Modern KM tools and techniques for collaboration
A significant part of the value derived from centralization tools is the oversight they provide into decentralized efforts across departments, use cases, tool selection, and ecosystems—which is pivotal for maintaining data governance. “As you’re delegating control and ownership from a compliance and security perspective, you still need a global view of what’s happening in the platform,” Claude Zwicker, Immuta senior product manager, remarked. “Meaning, who’s accessing what data? Are they trying to do something they shouldn’t?” These pragmatic considerations are foundational to expanding collaboration efforts across organizations, since failure to govern who’s accessing which resources—and how—can result in hefty fines for regulatory compliance and data privacy.
Modern data access governance solutions furnish a single pane of glass through which to monitor user behavior—including analytics, dashboards, reporting, and auditing capabilities—to ensure data governance protocols aren’t forsaken. This oversight is another expression of collaboration among IT teams, governance personnel, and the business users working with enterprise content. Variations of it are included in modules for data catalogs, process automation, and data fabrics. Consequently, for different applications, users can control “who should use it, when they should use it, and which parts they should be able to use,” Glaser posited. “And, you can do that based on who they are and based on attributes about them.”
Collaboration features
Creating, defining, assembling, curating, and maintaining KM require an ongoing effort best implemented by subject matter experts and specialists from different parts of an organization. Consequently, despite these inherent differences, solutions designed to centralize this endeavor offer myriad features for different user types to work together. The second most meaningful type of collaboration, therefore, is likely one designed for business users in dissimilar departments, with different levels of expertise, experience, and technical aptitude. Data catalogs in particular take a crowdsourcing approach, enabling users to look for, compare, and share enterprise knowledge and content. In addition to offering capabilities for organizations to assign ownership to assets, communicate with subject matter experts about them, issue comments, and conduct threaded conversations about such assets, catalogs also provide numeric rankings and scores.
“Each person’s voice matters,” Laine reflected. “They can rank it from one to five stars, and that goes behind the data score: the overall scoring. So, together they’re collaborating on the score: how well it’s used and the insights they’re getting from the dataset.” Other factors that contribute to the value score of a particular data asset or type of content are determined by data quality scores predicated on data profiling. Moreover, the data stewardship capabilities involved in curating data according to semantic models and enterprise knowledge are also apposite to these trust scores and include factors such as, “Does it have a business term?” Laine commented. “Does it have a policy? Does it have a rule? All the hard work that the stewards did on that data is represented through that value score.”
With each of these factors contributing to how cataloged assets are scored, organizations across departments can collaborate with one another about their valuation for them. Additionally, contemporary catalogs contain front-end data marketplaces designed to mimic a retail-like shopping experience for cataloged assets, which employees can look for and access. This functionality is assisted by what Laine termed “compare features, where you can compare the different datasets, just like you would compare it in an Amazon-like experience. So, the more familiarity, the concept, and the features of that concept, the more collaboration you create.”