Big Data Startups - Top Visualization and Data Analytics Startups

Download Big Data Startups - Top Visualization and Data Analytics Startups

Post on 15-Jul-2015



Data & Analytics

9 download

Embed Size (px)


<ul><li><p> Wale Ayeni, 2013 </p><p> </p><p>Big Data Analytics and Visualization landscape </p><p>Confidential. No part of this publication may be reproduced, stored in a retrieval system or </p><p>transmitted in any form or by any means without the prior written permission of the publisher </p></li><li><p>About the author </p><p> Wale Ayeni, 2013 </p><p> </p><p>Confidential. No part of this publication may be reproduced, stored in a retrieval system or </p><p>transmitted in any form or by any means without the prior written permission of the publisher </p><p>Professional </p><p>With about 10-years of domain expertise in technology spanning roles from chip design engineering, consulting, investment banking and </p><p>venture capital, Wale has always had a passion for science and technology. Currently working in a consultancy capacity with EchoVC </p><p>Partners, an early stage technology-focused Venture Capital firm in California, and before that, most recently was a Sr. Associate with </p><p>J.P. Morgan Investment Banking group covering technology, media and telecommunication companies (TMT) in San Francisco where he </p><p>was involved in strategic and financial advisory for leading public and private companies. He was a pivotal member of teams that </p><p>successfully closed deals valued at over $4 billion. His role also entailed researching and analyzing various technology trends, and </p><p>developing strategic alternatives for companies in the TMT space; in consumer internet and digital media, payments, semiconductor and </p><p>software. In addition, he was nominated to a leadership position and served on the Analyst and Associate Council of the firm interfacing with the Executive Committee of JPMorgan Investment Bank on company wide efficiency and transformative initiatives. </p><p>Prior to J.P. Morgan Wale was a Chip Design Engineer working at the frontier of high-technology developing next-generation chipsets, </p><p>communication platforms and microprocessors at Fortune 100 companies Motorola, Intel Corporation, and Qualcomm Inc. In his first 2 years at Intel Wale received the honor of being awarded block design ownership, a distinction rarely granted to new Engineers. In this role he led critical aspects of Intels QuickPath chip design, which resulted in the biggest change in PC platform architecture in Intels history. For Qualcomm he led Bus protocol verification of state-of-the-art mobile HDTV chipsets for wireless multimedia ecosystem enabling the first mobile devices capable of high definition content in the market. </p><p>In Wales consulting roles he developed strategic growth alternatives into China for a US automation client, worked on branding strategy for Adimab, a startup backed by Google ventures, and regularly mentors startup entrepreneurs in the developing world. </p><p>Education Summary </p><p>Wale has an MBA from Dartmouth College Tuck School of Business where he served as the Co-Chair of the Finance club and was an MBA fellow with the Center for Private Equity and Entrepreneurship. He has a Masters in Electrical Engineering from University of </p><p>Maryland, College Park and was a national merit recipient earning his B.Eng under academic scholarship in Moscow, Russia, at </p><p>Moskovskij Energeticeskij Institut, graduating with the prestigious red diploma (summa cum laude) . He is also a Kauffman Venture Capital fellow. The society of Kauffman fellows is a pre-eminent world-wide network of innovation investors who provide smart, connected capital to fuel entrepreneurial change. </p><p>Personal </p><p>Wale enjoys international travel and has been to over 25 countries. In addition to his native English he speaks Russian, Yoruba, and </p><p>Hausa fluently and speaks several other languages passively. In his free time he enjoys playing and watching soccer; and proudly took </p><p>the Tuck soccer team to the MBA World Cup. His other pastimes include career mentoring, watching movies, reading and defending </p><p>Formula 1 as a superior sport to NASCAR </p></li><li><p>Big Data opportunity driven by people and devices </p><p>Connecting people </p><p>500 million users </p><p>145 billion/day </p><p>Over 1 billion accounts </p><p>4 billion views/day </p><p>More mobile connected </p><p>devices than people </p><p>on the planet1 </p><p>More than 50 billion </p><p>connected devices </p><p>by 20202 </p><p>Twitter </p><p>Facebook </p><p>YouTube </p><p>E-mail </p><p>2 </p><p>Variety </p><p>90% of data in the world today was created in the last 2 years alone3 </p><p>Volume Velocity </p><p>Unstructured </p><p>+ </p><p>Structured data </p><p>5 exabytes of data </p><p>created every 48 hours </p><p>From Terabytes of data </p><p>to Petabytes of data </p><p>BIG DATA </p><p>Connecting devices </p><p>Source: 1CISCO VNI; 2Ericsson; 3IBM </p></li><li><p>Explosive growth in digital data is creating massive ~$100bn Big Data opportunity </p><p>Growing share of enterprise data is unstructured Volume of digital information created and replicated (ZB) </p><p>Structured data </p><p>64% </p><p>Unstructured data </p><p>36% Structured data </p><p>23% </p><p>Unstructured data </p><p>77% </p><p>Source: IDC, Wall Street research </p><p>2006 2015E </p><p>1,800 </p><p>7,900 </p><p>2011A 2015E</p><p>Source: IDC </p><p>Relational database </p><p>$27 </p><p>Hadoop </p><p>$14 </p><p>Business intelligence </p><p>$13 </p><p>Server for DB </p><p>$12 </p><p>Industry applications </p><p>$12 </p><p>Storage of DB </p><p>$9 </p><p>Unstructured data </p><p>$6 </p><p>Enterprise applications </p><p>$5 </p><p>Data integration and quality </p><p>$4 NoSQL </p><p>$2 </p><p>Total addressable market for Big Data = $100bn </p><p>Massive ~$100bn TAM ($bn) </p><p>Source: Garter, IDC, BofA Merrill Lynch Global Research </p><p>3 </p></li><li><p>The scale is tipping towards Big Data </p><p>Control </p><p>Consistency </p><p>Data management and planning </p><p>Predefined, structured data </p><p>Terabytes or petabyte range </p><p>Enforced by the database </p><p>management system </p><p>Mature standards </p><p>Traditional methods Big Data </p><p>Tolerating chaos </p><p>Agility </p><p>Data discovery </p><p>Diverse </p><p>Petabyte scale at relatively reasonable cost </p><p>Programmer and application dependent </p><p>Largely nonexistent standards </p><p>4 </p></li><li><p>Key trends in information management </p><p>Data </p><p>warehousing </p><p>Data </p><p>integration </p><p>Analytical </p><p>solutions </p><p>Search and </p><p>discovery </p><p> Growth in the data warehousing market due to replacement of performance-constrained environments and the value-add of emerging applications </p><p>(e.g., performance management and advanced analytics) </p><p> Significant consolidation as incumbents pick up smaller vendors with compelling technologies and large IT vendors seek to add data warehousing technologies to </p><p>centralize their presence within clients data ecosystem </p><p> Warehouses continue to evolve from pure-play data repositories to comprehensive information platforms that leverage multiple technologies </p><p>(e.g., Hadoop, MapReduce) to handle real-time, unstructured data </p><p> Addressing increased demandparticularly from marketersfor access to third-party consumer data and real-time pricing information </p><p> Leading vendors offer sophisticated products with a high level of service and perceived high value, but at a high cost </p><p> Vendors with less brand recognition offer less mature products, but may be positioned to gain share as capabilities expand </p><p> SaaS and cloud models are becoming more sought after to capitalize on cost and ease of deployment features </p><p> General data integration as a service is available, but most common with synchronization </p><p> Organizations increasingly layer large volumes of data from external parties (e.g., offline customer data, market research) onto internal structured data, driving </p><p>demand for automated integration services in Big Data offerings </p><p> Pure-play suppliers of other Big Data solutions continue adding analytical capabilities to support growing demand for real-time analysis of operational and </p><p>corporate performance </p><p> Specialized offerings from vendors like Splunk and broad offerings from vendors like Palantir are expanding the analytics use case, driving adoption across the </p><p>enterprise </p><p> Vendors like Tableau are developing a new market of business users who demand access to information with graphical interfaces to access data real-time </p><p> Despite growth of vendors that target specific industries, there remains high demand for customer-focused solutions (marketing, sales, service and social </p><p>dimensions), driving growth of cross-industry functional offerings like marketing automation </p><p> Large vendorswho are developing and acquiring search services to layer into existing applicationsrepresent default choices for customers, forcing independent vendors to specialize in particular business problems </p><p> Oracle's 2011 acquisition of Endeca and IBMs 2012 acquisition of Vivisimo are prime examples </p><p> Increasing popularity of appliances due to simplicity, speed and compliance </p><p> Search and discovery prove particularly beneficial to marketing teams, which increasingly leverage external market context (e.g., product reviews) in </p><p>campaign development </p><p> Adoption of search-based discovery is likely to increase in the near term with growing deployment of Big Data solutions </p><p>Content </p><p>management </p><p> Businesses are increasingly focused on content management due to the growth in volume and complexity of dataparticularly usergenerated social, video and mobile content </p><p> Enterprise adoption of cloud collaboration and filing tools (e.g., Box) is increasing as security concerns are addressed </p><p> Open source vendors and competitive stack offerings have driven price per seat down, stimulating demand </p><p> Point purchases are common, as no single person controls the budget for all of an organizations content management needs </p><p> Innovation in the management of contextual contentcurrent customer location, mobile device in-use, etc.will give enterprises the ability to more effectively target audiences in marketing campaigns </p><p>Source: Gartner, Forrester, IDC </p><p>5 </p></li><li><p>Big Data is not a vertical, it is a competency that cuts across industries </p><p>Analyze </p><p>+ </p><p>Visualize </p><p>Industry Key themes </p><p>Manufacturing </p><p>Retail and </p><p>transportation </p><p>Utilities </p><p>Healthcare </p><p>Oil and gas </p><p>Government and </p><p>research </p><p>Financial </p><p> Surveillance </p><p> Sensors </p><p> RFID </p><p> RFID </p><p> Marketing/Branding </p><p> Merchandizing </p><p> Smart meters </p><p> Smart grids </p><p> Massive digitization </p><p> Medical imaging </p><p> Demographicsbaby boomers </p><p> Digital oil fields </p><p> Sensors </p><p> Seismic and geographical devices </p><p> Climate research </p><p> Seismology </p><p> Environmental modeling </p><p> Payments </p><p> Fraud detection </p><p> Capital markets / Trading </p><p>Generate data </p><p>Capture data </p><p>Store data </p><p>Search data </p><p>6 </p></li><li><p>Evolution of next generation data analytics and visualization </p><p>Data warehouse </p><p>GBs of Data </p><p>Tra</p><p>din</p><p>g </p><p>HR</p><p> G</p><p>L </p><p>Web L</p><p>ogs </p><p>Mobile</p><p> S</p><p>ensors</p><p>Datamart </p><p>OLAP </p><p>cubes </p><p>Data </p><p>Sets </p><p>Next </p><p>generation </p><p>analytics and </p><p>visualization </p><p>Interactive </p><p>development </p><p>environment </p><p>TBs of Data </p><p>Big</p><p> Da</p><p>ta a</p><p>na</p><p>lyti</p><p>cs </p><p>Tra</p><p>dit</p><p>ion</p><p>al </p><p>an</p><p>aly</p><p>tic</p><p>s </p><p>TBs of Data </p><p>PBs of Data </p><p>7 </p></li><li><p>Demand for next-generation analytical solutions is driving a revolution and significant value shift </p><p>Note: Represents illustrative portion of information management expenditures 1 As of 2011. Source: IDC, June 2012 2 As of 2011. Includes Business-to-Business Middleware, Enterprise Service Business and Connectivity Middleware, Event-Driven Middleware, Other Integration and Process Automation </p><p>Middleware, Process Automation Middleware, Data Integration and Access Software, Database Development and Management Tools. Source: IDC, May 2012 3 As of 2011. Includes relational database, non-relational database, data warehouse generation and management, search and discovery, open-source and Hadoop markets. </p><p>Sources: IDC, Gartner </p><p>Database </p><p>Integration </p><p>BI </p><p>Database </p><p>Integration </p><p>BI </p><p>Today Future </p><p>Data analytics </p><p>in action </p><p>Data </p><p>infrastructure </p><p>$10bn1 </p><p>$17bn2 </p><p>$43bn3 </p><p>Waves of change </p><p> Big Data </p><p> Internet of everything </p><p> Mobility </p><p> Real time </p><p> Social </p><p> Cloud </p><p>8 </p></li><li><p>Incumbents and other large players invest and acquire heavily in Big Data </p><p> Greenplum acquisition provides a suite of Big Data management and analysis tools; Greenplum HD enables even easier </p><p>Hadoop deployments </p><p> Strong storage solutions support Big Data deployments; Atmos cloud storage platform enables distributed storage for </p><p>Big Data analytics </p><p> Acquisition of Autonomy and announced restructuring of business in August 2011 demonstrated the importance of information </p><p>management software to HP </p><p> Acquired Vertica in early 2011 </p><p> Released Vertica 5.0 in June 2011, including a software development kit enabling users to implement MapReduce like analytic jobs </p><p>and user-defined functions through standard SQL queries </p><p> Big Data viewed as key to its SmartPlanet and SmartEnterprise initiatives </p><p> Most of recent acquisitions (DemandTec, Q1 Labs, i2) positioned under its definition of Big Data and analytics </p><p> Expanding market reach with new Netezza High Capacity Appliance </p><p> New Biglnsights 1.1 release reflects its significant investment in Hadoop </p><p> Big Data strategy largely based on its Exadata platform </p><p> Acquisition of Endeca viewed as response to HPs acquisition of Autonomy </p><p> Recently announced plan for its Hadoop-based Big Data accelerator </p><p> Acquisition of SuccessFactors helps extend its Big Data initiatives into the cloud </p><p> HANA platform enables analytics across SAPs broad set of applications and customer base </p><p> Acquisitions of Sybase and Business Objects viewed as successes and encouragement for additional deals </p><p> Aster Data acquisition significantly expanded its Big Data capabilities </p><p> Addressing clients Hadoop needs via recent partnership with Hortonworks </p><p> Acquired Aprimo to enable event-based marketing together with its data solutions </p><p> Strong track record in enterprise data warehousing helps its Big Data initiatives </p><p>Source: Company information, Factiva, CapitalIQ </p><p>9 </p></li><li><p>Big data landscape Emerging players : Data Analytics and Visualization </p><p>Marketing and e-Commerce </p><p>Social </p><p>Location/future events Market places </p><p>Analytics and visualization </p><p>Machine data </p><p>Statistical </p><p>10 </p></li><li><p>Profiles </p><p>Analytics and visualization </p><p>Machine data </p><p>Marketing and e-commerce </p><p>Social </p><p> Market places </p><p>Location/future events </p><p>Statistical </p><p>11 </p><p>71 </p><p>65 </p><p>61 </p><p>52 </p><p>44 </p><p>39 </p><p>11 </p></li><li><p>1010Data - Company overview </p><p>Management </p><p>Name Designation Prior experience </p><p>Sandy Steier CEO and Co-founder UBS, Lehman Brothers, </p><p>Joel Kaplan CTO and Co-founder UBS, Morgan Stanley </p><p>Greg Munves Executive VP </p><p>Adam Jacobs VP and Chief Scientist Weill Cornell Medical College, UCLA </p><p>T.C Fleming Director of finance Duff &amp; Phelps, Deloitte </p><p>Recent news </p><p> May 14, 2012: Announced that the nations largest chain of drive-in restaurants SONIC, will be using Big Data Warehouse to enable critical </p><p>business decisions ranging from operations to marketing </p><p> Feb 28, 2012: Launched Micro Segmentation Wizard tool to enable </p><p>companies to model and predict individual consumer behavior and social </p><p>network relationships </p><p> Sep 16, 2011: Received Safe Harbor Certification for its cloud based big data </p><p>analytics platform </p><p> Feb 7, 2011: Partnered with American Securitization Forum and launched </p><p>Agency MBS Market Review reports </p><p>Business overview </p><p> Provides cloud based analytical platform for Big Data </p><p> Solutions include analytics, business intelligence, data publishing and </p><p>warehousing services </p><p> Value proposition includes ultra fast database technology, scalability, easy to </p><p>use interface, powerful analytics-on-demand data integration and access </p><p>through web interface </p><p> Raised first institutional round of funding in 2010...</p></li></ul>