Many organisations in the 21st century depend on data to reach their goals. Marketing and Sales need clear, accurate information that they can rely on to send communications and close deals. Businesses that use fragmented data that is utilised across different platforms lead to data duplication, poor internal communication and usually result in a downgraded sales performance. To address this, we first need to recognise why this happens. We've put together some of the common reasons for poor data quality in CRM systems.
- Lack of clear data-entry procedures: Without clear guidelines for how data should be entered and managed, it's more likely that errors will occur.
- Insufficient training: If employees who handle data are not properly trained on data management best practices, they may be more prone to making errors.
- Incomplete or inconsistent data: If data is incomplete or inconsistent, it can be more difficult to accurately track and manage customer interactions.
- Lack of data quality checks: Regular checks of data quality can help to identify and correct errors. If these checks are not in place, errors may go undetected.
- Outdated or obsolete data: Over time, customer data may become outdated or obsolete. If this data is not regularly cleaned or updated, it can lead to incorrect insights and decision-making.
- Complex data structures: If a company's data structures are complex or difficult to navigate, it can be more challenging to manage data effectively.
- Lack of resources: Poor data management can also occur if a company does not have the resources (e.g., time, budget) to invest in data management best practices.
There are several steps that a CRM leader can take to ensure clean data in an organisation:
Establish clear data-entry procedures: It's important to have clear guidelines in place for how data should be entered and managed. This can help to reduce errors and ensure that data is consistently entered in a consistent format.
Use data validation tools: Many CRM systems include data validation tools that can help to identify and correct errors in data as it is entered.
Implement data quality checks: Regular checks of data quality can help to identify and correct errors in data. This may involve comparing data to external sources, performing spot checks, or using data cleansing tools.
Provide training on data management: It's important to ensure that all employees who handle data are trained on proper data management practices. This can help to reduce errors and improve the overall quality of the data.
The dangers of poor data management are numerous. Poor data quality can lead to incorrect decisions and actions, which can have serious consequences for an organisation. For example, if customer data is incorrect, a company may send marketing materials to the wrong addresses, leading to wasted resources and a poor customer experience. Poor data management can also lead to regulatory issues, as many organisations are required to maintain accurate data for compliance purposes. In addition, poor data management can damage a company's reputation if customers lose trust in the accuracy and reliability of the data the company holds on them.
Inaccurate insights and decision-making: Poor data quality can lead to inaccurate insights and decision-making. For example, if customer data is incorrect, a company may make decisions about marketing or sales strategies based on incorrect assumptions about its customer base. This can lead to wasted resources and missed opportunities.
Regulatory issues: Many organisations are required to maintain accurate data for compliance purposes. For example, a healthcare organisation may be required to maintain accurate patient records in order to meet regulatory requirements. Poor data management can lead to regulatory issues and fines.
Poor customer experiences: Incorrect customer data can lead to poor customer experiences. For example, if a company sends marketing materials to the wrong address, the customer may become frustrated and lose trust in the company.
Loss of competitive advantage: In today's data-driven business environment, having accurate and reliable data is essential for making informed decisions and staying competitive. Poor data management can lead to a loss of competitive advantage as a company struggles to make informed decisions.
Damage to reputation: Poor data management can damage a company's reputation if customers lose trust in the accuracy and reliability of the data the company holds on them. This can lead to a decline in customer loyalty and a negative impact on the company's bottom line.
If your business is negatively impacted by poor data health, Serversys can help address these issues by deploying a tailored strategy to uncover why this happens and what actions to take so you can rely on the data you depend on for success.