Your Salesforce Has Data. But Can You Actually Trust It? 

Most businesses have no shortage of data sitting inside Salesforce. There are customer records, leads, opportunities, contact details, sales activities and years of information gathered by different teams at different points in the customer journey. From the outside, it can look like a complete picture of the business.

The problem is that having a lot of data does not necessarily mean having good data.

Over time, small inaccuracies have a way of piling up. A contact changes roles but the old information stays in the system. Two people create records for the same customer. One sales representative enters information differently from another. An opportunity remains open long after it has stopped being relevant. A field that was important a few years ago is no longer maintained, but the business continues to use it in reports.

None of these things are particularly dramatic on their own, which is exactly why they are so easy to overlook. The problem starts when this information becomes the basis for decisions.

Salesforce data does not simply sit in a database. It gets turned into reports, forecasts, dashboards, and analysis that people use to understand what is happening in the business.

A sales leader may look at the pipeline to understand what the next quarter could look like. A marketing team may use customer information to decide where to focus its efforts. Senior management may rely on reports to understand performance and plan what comes next.

If the underlying information is incomplete or outdated, the picture presented by those reports can be misleading even when the report itself has been built correctly.

This is one of the less obvious problems with poor data quality. It does not always announce itself. A broken system is easy to notice because people cannot use it. Bad data is much quieter. The system continues to work, the dashboard continues to load, and the numbers continue to appear on the screen. They just may not mean what people think they mean.

Salesforce describes data quality in terms of accuracy, completeness, consistency, and reliability, among other dimensions, because each of these affects whether information can be used with confidence.

Data quality is often treated as a question of whether all the required fields have been filled in. In practice, it is a little more complicated. 

Useful business data needs to be exact, current, consistent, and relevant to the work being done. A customer record containing every field is of little use if half of the information is outdated. Similarly, having thousands of records does not help much if there are duplicates or if different teams follow completely different standards when entering information. 

This becomes particularly important as organisations start relying more heavily on analytics and AI. 

A Salesforce report on trusted AI found that while three-quarters of workers surveyed considered accurate, complete and secure data important for building trust in AI, more than half said they did not trust the data being used to train AI systems. 

That gap says quite a lot. 

Businesses may be excited about what AI can do with their data, while still being unsure about whether the data itself deserves to be trusted. 

It is easy to blame the CRM when the data inside it becomes difficult to manage, but the system is rarely the whole story.

Data quality is usually shaped by the way people and processes work around the system. New employees are trained differently from the people who joined a few years earlier. Processes change as the company grows. Teams start using new tools. Information gets imported from other sources. Fields are added, removed, or repurposed.

Then there is the everyday pressure of work.

A salesperson trying to get through a busy afternoon may not think twice about leaving a field blank. Someone creating a new customer record may not realise that another record already exists. A manager may know that an opportunity is no longer active but forget to update it because there are ten other things demanding attention.

These are ordinary things. They happen in perfectly well-run organisations.

The challenge is that they accumulate.

After a while, what began as a few small inconsistencies becomes a data-quality problem that is much harder to untangle.

A large data cleanup exercise can certainly help, particularly when a business has accumulated years of inconsistent or duplicated information. But cleaning everything once does not guarantee that the problem will stay fixed.

If the processes that created the problem remain unchanged, the same issues will gradually return.

This is why businesses need visibility into the health of their Salesforce data rather than treating data quality as a once-a-year housekeeping exercise. It becomes much easier to address problems when they can be showed early, understood in context, and prioritised according to their impact on the business.

Not every imperfect record deserves the same level of attention. A missing piece of information on a record that is rarely used is quite different from inaccurate information feeding a critical sales report.

The aim should not be to create a database that looks perfect on paper. It should be to make sure the information people rely on is dependable enough for the decisions they are making.

This is where tools such as Salesforce Analyser can have a practical role to play.

Instead of relying entirely on manual checks or waiting until a reporting problem becomes obvious, businesses can use analysis to get a clearer view of the information already sitting inside Salesforce. That visibility can help teams identify areas that need attention and understand where data quality may be affecting the wider business.

It is a simple idea, but an important one. Before an organisation can get more value from its CRM, it needs to know whether the information inside it deserves that confidence.

The same principle becomes even more relevant as businesses introduce more automation and AI into their operations. Salesforce has pointed to data readiness as one of the barriers to scaling AI, with its research finding that 48% of IT leaders believe their data is not ready for AI.

That puts the issue into perspective. The challenge is not always finding another AI tool. Sometimes, it is getting the foundations right first.

A Salesforce environment with thousands of records may look impressive, but the real value lies in whether those records accurately represent the customers, opportunities, and relationships the business is dealing with.

Good data is not about having more information. It is about having information that people can rely on when it matters.

And that is what makes a CRM valuable in the first place: not the amount of data it stores, but the confidence people have in using it.