Harvard Business Review’s recent article, “Managing Your Mission-Critical Knowledge,” prompted me to speak my mind about what really works in KM. Authors Martin Ihrig and Ian MacMillan emphasize the importance of knowing your organization’s knowledge assets, and I’d agree with them. However, I think the authors overlook important and foundational principles of knowledge management. Let me point out 2 big misnomers from the article, as well as 3 key insights that we’ve found that work.
Misnomer #1: Organizations should manage “all” of their strategic knowledge assets.
The type of knowledge that people REALLY want—and the kind that we think is most useful—is tacit: based on experience, judgment, tried and proven learnings, years of repeating habits, and testing with new hypotheses.
Key Insight #1: You can’t really “manage” tacit knowledge, but you can manage the processes, tools, and systems that allow it to flow. Oh, and you can manage explicit knowledge (e.g., data, content, information) – and you should – it just requires a different set of tools and processes than tacit does. Once you have established that knowledge management is about “flow” and not “management”, you will be able to tweak your culture to get employees to share, transfer, and use knowledge that helps them do their jobs.
How to start? The HBR article includes a map of a “generic engineering company’s knowledge assets,” presumably across the whole organization. But we know that mapping knowledge at an enterprise level takes a lot of time and is outdated before you get finished. And then, no one seems to know what to do with it—much less own it or update it. In other words, it’s a waste of time.
Key Insight #2: It’s good to have a clear understanding (and agreement) of what is important to the business because knowledge maps needs context. The authors recommend that organizations map their knowledge assets before trying to reap the benefits of Big Data. We agree, but it’s important to identify critical business goals—the why behind knowledge mapping—before you even get started to ensure your map yields actionable results. Their example of Boeing’s global sourcing was good as it focused on a specific business problem where “knowledge” was important to the solution.
Misnomer #2: Enterprise data is hard to translate into useful knowledge.
While this may be true in some contexts, I look at the interplay between KM and Big Data through a different lens. APQC’s Knowledge Analytics process suggests ways to combine measures and information from KM approaches and other business processes in order to support strategic decision making.
Key Insight #3: When KM statistics are combined with data from HR, social analytics, finance, marketing, operations, etc., (as with Knowledge Analytics) organizations can tease out important patterns in employee knowledge creation, sharing, and use. This, in turn, helps leaders make smart knowledge-related investment decisions, use organizational capabilities more wisely, and predict outcomes based on patterns of behavior and performance.
Check out this video for an introduction:
One of KM’s biggest challenges is evolving from a corporate “initiative” to just the way business is done, and I suspect Big Data will suffer from similar growing pains. But perhaps the biggest misnomer in the whole article is the claim that leveraging organizational knowledge is a “new kind of expertise.” It’s not! For the past two decades, APQC has worked with organizations that recognize the business value of their enterprise knowledge, and we’ve seen organizations build KM programs that can flex to meet new challenges and incorporate new technologies.
What other techniques or methods are you using to solve the Big Data problems?