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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2 Types of Collaboration
- 1. #AIIM12 2 Types of Collaboration & 10 Requirements for Using Them Billy Cripe Principal BloomThinker - BloomThink VP Marketing â Digitiliti Board Member â AIIM Minnesota #AIIM12
- 2. 2 Types of Collaboration #1 Intentional #2 Accidental #AIIM12
- 3. âIf I have seen farther, it is by standing on the shoulders of giants. -sir Isaac Newton, 1676 #AIIM12
- 4. Amazing (ly Slow) Accidental Collaboration 1676 â Leibniz seeks to find the truth of any mathematical statement given a universal language. #AIIM12
- 5. Amazing (ly Slow) Accidental Collaboration 1882 â David Hilbert hangs out with Hermann Minkowski and Adolf Hurwitz 1928 â Hilbert poses Entscheidungsproblem to challenge Leibniz #AIIM12
- 6. Amazing (ly Slow) Accidental Collaboration 1936 â Alonzo Church creates Lambda Calculus to prove Hilbertâs problem is unsolvable #AIIM12
- 7. Amazing (ly Slow) Accidental Collaboration 1958 â John McCarthy designs LISP based on Churchâs Lambda Calculus. LISP includes mapping and folding⌠#AIIM12
- 8. Amazing (ly Slow) Accidental Collaboration 2004 â Jeffrey Dean & Sanjay Ghemawat publish MapReduce: Simplified Data Processing on Large Clusters â Our abstraction is inspired by the map and reduce primitives present in Lisp #AIIM12
- 9. Amazing (ly Slow) Accidental Collaboration 2004 â Doug Cutting âreads the paper 2004-2006 - Hadoop created December, 2011 - Version 1.0.0 of Hadoop released #AIIM12
- 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 to Wanted a way to tease insight out of find the truth of everything anything #AIIM12
- 11. Modern Accidental Collaboration #AIIM12
- 12. Key Requirements for Accidental Collaboration DATA Collection | Aggregation | Tracking |Prediction | Delivery #AIIM12
- 13. Evolve Status Quo Strategies of Data Collection ď§ we helping computers ď computers helping us ď§ our goal is awareness and interaction with all kinds of information #AIIM12
- 14. Automatically Aggregate Data ď§ encourage voluntary participation ď interest & gamification ď§ create springboards for further collaboration ď§ aggregates reveal new meaning & insight #AIIM12
- 15. Tracking Data & Data Tracking ď§ Usage & Context Patterns ď§ DLP & GRC ď§ Digitiliti #AIIM12
- 16. Proactively Predict ď§ Agile Creation of Business Intelligence ď§ Anticipate Demand, Need, Desire & Request #AIIM12
- 17. Deliver Insight Through Information ď§ Bring it all together ď§ Enterprise Information Ecosystem #AIIM12
- 18. An Army Of Davids #AIIM12
- 19. Thank You Billy Cripe Principal BloomThinker BloomThink.com billy.cripe@bloomthink.com +1 (612) 205-3762 Twitter: @billycripe Facebook: facebook.com/bloomthink AIIM Minnesota #AIIM12
Editor's Notes
- 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 â from how things are made to how theyâre transported from here to there. These gave rise to HCM systems which took the same fundamental approach and applied it to how people â human beings â are managed and placed and categorized and âquality assuredâ in an organization (that last part being your annual review). The fundamental assumption and fatal flaw in all of these software âsystemsâ is that process â those recipes of business â 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 weâll increase success in business. In many ways weâre the victims of our own technological success. Weâve succumbed to what I call âSmall Earth Syndromeâ. The industrial and information revolutions are said to have âshrunk the planetâ. Thatâs 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 â 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 â be it code or physical. Intentional: Common Purpose, Traditional Interactions , Boring Focused & shared purpose by a defined team Interactions are introductions, updates, âtake a look at thisâ, âthanksâ Most âcollaborationâ technology fits into this category Boring. Assembly Line., Line Cook Kitchen Model â 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 cowâs udders! WHOAH! The cow never intended that! Evolution and/or God never intended that. We got creative and now look where weâre at! REUSE in NEW Context Examples in technology include re-blgging (tumblr, pinterest) reporting, content curation, re-use, re-purposing, re-search Big Data â Is the Biggest Buzzword of 2012. Part of the âcloudâ, 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 â in the same year that Isaac Newton wrote this famous quote to one of his rivalsâŚ
- 1676 â 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 â 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 â Prussian born David Hilbert his best friend Hermann Minkowski (who invented space-time to better understand Einsteinâs 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 â He formulates the Entscheidungsproblem (decision problem) challenge to see if Leibniz universal language statement can be determined true or false even if a proof doesnât exist yet.
- 1936 â 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 Hilbertâs problem is unsolvable.
- 1958 â John McCarthy, the man who coined the phrase âartificial intelligenceâ and launched the line of research that has given us IBMâs Watson, designs the programming language LISP based on Churchâs lambda calculus. LISP has mapping and folding. Together these give us recursion â doing something to every item in a list and combining the results in some way.
- 2004 â 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 â Doug Cutting â 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 â Everywhere Discovering New Insight from Heterogeneous Data Sources in Different Contexts
- 2004 â Doug Cutting â 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 â 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 â 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 âchiming inâ 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 youâll 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 â 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.