5 Key Elements of Good Salesforce Data Management 

Data is at the heart of every modern business — with the potential to drive innovation, enhance decision-making and improve operational efficiency. For many, this can manifest most obviously in their Salesforce CRM platform — be it sales, service, marketing, compliance or finance. This is where the state of the data is first noticed. However, getting the most from the data requires the appropriate levels of data governance and quality management. 

With robust data-management measures and well-established governance policies, you can expect higher levels of accuracy, consistency and security — providing a solid foundation for improved sales and operational processes. Not only can reliable, high-quality data enhance insights, helping you gain a competitive edge, but it can also enhance compliance processes, limiting the risk of costly errors and data breaches.

So, what are the five key elements of good Salesforce data management that will help you guarantee the integrity and quality of your data?

1. Understanding your data model

The legacy of how a system has been implemented — from account hierarchies and different record types for customers or partners to validation rules and complex flows — has many implications. Understanding your CRM metadata is about recognising the impact of these implications on your processes, integrated systems, reporting and insights. If you do not understand why and how the data is created, you will struggle to get to the root cause of your data management issues.  

To help you understand your CRM data structure, it is critical to maintain and update data in line with revised processes and define a data dictionary for organisational use. 

2. Continuous data cleansing and duplicate management

Ongoing data cleansing is critical for maintaining accurate data and enabling new opportunities for automation. This can be encouraged at the individual user level or by applying specific tools and data processing to address bigger issues. 

One crucial aspect of data cleansing is deploying the correct deduplication logic across accounts, contacts, leads and opportunities. Identifying and removing duplicate records helps to ensure data uniqueness; however, doing it to ensure maximum data integrity and minimal loss of relevant child records can be much harder than it looks. 

By continuously removing duplicates, correcting errors and ensuring data consistency, you can maintain high data quality. Not only does this practice improve reporting and forecasting accuracy, but it also saves user time, drives productivity and, ultimately, enhances customer service. 

3. Configuring data-quality dashboards

If you want to maintain high data standards, building data-quality work into your daily activities is essential.  

A data-quality dashboard is a visual tool that helps you grasp your risk exposure in line with privacy laws and understand crucial data metrics that impact user, client and candidate experiences. These dashboards also help drive credibility and trust in the system, as well as track and gamify progress.  

By setting up dashboards and customising reports to meet your specific organisational needs, you can ensure you are focusing on the most relevant metrics that link to customer satisfaction — gaining real-time insights into data-quality issues that allow for immediate action and resolution. 

4. Regular data-quality analysis

Dashboards provide a micro view of data quality and prompt individual actions to address quality issues. However, a more thorough analysis can deliver significantly more powerful insights with regard to the effectiveness of flows, process improvement, automation and self-serve opportunities — as well as adoption levels and platform engagement.  

Thinking of data as the fourth leg of the traditional three-legged stool of people, processes and technology will significantly enhance the capability of your platform, and ensuring it plays a critical role in the evolution of your platform will pay huge dividends. 

5. Adhering to data privacy, residency and AI regulations

The responsibility to adhere to ever-expanding data privacy, residency and AI laws (GDPR, CPRA, POPIA, DSL, EU Artificial Intelligence Act) will continue, and this requires implementing the correct data lifecycle practices and ensuring personal data is deleted or processed correctly at the right time. 

Defining what constitutes ‘meaningful activity’, applying privacy policies at every stage of interaction and ensuring there are no gaps in privacy obligations — such as Subject Access Requests (SAR) and the Right to Be Forgotten (RTBF) — are essential practices and can significantly reduce the ‘admin’ burden on sales and marketing teams.

Technicus’ Data Management Services are designed to help you optimise the value of your data assets and get the most out of Salesforce. We provide comprehensive support to ensure your data is governed according to best practices and industry standards by creating and enforcing data policies, procedures and standards. Contact us today at jamie.hart@technicus.co.uk to see how we can help your business maintain high data quality and integrity.