Dot Voting and the Four “R”s: Prediction Markets, Part 2

April 28, 2008 at 3:20 pm · Filed under Change Management, Collective intelligence, Enterprise 2.0, Prediction markets, Web 2.0

In my first post on this topic, I looked at how prediction markets can work like a stock market — buying and trading “shares” that reflect a specific value, such as a ship date, sales forecast, and so on.  Another popular way to use prediction market technologies is an opinion forecast. Employees (or members of a community) can vote on the likelihood that an event will occur or the positive or negative impact of an event.   For instance, one person might enter a value range that indicates “I think there is a 25% to 45% chance that this will happen,” another person might think the probability is 40-50%, another may use wider or narrower ranges, etc.  In this variant of a prediction market, the “winner” is the person who comes closest to the final calculated aggregate percentage within the narrowest margin. For example, if the collective intelligence says the answer is 45%, then the user with the narrower range (40% to 50%) gets more points.

I happened to be in a room with a pair of scenario planning experts and a leadership development consultant during this informal discussion with Maurice Balick when we noticed the power of seeing others’ predictions. We’ve all used facilitation methods that rely on “dot voting” to display the collective wisdom in a room.  Dot voting is a great technique to use when there are multiple courses of action identified during a workshop or meeting and it’s important to narrow down the choices or see where people’s heads are at. Everyone in the room gets a certain number of colored adhesive dots and can use these as votes on particular topics or items. As dots accumulate on one or two of a long list, it’s easy to see where the room is, collectively.

Balick had previously explained that Newsfutures uses four “R”s as design principles in setting up prediction markets. These must be designed into the environment for successful adoption of prediction markets:

• Relevance - it has to matter to the company or participants
• Rewards - appropriate rewards (monetary rewards, stickers, tee-shirts) need to be in place
• Recognition - people who are the most successful should have “bragging rights”
• Relationship - the market must engender conversation

“Aha!” I said again (but only to myself this time). It comes back to relationships and conversations. While the specific value of the knowledge created by the collective wisdom provides value data to management (who must ultimately decide and act), the process of participating in this medium sparks conversations: it’s about getting people to see not just where they agree with others, but also to see that there are a range of positions possible. By seeking the outliers in estimating and rating, there is the opportunity for the isolated expert or group who may have special knowledge to be recognized and listened to.

When we facilitate a dot-voting session, it’s always important to understand the minority view. The workplace of the future is inclusive; tools like prediction markets  can leverage the necessary diversity.

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From Need to Know to Need to Share

March 24, 2008 at 11:41 am · Filed under Change Management, Collaboration, Enterprise 2.0, Web 2.0

The basic Web 2.0 applications (social networking, blogs, wikis, tagging, “twittering,” RSS) and underlying technologies (collaborative filtering, social network and link analysis, data mining, mashups and plug-ins) continue to add to and support the collaborative capability in organizations. But, as we know, inside organizations the critical factor is not how the technologies and applications will be deployed and (most importantly) integrated, but how will managers guide the adoption to achieve maximum effectiveness (or, as some say, the ROI).

Enabling adoption is a complex mix of providing motivation and incentives, using a focused pilot process to generate success stories and pull from early adopters, integrating (there’s that word again) the tools and technologies into critical business processes. Since the early days of knowledge management, we (myself, my colleagues, and the legions of practitioners) have lobbied HR organizations to add “knowledge sharing” components to annual performance reviews. (I once, in a past life, participated in revamping criteria for promotion of senior technical people to an elite, consulting, status, by adding in requirements that candidates had demonstrated knowledge sharing by virtue of — among other things — being available and accessible.)

Consider that such a move is one way of putting a boundary around behaviors and that as it beings to shift the culture can move to other domains. The U.S. intelligence community began the culture shift from “need to know” to “need to share” by implementing just such a performance review component. According to a recent story in Government Executive, the current program manager for the Information Sharing Environment (a job established by the 2004 Intelligence Reform and Terrorism Protection Act), Thomas McNamara, is now calling for all federal and state and local agencies to adopt similar criteria.

Sharing in government agencies, particularly the intelligence community and military, is hampered by the levels of classified systems and information. Similarly, state and municipal law enforcement and other local agencies have their own distinguishing needs to protect sensitive information. Enabling the right level of sharing across currently rigid boundaries will take time, but the pressure is on.

What I have not seen addressed, and what may become perplexing to organizations adopting Enterprise 2.0 practices is, “what constitutes sharing?” Will the performance review reflect data captured from the volumes of information accumulated about both explicit and implicit content contributions? I suspect not, as such measures only encourage gaming the system (as we have seen in some knowledge management implementations) to get higher participation scores without producing contributions that are actually useful. But the performance review process — which ought ultimately to be a conversation about learning and development — can provide an individual with data leading to insight into how social tools can improve their performance and access to more interesting work and promotions.

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The Balance in Enterprise 2.0

February 27, 2008 at 5:49 pm · Filed under Change Management, Web 2.0

Via Patrick Lambe, a reference to a Matt Moore posting on implementing Enterprise 2.0. Matt uses the metaphor of how we help youngsters learn to ride bicycles by putting on training wheels that stabilize the bike. Eventually, the training wheels come off and there is an inevitable crash (or two or three) before that wonderful thing called balance emerges. Once we have learned to ride a bike, we never lose that particular sense of balance.

The “training wheels” for using Enterprise 2.0 technologies are also stabilizers: templates, governance models, moderators, and various artifacts that provide a sense of control that makes IT folks and executives a little more comfortable with allowing the social software revolution to slip into the enterprise. But these stabilizers must also at some point be removed if the true value of the integrated innovative enterprise is to be realized. But what, Matt asks, happens when the inevitable crash occurs? The organization has a choice, he says, between putting the wheels back on and keeping them there or letting the falls happen until the organization finds its balance.

This notion of balance in Enterprise 2.0 reminded me of the Davenport/McAfee debates in June 2007 and January 2008. In the first, the question centered on the need for corporate control of assets (Davenport) versus the benefits of opening the enterprise to emergence (McAfee). In the second debate, the argument shifted to the question of whether Web 2.0 was an evolution (Davenport) rather than a discontinuity (McAfee).  Davenport came a bit around last week to suggest that Enterprise 2.0 is the “new, new knowledge management.” (Thanks to David Gurteen’s newsletter for helping me catch up on the blogs I missed during vacation!)

The rapprochement is important, because it illustrates the many levels of learning to balance at work. We are learning to work with a new language, a set of distinctions that make us a bit wobbly in our thinking until we get them sorted out in our minds and speak more confidently. Emergence is one big distinction, as Davenport suggests: “Perhaps the most important difference is the emphasis on emergence of content structures in E2.0, rather than specifying them in advance.”

Another big distinction that we need to come to terms with is “boundaries.” Despite the urge on the part of some to let loose these new technologies within the enterprise, we need to have some governance, some rules about privacy and confidentiality, and perhaps even a bit of hierarchy with respect to editorial norms and appropriateness. This is the balance that companies undertaking E2.0 must learn. And they must let themselves wobble and, sometimes, crash. But the crashes must be source of learning, and not an excuse for withdrawal.

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