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The worldwide volume of digital data is increasing dramatically. According to the large consulting firm IDS, the data volume in the world will reach an impressive 175 zettabytes by 2025. Business data account for most of this volume. Businesses face the challenge of making their data pro-cessing more efficient because this will ultimately optimise their business processes. This, in turn, has a direct impact on customer satisfaction and competitiveness.
Business data received at companies are usually unstructured: they include different kinds of business documents, e-mails, graphics, and images. To process them efficiently and use them, they need to be structured. For this purpose, an automated process called Intelligent Document Processing (IDP) is used. Various technologies in the area of artificial intelligence (AI) rely on this process, one of them being machine learning. Better data – better output Machine learning relies on algorithms to allow IT systems to recognise patterns and relations in existing data. On this basis, predictions can be made and business processes can subsequently be automated. For large data volumes, deep learning, which is part of machine learning, has been gaining importance. It works with algorithms that emulate the human brain and can recognise texts and data patterns. Data: specific and relevant For machine learning and deep learning to actually add value for a company, high data quality is required. More specifically, in order to be able to draw the right conclusions and develop solutions, machine learning algorithms need to be based on high-quality data. The following two concepts are key: relevance and accuracy. The more relevant and accurate a data set is, the better the output of the machine learning algorithm will be. Business processes will then be handled more efficiently, which allows companies to increase their bottom line. Current studies underscore the importance of high data quality. They show that on average, 8 to 12 percent of the operating income at companies is lost due to poor data quality. Other studies estimate that as much as 20 to 30 percent of all corporate data is inaccurate. Business data change constantly, because of clients changing their address, phone number or bank account details. To remove such obsolete data or duplicates from their databases, businesses need to find consistent standards for their data maintenance on different hierarchy levels, across departments and for different processes. Efficient document processing To process unstructured data (e.g., documents), we rely on AI technology, combined with human quality assurance. This allows us to achieve an unsurpassed combination of data quality and efficiency in the automation of business processes.
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