Yesterday, I went along to the InformatieProfessional Conference here in Amsterdam. As with all things associated with the web these days, the theme of the conference was Integrity 2.0. Key issues revolved around data privacy, information reliability and management of information overload. Lee gave a great presentation on the use of “Social network as information filters”. Here’s what he had to say:
The affordances of social web allow us to build a new relationshipwith each other and with information. New forms of media consumptionand architecture of participation hold important implications forinformation management:
Sharing as a by product of action: During the 1990s we saw arise in interest in KM, which came up with a host of ideas that werenever implemented. The problem lay in the precepts on which knowledge’management’ was built, i.e. that people could and should share becausesharing is a good idea. But people are fundamentally lazy andselfish. They don’t share unless they have to. And even if theywanted to, the tools available to them have been so difficult to useand unfit for the purpose that they themselves created barriers toparticipation. Now, we have the ability to support effective sharingby placing flexible, user-friendly social tools like wikis, blogs andstatus updates ‘in the flow’ of people’s daily work. Contributing tothe collective intelligence of the organisation takes no extra effortand flows from the very activities necessary for people to get theirjobs done.
Socialisation of information: The second phenomenon is thatsocial computing makes invisible data visible. Information that waspreviously private or hidden in databases or behind firewalls is being’socialised’. A key difference that the social web holds forinformation professionals is the way it enables individuals to managetheir feeds and flows of information. It also offers new ways ofaggregating information, which provides new levels of meaning and addssignificant value. For instance, we have seen how Google employs pagevisits to feed back and improve page rankings, which acts as anextremely effective recommendation system. Even in the absence ofsocial tools or sophisticated algorithms, organisations have a wealthof meta-data available to them e.g. what people are reading, browsingand searching behaviours, time spent on pages, and so on. However, todate they have been terrible at surfacing this information, wastingextremely valuable data.
Rapid feedback is key to evolution: These days we seethousands of new internet businesses launch, and they expose themselvesto constant feedback. That means they will fail quickly or succeedbecause they are robust and responsive to market conditions. Insidecompanies, the situation is very different. Traditional staticintranets don’t have the same evolutionary forces at play. Informationis simply broadcast and there are no feedback loops through whichpeople can signal their preferences, and improve or change what theyare being given. Internal tools are evolving very slowly compared totheir web-counterparts, because the lessons of the web about the needfor interaction, transparency and feedback are not being applied in theenterprise context.
Networked productivity: Companies also need to move beyondthe obsession with personal productivity and look to networkedproductivity. That requires more and better information sharing, andits aggregation to create ambient intelligence. We are still exploringand tapping into the great source of value of networks in theenterprise. Consider for instance the ways in which following peopleon twitter, reading blogs, discovering new information via Digg ordelicious tags makes us more productive collectively.
What does this mean for managing information?
The answer often results in a binary debate about focusing onexperts on the one hand and the wisdom of crowds on the other. Buteach of these views is too simplistic, but not mutually exclusive. Ifyou were to look on Google for the best restaurants in New York City, page rank driven user recommendations would provide you with a set ofde-facto facts about the ‘best restaurants’ based on people’s searchand browse behaviours. We don’t know they are the best, but we do knowthat enough people have clicked on the page to make it worthwhileconsidering. On the other hand, WolframAlphaseeks to establish a fact, but the problem it that it hasn’t got a clueabout how to do so. The ‘fact’ simply can’t be established throughsemantic data because there are different ways of establishing what is’true’ in this context.
So which do we use: the individual or distributed model? On the onehand, gurus like Steve Jobs commonly do an outstanding job of decidingwhat it is that everybody will have and love. On the other, we havethe development of the Linux kernel using distributed expertise. Twoequally powerful scenarios. However, recently we have also seenexamples of experts testifying in trials based on their interpretationof information behaviour, and getting their opinions very wrong. Similarly, in healthcare, we are being advised that what was said to begood for us yesterday is not good today ‘in light of what we nowknow’. Being open to interpretation, knowledge and truth meandifferent things to different people and change o
ver time.
That has ramifications for the way we manage information – using networks and human signals to improve information findability:
- Findability: Making something increasingly easy to find is much better than search. Whilst some companies look to black-box solutions like Autonomyto find ‘right’ answer, others are using social tagging to build anaccurate picture of what information is and isn’t important in theirsystems. Leaving trails is a far better way to find information.
- Human signals: Signals are a very powerful way of validatinginformation. Working through our networks, we see what people haveread, commented or voted on the most, and use that contextual levelinformation to help guide us in our search for our ‘facts’ or meaning.
If we continue to manage information as we did in the past we willinevitably create information overload and increasing sources offrustration for our consumers. In the past, the job of informationmanagers was to codify and store information. Most of the metaphorssurrounding this work related to about putting information into boxes. This approach is not robust or scalable and leads to filter failure. Weneed to move away from the obsession with storage, and to a weavefabric of information through which people operate. Notably, theconnective tissue (e.g. signals, links and tags) is as important as theinformation it points to. All of this is based on people who by theiractions indicate what they think is important and useful.
It is this human generated web of information that is the onlyeffective way of dealing with the information deluge. Everyday, wehave too much information pushed at us via email. We sit like Pavlov’sdog waiting for the tinkle to alert us to the arrival of new mail, onlyto dutifully go to our inbox (and salivate) over what usually turns outto be spam. This is a disturbing productivity drain. Too much of thewrong kind of information commands people’s attention. In addition,most enterprise communication and collaboration tools cannotdistinguish between the variable velocity and life span ofinformation. Which information is current only in the moment, andwhich has more durable and lasting significance?
To cope with these problems, we need better filters and betterradars. Your ‘filters’ are your network including Twitter, Delicious,Digg, Stumblupon, etc, signaling links or sites you should read becausepeople you trust think they are important. But using your network asfilters, in isolation, can lead to group think as you tend to beattracted to people with similar interests, views or roles. In builtbias is not a bad thing as long as you have other mechanisms forfinding new information. This is where your ‘radar’ comes in. Itcomprises alerts, searches and smart feeds, which are always on thelook out for new stuff. The combination of the two things is needed tocapitalise on ambient awareness.
In fact, one of main purposes of knowledge management is to helppeople find good information on which to make better decisions. Thisis far more involved than people processing email, memos and otherdocument-centric communications. People are incredibly adept atreceiving and processing ambient information. In the office weoverhear other people’s conversations, we see what people are workingon, we receive snippets of news from our feeds or the paper, and soon. This information is constantly feeding our consciousness. And thehuman brain has evolved process these huge volumes of fragmentedambiguous information. But if people constantly have their noses intheir inbox, or they are forced into document-centric models ofinformation sharing, they are cut off from valuable informationsources and flows.
Online social networking acts as an excellent operationalinformation filter. We are used to connecting with people andexchanging information in spaces, and this behaviour is reflectedonline in social and business networking sites like Facebook andLinkedIn. Instead of going to Google to search for the bestrestaurants in NYC, people now go to their network and get better morerelevant results.
These activities socialise the information, along with the languageand meaning. An experiment run by the Sony computer lab used robots todescribe images projected onto a wall. The robots had to rapidly learnhow to communicate with each other to come up with a description. Theyfound that at the beginning of the experiment, the number of words usedfor a concept was quite large but declined over time as the robotsnegotiated meaning and converged on the designated concept. Thefinding: Polysemy declines rapidly for new concepts as dominant termsemerge.
Likewise, the process of social tagging is fascinating, especiallyits effect on interactions and understanding. As we label ourinformation, we find people that share our perceptions or interests, orwe even add new meaning through the label itself. This is the power offolksonomies over taxonomies which for decades have made informationimpossible to find for most people. Instead of trying to structureeverything and remove all ambiguity, we should use a top-downcategorisation system for things that are broadly correct (e.g.regions, products, practice areas) and below that allow human-generatedemergent metadata like labels to act as a more effective social way ofnavigating through information. Allowing the structure of the languageto come from people in the field.
For information professionals, this means moving from tending boxesand labels to becoming information networkers. It means being guidesrather than gate keepers. Information professionals need to share 21stcentury competencies with people, helping them to use their networks asfilters and establish their radars giving greater control to theindividual. All of this points to a much more interesting future rolefor information professionals.
