The art of the start. Guy Kawasaki wrote a famous management book about it. How to launch a great idea into any type of business project. For CFO’s the same applies to Finance automation- and transformation projects.
According to this Accenture study, 99% of CFO’s want to use real-time data when making decisions, but only 16% believe they are (fully) capable of starting on this topic. A clear struggle about bringing a good idea into practice, even while they channel 33% of their departments’ budget towards building solutions with respect to real-time operations and processes.
Automated data extraction
I strongly believe that using real-time data has major advantages with regards to monitoring, forecasting and creating reliable input for faster decision making. It’s inevitable to use more data and of course: the more real-time you do so, the better you are capable to use it in your advantage. But what is the definition of real-time? Is near real-time also satisfactory to empower organizations to make better and sound decisions?
In my opinion it’s not about focusing on real-time or near real-time per se, but about building an infrastructure where the data-extraction process is automated and the involved stakeholders understand the definitions and quality of the dataset in detail. On transactional level.
So where and how to start?
Here is an example approach that is based on our own experiences with clients:
- Assess which systems and applications have the option to schedule data-exports;
- Assess which systems and applications have API connections which can push or pull data on transactional level;
- Then assess which systems contain the same unique identifiers and investigate whether a connection between these systems can be established and if possible
- Conceptionally design a (near) real-time reconciliation which can be the start of automating important check & balances which, as a result, lead to automated standardized reports, better insights and high-quality decisions making.
But where to apply this in practice?
We learned that, when it comes to applying these checks and balances, the quickest wins are often gained with respect to the high-volume B2B and B2C transactions and in particular related to gaining insights on chargeback and refunds. on transactional level that allow for better prevention of revenue leakage.
Based on these findings, my question to you would be: Do you truly know which debtors are outstanding and where you still are able to collect outstanding amounts due to chargebacks?
Quick win tip: automated data extraction from PSP’s
A major struggle for finance teams in the high volume transaction domain is the challenge to connect core systems with payments and settlements of multiple payment service providers (PSP) and ultimately with the general ledger.
If you ask me, this situation offers a great use case to start with real-time data analysis. Perhaps not a field you would think of? Probably. But a very good start to get familiar and gain experience in how to establish real-time data insights: by establishing automated data extraction.
Want to learn more about automated data extraction and quick wins in finance? I’d be more than happy to demostrate some of our best practices. Feel free to contact me.