Machine Learning and Processing Unstructured data

Author: Gina Gray, Commercial Director

The amount of data organisations receive is on the rise, with the vast majority arriving in the form of documents. These documents are crucial for daily operations and need processing for businesses to gain insight and perform critical actions such as payments or customer responses. It is not always easy for companies to make sense of these documents because of the sheer volume, variety and complexity, particularly as many of the tasks involved with the processing are still manually intensive and repetitive. Crucially, these labour-intensive tasks can result in not only delays but are also error-prone and can lead to low employee morale and negatively impact supplier and customer relationships.

Organisations are faced with challenges that they would not normally experience, and for many are outside of normal daily operations. It is, therefore, more important than ever that businesses are able to adopt an agile approach and respond to their changing environments.

In recent years technology solutions such as Digital Process Automation (DPA), which includes applications such as OCR and RPA, have offered organisations a solution for these manual tasks through automation. DPA is an evolution of Business Process Management (BPM), something organisations have been doing in some form for decades. DPA takes the appropriate manual tasks within a process and utilises computer systems to help organise and perform them more efficiently, eliminating unnecessary repetitive tasks altogether by having the computer system carry them out instead.

Traditional DPA, as defined previously, is extremely effective when structured data is required to be processed because the technology relies on rules-based decision making to perform tasks. For example, DPA could be applied to locate and extract data from a structured spreadsheet and deliver it to a line of business system for further action. In this instance, the location of the data required to be extracted will not change from document to document making it easy for the technology to find the key data with pre-programmed rules. This technology works well when there are high volumes of the same document layout or just a few anticipated variations.

The problem for many organisations, however, is that the data they receive is unpredictable and can be structured, semi-structured or unstructured. Organisations often have very little control over inbound documents and when received at higher volumes can become a real stumbling block for traditional DPA. This is due to the unpredictability of document types making it difficult and ineffective to anticipate and preprogramme the software for every eventuality.

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