How to Create Efficient Data Streams Across the Enterprise 

Data collection technologies such as robotic process automation (RPA) and intelligent automation (IA) reveal patterns that can save companies millions of dollars. According to a McKinsey survey, more than a third of organizations will adopt smart technologies to digitize unstructured data and weather the storm in the near future.

This article addresses the importance of unstructured data digitization and shares how to create seamless data streams across the enterprise by leveraging Machine Learning (ML) and Cognitive Machine Reading (CMR).

Contents

What is unstructured data?

According to MongoDB, “unstructured data is information that is not arranged according to a pre-set data model or schema, and therefore cannot be stored in a traditional relational database or RDBMS.” Unstructured data accounts for 80 to 90% of data generated and collected by organizations today.

Unstructured data, including images, handwriting, signatures, and mobile content, significantly complicates data collection and digitization. Cognitive Machine Reading (CMR) and Machine Learning (ML) can be a solution to this problem. They are ahead of Optical Character Recognition (OCR), which only captures structured data.

CMR classifies data in dozens of languages and provides end-to-end process automation that’s defined as “using a machine, or even a set of machines or decision trees, to perform different human actions to achieve a more cognitive decision making or more added value.”

Why is it important to digitize data?

Any process automation requires fewer resources, improves quality, speeds up processing, and improves customer response times. 

Intelligent technologies identify bottlenecks in processes, find areas for improvement, and create bots that enable companies to provide end-to-end business solutions to customers. Organizations need analytics to thrive in today’s markets, but automation is only effective if it can capture structured digitized data. In most cases, it can’t.

According to the IA Week, the biggest challenge in moving to intelligent process automation is converting data into a structured format.

Structured data characterize areas that achieve the highest level of success. All other processes include “a high number of unstructured documents, whether they be contact forms, driving licenses, claims or financial reports.”

Benefits of enterprise data digitization

Document digitization allows organizations to access digital data from anywhere, breaking down the barriers of location, time, and concurrent access. Easy access to data improves the flow of data within an organization, resulting in increased productivity.

Digitization of documents allows an enterprise to get rid of physical documents that consume various resources, such as office space or security personnel. Once the data is digitized, we can optimize these resources to reduce costs.

Multiple departments within the business can access data at the same time. If the data is not digitized, it takes more time and effort to access it.

Because documents are digitized, access to digital data is easy to determine. Digital access reduces the time required to provide access and allows you to determine the level of accessibility. This enhances data security and protects you from data leaks. With proper cybersecurity, storing data is safer.

With digitized data, there is no need to print it out for distribution, and the processing of digitized data is easy. Email is all you need to exchange data to avoid unnecessary printing. Ultimately, this will lead to saving natural resources.

Digitized data is easy to store in multiple locations, keeping the data secure. Storing data in multiple locations provides additional protection against data corruption or loss. Physical documents are more likely to be damaged in natural disasters, while digital data is more secure and easily reproduced. Natural disasters are often unpredictable, so storing documents in multiple secure locations increases the security of your documents.

Cognitive Machine Reading (CMR) as a solution

Automation refines the next steps with machine learning and cognitive decisions, as digitized structured data enables seamless further processing. The goal is to read, recognize and transform data in an integrated way. A 2019 SSON Analytics survey found a trend to view end-to-end workflows and data as mission-critical. According to the SSON Analytics 2022 State of the Industry Survey, 81% of respondents said end-to-end process integration is a top priority for shared services this year.

While optical character recognition (OCR) is inefficient at representing images, artificial intelligence (AI) can use pattern recognition to recognize documents and classify them without human intervention. Once a machine vision and machine learning model is identified, it can be trained to extract data. 

Cognitive Machine Reading (CMR) is one of the newest technologies on the market. It uses pattern matching with content-based object lookup techniques and proves to be very efficient at

  • digitizing a full range of data formats, 
  • extracting and structuring data, 
  • applying business rules, and 
  • enabling fast post-processing.

 

Companies that rebrand due to digital transformation no longer see it as a choice but a necessity. Even before the pandemic, organizations were forced to cut costs through automation; what used to require 100 people may take only a fraction of them. For typical large-scale, repetitive, transactional work, RPA was a good solution. But this is a short-term solution that doesn’t scale. On the other hand, CMR offers the opportunity to transfer this work to smart technologies.

This means a solution that can retrieve content, respond, read attachments, extract data, and automate processes. Many businesses scan images and consider them “digitized”. The only solution is to digitize correctly by implementing a CMR that digitizes data at the point of entry and then integrates automated decision-making and execution.

CMR’s ability to successfully handle a wide range of data—from tables, checkboxes, handwriting, italics, images, and signatures—opens the door to continuous, seamless end-to-end process automation. CMR is integrated into leading IA platforms and allows you to process large amounts of data and workflows in a fraction of the time that is normally required.

Are you looking to digitize your unstructured data with ML techniques fast and cost-effectively?

Persistent Data Challenges

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.

Conclusion

Machine learning can overcome the limitations of rule-based approaches to data mining, but these projects have their own challenges. A team of data scientists, data scientists, and SMBs must work on data labeling before a model can be trained, a process that is difficult to scale across multiple parallel projects. Once deployed, machines become less accurate over time as data evolves, so models need to be periodically retrained.

Even the data labeling barrier can be overcome with the help of AI. This new approach is perfect for the digital ecosystem evolving around us. 

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