Key Considerations for ECM and Content Services
The building blocks of content services – the modern incarnation of enterprise content management (ECM) – remain largely unchanged. Metadata management, process automation, information governance and security issues are still the basis of a number of document-based workflows, business processes and management activities knowledge.
What continues to progress, however, is how this field enables organizations to meet these vital needs. Cognitive computing applications, hybrid cloud deployments, business connectors, APIs, and productivity intelligence enable the business to perform these tasks at scale, cost-effectively, and within the of the decentralized work paradigm that has been the defining element of the new decade so far.
“Cloud SaaS solutions, artificial intelligence and big data analytics are where content services are headed,” said Larry Reynolds, vice president of sales, GRM Information Management. “We still find that most Fortune 500 companies and most government entities are still struggling with many old-school document-based ECM applications. AI and big data.
Ultimately, content services’ current reliance on advanced analytics and cloud connectivity means more than an improved ability to achieve their goals, which increasingly take the form of process automation. These new approaches are also creating a new reality in which developments such as voice, mobile technologies and predictive analytics are expanding what the field is capable of in ways that did not seem possible just a few years ago.
In terms of document-based workflows and process automation, Fred Sass, senior director of product marketing for content services at OpenText, defined the four pillars of content intelligence as “capture, manage, seek and find, and govern in terms of managing through its lifecycle and retaining or disposing of it Capabilities to extract, classify, and mark up information to make it available for workflows are essential to these modules. Content intelligence relies on various cognitive computing technologies to illuminate these phases. Its goal is to derive structure from unstructured content so that it is usable in downstream applications. of content includes these critical facts:
♦ Data Capture: Ingesting or capturing content initiates workflows by placing that information into computer systems that understand it. Capture involves “machine learning to improve the accuracy of capturing documents over time so that more of them are circulating intact,” Sass said. Capture frequently uses Optical Character Recognition (OCR) or Intelligent Character Recognition (ICR) and encompasses paper, email, web forms, print screens, and more. Healthcare use cases include “HL7, XML, X12, images or any other format,” Reynolds added.
♦ Extraction: Extraction involves copying or “removing” specific information from content for purposes such as classification or rating. It frequently uses cognitive computer models to “extract meaningful objects and the meaning behind it from the document,” said Prince Kohli, Automation Anywhere’s chief technology officer.
♦ Classification: AI, combined with other techniques such as cross-reference lists and content mining with regular expressions, allows organizations to automatically categorize and tag content. Regulatory concerns can make it difficult for organizations to find large enough datasets on which to train AI models. Credible solutions allow “people to classify data, and they implicitly classify as part of the classification,” explained David Robertson, director of business engineering at M-Files. “This means that the machine learning algorithm is effectively self-reinforcing and has a very large training data set of real data in the system that it can learn from. The algorithms are trained in the data you store in the system. “
According to Kurt Rapelje, Director of Analyst Relations and Product Support at Laserfiche, “Records management is a whole discipline of categorizing content into the required records retention policies that are typically established by a state, for example , for the government.
It is a vital downstream application (and a natural extension) of content intelligence, whose capabilities are based on the following considerations:
♦ Supervised and unsupervised learning: Supervised learning – which requires labeled learning data – is invaluable for extracting insights. This approach allows organizations to easily extract the required information from invoices (dates, total amounts, company receiving payment, etc.), regardless of their location on the form. Additionally, e-discovery and other use cases benefit from unsupervised learning. “Unsupervised learning works best for classifications where there’s a whole set of documents and you’re not really sure what those documents are about,” Rapelje remarked. “You let the subject of these documents emerge based on machine learning that looks for commonalities between them.”
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