Washington DC | Melbourne Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Managing Innovation @IanMulvany Head of Product Innovation
Washington DC | Melbourne Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne SAGE Publishing • Founded in 1965 • Independent • 1,500 employees globally • 5th largest journal publisher & fastest growing STM publisher • Research Methods is at the heart of what we do • Product Management team started in 2011
in which big data is used responsibly to improve social outcomes and governance. • Our mission is to equip every social scientist with the skills and tools they need to do big data research. • Do this by creating new products and services Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Existing process, meetings, systems Split roles Too much excitement Too many people wanting to get involved Reality not matching up to expectation Too much or too little sponsor involvement Pigs vs Chickens Challenges of innovating from within a large company CC0 Public Domain
Washington DC | Melbourne Innovation Incubator • Establish an incubator initially with 3 people • Product Innovation budget • Test, prototype, fail, test, prototype, build • 2 year limit to identify scaleable commercial proposition for SAGE
Washington DC | Melbourne What is our problem domain Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Why Big Data? “The social sciences are undergoing a dramatic transformation from studying problems to solving them; from making do with a small number of sparse data sets to analyzing increasing quantities of diverse, highly informative data; from isolated scholars toiling away on their own to larger scale, collaborative, interdisciplinary, lab-style research teams…”
Washington DC | Melbourne What is our problem domain Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Why Big Data? “The social sciences are undergoing a dramatic transformation from studying problems to solving them; from making do with a small number of sparse data sets to analyzing increasing quantities of diverse, highly informative data; from isolated scholars toiling away on their own to larger scale, collaborative, interdisciplinary, lab-style research teams…” Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Our Problem Domain
Washington DC | Melbourne •Our vision is a world in which big data is used responsibly to improve social outcomes and governance. •Our mission is to equip every social scientist with the skills and tools they need to do big data research. • Do this by creating new products and services
Washington DC | Melbourne Lean Discovery Framework Portfolio Product Experiment define refine refine hypothesize test refine hypothesize refine test This is the framework we’re using to learn about potential areas of investment:
scientist with the skills and tools they need to do big data research. Mission Goals Bets Product ideas Help researchers gain SKILLS Provide TOOLS Support COLLABORATION Support access to DATA Add collaborations through common PROBLEMS Allow CS and Social researchers to find Problems Help researchers collaborate effectively Help researchers collaborate successfully Matchmaking tool Match open data w/ funding opps Make a collaboration metric for Unis Tie SS proposals and tech needs to nonprofit platforms like KIVA Incentivise tech infra folks to provide infra free if data is open Pitch decks to funders Word/topic meaning suggestion engine Grant evaluation
Washington DC | Melbourne Grounded by metrics if you can’t confirm those metrics to begin with, you can use your projections as a feedback mechanism, but you need to have a place to start from
Washington DC | Melbourne Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Population of Social Scientists 50% Year 2M 2M Year 1 Year 10 Interested in CSS 6.5% Population Interest Growth
Washington DC | Melbourne lean Canvas Academic Data Scientist ! Can’t do this myself ! Lack quality question ! Lack right method ! Problem getting funding Industry Data Scientist: ! Talent gone to waste ! Lack of social impact ! Lack of new learning ! BD research centre, conferences/seminars, linkedin/Twitter, Uni directory, data.world, blogs, Kagle. ! Hackathonsm meetups, linkedIn, idea marketplace, IDEO Academic Data Scientist ! Find SS researcher (by method, data, topic, funding, problem) Industry Data Scientist: ! Find researcher (by data, problem, topic) ! 600 new technical collaborations by mid-2019 Academic Data Scientist ! Enhanced reputation ◦ Output ◦ New opportunities ! Power to affect the world / discipline Industry Data Scientist: ! Impact the world ! Confirm my super powers Academic Data Scientist Industry Data Scientist: ! Charge to sign up ! Charge upon starting collaboration ! Institutional subscription ! Funders pay
Washington DC | Melbourne Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Identify Riskiest Assumptions ONLINE CSS COURSE - EXPERIMENT PHOTOS Cycle 1 Lean Experiments: Test Riskiest Assumptions
Washington DC | Melbourne Closing the feedback loop daily standouts toy kan ban stakeholder management Retrospectives Need for Tool usage is inverse proportion to how tight knit the team is Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Lessons Learnt - product and market
Washington DC | Melbourne Some examples of what we learnt Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne - People like compelling demos - Our Core assumption around challenges to cross disciplinary collaboration was wrong - Spent some time prototyping a product that didn’t have a business model, and we could have saved that time if we had done the business analysis first - Later this approach led us quickly from designing an offering that we realised would not be aligned with what our users need, or what our goal is
Washington DC | Melbourne Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Specific project $$ is more powerful for alignment than a general project budget
Washington DC | Melbourne Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne flickr: nsalt cc-by flickr: hommedechevre. Cc-by-nc-sa flickr: cakeinmilk cc-by-nc-sa Pigs vs Chickens vs ???
Washington DC | Melbourne Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Need for feedback cycles is inversely in proportion to how tightly knit the team is
Washington DC | Melbourne Los Angeles | London | New Delhi | Singapore | Washington DC | Melbourne Finally Confidence in our team is our most valuable resource
Washington DC | Melbourne Individuals and interactions over processes and tools The Value of Conversation Working software over comprehensive documentation Early testing, low-fi prototypes Customer collaboration over contract negotiation Co-develop, “Get out of the building” Responding to change over following a plan Evidence based decision making Agile Manifesto Translated for Product Development