Big Data Next: Capturing the Promise of Big Data Big Data Report 2015
The decade of big data is here. Ninety percent of all of the world s data has been created in the last two years, buoyed by the rapid growth of the Internet of Things and mobile devices. Data collection, storage and analysis costs have plummeted. Now, entire industries are turning to data-generated insights to gain a competitive advantage. The future of big data holds an even greater promise to expand insights for the largest industries and solve some of the world s most complex problems. SVB Analytics, in conversations with big data developers and users, including our clients, is identifying the best opportunities for innovators, enterprises and investors in the next phase, which we call Big Data Next. 3
The value of data-driven insights grows as infrastructure costs decline and analytics improve. The amount of data collected is growing exponentially, and the costs for processing and storing these huge quantities are dropping. These two trends are creating more powerful use cases for big data. At the same time, demand for skill sets to use big data in practical applications is growing, as enterprises seek to leverage data for competitive advantage. Venture investments in big data are quickly accelerating, and changing focus. Global Internet traffic is exploding and the cost of big data infrastructure is dropping. 30,000X $11K to 3 cents Increase in Internet traffic by petabytes, 1995-2014. 1 Decline in average storage costs per gigabyte, 1990-2014. 2 $527 to 5 cents $1.2K to 63 cents Decline in computing costs per 1MM transistors, 1990-2013. 3 Decline in Internet transit prices per Mbps, 1998-2013. 4 The demand for data scientists tripled in three years. Overall data job growth, 2006-2015 5 Percentage of matching job postings 0.6 0.4 0.2 3X 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Postings that include terms data scientist, data architect, data engineer, big data 4
Big data is driving big values, signaling expectations of large returns. Venture investments in big data analytics companies, 2004-2014 6 Venture investment growth: big data analytics vs. B2B, 2009-2014 7 $6.0 600 1800% $5.0 500 1500% $4.0 400 1200% $3.0 300 900% $2.0 200 600% $1.0 100 300% $ 0 0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 0% B2B IC Growth Big Data IC Growth Invested Capital ($B) # of Deals Invested capital multiples for big data companies exceed those for all technology companies. Invested capital multiples: big data analytics vs. all tech companies 8 20x 90 th % (big data) 15x 75 th % (big data) 10x 50 th % (big data) 25 th % (big data) 5x 10 th % (big data) 0x 50 th % median (all tech) < $2M $2 5M $5 20M $20 40M > $40M Invested Capital (Pre-Financing) 5
Big data 2.0 is driving action and value across many industries. Think of the evolution of big data as a 21st century alchemy process, turning data from digital exhaust to digital gold. Big data 1.0 had limited inputs and analytical tools, held back by high costs. The end result was narrowly focused insights of limited value to specific industries. Big data 2.0 features sensors and connected devices that are vastly expanding the capture of data as infrastructure costs are dropping. These trends, combined with improved analytics, are producing powerful cost-effective use cases for big data across many industries. How the big data alchemy process works. Big Data 1.0 Big Data 2.0 @ @ @ IoT bridges physical & digital worlds, creating data explosion. INFRASTRUCTURE Storage is commoditized and costs drop. INFRASTRUCTURE DATA MANAGEMENT Computing power and speed increase. DATA MANAGEMENT ANALYTICS New data-driven insights lead to broader adoption. ANALYTICS 6
Big data 2.0 has applications for many industries, particularly as technology enables more data capture and analysis. Big data alchemy across industries Retail Financial Services Cybersecurity Energy Advertising Healthcare Agriculture Travel & Hospitality Key use cases driving big data adoption across industries Know the customer Detect fraud and improve security Increase operational efficiency Turn targets ads to consumers through multichannels based on real-time data. Real-time audience data in a single dashboard (1st, 2nd, 3rd party) Segment and target prospects where and when it matters Deliver the right message on the best media channel at the right moment in time Result: Increased ROI Pindrop Security identifies potential fraudsters by analyzing caller attributes and linking to fraud databases. Combines authentication and anti-fraud detection technology to verify legitimate callers while detecting malicious callers Ability to determine a caller s true location and calling device and match them to Pindrop fraud database Sight Machine uses sensors to collect data to maximize operations in real time. Big data solution for manufacturers Platform collects data from sensors, automation systems and other factory systems, analyzes it and delivers insights in real time Structured and unstructured data transformed into actionable reports 7
SVB Big Data Maturity Index: Finding opportunities for growth SVB Analytics created the SVB Big Data Maturity Index to analyze the pace of development of big data adoption across industries. We looked at three attributes that impact adoption: regulations on data collection, ease of data capture and level of technology integration and ranked whether these attributes enhanced or impeded adoption for each major industry. The higher the overall score indicates more developed adoption but that leaves a smaller opportunity for growth. The lower the score indicates underdeveloped data adoption but that leaves a bigger opportunity for growth, especially if it is a large industry. 9 Industry Level of Regulatory Oversight Ease of Data Capture Level of Technology Integration Maturity Index Advertising 3 3 3 3.0 Developed Travel & Hospitality 3 2 3 2.7 Cybersecurity 2 2 3 2.3 Retail 3 2 2 2.3 Energy 2 2 1 1.7 Big Data Adoption Attributes Enhances Neutral Impedes Healthcare 1 1 2 1.3 Financial Services 1 2 1 1.3 Agriculture 2 1 1 1.3 Underdeveloped 8
Large industries have significant untapped value in big data adoption. U.S. market size vs. SVB Big Data Maturity Index 10 Industry value ($B) Complex large-market industries, including financial services and healthcare, are underdeveloped when considering the potential big data adoption has for significant disruption and value creation. Big data strategies in these sectors have been slowed by difficulty of data capture and level of regulation. $1400 $1200 $1000 $800 $600 $400 $200 Large market size/ Underdeveloped Financial Services Healthcare Small market size/ Underdeveloped Agriculture Energy $- 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 Maturity Index Retail Cybersecurity Large market size/ Developed Small market size/ Developed Travel & Hospitality Advertising Big Data underdeveloped Big Data developed How big data adoption impacts VC investment. Distribution of venture capital deals by stage, 2008-2015 11 Advertising Big Data: Developed 100% 80% 60% 40% 20% Healthcare Big Data: Underdeveloped 100% 80% 60% 40% 20% 0% 0% 2008 2009 2010 2011 2012 2013 2014 2015 2008 2009 2010 2011 2012 2013 2014 2015 Early Mid Late Early Mid Late Venture investments are flowing from smaller market, more developed users of big data (advertising) to larger market industries (healthcare) that are only beginning to leverage big data infrastructure development of the last decade. 9
Big Data Next: The alchemy process reimagined As data infrastructure, management and analytical tools become commoditized and more commonly adopted, the value will shift back to the data. Owning or gaining access through partners to proprietary data, which is protectable and non-replicable, will be vital to maintain a competitive advantage. With the advance of machine learning, we are poised to see increasingly valuable insights derived from data and applied with profound results. The innovations of Big Data Next will enable game-changing advancements that are difficult for us to imagine right now. Big Data 2.0 Big Data Next Public or shared private data Proprietary data @ @ @ INFRASTRUCTURE The highest value will belong to proprietary data that can generate real-world solutions. DATA MANAGEMENT ANALYTICS 11
About Silicon Valley Bank For more than 30 years, Silicon Valley Bank (SVB) has helped innovative companies and their investors move bold ideas forward, fast. SVB provides targeted financial services and expertise through its offices in innovation centers around the world. With commercial, international and private banking services, SVB helps address the unique needs of innovators. Forbes named SVB one of America s best banks (2015) and one of America s best-managed companies (2014). SVB Analytics provides strategic advisory, research and valuation services. Learn more at svb.com/svbanalytics. 1 Dr. William P. Norton 2 The Wayback Machine 3 The Wayback Machine 4 DrPeering.net 5 Indeed.com 6 Pitchbook 7 Pitchbook 8 SVB Analytics proprietary data 9 SVB Analytics proprietary data 10 U.S. Department of Commerce, Gartner, emarketer 11 Pitchbook Silicon Valley Bank is the California bank subsidiary and commercial banking operation of SVB Financial Group (Nasdaq: SIVB), and a member of the FDIC. Silicon Valley Bank and SVB Financial Group are members of the Federal Reserve System. 2015 SVB Financial Group. All rights reserved. Silicon Valley Bank is a Member of FDIC and Federal Reserve System. SVB>, SVB>Find a way, SVB Financial Group, and Silicon Valley Bank are registered trademarks. B-15-14202 Rev. 08-11-15.