Data Retrieval in Multiple ECM Systems Is Challenging

by Brad Porter on Aug 23, 2017

These days, organizations storing business information amongst multiple Enterprise Content Management (ECM) systems is a common practice. According to an AIIM survey, 22% of today’s businesses utilize at least five Enterprise Content Management (ECM) solutions while 52% use three or more. However, businesses are learning this procedure often leads to a number of challenges for employees. For example, users are often confused as to which system to use for uploading and tagging their documents. Plus, these separate systems can turn into information silos which make data retrieval difficult and time intensive.

Organizations don’t set out to purposely complicate the process. There are often scenarios that contribute to this situation, such as:

  • Departments within an organization don’t communicate with each other as to which system is storing what content.
  • Departments deploy their own systems and solutions without the organization’s or IT department’s approval.
  • Enterprises merge or an acquisition occurs, yet the systems containing business content remain separated.
  • Organizations transition certain information to a cloud-based solution.

In any of these situations, a best practice is to use a unified document capture approach designed to integrate with disparate ECM systems.

Capture and connect data retrieval
This is where KnowledgeLake’s powerful, cloud capture platform can be of optimal service. The lightweight, 100% browser-based solution enables users to scan, tag and release content to multiple ECM systems. The solution’s connectivity with ECM systems is bi-directional — the platform can tap into ECM systems to understand the taxonomy of each, then use that knowledge to route incoming documents to the appropriate system.

Unifying through taxonomies
A taxonomy is an enterprise-wide schema to classify, structure, and organize groups of business data. Most ECM systems support the creation of content types and fields which can be used to enforce adherence to the schema via classification and tagging of new content entering the system.

KnowledgeLake’s solution makes retrieving a taxonomy — including content types, fields and behaviors — simple. Documents can be automatically tagged, allowing for fast data retrieval later. This means different kinds of content can be automatically routed to different systems, enabling the user to upload or easily retrieve data without being expected to understand complicated SharePoint hierarchies or folder structures.

Extensible architecture
Integration can be configured by an administrator in a couple of clicks, with no coding or complicated field mapping. Plus, when changes are made to the source ECM system, KnowledgeLake’s cloud capture platform can be configured to automatically pull in those updates.

The beauty in this approach is that even though your organization may use multiple ECM systems, you now have a unified approach for document capture. This includes scanning, uploading and tagging in an easy-to-use web interface, enforcing validation rules on all scanned and born-digital content, as well as enforcing information governance policies. This means a much more positive experience for your users and easy data retrieval.

KnowledgeLake’s flexible capture solution supports SharePoint 2013 & 2016, SharePoint Online, Box, AODocs, Egnyte, Hyland OnBase and many other repositories.

In my next blog post, I will discuss the additional extensibility points of KnowledgeLake’s powerful, cloud capture platform and how this can be leveraged to work with a home-grown ECM system. Stay tuned…

Learn how KnowledgeLake’s cloud-based capture platform can work with your repositories!

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Brad Porter
Brad Porter

Brad is an industry veteran with over 12 years of experience in designing and building enterprise content management software. He is responsible for setting and maintaining the technical culture at KnowledgeLake with a focus on attracting and retaining top talent. He has a passion for engineering execution, including continuous improvement of the software development lifecycle (SDLC).

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