improved data quality has led to better outcomes, such as more accurate reporting, successful projects, or enhanced customer satisfaction. These stories help non-technical colleagues visualize the positive impact of data quality standards on their own work and on the organization as a whole.

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5 Provide Support

Create a supportive environment where your colleagues can ask questions and receive help as they adapt to the new standards. Establish a helpdesk or a dedicated team member who can provide assistance when needed. Encourage an open dialogue about the challenges faced during the transition and work together to find solutions. Regular check-ins can help ensure that everyone feels supported and remains engaged with the process.

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6 Recognize Efforts

Finally, acknowledge and reward the efforts your colleagues make in learning and applying data quality standards. Recognition can take many forms, from a simple thank-you to more formal rewards systems. Celebrate milestones and improvements, no matter how small, to foster a positive association with data governance initiatives. This not only encourages continued adherence but also builds a culture of continuous improvement.

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Your non-technical colleagues are resistant to data quality standards. How can you get them on board?

1

2

3

4

5

6

1 Educate Gently

2 Highlight Benefits

3 Simplify Processes

4 Share Success Stories

5 Provide Support

6 Recognize Efforts

Data Governance

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Your non-technical colleagues are resistant to data quality standards. How can you get them on board?

1

2

3

4

5

6

1 Educate Gently

2 Highlight Benefits

3 Simplify Processes

4 Share Success Stories

5 Provide Support

6 Recognize Efforts

Data Governance

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感谢您的反馈

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