How can you maintain data quality in CRM post-go-live?

You've invested a lot of time and resources to implement a CRM system that can help you manage your customer relationships, optimize your sales and marketing processes, and gain valuable insights from your data. But what happens after you go live? How can you ensure that your CRM data remains accurate, consistent, and relevant over time? In this article, we'll share some best practices and tips on how to maintain data quality in CRM post-go-live.

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1 Define data quality standards and metrics

Before you can measure and improve your data quality, you need to define what data quality means for your organization and your CRM goals. Data quality standards are the rules and criteria that specify how your data should be formatted, validated, updated, and stored. Data quality metrics are the indicators and measures that track how well your data meets those standards. For example, you can use metrics such as completeness, accuracy, timeliness, consistency, and relevance to assess your data quality.

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Mohamed Nashaat

Customer Services Consultant | Telecommunications & Healthcare Operations Expert | Customer Experience Excellence & Operations Guru l CPHQ, TOT, Six Sigma White Belt, PMP Certified l MBA Holder l DBA In Process
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in my opinion -Define data quality standards and metrics. -Implement data quality tools and processes. -Train and engage your CRM users. -Monitor and evaluate your data quality performance.

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Dheeraj Kumar, PMP®

Senior Project Manager @ Ministry of Electronics and Information Technology | PMP, Agile Project Management
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Maintaining the data quality standards in a Customer Relationship Management system is critical for ensuring the system's continued effectiveness and reliability. Some strategies that can contribute effectively towards maintaining the standards can be attributed as follows. a) Standard Setting : create data quality standard guidelines and ensure they are getting enforced across the system b) Conduct periodic audits to identify and rectify any inconsistencies, errors. c) Inculcate a training and support system to ensure the end users understand the importance of maintaining data quality d) Regular monitoring and tracking on well defined KPIs and metrics can help us identify the trends, anomalies or the areas for improvement.

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2 Implement data quality tools and processes

Once you have your data quality standards and metrics in place, you need to implement tools and processes that can help you maintain and enhance your data quality. Data quality tools are software applications or features that can automate or assist with data cleansing, validation, deduplication, enrichment, and integration. Data quality processes are the workflows and procedures that define who, when, how, and why data quality tasks are performed. For example, you can use tools such as validation rules, lookup tables, matching rules, and third-party data sources to improve your data quality. You can also use processes such as data governance, data stewardship, data audits, and data quality feedback to ensure data quality accountability and continuous improvement.

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3 Train and engage your CRM users

Your CRM users are the key stakeholders and contributors of your data quality. They are the ones who enter, update, access, and analyze your CRM data on a daily basis. Therefore, you need to train and engage them on the importance of data quality and the best practices and policies for data entry and maintenance. You can use various methods and channels to educate and motivate your CRM users, such as online courses, webinars, manuals, newsletters, gamification, incentives, and recognition. You can also solicit their feedback and suggestions on how to improve your data quality and address any issues or challenges they face.

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4 Monitor and evaluate your data quality performance

Finally, you need to monitor and evaluate your data quality performance on a regular basis to identify any gaps, errors, or opportunities for improvement. You can use various tools and methods to measure and analyze your data quality, such as dashboards, reports, charts, graphs, scorecards, and benchmarks. You can also use various sources and perspectives to evaluate your data quality, such as internal audits, external reviews, customer feedback, and business outcomes. By monitoring and evaluating your data quality performance, you can gain insights into your data quality strengths and weaknesses, and take corrective or preventive actions accordingly.

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Sven Sauder

Automatisierter Sales für IT Dienstleister | | Wie?: 8 Wochen Projekt ✅
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Meiner Erfahrung nach sind gerade Reports und Dashboards sehr wertvoll um einen schnellen Überblick über die eigene Datenqualität zu erhalten. Aber nur, wenn damit auch konkrete Verbesserungsmaßnahmen durchgeführt werden. Es fällt den Teams meist deutlich einfacher, wenn diese bereits vorab definiert wurden. Immer in Verbindung mit definierten Schwellwerten, um den Prozess der Verbesserung anzustoßen. Hat man das nicht, bringen einem auch die besten Reports und Dashboards nichts.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you maintain data quality in CRM post-go-live?

1

2

3

4

5

1 Define data quality standards and metrics

2 Implement data quality tools and processes

3 Train and engage your CRM users

4 Monitor and evaluate your data quality performance

5 Here’s what else to consider

Customer Relationship Management (CRM)

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How can you maintain data quality in CRM post-go-live?

1

2

3

4

5

1 Define data quality standards and metrics

2 Implement data quality tools and processes

3 Train and engage your CRM users

4 Monitor and evaluate your data quality performance

5 Here’s what else to consider

Customer Relationship Management (CRM)

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