Trends in knowledge management

Editor's Notes

• #3: The power of threes: like cycles of improvement or the impact of “fun“ activities. The ones that are deeper, more meaningful and based on substance – their power and stature grows over time.
• #7: On a ship and see the passing scene. But if you don’t look more closely, you might miss an opportunity.Today is just a glimpse but the opportunity is to see how these trends can keep your KM relevant and constantly improving.
• #8: It takes three years to make a trend (see: Rogers Adoption of Innovation Curve)Year 1: an intriguing idea emerges, gets some buzzYear 2: promising approach– a few organizations are trying itYear 3: has gone mainstream. Many organizations implementing or seriously considering. The power of threes: like cycles of improvement or the impact of “fun“ activities. The ones that are deeper, more meaningful and based on substance – their power and stature grows over time.
• #9: 3 and to understand the flow of knowledge process
• #10: Nudge instead of force – make it easier for people to do what you want them to.Three ways to nudge in KM Put it in the flow of workMake career progression a pay offHave something for all levels of people at all stages of their career. – across the employee life cycle.
• #11: The Big IdeaKM: above the flow and in the flowThe biggest enduring barriers in KM areGetting senior management attention and fundingGetting people to participate and advocateWorking in the flow overcomes those problems.At IBM if you want best practices, you will find them through your knowledge sharing communities and networksAt MITRE, you have to fill out a time sheet, and you find it on the KM portal.At Fluor or ConocoPhillips, if you want the best engineering knowledge, you have to participate in communities. Are you doing KM or are you doing the business of collaborative knowledge work?Above the flow is critical to enable in the flowAsking people to step out of the flow of their work is resource intensive – requires branding, Persuasion, business case, demonstrated results. It is easier to build with their flow in mind? That’s why APQC advocates design teams made of people who do the work.
• #15: Using analytics to tie KM to business resultsPredictive (Knowledge) analytics emerges out of business and social analyticsKM maturity tied to resultsVisualization and imagery growing as a way to portray informationWhat else?
• #16: It all starts with big data – you have got to have a lot of it – not as much as Target, but moRe than a dozen. As people participate in KS activities – whether on line or face to face– if you begin to track it you can begin to correlate with measures your organization cares about. It requires a mashup of data from KM and process.Examples of sensemakingAmazon and Netflix recommendation systems, predictive analytics LIKE Target and have a slide for sentiment analysis (could transform HR surveying)When you click on a web page, Before the site even loads, 10 ad and marketing agencies/departments have profiled you or found your (nameless) profile and are tracking you to target ads to you. over 24 hours dozens can be tracking you.Payoff to you is targeted content. In exchange, the tradeoff is that you let them have insights about how to enticSmarter analytics—uses more advanced statistics to do Predictive analytics: defined as understanding patterns of behavior that will allow you to predict with grater accuracy what your “custoemrs” will do next.e you. It also leads to predictive analytics – what will people want or do next – digitally?
• #17: 2 min1 quarter of data – Q1 Fy11 (Oct – Dec)1st chart - As the number of CoP participants increases, the percentage of defects that escape upstream in the process decreases. Positive correlation.2nd chart – As the number of articles related to Mbe increased in Epedia knowledge base, the percentage of defects that escape upstream in the process decreases. Positive correlation.Moving up in the assessment we need to start focusing on measures.Still trying to prove the correlation is “real” – still very anectodal, too many variables.
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