Businesses in a wide range of industry sectors are eagerly pursuing Robotic Process Automation (RPA) initiatives. While perhaps the most apparent benefit is cost reduction through dramatic reductions in human workforce requirements, enterprises are discovering that RPA drives significant improvements in speed, process efficiency and data quality. These improvements, in turn, enable business benefits such as better analytics, customer insight and more effective regulatory compliance.
The way that robots collect data is key to achieving these ancillary benefits. In a typical operational environment, data is collected from a variety of sources such as point-of-sale systems, websites or shared folders and used to create reports that drive analysis on business performance. A fast food chain, for example, receives daily data uploads from all of its individual restaurants and pulls that data into a central database that fuels analysis on individual restaurant and overall group performance. The problem with this approach is that there are multiple ways for people to pull and interpret the data housed in the database. Multiple data points may appear to be similar, but have distinct differences that can lead to different answers or interpretations. As analysts take on new roles or leave the company, knowledge transfer further erodes data quality, leading to discrepancies in reporting and requiring extensive reworks.
To take another example, human analysts at a food retailer pull information from public websites to determine pricing data on dairy commodities. That data is used to project cost forecasts as well as determine prior period performance. The issue here is simply one of human error. Any time a person is called upon to go to a website to pull certain information, an opportunity for mistakes is created. Mistakes compromise the accuracy of forecasts, which in turn can have a direct impact on pricing for external transactions.
RPA addresses both issues by providing a faster and a more reliable, accurate and consistent form of data collection and calculation. Once pointed at a data source, a robot collects the data the same way every time, over and over, without transposing a number or misplacing a decimal. And when told how to calculate a metric, it pulls the same inputs and applies the same methodology to make the calculations.
Bottom line: process consistency yields data quality, which drives business benefits.
About the authorJames is a Senior Consultant on the Intelligent Process Automation team. He brings more than 12 years of experience in financial modeling, budgeting, supply chain procurement and project management. Before joining, James led the Field Finance team at Pizza Hut, overseeing the financial and operational performance of their 500+ company-owned restaurants. In addition to financial planning and budgeting, he also managed the business analytics for 6,000+ restaurants nationwide and provided thought partnership and financial modeling expertise to their global business units.
Before Pizza Hut, James worked on the Commodity Procurement team at Dean Foods. He created and managed financial models for multiple business segments and commodities, including a direct procurement initiative that continues to be the company’s largest strategic effort.
