Why is CRM Data so Important?

Why is Data Important?

Data is the lifeblood of CRM systems. Plus, with the advent of Artificial Intelligence (AI) and all of its capabilities, data quality is even more important. The quality of data affects the performance of the CRM system. Regrettably, data can be expensive, especially during the first phase of a CRM implementation. In this phase, data work may be the most expensive and time-consuming. It is essential to determine where data is located within the organization, including in Outlook, Excel, or legacy systems.

Consolidating and centralizing information is a common objective of CRM systems. However, this involves extra processes that take time, such as pulling data from all current systems, reformatting data, importing data into the new CRM, and data cleansing processes, such as deduplication and address verification. The costs of these processes can be significant and are often underestimated. However, neglecting these processes can result in even greater costs in the long run.

Initial Data Load

Where is your initial data coming from? This could be a previous CRM system, or from several data sources including Outlook contacts lists, ERP system, separate mailing lists, spreadsheets etc.

The layout, names of data fields, data format, and data representation will differ for each data source, and they may be significantly different from the layout, format, etc., of the new CRM system. For example, the format of the address may be different for each data source, which can create challenges when consolidating and centralizing information. Therefore, it is crucial to understand the data factor fully before implementing a CRM system.

Data Quality

AI in CRM systems has the potential to transform decision-making and increase efficiency. AI improves decision-making and user efficiency across all CRM systems by analyzing data and offering insights, such as lead scoring and opportunity analysis. However, these benefits are compromised if the underlying CRM data contains duplicates, inaccuracies, or missing information. If your CRM data quality is low, your AI snapshot will be unreliable and will not contain the meaningful insights you are looking for.

Low-quality data is one of the most common reasons for low user adoption of the CRM system. Even if the system has user-friendly, intuitive functionality, users will quickly notice if the data is not current, correct, and duplicate-free. This lack of confidence in the system can lead to users entering less accurate data, ultimately leading to employees abandoning the CRM system and creating their own systems, usually through spreadsheets.

Inaccurate, duplicate, or out-of-date data also results in low confidence in the output from the CRM system, particularly with regards to marketing, forecasts, reporting, and customer service. It is also essential to consider where the initial data will come from, whether from a previous CRM system or various data sources, such as Outlook contact lists, ERP systems, separate mailing lists, or spreadsheets.  Read How to Achieve Clean Data article for more details.

Data Governance

This is about setting out your Data Standards in terms of CRM Data Entry Standards, security (who can see what), and how it is maintained.

Setting out your standards and implementing monitoring and controls helps to maintain the quality and health of the data. Simple things, like ensuring data entry is managed wherever possible via defined option sets (A.K.A. pick lists), can aid data quality. When we start talking about this in projects, a common request is to make lots of fields mandatory. A reasonable request, but what happens if at the point in time of adding the records, the user simply does not know what to enter? Usually, users will simply make something up to progress what they are doing. Use mandatory fields sparingly, with checks in place to highlight those key fields in your CRM that still require entry.  This is simple to achieve using a lookup/query with the system and making this available to the users.

Data Governance is also crucial to maintaining the quality and health of the data, which involves setting data standards in terms of data entry standards, security, and maintenance.

Implementing monitoring and controls helps to maintain the quality and health of the data. Simple things, such as managing data entry via defined option sets, can aid data quality. However, it is important to use mandatory fields sparingly and have checks in place to highlight the key fields that still require entry in the CRM system. There are data providers that offer point of entry validation for addresses and other data, making data quality management at this point more cost-effective. Preventing data quality issues is always better than curing them.   

Here are some essential steps for your CRM Data 

1. Audit Existing Data Sources

• Identify all current data repositories (Outlook, spreadsheets, ERP and legacy systems).
• Assess data formats, field structures, and quality levels.
• Avoid data silos: Ensure integration rules are clear to prevent conflicting records (for example, sales vs. finance figures).

2. Define a Data Consolidation Strategy

• Plan how data will be extracted, transformed, and loaded (ETL) into the new CRM.
• Include deduplication, reformatting, and validation steps.

3. Establish Data Governance Framework

• Create clear standards for data entry, access control, and maintenance.
• Decide which fields are truly mandatory and implement smart reminders for incomplete entries.
• Assign roles: Appoint an Executive Sponsor, Data Governance Administrator, Council, and Stewards to oversee and enforce governance.

4. Implement Data Quality Tools

• Use option sets (pick lists) and validation services at the point of entry.
• Consider third-party tools for address verification and duplicate detection. Paribus 365 will help with the duplicate detection and cleansing.

5. Engage Stakeholders Early

• Involve marketing, sales, and customer service teams in defining data needs and standards.
• Ensure buy-in for governance policies and training on data quality practices from both leadership and the team.

6. Plan for Ongoing Monitoring

• Set up dashboards or queries to track data health (for example, missing fields, duplicates).
• Schedule regular reviews and updates to data standards.
• Use KPIs to evaluate compliance and data quality. Update documentation and training as needed.

Read our CRM Data Governance Best Practices Series for more details on this topic.