1. Business/Data Analytics Organizational & Strategic Opportunities

2. 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)

3. 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)

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

5. 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)

6. Gartner Technology Hype Cycle

7. Gartner Hype Life Cycle – Big Data

8. 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.“

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

10. “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

11. 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

12. 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

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

14. 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

15. 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

16. 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”

17. Some Suggested Reading