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GiGo: Ensure your IN is Valid

Forward-thinking companies are harnessing Big Data to unlock valuable analytics. These critical insights inform business decisions from operations and marketing, to long-term planning and growth strategy. According to IDC, revenues for the Big Data and business analytics market will be worth $260 billion in 2022 (up from $130.1 billion in 2016). However, IBM found that bad (aka poor quality) data cost U.S. companies a staggering $3.1 trillion in 2016 alone.

Harvard Business Review contributor Thomas Redman defines a concept he calls The Hidden Data Factory to explain how bad data may get into your business processes. In this process, a person will quickly update what they perceive to be bad data values, in lieu of acquiring the correct value from the original source, and unwittingly enter erroneous data.

This offers a stark reminder: even with the most advanced Big Data management solutions, valuable output can only be obtained with the ingestion of accurate data. Clean, reliable data – compiled with automated, intuitive processes that lend efficiencies and avoid these hidden errors – is essential to successful Big Data initiatives. You cannot smelt gold from coal.

Eliminate the Hidden Data Factory

Robotic Process Automation (RPA) software is customized to complete a variety of tasks typically handled by people. The robotic process automates retrieval of specific information, its processing, document creation, and timely sharing of the final information product.

RPA drastically decreases time required for functions like data collection. The robot knows how to access data sources, download these data files, and then parse and normalize the data to meet the needs of specific business processes. Robotic process automation significantly reduces manual labor time and effectively eliminates data entry errors – bad data – at the source extraction point, so the Hidden Data Factory is destroyed.

Keep the Garbage Out

It’s not uncommon for companies to have employees manually process high page volume documents – seemingly making source errors inevitable. As an example, Contact Telecom successfully collected, parsed, and normalized a complex set of client invoice files that historically would be manually compiled by the client’s production team. The client compared Contact Telecom’s output to its most current system feed, and found nearly 30% of the data fields held erroneous values. Following a post mortem review (reconciling the three data sets; the client’s manually compiled data, Contact Telecom’s data and the original source data), the errors were discovered to be in the client’s manually compiled data sets – an unfortunate product of the Hidden Data Factory.

Contact Telecom Delivers Accurate Clean Data

Contact Telecom leverages its RPA technology to streamline and automate invoice processing for clients across industries. From invoice management and processing, to authorization and accounting system integration, our Billing Data Analyzer (BDA) software drives efficiencies by utilizing robotics – ensuring consistent work product and data integrity. Our robots collect data from many diverse sources and uses artificial intelligence (AI) capabilities to normalize data to fit client processes. This data then feeds business intelligence reporting tools to drive strategic sourcing and operating decisions.

#RoboticDataProcessing #DataIngestion #InvoiceWorkflowAutomation