Business/Data Analytics Organizational & Strategic Opportunities

What is Big Data Analytics? Volume Variety Velocity Volume: “More data cross the internet every second than were stored in the entire internet just 20 years ago.” McAfee and Brynjolfson 2012, p.63 Velocity: MIT used sensor data in Macy’s parking lots to estimate sales that day. P.63 Variety: social networks, sensors, mobile phones (we are now a walking data generator)

Big Data Analytics Defined Large structured and unstructured data sets Captured by sensors, web server logs, Internet clickstream data, social media activity reports, mobile-phone call records Examples include sales data, process operating data, spend data Require new forms of processing capability “Big Data Analytics” Process of examining Big Data using advanced technologies Data management Open-source programming Statistical analysis Visualization tools In-memory computing Data management (e.g., massively parallel-processing databases), Process of examining Big Data using advanced technologies Open-source programming (e.g., Hadoop, MapReduce), statistical analysis (e.g., sentiment analysis, time-series analysis) Visualization tools that help structure and connect data to uncover hidden patterns, anomalies, unknown correlations, and other actionable insights In-memory computing (IMC) (e.g., SAP’s HANA)

Drivers of Big Data Analytics Sophisticated consumers Savvy, technology equipped, connected and social media users Automation Scans, sensors, bar codes, kiosks, email, chat, blogs, social media... Monetization Experiences anonymized, packaged and sold Competition Fierce global competition

H. Watson: Business Intelligence Journal, 2012 vol. 16(1) “Analytics: Hype or Here to Stay” Analytics Has a Longer History than Many People Think Analytics Means Different Things to Different People Analytics Requires a Diverse Set of Skills There Is a Shortage of People with Analytics Skills Analytics Is Becoming a Competitive Requirement Analytics Is Overhyped but Is Here to Stay H. Watson: Business Intelligence Journal, 2012 vol. 16(1)

Gartner Technology Hype Cycle

Gartner Hype Life Cycle – Big Data

Importance of Big Data and Analytics Wall Street Journal 9/16/13 44% of CIOs consider Business Intelligence/Analytics as the top priority for technology spending 51% of the companies plan to increase spending on Business Intelligence and Analytics software this year McKinsey (2014) Considers Big Data as “The next frontier for competition” “The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.“

“The World’s Most Valuable Resource” Data and the New Rules of Competition The Economist: May 6-12, 2017

“Data Driven” Companies IBM’s evolution Hardware → Software → Consulting → Analytics Recorded Future originally funded by Google and In-Q-tel (CIA venture capital) forecasts evaluates geopolitical threats via web scraping and via machine learning Uber valuation of $68 billion pool of data linking supply (drivers) to demand (passengers) Tesla valuation 50.9 billion surpassing General Motors collected 1.3 billion driving miles of data (self driving algorithms) Sports teams Oakland A’s (Moneyball) FC Midtjylland

Partnerships, Mergers, and Valuations “Infonomics”: Gartner’s term for pricing methodologies IBM purchased the Weather Company for $2 bn Caesars Entertainment 2015 bankruptcy Most valuable asset ($ 1 bn) = data on its 45 million customers Data trading is complicated process Purchasing data alone will rarely provide a competitive advantage Data sharing in healthcare Swapping anonymous data for greater medical insight Data is “non-rivalrous” Can be copied and used by more than one user at a time

Big Data Opportunity Both the amount of data and the % of available data are growing. A large amount of this data is coming from machine to machine learning. Source: IDC’s Digital Universe Study, sponsored by EMC, December 2012

“Analytics: The New Path to Value” MIT research report: 30 industries, 100 countries Analytics is the differentiator for the top performing companies

Big Data Analytics – Organizational Benefits BDA potentially has many organizational benefits BDA represents a new way of running business i.e., converting data into insights and intelligence, delivered when and where they are needed to help companies make better strategic and operational decisions BDA can potentially transform all organizational aspects: Strategy and business model design Marketing and product development Operations and SCM HR / Talent Analytics

Ransbotham et al. MIT Sloan Mgmt Rev 2016 Capabilities via BDA Types of BDA Descriptive Analytics: analyzing the past and why Predictive Analytics: forecasting the future Prescriptive Analytics: modeling the optimal solution for a given situation Organizational Innovation via BDA Analytical Laggards Analytical Practitioners Analytical Innovators Ransbotham et al. MIT Sloan Mgmt Rev 2016

Big Data - Organizational Issues Despite the potential of “Big Data”, many organizations lack the ability to initiate and execute such initiatives BDA Issues generate from several sources: Uncertain value & ROI Lack of rare BI & analytical skills Organizational risks and concern for failure Competing areas for investment As such, most organizations remain: “BDA Laggards”

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