Achieving quality data for your sales system

A single source of the truth

Inaccurate data wastes everyone’s time, but with some adjustments, this same data can be transformed into a smart business tool. Better data increases the users’ confidence; the system is used more and as a result increases its value to your business. In extreme cases, inaccurate data can lead to incorrect decisions being made.

Efficient Business Process

If data entry is a bit of a chore, users are going to be less keen to enter it correctly and keep it up to date. Incorporating it as part of the job is a good way to remedy this. For example, your company typically will have less data problems with call centre staff than field reps. The former fill in data as they speak to customers on the phone, while the latter have to do it as a separate task.

If your business processes can be automated by the system itself and capture data at the same time, users will be more keen to do it as it will save them time. Giving users a positive reason to get data correct (like regular use of it in sales pipeline reviews) also helps.

System design

The number of data fields you ask users to fill in can also have a bearing on the final result. Avoid data overload - only get users to populate data that will be vital for your business.

Make it as simple as possible to enter. Ideally the system will capture data without the users having to do anything (e.g. record creation date).

But some data obviously does require input, so for critical data consider introducing data controls - either preventing or highlighting missing or incorrect data. However it should be noted that there is a trade off between making the system difficult and cumbersome to use and ensuring data quality.

Training and communications

This often gets overlooked, but is probably the most cost effective way of improving your data capture. The key is for users to understand what they need to enter, how this relates to your business processes and why it is important.

Make sure you have simple definitions for each data field and ensure that your users know what these definitions are (especially those that are not self explanatory). Get your message across both via push (presentations, e-mails, training courses) and pull (guidance materials, online help) methods.

Monitor progress

If you are undertaking a data improvement programme it is important to review your progress.

Do this by first identifying which data you want to improve; build a report to identify it and those individuals getting it wrong; then mentor them to improve. Unfortunately, the report will not be able to capture all problem data (for example old data). There is danger of users correcting data just for the report, rather than concentrating on overall quality. So the report should not be used as a stick to get users to improve, but more of a tool to identify problem areas requiring action and viewing overall progress.


To be effective, data must be entered into a system correctly the first time and then kept up to date as soon as it changes. But what if your sales data is inaccurate or incomplete? Getting this to improve can be challenging; here I discuss some of the things you can do to remedy the situation:

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