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Editor's Notes

• #2: HOW DID WE GET HERE?WHAT IS BIG DATA?WHAT REALLY CREATES TRUE COMPETITIVE BARRIERS IN DATA-DRIVEN BUSINESSES?
• #3: As I wrote a post recently, DATA IS THE NEW DOT COM. Funds are announcing a new focus on “Big Data.” – IT’S HOT. WHY NOW?
• #4: Big Data is pervasive - permeating every industryAdvertisingGovernmentFinancial ServicesCommercePharma Biotech & HealthcareThe good news: data is becoming MORE ACTIONABLEThe bad news: it is INCREASINGLY DIFFICULT TO EXTRACT VALUE given the VOLUME, VELOCITY AND MULTIPLE DATA TYPES
• #5: MASSINVE ADVANCES IN INFRASTRUCTURE HAS SEEDED THE BIG DATA REVOLUTION OVER THE PAST 50 years
• #6: THESE TRENDS HAVE A DIRECT IMPACT UPON BUSINESS – AND THE BOTTOM LINEe.g., RECOMMENDATION ENGINESTHAT LEVERAGEHISTORICAL DATA andPREDICTIVE ANALYTICS to generateACTIONABLE REAL-TIME INSIGHT for customers
• #7: CAN WE AGREE ON A SET OF DEFINITIONS GIVEN THE AMBIGUITY OF THE TERM?
• #8: Sizes that were unimaginable a few years ago are now commonplaceJust storing and accessing the data can be difficultSIZE – MANAGED WITH – STOREDSmall :: Excel, R :: fits in memory on one machineMedium :: indexed files, monolithic DB :: fits on disk on one machineLarge :: Hadoop, Distributed DB :: stored across many machines Example in the IA Ventures portfolio: METAMARKETS – LARGE + REAL-TIMEPROBLEM: WHEN YOU MOVE TO DISTRIBUTED DATABASES, even the most simple mathematical tasks which are trivial for small and medium size systems are challenging
• #9: Data that DIFFICULT FOR COMPUTERS TO UNDERSTANDPrincipal example being NATURAL LANGUAGETEXT, IMAGES, VIDEOVALUABLE INFORMATION TRAPPED INSIDE THIS DATA, e.g., Twitter, earnings releasesExample in the IA Ventures portfolio: RECORDED FUTURE – LARGE + UNSTRUCTURED
• #10: More data coming in fasterDecision windows getting shorterValuable to worthless in a matter of minutes. (seconds … no milliseconds) :: RAPID VALUE DECAY – EVERYTHING IS BEGINNING TO LOOK LIKE TRADINGe.g., trading, ad servingSTREAMS ARE WHERE REAL-TIME INSIGHT COME FROM:: Stream processing – insight is extracted as soon as the data shows upExample in the IA Ventures portfolio: DATASIFT – LARGE + UNSTRUCTURED + REAL-TIME
• #11: BIG DATA = COMPLEX DATAExtracting value from Big Data is FREAKING HARDBig Data companies are mash-ups of these different attributes :: we like that at IA Ventures. WE BELIEVE THIS CREATES BARRIERSSTORAGE AND ANALYTICS generally go hand in hand :: LOTS OF DEPENDENCIES
• #12: THE IA VENTURE DEFINITION
• #13: At IA Ventures we call this the DATA TAXONOMYINPUTS on the y-axisOUTPUTS on the x-axis
• #14: SINGLE SOURCE DATA PLATFORMS TWITTERData generated on its platform – consumed as a discrete data streamPeople come to Twitter for the streamHigher order enrichment delivered by others
• #15: THIRD PARTY DATA PLATFORMSDATASIFTIngests a variety of streams from a range of platforms – Twitter, Wordpress,LinkedIn, etc.ENRICHES THOSE STREAMS with analytics and other forms of data like SENTIMENT AND REPUTATIONCan either consume a pure data product (the Twitter firehose) or OVERLAY ADDED VALUE TO EXTRACT INSIGHT
• #16: MORE SOPHISTICATED PRODUCTIZATION AROUND THE DATA ASSETPLACE IQMULTI-SOURCE – GEO DATA, WEATHER DATA, TRAFFIC DATA, ETC.COMPLEX ALGORITHMS, e.g., looking at the relationship among brand, weather forecast and time of day to optimize ad placement and offersCreate and maintain competitive advantage through FRESHNESS – TIMELY and ACTIONABLE information
• #17: SINGLE SOURCE PLATFORMS WITH RICH PRODUCT OFFERINGSRepresent a phase change – Big Data companies who don’t sell data BUT USE DATA TO OPTIMIZE PRODUCT AMAZON – rich trove of user data that is leveraged to optimize both user experience and economic outcomes. REAL-TIME PERSONALIZATION, HYPER-CONTEXTUAL
• #18: MULTI-SOURCE, HIGHLY REFINED PRODUCT –FUSING INTERNAL AND EXTERNAL DATA FOR MAXIMUM COMPETITIVE ADVANTAGEWAL-MARTIntersection of historical user behavior, inventory levels and weather data to optimize a promotion, shipping patterns, buying policy, etc.RENAISSANCE TECHNOLOGIESBuy massive amounts of external dataCreate their own metadataIndex and archive petabytes of data for historical analysis, model creation and calibrationThe firm’s success – massive absolute and relative returns – is the ultimate example of A HIGHER-ORDER DATA DRIVEN PRODUCT
• #19: THE TREND AS SIMPLE DATA BECOMES COMMODITIZED andACTIONABLE INSIGHTS ARE WHAT CUSTOMERS REALLY WANT – AND ARE WILLING TO PAY FOR
• #21: EXECUTION is TABLE STAKES TO PLAY THE GAME
• #22: SO IF IT’S NOT ABOUT TECHNOLOGY AND ALGORITHMS, WHAT IS IT ABOUT??
• #24: The rise of the CONTRIBUTORY DATABASE – DATA EXHIBITING TRADITIONAL NETWORK EFFECTSThese companies TRANSCEND SMART ALGORITHMSIn the SHORT RUN, SMARTER ALGOS provide a needed edge to gain early adoption (OUT-EXECUTE everyone else)In the LONG RUN, at scale, USER CONTRIBUTED DATA IS WHAT CREATES THE COMPETITIVE MOATBILLGUARD
• #25: The rise of DATA ECONOMIES OF SCALEDay 1: not much data, not much valueAs the data asset builds, insights are gleaned, fed back into the product, users interact with the product and create more valuable usage dataBANKSIMPLE, PLACEIQ
• #29: CORE COMPETENCIES FOR A BIG DATA COMPANY
• #30: Machine learning: great skills, mathematically grounded but inability to bring deep industry knowledge to problem-solvingResearch: strong industry knowledge and mathematical grounding but inability to operate at scaleDanger zone: strong dev skills plus industry knowledge but without analytical rigorDATA SCIENTSTS ARE TRUE UNICORNS
• #31: NOT ONLY ABOUT DATA SCIENTISTS AND TECHNOLOGISTS, but DATA CENTRIC LEADERSHIP