2 Types of Collaboration


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Presentation given at AIIM International 2012 on 2 types of Collaboration. Lecture Notes in the To view the session on YouTube see: http://www.youtube.com/watch?v=j-m0OqT0gRE

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  • There is an unseemly Hubris in content and collaboration software. It is the assumption that knowledge and innovation can come out of them. As if our companies were some sort of orange to be squeezed into a pitcher and served up as a healthy and tasty accompaniment to breakfast. The trend has a long history that is still being played out today though we see some hints of change. ERP, MRP & Supply Chain systems took the principles of the assembly line and applied them to business processes – from how things are made to how they’re transported from here to there. These gave rise to HCM systems which took the same fundamental approach and applied it to how people – human beings – are managed and placed and categorized and “quality assured” in an organization (that last part being your annual review). The fundamental assumption and fatal flaw in all of these software “systems” is that process – those recipes of business – are actually what make the product, bring in the revenue, and service the customer. They rely on a terribly thin lie; that the process is understood, the recipe is known and all that is left to do is find more efficient ways of executing it. It is shake and bake business. If we just automate our phone help desk with push-button easiness and route exception calls overseas we’ll increase success in business. In many ways we’re the victims of our own technological success. We’ve succumbed to what I call “Small Earth Syndrome”. The industrial and information revolutions are said to have “shrunk the planet”. That’s a lie. They extended our reach but they did not shrink and they did not simplify. Complexity has increased magnificently and terribly in order to access our large planet. Sophistication of our systems increased to handle the complexity. Each new permutation of observer-induced cybernetic feedback was identified as the last bug to fix, rolled into our software systems and re-re-re-released with great fanfare. And all the while the information was coming in – a tsunami made of 8 billion individual spigots left running and our collective sinks overflowing. We see huge potential in this ocean of information. Our flaw is in thinking that our old assembly-line thinking and linear systems can classify, index and handle it if only it gets just a bit more efficient. This is not what brought us the revolutions in industry and information. Intentional collaboration - directed, focused and goal oriented gave us superior suspension in horse buggies, line-cook short-order restaurants and enterprise content management software systems. Accidental collaboration gave us GPS from people listening to Sputnick and doodling calculations that determined its location in the sky. Accidental collaboration gave us Big Data, Big Insight and will drive the next phase of innovation. Agile management, a flexible serendipity-allowing reaction to process-centric thinking, is a small step in the right direction. It is an attempt to inject some organic fuzziness into the creative sculpting of product – be it code or physical. Intentional: Common Purpose, Traditional Interactions , Boring Focused & shared purpose by a defined team Interactions are introductions, updates, “take a look at this”, “thanks” Most “collaboration” technology fits into this category Boring. Assembly Line., Line Cook Kitchen Model – Recipe, Ingredients, Cook, Sauce, Side, Plate, Serve. No innovation. Efficient Repetition, Where do new recipes come from? Accidental: Subvert Original Intent, Time & Context Shifted, Disruptive, Innovative & Amazing Also as old as time but massively more interesting! Drinking the stuff that came out of a cow’s udders! WHOAH! The cow never intended that! Evolution and/or God never intended that. We got creative and now look where we’re at! REUSE in NEW Context Examples in technology include re-blgging (tumblr, pinterest) reporting, content curation, re-use, re-purposing, re-search Big Data – Is the Biggest Buzzword of 2012. Part of the “cloud”, vital for better business. Purpose is to Yield insight by bringing together disparate data and deriving insight. The original intent of the data is largely irrelevant. The data is important. Call center logs combined with field sales pipeline updates and marketing campaigns to create Social CRM. Transaction information against weather patterns to predict when to increase inventory in one part of the supply chain.
  • The giants were men of renown, well known, well published, terrible and wonderful. Some were like Prometheus, Some were like Goliath. The battles these men fought over ideas, theories and discovery were legendary. They drew people to them and their ideas, through time, to engage, to collaborate and innovate. One wonderful story that illustrates this engagement of Goliaths gives us one of our most prominent industry buzz words today. It all starts in the 17 th century – in the same year that Isaac Newton wrote this famous quote to one of his rivals…
  • 1676 – raised by a single mother Leibniz develops a calculating machine based on his universal language to find the truth of any mathematical statement. He traveled widely, met many other incredibly influential scientists, philosophers and mathematicians like Spinoza, like Leeuwenhoek who discovered microorganisims and members of the Royal Academy of Sciences – which is still in existence today. He wrote tens of thousands of letters and published papers in which he exchanged ideas with contemporaries and from which many who came after studied, clashed and enhanced.
  • 1882 – Prussian born David Hilbert his best friend Hermann Minkowski (who invented space-time to better understand Einstein’s Relativity who he taught) and their teacher, Adolf Hurwitz start sharing scientific and mathematic ideas. In 1900 he outlined 23 problems that charted the course of mathematics for most of the 20 th century and some that are still unresolved. In 1928 – He formulates the Entscheidungsproblem (decision problem) challenge to see if Leibniz universal language statement can be determined true or false even if a proof doesn’t exist yet.
  • 1936 – Alonzo Church the son of a blind judge in Washington DC with a rich uncle who helps him go to an exclusive boarding school in Connecticut and then college at Princeton, creates Lambda Calculus to prove that Hilbert’s problem is unsolvable.
  • 1958 – John McCarthy, the man who coined the phrase “artificial intelligence” and launched the line of research that has given us IBM’s Watson, designs the programming language LISP based on Church’s lambda calculus. LISP has mapping and folding. Together these give us recursion – doing something to every item in a list and combining the results in some way.
  • 2004 – Jeffrey Dean and Sanjay Ghemawat from Google Labs publish MapReduce: Simplified Data Processing on Large Clusters which directly states that they the name and functionality was directly inspired by LISP primitives. MapReduce: Simplified Data Processing on Large Clusters, 2004, Jeffrey Dean and Sanjay Ghemawat, Google, http://static.usenix.org/events/osdi04/tech/full_papers/dean/dean.pdf
  • 2004 – Doug Cutting – the man who invented Lucene reads the paper and sees the connectivity between the MapReduce approach and Lucene. 2004-2006 - He Creates Hadoop. With OpenSourcing Help From Yahoo! December, 2011 , Version 1.0.0 of Hadoop released under Apache Hadoop Gives us Big Data – Everywhere Discovering New Insight from Heterogeneous Data Sources in Different Contexts
  • 2004 – Doug Cutting – the man who invented Lucene reads the paper and sees the connectivity between the MapReduce approach and Lucene. 2004-2006 - He Creates Hadoop. With OpenSourcing Help From Yahoo! December, 2011 , Version 1.0.0 of Hadoop released under Apache Hadoop Gives us Big Data – Everywhere Discovering New Insight from Heterogeneous Data Sources in Different Contexts
  • Accidental collaboration throughout history has been incredibly slow. Modern information management technology can speed it up and deliver to us amazing insight in incredibly short periods of time. Computable Genomix story. http://spiritbreath.deviantart.com/art/harder-better-faster-stronger-25925803 By deviantArt user Spritbreath
  • Image: http://ipadwallpapergallery.com/uploads/mattly-monome-ipad-wallpaper.jpg Source: mattly License: Creative Commons Share, remix, attribution, non commercial, share alike
  • 1)We spend so much time helping computers understand what we mean: metadata, tagging, summarizations, search within, drill-down change that paradigm! 2) There is so much information, chances are what we want / need is out there, if only something could help bubble it up to the surface. 3) Infographics example – amazing insights that come out of a wealth of minute, banal, hidden & heterogenous data http://www.flickr.com/photos/pagedooley/1856663523/sizes/l/in/photostream/ Creative Commons Attribution Some rights reserved by kevin dooley
  • Encourage voluntary participation with entertainment, ease & rewards Aggregations become curated collections. Collections spur additional participation and “chiming in” which is always easier than starting anew. Example Pinterest: collections of interesting items originally created by different sources, but the collection becomes its own new artifact. Pinterest automatically brings you new aggregated data based on your preferences and past usage with the likelihood that you’ll be interested.
  • 1)Track usage and context patterns. Track when people use certain information and what they do while using it 2) Acceptable in the enterprise because there is no expectation of anonymity. Different standards than the public web. 3) DLP & GRC systems perform this well – Digitiliti is an example..
  • As you gather the data you can process it to anticipate what people will want, even before they know it. Hadoop is one compute process than can help get to the insight in the information. Deliver the content items that people need as starting points & springboards. Image cite: Flicker User: by kjarrett creative commons attribution http://www.flickr.com/photos/kjarrett/4103092955/sizes/l/in/photostream/
  • Data can be woven into individualized business information ecosystems Enterprise search, ecm, portal, email sms, Social, Local, Mobile The promise of accidental collaboration is an accessibility of ideas. We realize that innovation and insight comes not just from the hand-me-downs of original programmers, founders and CEOs. It is not as much about a clash of ideas between the giants of technology and the goliaths of our respective industries. Accidental Collaboration yields information and insight that come from very different sources, hidden our org charts, buried by time, by project team, by anonymity. In Short, at this the dawn of an age of insight and innovation we stand not on the shoulders of giants… Image cite: Flicker User: by tofumonstruo creative commons attribution, http://www.flickr.com/photos/tofubakemono/368327034/sizes/l/in/photostream/
  • … . but in an army of Davids.
  • 2 Types of Collaboration

    1. 1. #AIIM122 Types of Collaboration &10 Requirements for Using Them Billy Cripe Principal BloomThinker - BloomThink VP Marketing – Digitiliti Board Member – AIIM Minnesota #AIIM12
    2. 2. 2 Types of Collaboration#1 Intentional#2 Accidental #AIIM12
    3. 3. “If I have seen farther, it is by standing on the shoulders of giants. -sir Isaac Newton, 1676#AIIM12
    4. 4. Amazing (ly Slow) Accidental Collaboration1676 – Leibniz seeks to find the truth of any mathematical statement given a universal language. #AIIM12
    5. 5. Amazing (ly Slow) Accidental Collaboration1882 – David Hilberthangs out withHermann Minkowskiand Adolf Hurwitz1928 – Hilbert posesEntscheidungsproblemto challenge Leibniz #AIIM12
    6. 6. Amazing (ly Slow) Accidental Collaboration1936 – Alonzo Church creates Lambda Calculus to prove Hilbert’s problem is unsolvable #AIIM12
    7. 7. Amazing (ly Slow) Accidental Collaboration1958 – John McCarthy designs LISPbased on Church’s Lambda Calculus.LISP includes mapping and folding… #AIIM12
    8. 8. Amazing (ly Slow) Accidental Collaboration2004 – Jeffrey Dean & Sanjay GhemawatpublishMapReduce: Simplified Data Processing on LargeClusters“ Our abstraction is inspired by the map and reduce primitives present in Lisp #AIIM12
    9. 9. Amazing (ly Slow) Accidental Collaboration2004 – Doug Cutting –reads the paper2004-2006 - Hadoop createdDecember, 2011 - Version 1.0.0 of Hadoop released #AIIM12
    10. 10. Accidental Collaboration:The soul of an Idea Weaves Through Time Forward pressure of the idea influences those who come next… Building on the insights & data of the progenitors in ways they never imagined… Created a way toWanted a way to tease insight out offind the truth of everythinganything #AIIM12
    11. 11. Modern Accidental Collaboration #AIIM12
    12. 12. Key Requirements for Accidental Collaboration DATACollection | Aggregation | Tracking |Prediction | Delivery #AIIM12
    13. 13. Evolve Status Quo Strategies of Data Collection we helping computers  computers helping us our goal is awareness and interaction with all kinds of information #AIIM12
    14. 14. Automatically Aggregate Data encourage voluntary participation  interest & gamification create springboards for further collaboration aggregates reveal new meaning & insight #AIIM12
    15. 15. Tracking Data & Data Tracking Usage & Context Patterns DLP & GRC Digitiliti #AIIM12
    16. 16. Proactively Predict Agile Creation of Business Intelligence Anticipate Demand, Need, Desire & Request #AIIM12
    17. 17. Deliver Insight Through Information Bring it all together Enterprise Information Ecosystem #AIIM12
    18. 18. An Army Of Davids #AIIM12
    19. 19. Thank YouBilly CripePrincipal BloomThinkerBloomThink.combilly.cripe@bloomthink.com+1 (612) 205-3762Twitter: @billycripeFacebook: facebook.com/bloomthinkAIIM Minnesota #AIIM12