Urging a new KM team to establish baseline measures is a little like begging a toddler to finish her broccoli before digging into the chocolate cookies. When KMers first set out, they are excited to start doing things. They want to build systems, connect people, and solve problems. They are less enthusiastic about collecting data to show that processes they already know are broken are, in fact, broken, and then calculating the exact impact of each KM intervention. After all, the more time KM spends bean counting, the less it has left to generate business value, right?
This attitude may be tempting, but it is also shortsighted. Measurement has become a KM critical success factor in today’s data-driven environment. That’s why APQC calls it out as one of 12 key capabilities in the KM Capability Assessment Tool and why it is the final step in our new white paper, 10 Steps to Get a KM Program off the Ground. A strong measurement game can help you convey KM’s purpose, allocate resources, root out resistance, and make incremental improvements.
What Kind of Measurement Are We Talking About?
Early measurement can be thought of in two categories: (1) baseline data that describes what’s going on prior to KM implementation and (2) data from KM pilots, initial projects, or phases.
Baseline data:
- Illuminates the KM business case. These measures quantify the breadth and depth of the problems KM aims to solve and show how those problems hurt the business. The data can win over skeptics and help the KM team prioritize initial projects.
- Provides a comparison point to illustrate progress. Think about this like a cheesy ad for exercise equipment on late-night TV: The super-fit spokesperson wouldn’t be nearly as compelling if their current physique wasn’t juxtaposed with a “before” photo. Even if things aren’t perfect, the KM team needs evidence that it has made headway.
Data from initial KM projects:
- Proves out the KM business case. The KM team may get approval to carry out a few limited-scope projects, but these are usually test cases. To expand its scope, KM must show that its initial efforts generated meaningful results. And if the stats do not prove out the business case, it may be an early warning that things are off track.
- Informs iterative program development. No KM effort is perfect right out of the gate. Data can clarify what’s working well, what’s not, and what the roadblocks are. This allows the KM team to better target its improvements going forward.
As you select measures, think about the business value you expect to create, the story you want to tell to justify KM investment, and the operational data that will help the KM team make good decisions. Don’t limit yourself to data that’s automatically captured in your systems. Use those metrics where you can, but you may need additional surveys or controlled experiments to complete the picture.
I also recommend adopting a broad view of what constitutes “measurement.” Quantitative data about who’s using KM, how they’re using it, and the outcome of that usage is more impactful when paired with qualitative feedback and success stories. As you measure, make sure you’re proactively seeking feedback through focus groups, local champions, and other forms of outreach.
How to Measure KM in the Real World
APQC’s research on KM programs illustrates the benefits of establishing good measurement practices from the start. U.S. Pharmacopeia (USP), a nonprofit that develops scientific standards to ensure the quality and safety of medicines, is a great example.
When USP decided to reinvest in KM, it took the time to thoroughly investigate its current state. Leaders of the new KM effort:
- surveyed employees about their ability to find information and their satisfaction with existing KM tools and resources;
- completed APQC’s KM Capability Assessment Tool to get an objective view of the strengths and weaknesses of current KM efforts;
- held meetings, workshops, and open houses to learn about employees’ knowledge-seeking behaviors and challenges; and
- examined grassroots KM in different pockets of the business to identify the most relevant, widely applicable solutions.
This baseline analysis showed KM leaders where the biggest pain points were, which projects were likely to generate the most short-term value, and which business groups were enthusiastic about KM and had established processes that might be scaled up.
Based on the data, USP launched a series of pilot projects to address urgent needs and further validate the business case for an enterprise KM program. It then closely monitored the pilots to identify discrete gains that KM had brought to individuals and the organization. KM leaders collected success stories and ad-hoc feedback that showed people were interested in KM and had started to see its value. They also gathered quantitative data where possible. For example, a chemical information repository pilot saved a team several hours of work each week. The KM group also surveyed employees about their ability to access knowledge when they need it, using the same survey it sent out prior to the KM program overhaul. The survey found that knowledge access improved in areas where the KM group launched pilots.
These early gains helped the KM group develop baseline and initial targets in four measurement areas: awareness, adoption, improvement, and feedback. The team included target measures when proposing a new enterprise KM strategy to executives for approval. Leaders liked that the KM team had specific goals and leaders knew exactly what expectations they were funding. This helped USP move forward on implementing a revamped holistic KM strategy.
To learn more about getting a fledgling KM program up and running, see 10 Steps to Get a KM Program off the Ground. To learn more about measuring KM, see Measuring Your Knowledge Management Activities and Impact and How Excellent KM Programs Measure Progress and Value.