The APQC Blog

Simplicity: It’s the 2020 Word for KM

KM Success in 2020 Starts with Simplicity

As 2020 begins, countless self-help articles are encouraging people to choose a “word of the year” to help motivate and guide them. While setting a theme for personal growth makes sense, the same tactic can be applied to a team, company, or broader discipline. The right rallying cry helps bring people together and keep them focused on what’s really important. But what word will define knowledge management’s trajectory this year?

My first instinct was to pick something on trend. Vendors want us to think the word is “cognitive,” “digital,” or “smart.” But while integrating new tools is table stakes for KM success this year—and this decade—the KM story must transcend the rise and fall of technology trends. Similarly, KM shouldn’t box itself into specific methodologies, even compelling ones like Agile and Design Thinking.

The word I ultimately settled on is simplicity.

Both knowledge management teams and the businesses they support are operating in increasingly complex, fast-moving ecosystems. Employees and customers alike are overwhelmed by the amount of knowledge available, the effort required to wade through it, and the speed at which it is evolving. At the same time, cloud platforms roll out new collaboration capabilities every week, leaving people justifiably mystified about what to use when (“Why do we have Teams and Chat and Yammer, and what do I post where?”). In this context, the biggest problem KM can solve for the business is to simplify the user experience with knowledge in order to reduce confusion and deliver answers that really are “just enough, just in time, and just for me.”

The effort to simplify breaks down into three component goals.

1. Streamline systems, features, and options.

We’ve understood the benefit of a clean, simple UX for decades. After all, that’s part of how Google beat out Yahoo: one box that answers all your questions, without navigating 30 categories and scanning a forest of links. But as companies race to deploy the latest technology, some have lost sight of their end users.

One challenge is that IT departments are layering content and collaboration tools on top of one another, without taking the time to decommission old systems or migrate relevant resources. The cloud has done a lot to simplify access, but it has further complicated the overall landscape. The information employees need is scattered across new cloud-based tools, old SharePoint-style sites, and traditional shared drives. And as organizations dutifully roll out the latest and greatest, few are investing in the content strategies and processes needed to tame this jungle.

Simplicity in this arena starts with intentionality. Someone has to be responsible for optimizing the user experience with knowledge. This team must gather feedback on current options for knowledge sharing and access, identify pain points, and advocate on employees’ behalf. When rolling out new KM technology, the team can work with vendors and IT to apply human-centric design principles and ensure what’s under development is what's actually needed. Often, firms find that employees don’t want more tools and features—they want better ones. As a company moves to the cloud and embraces the digital workplace, it must retire outdated communities and collaboration approaches, get rid of legacy systems and functionality, and make it easier for employees to access and use new capabilities.

2. Curate and filter content so users see only what's most relevant.

Perhaps the biggest challenge in the quest to simplify is the sheer volume of information organizations have amassed. Over the last decade we got excited by how much stuff we could collect: through automated systems, authorship pushes, and by persuading employees to submit templates and examples. Storage became cheaper and we encouraged people to save everything and share, share, share—sometimes assorted versions in multiple places without any differentiating metadata. The result is that enterprise repositories have ballooned with irrelevant information.

The hope that search could guide us through this mess has proven overly optimistic, at best. Sensitive algorithms can actually aggravate the problem when they return everything ever created across the company containing a particular keyword. Even advanced search tools cannot identify the best content on a particular topic without large volumes of data to analyze or the right contextual clues, which many systems fail to provide (the old “garbage in, garbage out” conundrum).

The answer starts with better curation. Organizations must do more to break complex knowledge into bite-sized chunks and eliminate redundant, outdated, and trivial (ROT) material. Machine learning can help, but there is no substitute for the basics: assign owners for content, evaluate items according to standard review cycles, auto-archive or delete lower-value items that have not been reviewed or accessed within a set time frame, and schedule routine clean-outs. Employees don’t want more content, the want a clear path to the right content.

The other side of the equation is contextual filtering—and this is where technology takes center stage. The current KM mandate is less about making everything visible than simplifying search and discovery so users see only the most relevant results. Smart recommendations, which I blogged about last fall, require a combination of item- and user-based filtering. The goal is to apply information about each user (job role, location, current projects, community affiliations) and how similar users have interacted with the system (terms searched, items downloaded, questions posted and answered) to predict what will be most pertinent in a given situation. The technology is still immature, but ultimately it should help simplify search and create a more seamless connection between people and the information they need to work effectively.

3. Simplify the story KM tells and the measures it communicates.

In addition to being deluged with content, organizations are drowning in data about their knowledge and collaboration systems. The ability to monitor and track users at every turn has encouraged teams to set up complex dashboards with dozens of metrics. Some of this data is useful at the operational level—KM specialists need the details to tweak existing systems and prioritize future investments. But where many KM programs fall down is in curating a set of key performance indicators to share with leaders and participants.

Stakeholders out in the business don’t need to see every KM activity metric, even if the information is collated in a sleek real-time dashboard. Displaying so much data—with little indication of its relative importance—can actually make it harder for people to interpret the overall trajectory of KM and whether it’s achieving its purpose. The data is also prone to misinterpretation since managers read too much into what are natural undulations in usage.

Instead, KM programs should project a clear narrative about what KM does for the business, even if the details change in response to evolving business needs. Team members should be able to communicate the value that KM provides and cite two or three data points that encapsulate that value. A concise business case, linked to thoughtfully chosen success measures, is much more likely to drive investment and participation than is a complicated dashboard of bells and whistles. Have the detailed measures at hand if someone asks, but take the time to roll them up into a simple story of KM purpose and business results.