Why data quality is key in any Quote-to-Cash programme

Posted On by Michelle Stölting

Data is undervalued and underrated. Even though every company wants to be data-driven, many are not aware of the quality their data holds and of the impact it may have on business decisions. From my experience in Quote-to-Cash (QTC) programmes, I can safely say that data quality is key to a successful transformation.

Before we engage in a QTC programme, we usually propose an organisational readiness assessment. This assessment detects the gaps that need to be addressed in order to make the programme a success. The assessment looks at roadmaps and workstreams, business unit alignment, change management experience, … The gap that never fails to present itself is the lack of clean data. Allow me to share some insights on why data quality is so crucial.

Better for business

Clean data holds a lot of advantages: it is only natural that clean data will lead to better reporting, hence better decision-making. It can also prevent loss of margin and revenue: when the data is correct, it is much easier to get insights and track everything that is happening. Bad data may lead to contracts not being fulfilled, or contract renewals being forgotten. Clean data also leads to better collaboration, because people will know exactly where to find what information. This makes it easier to bring in a colleague during vacation or sickness since it is very simple to tell the co-worker where to get the data from and how to use it.

Better for user adoption

User adoption is critical for any new programme, and even more so when data is involved. Data entry is very often a manual process, thus prone to errors. How can a company expect users to be careful when keying in data if the user knows that the bulk of the data is garbage, making accurate reporting impossible? If all other data is clean and you are the only one entering rubbish, people are more inclined to make an effort. Fortunately, systems can help and enforce data hygiene, for instance by only accepting valid email addresses in an email field.

Better integration after mergers

When two or more companies merge, this affords an opportunity to start with a clean slate. Companies have to grab that chance to sift through the data and keep only what is current and valid. The worst thing a company can do is ‘lift & shift’ the legacy data to a new system. Maybe there is garbage in there that is 10 years old and no longer valid or there are duplicates that will make the life of the users harder, decreasing adoption to the new system. Anything that potentially excites the users, you want to look into.

Better flow of a programme

While it is not necessary that all data be cleansed at the start of the design-and-build phase, user acceptance testing should not start without at least having the basic information in the new system. If you have a Quote to Cash programme, you will always need a base layer of data such as account, contact, opportunity, subscriptions, contracts, renewals, products, prices,… You need at least these for a system test to be valid. Extra information for customisations or other requirements may come in a later phase.

In our experience, it’s best to sit down once, clean everything and be done with it. There are always dependencies that you perhaps only find out about when it is too late. And that may extend the programme timeline – and the budget. Taking care of data quality is really a mindset. A data-driven company will always start with the data. Cleansing data is a dirty job, but someone has to do it.

At Wipro, we make this task a bit easier by offering data advisory services and providing proven templates; these make sure that only the right data gets migrated to the new system. Data is precious and deserves your full attention.