Even today, the promise of content management software to make content available and accessible across the enterprise for better business outcomes is not being achieved at many organizations.
With so many different document types in many formats arriving from many different sources inside and outside the organization, it is not a simple challenge. On top of that, many organizations have more than one, if not several, content repositories creating information silos that are difficult to transcend.
Here are five common obstacles and what successful organizations are doing to address them:
Problem 1: Organizations need expensive technical experts to set up and run capture systems.
Business analysts — the ones with the business intelligence — should be empowered with technology to improve business operations. Instead, with current solutions, most business analysts have to engage technical experts to create system templates needed to classify each new document type, which can negatively impact their efficiency, agility, productivity and ability to provide the right customer experience.
Look for a new generation of AI-enabled capture services that remove the dependency on templates. After the initial deployment, these new solutions can be configured and updated by non-technical personnel, which alleviates the need for technical intervention and provide faster time to value.
Problem 2: It’s expensive to extract and classify data from unstructured document formats, such as contracts, correspondence and many business documents.
Documents come in many layouts, from simple, structured forms to freeform text on a page. If you’re only processing structured forms in standardized formats, you’re getting a percentage of the benefit of automation, but not the full potential. Processing unstructured, freeform document layouts requires costly manual processing, which is prone to error and expensive.
Look for non-traditional capture solutions that allow you to extract and classify data from both structured and unstructured documents, freeing your employees to focus on more meaningful activities.
Problem 3: Many organizations must install additional servers to keep up with document processing demand.
If you host your data capture application on premises, you may have to spend more money to deploy additional servers to avoid creating a back log of valuable, unprocessed data.
Look for a cloud document-processing service that can provide scalability when demand for document capture increases. Using a cloud-based service reduces the costs of deploying and managing servers on premises.
Problem 4: Organizations lack ways to automatically classify and index sensitive or protected data in documents before it’s sent to the content management system.
If you’re just applying a document number and some basic metadata as you store new documents in your repository, you could be risking disclosure of sensitive or personally identifiable information (PII), which runs the risk of a fine or worse.
Look for tools that enable you to flag sensitive and protected data so that it can be redacted or directed to specified streams. This protects your organization from the risk of non-compliance and will help you to react quicker and with confidence when new compliance requirements are put in place.
Problem 5: Organizations have millions of documents in repositories, but no way to make them available to data scientists for analytics.
Data scientists cannot apply their technologies to TIFF image files with only minimal metadata provided. It’s not cost effective to manually type the data, so you need a way to automatically extract meaningful information from documents and provide them in a format that data modeling systems can use.
Look for a solution that
Can process multiple document types and layouts
Uses the latest data extraction strategies, such as full-page optical character recognition (OCR) and key-value pair matching
Can export to a common JSON format across all document types
IBM recently announced the launch of a new document processing cloud service, IBM Business Automation Content Analyzer, that addresses all of these problems with an intelligent capture-as-a-service API that can be added to a content repository, capture system or business application.
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