Data discrepancies between your CRM and other systems can cause confusion, frustration, and errors in your customer interactions. They can also affect your reporting, analytics, and decision-making. To avoid these problems, you need to manage your CRM data quality and security effectively. Here are some tips to help you do that.
Identifying the source of data discrepancies is the first step to managing them. Data discrepancies can come from different data formats, standards, and definitions across systems, data entry errors, duplicates, or missing values. They can also be caused by synchronization delays or failures, data manipulation or corruption by unauthorized users, and data changes or updates by customers or third parties. To detect and analyze data discrepancies, you can use data audits, quality reports, validation rules, integration tools, or governance software.
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It is also important to consider data lineage. Data lineage is the process of tracking the origin, transformation, and movement of data throughout its lifecycle. This can be a valuable tool for identifying the sources of data discrepancies, as it allows you to trace the data back to its source to see where the discrepancy occurred.
The second step to manage data discrepancies is to establish a data governance framework that defines the roles, responsibilities, policies, and procedures for managing your CRM data. Doing so can help you align your CRM data with business objectives and customer needs, as well as ensure data quality, accuracy, consistency, and completeness. Additionally, a data governance framework can protect data privacy, security, and compliance; monitor and measure data performance and outcomes; and improve data collaboration and communication across teams and systems. Various frameworks and best practices can be used to design and implement your strategy, such as the DAMA-DMBOK, the ISO 8000, or the CRM Data Governance Maturity Model.
It is also important to choose the right data governance frameworks for your organization. When choosing a data governance framework, consider the following factors: The size and complexity of your organization The types of data that you collect and manage Your industry and regulatory requirements Your budget and resources
The third step to manage data discrepancies is to implement data cleansing and enrichment processes that correct, update, and enhance your CRM data. This can include removing or merging duplicate records, fixing or filling in missing or incorrect values, standardizing or normalizing data formats and definitions, verifying or validating data accuracy and relevance, and appending or supplementing data with additional information or insights. To do this, you can use various tools and techniques such as data deduplication tools, data verification services, data enrichment platforms, or data append solutions.
The most important aspect of data cleansing tools or data hygiene ones is that they run periodically and as frequently as needed per business to ensure the data is maintained at all times. Another critical process is to identify when syncs fail, in due time, to rectify the issues and run the tools to ensure data integrity.
The fourth step to manage data discrepancies is to automate data integration and synchronization that connect and align your CRM data with other systems. This can streamline data flows, reduce silos and redundancies, increase visibility and accessibility, enhance reliability and timeliness, and support data analysis and reporting. To do this, you can use various tools and methods, such as data integration platforms, synchronization software, APIs, webhooks, or ETL processes.
The fifth step to manage data discrepancies is to educate and empower your data users - such as employees, customers, partners, or vendors - that interact with your CRM data. Doing so can help you raise data awareness and literacy, promote a culture of data quality and security, encourage data feedback and improvement, foster data ownership and accountability, and enable data-driven actions and decisions. To do this, you can use various tools and resources, such as data training programs, guidelines and manuals, dashboards and alerts, feedback mechanisms, or incentives and rewards.