Understanding the State of Your Data: Before and After Salesforce Implementation

When implementing a new customer relationship management (CRM) system like Salesforce, organisations will be focused on the exciting new features, enhanced user interfaces and better workflow automation — and no doubt, new AI opportunities.

For many, implementing Salesforce will be the first step for the organisation in building a whole new relationship with data and the opportunities it may present. However, understanding your data today — how it is structured, where it comes from, how it is used and the data quality — will significantly affect the success of your Salesforce implementation and its ability to meet your organisational needs.

Pre-implementation analysis of your data

Before embarking on their Salesforce optimisation journey, organisations should assess their data, often known as data profiling.

Understanding the KPIs and reports that drive your business

It is essential to understand the key performance indicators (KPIs) and reports that drive decision-making in your organisation. Salesforce optimisation will require data that feeds into accurate and meaningful reports, which means ensuring your current data is aligned with the standard KPI definitions your organisation values the most.

By evaluating these critical metrics, you can ensure the new system is capable of supporting and improving the way you track and measure success, as well as identifying any issues with definitions or standardisation in your organisation. Arriving late with your standardised and agreed KPIs on a Salesforce build will probably result in a lot of rework.

Understanding the data that underpins your business processes

In any organisation, data drives processes. Before moving to Salesforce, you need to understand the data that underpins your workflows — whether it is sales pipelines, customer service interactions or marketing campaigns. This will help you configure Salesforce to support, rather than hinder, your processes.

If your data for leads, contacts or opportunities is inaccurate or incomplete, Salesforce will only amplify these issues, hampering efficiency. Aligning validation rules with when data comes into a process is critical to ensure a decent user and customer experience.

Understanding data sources and integrations

Many businesses rely on multiple systems and data sources that feed into their workflows. You would be amazed at how many workflows are underpinned by a random Excel spreadsheet.

Part of your pre-implementation analysis should involve mapping these systems and data flows, truly understanding how they integrate. For example, are some data sources redundant or outdated? Identifying which integrations are necessary and which can be retired helps prevent the platform from becoming unnecessarily complex — enabling further Salesforce optimisation.

Understanding the quality of your data and privacy impacts

With ever-changing data regulations, understanding how new lifecycle and privacy policies might impact your data is critical. If your data quality is poor or your data management does not align with privacy regulations, you risk running into trouble with compliance laws.

A pre-implementation analysis will help you set up Salesforce in a way that complies with these policies and ensures personal data is handled responsibly.

Understanding how data quality affects data migration

One of the most complex tasks during a Salesforce implementation is migrating your data. If your data is messy — riddled with duplicates, inconsistencies or inaccuracies — your migration will be badly disrupted, giving users a miserable ‘go-live’ experience and damaging the credibility of your shiny new platform.

Understanding the quality of your data before the move allows you to clean and prepare it, reducing the risk of errors during migration. A thorough pre-implementation analysis and audit helps to map old data models to the new Salesforce structure more effectively, ensuring a smooth transition and accurate records.

Post-implementation analysis of your data

Once Salesforce is implemented, it is important not to sit back and assume all your data problems are solved.

Post-implementation analysis will help you maintain data accuracy, compliance and relevance — so be sure to continue monitoring and maintaining data after the system is live.

Understanding your new privacy risk levels 

After implementing Salesforce and applying lifecycle decisions, such as data retention policies, you will need to reassess your risk levels in terms of data privacy and regulations. What areas could be improved? What processes could be automated?

Ensuring that your data quality aligns with your privacy policies will help maintain compliance with regulations like GDPR and CCPA, protecting both your business and your customers.

Understanding what data is being used and what is obsolete

Over time, data within a CRM can become obsolete — records get outdated, and customers become inactive. Ongoing analysis will help you regularly assess which data is being actively used and which is redundant. By identifying and archiving or removing obsolete data, you can keep your system streamlined and efficient, delighting your users.

Understanding how to visualise and gamifying data quality

Salesforce offers tools to help you visualise data trends and quality, making it easier to identify areas for improvement. By gamifying data management, you can encourage your team to actively participate in maintaining data quality. For instance, sales teams might compete to have the highest percentage of complete or accurate records, adding an element of fun to an otherwise mundane task.

Understanding how data quality impacts user adoption and customer experience

Data quality impacts user adoption of Salesforce. If users encounter outdated or inaccurate data, they may lose trust in the system, reducing their engagement. Identifying these correlations can help you address user frustrations, automate routine data collection processes and encourage better data management practices.

Similarly, data quality has a direct impact on customer experience and engagement. Poor data leads to inefficiencies, misunderstandings and broken workflows that can result in negative customer interactions. A post-implementation analysis can help you identify correlations between data quality and the customer experience, allowing you to refine your processes for better service delivery and improved customer satisfaction.

Your trusted partner for Salesforce optimisation

Understanding the state of your data before and after a Salesforce implementation is key to achieving the full potential of the platform.

With pre-implementation analysis, you can avoid common pitfalls and ensure a smooth transition. Post-implementation analysis then helps you maintain compliance, enhance workflows and continuously optimise your system.

By focusing on data quality, both before and after implementation, you can maximise Salesforce optimisation for your business.

To ensure your data is clean, compliant and structured for success, consider partnering with a trusted provider. Technicus’ data health check, data migration delivery and data management services are designed to help you at every stage of your Salesforce journey, from initial data audits to ongoing maintenance and optimisation. Contact us today at jamie.hart@technicus.co.uk to see how we can help your business.