Robotic Process Automation (RPA) is the solution for automating mundane, business-rule based processes so that organizations high value business users can be deployed to more valuable work.
McKinsey defines RPA as “software that performs redundant tasks on a timed basis and ensures that they are completed quickly, efficiently, and without error.” RPA has enormous savings potential. In RiskSpan’s experience, RPA reduces staff time spent on the target-state process by an average of 95 percent. On recent projects, RiskSpan RPA clients on average saved more than 500 staff hours per year through simple automation. That calculation does not include the potential additional savings gained from the improved accuracy of source data and downstream data-driven processes, which greatly reduces the need for rework.
The tedious, error-ridden, and time-consuming process of data normalization is familiar to almost all organizations. Complex data systems and downstream analytics are ubiquitous in today’s workplace. Staff that are tasked with data onboarding must verify that source data is complete and mappable to the target system. For example, they might ensure that original balance is expressed as dollar currency figures or that interest rates are expressed as percentages with three decimal places.
Effective data visualizations sometimes require additional steps, such as adding calculated columns or resorting data according to custom criteria. Staff must match the data formatting requirements with the requirements of the analytics engine and verify that the normalization allows the engine to interact with the dataset. When completed manually, all of these steps are susceptible to human error or oversight. This often results in a need for rework downstream and even more staff hours.
Recently, a client with a proprietary datastore approached RiskSpan with the challenge of normalizing and integrating irregular datasets to comply with their data engine. The non-standard original format and the size of the data made normalization difficult and time consuming.
After ensuring that the normalization process was optimized for automation, RiskSpan set to work automating data normalization and validation. Expert data consultants automated the process of restructuring data in the required format so that it could be easily ingested by the proprietary engine.
Our consultants built an automated process that normalized and merged disparate datasets, compared internal and external datasets, and added calculated columns to the data. The processed dataset was more than 100 million loans, and more than 4 billion records. To optimize for speed, our team programmed a highly resilient validation process that included automated validation checks, error logging (for client staff review) and data correction routines for post-processing and post-validation.
This custom solution reduced time spent onboarding data from one month of staff work down to two days of staff work. The end result is a fully–functional, normalized dataset that can be trusted for use with downstream applications.
RiskSpan’s experience automating routine business processes reduced redundancies, eliminated errors, and saved staff time. This solution reduced resources wasted on rework and its associated operational risk and key-person dependencies. Routine tasks were automated with customized validations. This customization effectively eliminated the need for staff intervention until certain error thresholds were breached. The client determined and set these thresholds during the design process.
RiskSpan data and analytics consultants are experienced in helping clients develop robotic process automation solutions for normalizing and aggregating data, creating routine, reliable data outputs, executing business rules, and automating quality control testing. Automating these processes addresses a wide range of business challenges and is particularly useful in routine reporting and analysis.
Talk to RiskSpan today about how custom solutions in robotic process automation can save time and money in your organization.