In today’s fast-paced business world, financial reconciliations are crucial processes finance teams rely on to identify discrepancies in their financial records and prevent fraud. However, manual financial reconciliation can be extremely time-consuming, prone to human error, and tedious.
Fortunately, Dataiku offers a range of powerful features that can support the automation of financial reconciliations, streamlining the process and helping teams realize the value of time savings and improved process accuracy. In this blog post, we will discuss five key features of Dataiku that are useful for automating financial reconciliation processes.
Join Recipe – Centralize & Merge Disparate Data Sources
One of the core tenants of any financial reconciliation process is combining the sources you wish to reconcile. The reconciled data often lives in disparate backend systems, and Dataiku provides the ability to connect to numerous data sources. Once the data is input to your flow, the join recipe allows you to merge two or more sources on a set of common fields.
The configured join recipe can perform an ‘inner join’ and left/right/full outer joins if you need to report information on records for which there is no match in the other source or sources you are trying to compare.
Click here to learn more about how the join recipe works.
Prepare Recipe – Standardized & Governable Data Transformations
The prepare recipe is Dataiku’s Swiss army knife. It allows you to clean, normalize, transform, and interactively enrich your dataset. It helps finance teams improve process governance by standardizing transformation steps in a visible, auditable format.
In this use case, you can use the prepare recipe to find the difference between the two values you are trying to reconcile or cleanse your dimensional data before merging your data sources.
Read the prepare recipe user guide to understand all of the different processors available within the prepare recipe.
Group Recipe – Reconciling at the Desired Level of Granularity
The Group Recipe is also helpful for financial reconciliation processes as it allows you to reconcile your data at the desired level of granularity. For instance, you may not need to reconcile your data at a transaction level, but rather at a higher level, such as for each business unit or by a custom transaction grouping. With the Group Recipe, you can aggregate your data to the level required for your particular analysis.
Understand how to leverage the group recipe to aggregate your data with this walkthrough.
Metrics and Checks – Monitoring & Alerting to Ensure Data Quality
Successfully implementing reconciliation process automation requires trust in the process. This process requires both the ability to reliably monitor data flowing into the process, to ensure inputs data are as expected, and to reliably flag where the data do not tie out at the point of reconciliation. Metrics and Checks are a great way to make your process robust.
You can also apply Metrics and Checks against any dataset. For example, they can be used on your input datasets to validate that the data format matches your expected schema or to check the size of the input data is within the expected range.
Specifically in financial reconciliation processes, Metrics and Checks can highlight instances where the numbers do not reconcile as expected, and users can use them as the basis to send alerts for investigating these occurrences.
Work through this tutorial to tune your knowledge of Metrics and Checks.
Applications – Accessibility and Interactivity for Business Users
In our experience, it is impossible to fully automate the financial reconciliation processes; instead, end-users will want some control over the process. Dataiku Applications allow non-technical business users to input parameters into the underlying Dataiku flow. For example, the user may want to define a tolerance level at which any variance identified is acceptable.
We have also found that financial reconciliation processes often rely on locally managed files which are critical inputs to the process. Dataiku applications provide a method for users to upload such files into the Dataiku flow, giving them the flexibility to influence the analysis.
Enroll in the Advanced Designer learning path within the Dataiku Academy to understand more about Dataiku Applications.
Partner With Aimpoint to Automate Your Financial Reconciliation Processes
In conclusion, Dataiku offers a range of powerful features that can help you automate financial reconciliation processes and streamline the entire process. By leveraging the Join Recipe, Prepare Recipe, Group Recipe, Metrics and Checks, and Dataiku Applications; you can reduce manual errors, save time, and improve process governance.
At Aimpoint Digital, our Data and Analytics team is ready to help you leverage these features to automate your financial reconciliation processes using the Dataiku platform. Contact us to learn more about how we can help your business become more efficient with process automation.
