How to Create Efficient Data Streams Across the Enterprise 

Unstructured data is a hurdle at every turn, but embedding IA creates its own challenges. New tools see what was once a vast implementation now automated.

IA capabilities such as process discovery and analysis and data discovery tools facilitate automation. Process discovery consists of three steps.

The first step is a computer implementation that collects data for process evaluation and then sends it to a machine learning application that determines what can be automated in the last step, detailed process evaluation. Process mining only monitors systems that generate structured logs, while data discovery uses scans to uncover patterns and gain deeper insights.

The 2022 GBS and Shared Services State of the Industry Survey finds that 43% of respondents voted for process mining as a tool or approach to support process optimization efforts, and 42% named process discovery. These tools are effective in implementing IA through the use of data.

Programs such as automatic code generation and automated machine learning (AutoML) are similar to data discovery technology in that they automate the steps data scientists once took. Automation code generation adds automation workflows, while AutoML prepares raw data for machine learning applications.

Organizations that use hundreds of IA programs can resolve process failure with a system that changes the programming environment or automation program.

Unstructured data is difficult to parse because rules cannot process it. Written in natural language, has limited/missing structure or metadata, and may include non-textual material. There is no easy way to get a one-size-fits-all solution for your unstructured data because, by its nature, it is unstructured and in different media. In some cases, there is no standardization for all these different types of documents or media types where valuable information is contained.

So it’s more of an opportunity cost when you don’t know what you’re missing out on when trying to structure unstructured data because it’s so big, so vast, and full of different shapes and sizes that you just don’t understand or know what to do with it.

Machine learning technology is one solution; it adapts and learns workflows and improves accuracy by 95%. For example, studying handwritten forms requires smaller datasets.

CMR is built using proprietary pattern matching with content-based object lookup methods to ensure a high level of accuracy. It is based on templates and their associated fidelity metrics and is therefore not limited to standard shape sets and font libraries with a limited generation of pre-AI automation technologies. It can capture data, including handwritten and cursive text, image and object recognition, and natural language.

Although OCR is widely used, CMR and ML are easily superior. CMR converts business documents such as handwritten forms, invoices, and correspondence into structured electronic information. It can scale from hundreds to hundreds of thousands of documents per day.

With automation and artificial intelligence, businesses respond to requests in a timely manner, resulting in a better customer experience. Eliminating human error improves quality, and freeing human labor from repetitive routine work allows it to focus on knowledge-based areas. Completing tasks quickly at a lower cost will generate more revenue and will also use data analytics to identify new revenue opportunities.