Copyright sight Consulting, All Rights Reserved. Founder and Principal 9sight Consulting,

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1 Big Information / Data New oil in old barrels? July 2015 Cape Town Dr Barry Devlin Founder & Principal 9sight Consulting Copyright sight Consulting, All Rights Reserved Dr. Barry Devlin Founder and Principal 9sight Consulting, Dr. Barry Devlin is a founder of the data warehousing industry, defining its first architecture in A foremost authority on business intelligence (BI), big data and beyond, he is respected worldwide as a visionary and thought-leader in the evolving industry. Barry has authored two ground-breaking books: the classic "Data Warehouse--from Architecture to Implementation" and Business unintelligence--insight and Innovation Beyond Analytics and Big Data ( in barry@9sight.com Barry has over 30 years of experience in the IT industry, previously with IBM, as a consultant, manager and distinguished engineer. As founder and principal of 9sight in 2008, Barry provides strategic consulting and thought-leadership to buyers and vendors of BI and Big Data solutions. He is an associate editor of TDWI's Journal of Business Intelligence, and a regular keynote speaker, teacher and writer on all aspects of information creation and use. Barry operates worldwide from Cape Town, South Africa. 2 Copyright 2015, 9sight Consulting 1

2 Agenda 1. Data as the new oil 2. Data like the Deepwater Horizon? 3. Oiling the wheels of the future with IT 3 Copyright 2015, 9sight Consulting Agenda 1. Data as the new oil An explosion of business opportunities 2. Data like the Deepwater Horizon? 3. Oiling the wheels of the future with IT 4 Copyright 2015, 9sight Consulting 2

3 Oil transformed 20 th century business; Will information/data do the same in the 21 st? 20 th Century: All business models transmuted by cheap oil Distance devalued Production costs reduced 21 st Century: All business innovation based on information & communications technology data is the new oil Time compressed Production costs minimized or eliminated Human interaction reduced Biz-tech ecosystem: Symbiosis between business and IT 5 Copyright 2015, 9sight Consulting Transport: peer-to-peer ride sharing goes data driven crazy Company valued at $40B plus $50B in sight Uber Series E venture financing up to $2.8B (Feb 2015) On top of $4B raised previously Data driven core business operation Data and comms key to business model Deep analytics drive & optimize business Cars available where needed Real-time demand-based pricing Highly disruptive to current transport model Convenience, availability, cost all data driven 6 Copyright 2015, 9sight Consulting 3

4 Agriculture: sowing the data driven seeds Monsanto acquires The Climate Corporation October, 2013 for $930million What did Monsanto get? Weather measurements from 2.5 million locations Forecasts from major climate models 150 billion soil observations to generate 10 trillion weather simulation data points Added to Monsanto s own extensive databases What is the business case? Counteract effects of climate change Improved yields (proven 5% over 2 years) and cost savings for farmers Lock in of farmers to Monsanto products I could easily see us in the next 5-10 years being an information technology company. CTO, Robb Fraley 7 Copyright 2015, 9sight Consulting Finance: data driven forex slashes customer fees Start-up TransferWise raises $58m in Series C funding round (Jan 2015) London-based firm, possible $1B valuation Pairs up currency buyers with sellers Avoids the need to actually convert money Saves on expensive foreign exchange fees Real-time data driven business model Potential to disrupt banking industry Banking relies on trust to charge high fees New models depends on relatively even flow of money between countries See also: CurrencyFair and others 8 Copyright 2015, 9sight Consulting 4

5 Agenda 1. Data as the new oil An explosion of business opportunities 2. Data like the Deepwater Horizon? Big data s governance, societal and economic impacts 3. Oiling the wheels of the future with IT 9 Copyright 2015, 9sight Consulting Big data potentially has explosive dangers for business and society All data vs. real information How much big data is trustworthy? Or even understood? Market segment of one vs. protecting privacy and preserving democracy How much should you know about one person? Productivity from algorithms and automation vs. impact on the economic system When does reduced production cost impact income and the consumer s ability to buy? 10 Copyright 2015, 9sight Consulting 5

6 Data is inherently hard to decipher big data only makes it more difficult External data is innately dirty Reflects providers viewpoints Often poorly documented Continuously changing meanings Missing, corrupted values and noise Data no longer necessarily = facts Correlation causation The long path from data to action Data information knowledge meaning decisions action Rationality and intuition both needed 11 Copyright 2015, 9sight Consulting How about marketing data gathered in your home? -that-watches-you-back-verizon-applies-ambient-actiondetecting-device-patent.shtml 12 Copyright 2015, 9sight Consulting 6

7 Algorithms and automation always replace jobs to drive down cost of production (capitalism) Travis Kalanick, CEO and founder of Uber, would replace human Uber drivers with a fleet of self-driving cars in a second Autonomous trucks could displace 4-8 million jobs in the US within 10+ years May 6, 2015, first self-driving truck on American road in Nevada See The ever reducing need for more advanced skills Researchers construct statistical model able to predict the outcome of 70% of U.S. Supreme Court cases Hong Kong-based venture capital firm Deep Knowledge Ventures adds AI program with equal voting rights to its board of directors See 13 Copyright 2015, 9sight Consulting Economics in a time of exponentially improving technology In the old/current model Work produces ever cheaper goods Income, to purchase goods, depends on work Leisure or meaning is earned through work In the forthcoming model Capital produces goods (via technology) Work is increasingly unavailable, poorly paid Lack of income to purchase goods Leisure and meaning become internalized See The capitalist model contains the seeds of its own demise 14 Copyright 2015, 9sight Consulting 7

8 Agenda 1. Data as the new oil An explosion of business opportunities 2. Data like the Deepwater Horizon? Big data s governance, societal and economic impacts 3. Oiling the wheels of the future with IT Big data demands that we rethink IT completely 15 Copyright 2015, 9sight Consulting The data architecture since the mid- 80s Two layers within the Data Warehouse 1. Enterprise data warehouse Reconciled data 2. Data marts What the users need fed from and separate to operational systems Data to run the business Created by the processes of the business Metadata Data An architecture for a business and information system, B. A. Devlin, P. T. Murphy, IBM Systems Journal, (1988) marts Data warehouse Enterprise data warehouse All data created within the enterprise (or partners) Operational systems 16 Copyright 2015, 9sight Consulting 8

9 The tri-domain model is the basis for a new data/ information architecture Process-mediated data Traditional operational & informational data Via data entry & cleansing processes Machine-generated data Output of machines and sensors The Internet of Things Human-sourced information Subjectively interpreted record of personal experiences From Tweets to Videos Structure/Context In-flight Human-sourced information Machinegenerated data Process-mediated data Stable Reconciled Timeliness/ Consistency Historical [ Data signifies well-structured and/or modeled and information is more loosely structured and human-centric.] Live 17 Copyright 2015, 9sight Consulting information pillars replace data layers One architecture for all types of information Mix/match technology as needed Relational, NoSQL, Hadoop, etc. Machinegenerated (data) Processmediated (data) Humansourced (information) Integration of sources and stores Instantiation gathers inputs Assimilation integrates stored info. Assimilation Context-setting (information) Data flows as fast as needed and reconciled when necessary No unnecessary storage or transformations Distinct data management / governance approaches as required Measures Transactional (data) Transactions Instantiation Events Messages 18 Copyright 2015, 9sight Consulting 9

10 From BI to Business unintelligence People: Rational thought and far beyond People make all decisions! Process: Logic predefined and emergent Decision making is a process Information: Data, knowledge and meaning Data/information is only the foundation People process information Not business intelligence Business unintelligence Amazon: Or 25% discount with code BIInsights25 19 Copyright 2015, 9sight Consulting New and vital responsibilities of technology providers (including IT) include: 1. Ensuring clean, valid information for decision making via data management and governance 2. A new focus on tools for the psychological and sociological aspect of decision making 3. Re-establishing data privacy and security as a foundation for all information processing 4. Deep engagement between IT and business to understand economic and social sideeffects of big data and the Internet of Things 5. Re-inventing business as a socially progressive force in line with its original intention: a social and organizational construct composed of, run by, and for (ordinary) people 20 Copyright 2015, 9sight Consulting 10

11 Thank You Dr Barry Devlin Founder & Principal 9sight Consulting Copyright sight Consulting, All Rights Reserved 11