Modern KM tools and techniques for collaboration
The business imperative for prioritizing collaboration across business units and between enterprise users is swiftly becoming paramount. Organizations are more geographically dispersed than ever before. Telecommuting has become normative. Data is regularly distributed throughout any number of on-prem, edge, hybrid, and multi-cloud deployments—as are the tools for processing, managing, and acting on them.
Modern collaboration methods enable organizations to maximize the usability (and reusability) of knowledge assets. Entire architectures have emerged to support this, including data fabric and data mesh implementations. Constructs predicated on centralizing enterprise knowledge via data catalogs are flourishing. Low-code process automation techniques that are reusable across applications and business units are centered on collaboration.
The results of each of these developments are as predictable as they are beneficial. Silos are disappearing; transparency is increasing; and outputs such as improved capabilities for search, regulatory compliance, and data privacy are regularly achieved.
Most of all, organizations are profiting from the collective experiences of their diverse personnel, across departments, to cultivate enterprise knowledge that’s truly reflective of all the resources that are at their disposal.
Data catalogs
One of the definitional traits of contemporary collaboration mechanisms for KM is the centralization of information that was previously separated in different IT systems. Data catalogs excel at this requisite for colocating the different elements of enterprise knowledge. Knowledge hubs house everything from fundamental aspects of data models to taxonomies, business glossaries, regular expressions, and other facets of enterprise terminology. According to Susan Laine, director of solution strategists at Quest Software, data catalogs enable users to “start with [their] own ontology or semantic model and have that be the base for everything else you have inside the catalog.”
Moreover, current approaches to centralizing knowledge across the enterprise facilitate different types of collaboration. Whether employing the data fabric architecture or a data catalog (options that are far from mutually exclusive, as many data fabrics are controlled by data catalogs), the first level of collaboration “is between the business and IT, which has historically been a very fractured collaboration effort,” maintained Adam Glaser, SVP of prod- uct management at Appian. Data catalogs enable such collaboration by providing a single location in which to store technical details about data’s structure, models, and contents alongside business metadata about definitions. According to Laine, these hubs incorporate certain mapping functionality that graphically illustrates “how all of that works together.”
Process automation and data governance
Technical and nontechnical user collaboration may be even more pronounced in low-code process automation solutions in which there is tooling for “visualizing your data structure, drag-and-drop interfaces, and visual process mapping that’s executable [as code],” Glaser mentioned. These constructs are equally accessible to the business units and developers for implementing applications for workflows related to regulatory compliance demands, such as Know Your Customer in financial services. They’re also comprised of reusable components, which encourage collaboration and span the gamut of needs for putting workflows in production, including “everything you need to build an integration,” Glaser said. “It’s got a UI. It’s got rules. It’s got data management. It’s got process, bots, and AI.”