Subscribe to our newsletter

10 data quality goals for practices

I frequently hear from our customers that they have to deal with too much information and too much complexity. Increasingly, they talk of their need to embrace technology and, specifically, to be much more data-driven.

10 data quality goals for practices
smsfadviser logo
  • Elaine Roche
  • March 24, 2021
share this article

Data is one of the most valuable assets advisors have at their disposal today. Most practices aspire to be completely data-driven and, to achieve this goal, they are embracing data quality and governance principals within their tax and accounting systems. However, for practices to become truly data-driven, data quality can’t be the responsibility of a sole practice manager, or even a few key members of the team. Data quality needs to be bought into at all levels across a practice.

Many practices experience challenges in assessing and maintaining data quality, and how it’s integrated within their systems. Simply put, it’s impossible to make business decisions or use data to communicate if the quality and accuracy isn’t there.

It is important to put processes in place for regular data housekeeping. Rather like cleaning your house, a ‘little and often’ approach makes it is a manageable task. If it is left for years without the right care and attention, data management can become a seemingly insurmountable task.

Setting standards for data, as well as rules that dictate consistency across all records, will help in the longer term with any sort of data analysis or management information reporting that practices want to explore. Before embarking on a data quality journey, here are a few goals to keep in mind:

  1. Firstly, consider what is going to make a difference to your practice and your clients. What is causing your team the most pain when it comes to data? Is it inconsistency, is it outdated data, or is it something deeper, like a lack of accessibility or an inability to find what you need? You may be able to start with a data cleansing exercise as a priority, before moving on to deal with accessibility or data management training issues.
  2. Ask your practice: what timeframe you do you want to work towards? A new system implementation is often a great time to look at your data and embark on a data cleansing exercise. However, you don’t need to wait to implement new software to begin your data quality journey. Recognise that this is a marathon, not a sprint, and it’s likely to be a journey that takes some time to find the right balance between accuracy and effort to correct the situation.
  3. Consider compliance, be that GDPR or any other regulations. How you gain, retain and dispose of data needs to take account of all of the relevant regulation.
  4. Involve the right team members. Data quality is something that affects everyone in your organisation. It is important to have representation from all departments as you will need everyone engaged if your data quality programme is going to be a success.
  5. Consider your culture and organisational mindset. Data quality requires all members of the team to look at things in a new way and really understand the value that accurate data will bring to everyone. You will need to make sure that you foster a culture that encourages and promotes people to care and to focus on the impact poor data quality can have on all clients and processes.
  6. Consider how you are going to monitor and benchmark your data quality going forward. Do you need to invest in any software, or is it enough to run some simple reports?
  7. Think about accessibility. How much freedom do you want to give people to enter new data into the system once it is in the desired state? Who should or shouldn’t have responsibility for entering certain types of data? Who can delete data relating to clients or contacts? It is important to consider the right protocols for your business.
  8. How will you deal with duplicate records, or situations where client or contact records need to be merged? What constitutes a duplicate for one practice will be different for another.
  9. What validation will you add to the system to minimise the chance of data degradation in the future? Something as simple as a dropdown list can mean that you don’t get five different versions of ‘Doctor’ entered as a title.
  10. In terms of technology, consider whether or not there any changes or updates to your software or hardware that can help further improve your data quality. Also, remember that new software platforms (particularly cloud platforms) by nature may provide more data visibility to customers. Self-service platforms, where customers are able to update their own contact details for example, require detailed rules for consistency.

In accountancy as in every profession, good quality, well managed data has become more important than ever before, but practices need to adopt the right attitude and approach from the ground up and maintaining good quality data can’t be an afterthought. If practices can achieve a high quality of data, they can move on to using data to extract powerful business insights to drive business decisions and future planning, which is the next step in the journey to becoming a truly data-driven enterprise.

Elaine Roche, head of customer success, Wolters Kluwer Tax and Accounting UK

Receive the latest Financial Accountant news,
opinion and features direct to your inbox.

related articles