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From big data to big profits : success with data and analytics / Russell Walker.

By: Walker, Russell, 1972- [author.].
Material type: TextTextPublisher: New York, NY : Oxford University Press, [2015]Description: xxvi, 283 pages : illustrations ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9780199378326; 0199378320.Subject(s): Big data | Business -- Data processing | Management -- Data processingDDC classification: 658/.0557 LOC classification: HF5548.2 | .W2933 2015Online resources: Contributor biographical information | Publisher description | Table of contents only
Contents:
Part I. Examining Big Data and its value to firms : What is "big data"? : Scale: how big is big? How big will it become? ; Data creation: a measure of how fast data is generated ; Data storage: a measure of scale and the data we keep ; Data processing: a measure of how much data we use ; Data consumption: a measure of our demand for data ; Implications of scale in Big Data; Exploratory data analysis: considering all of the data ; Data organization and metadata ; Variety : using more that numerical data ; Velocity: leveraging data within its window of opportunity ; Viral distribution of data: social networks take front stage ; Availability of data alters decisions for the better ; Where is Big Data being created? -- Benefits of scale and velocity in Big Data: the movement to now! : Overcoming complexity through scale in Big Data ; Yelp and TripAdvisor: case studies in the creation of value through Big Data ; Value of information: risk reduction ; Data velocity is the new normal ; Automated data creation: a necessary byproduct of scale and velocity ; Human interactions with the Internet of things: wearable devices : Mastering velocity and scale: creating advantages with Big Data ; Merging high velocity and high scale in data ; Getting to high return on Big Data ; Success in a high velocity and high precision data environment -- Big Data expands with passive data capture : Active data capture ; Example of passive data capture at work ; Passive data capture ; Mobile platforms expand passive data capture ; Passive data capture will change the driving experience ; Passive data capture adds value to agriculture ; Valuable features of passive data capture ; Passive data capture is in the home of the future ; Passive data capture is transforming health care ; Trade-offs are inevitable when passive data capture is collected and leveraged ; Passive data capture raises privacy concerns -- Novel measures in market activity: direct vs. indirect measurements : Direct measurement by active data capture ; From micro to macro ; Indirect measurement by passive data capture ; Measurement of assets by leveraging Dig Data and data inverting ; Media measurement by third parties ; Measurement of health care providers ; Considerations in the use of direct and indirect asset measurement -- Precision in data: new possibilities for mass customization and location-based services : New sensors and mobile phone systems enable precision in location-based data capture ; Social networks enable measuring the previously immeasurable ; Precision in measuring human performance is here now ; Precision agriculture is changing decision-making in powerful ways ; Precision medicine and genomics enable personalized care ; High precision in customer data leads to mass customization ; Digital platforms enable increased precision in data capture ; Precision in data is critical to unraveling complexity -- Data fusion: combining data to produce economic value : Data availability in the real estate industry ; Zillow: a real estate innovator ; History of Zillow: data opens opportunities ; Zillow focuses on data fusion and data productization ; Zillow's data product innovations ; Success with data breeds competition and innovation ; Data comes in all forms ; Lessons from Zillow ; Mint.com transforms personal finance ; Fusing of data at Mint.com creates novel data views for users and vendors ; Lessons from Mint.com --
Part II. Success in leveraging Big Data : Strategies for monetizing Big Data : Keep the data proprietary ; Data strategy: trade data to business partners for shared benefits ; Data strategy: sell the data product (to a host of possible clients) ; Stat strategy make the data available (and even free) to many users ; Novel data creation in advertisement on digital platforms ; Origins of the marketplace ; Overview of data strategies and monetization strategies ; Multi-sided business models form to monetize data from digital platforms ; LinkedIn.com creates Big Data ; Lessons from LinkedIn on multi-sided business models -- Monetizing Big Data through productization and data inverting : The origins of Netflix as a disruptive innovator ; Blockbuster: a history ; Netflix cultivates Big Data on customer preferences ; Netflix forms a data exchange with customers ; Big Data and analytics enable Netflix's success ; The future of Netflix: data wars have begun ; Lessons from Netflix -- Impact of analytics and Big Data on corporate culture and recruitment : The rise of the data scientist ; A portrait of a data scientist ; Graduate programs in data science are available ; Benefits of functionally assigned analytical teams ; Challenges of functionally assigned analytical teams ; Benefits of centralized analytic teams ; A new organizational model: chief data scientist ; Maximizing the impact of data scientists -- Stimulating innovation through Big Data : Leveraging and re-leverage data dynamically ; New data fuels innovation ; Digital platforms enable innovation ; New data and digital platforms can change markets ; Innovation in health care ; Innovation through data requires a data laboratory for data creation ; Nest and building new digital platforms for innovations ; Digital platforms and the Internet of things fuel innovation ; Stimulating innovation with Big Data challenges ; Experimenting with data at the enterprise -- Disrupting business models with new data from location-based services : Big Data possibilities from cellular networks ; Passive vs. active data capture in location-based services ; Leveraging location for data monetization ; Location-based services ; Trends in location-based services ; Foursquare: using customer location data to guide you where to go ; Opportunities created by leveraging location-based data ; Foursquare example: gaining precision in user location data ; San Francisco vs. New York ; Lessons from Foursquare ; Strategy implications of using location-based data -- Protecting data assets : Privacy concerns ; Tracking and monitoring ; Who owns the data?: Data ownership raises many questions ; Data ownership differs for actively shared and passively captured data ; Privacy in aggregate data views -- Future trends in Big Data : Increases in automation for data capture, creation, and use ; Cloud computing makes Big Data possible for most firms ; Flexible analytical tools make Big Data processing possible to more firms ; Mobile platforms drive location-based data and services to new levels ; Analytical talent will be in short supply due to high demand ; Aggregation of digital platforms will become more common ; Digital platforms will reduce market inefficiencies through new data ; Autonomy, not just automation, will become more mainstream -- Getting started: SIGMA framework for implementing a Big Data strategy: from Big Data to big profits : Sources of data ; Innovation ; Growth mindset Market opportunities ; Analytics ; Big Data to big profits diagnostic: scoring an enterprise with the SIGMA framework for Big Data readiness ; Getting started on the path from Big Data to big profits.
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HF5548.2 .W2933 2015 (Browse shelf) Available 0000002100014

Includes bibliographical references and index.

Part I. Examining Big Data and its value to firms : What is "big data"? : Scale: how big is big? How big will it become? ; Data creation: a measure of how fast data is generated ; Data storage: a measure of scale and the data we keep ; Data processing: a measure of how much data we use ; Data consumption: a measure of our demand for data ; Implications of scale in Big Data; Exploratory data analysis: considering all of the data ; Data organization and metadata ; Variety : using more that numerical data ; Velocity: leveraging data within its window of opportunity ; Viral distribution of data: social networks take front stage ; Availability of data alters decisions for the better ; Where is Big Data being created? -- Benefits of scale and velocity in Big Data: the movement to now! : Overcoming complexity through scale in Big Data ; Yelp and TripAdvisor: case studies in the creation of value through Big Data ; Value of information: risk reduction ; Data velocity is the new normal ; Automated data creation: a necessary byproduct of scale and velocity ; Human interactions with the Internet of things: wearable devices : Mastering velocity and scale: creating advantages with Big Data ; Merging high velocity and high scale in data ; Getting to high return on Big Data ; Success in a high velocity and high precision data environment -- Big Data expands with passive data capture : Active data capture ; Example of passive data capture at work ; Passive data capture ; Mobile platforms expand passive data capture ; Passive data capture will change the driving experience ; Passive data capture adds value to agriculture ; Valuable features of passive data capture ; Passive data capture is in the home of the future ; Passive data capture is transforming health care ; Trade-offs are inevitable when passive data capture is collected and leveraged ; Passive data capture raises privacy concerns -- Novel measures in market activity: direct vs. indirect measurements : Direct measurement by active data capture ; From micro to macro ; Indirect measurement by passive data capture ; Measurement of assets by leveraging Dig Data and data inverting ; Media measurement by third parties ; Measurement of health care providers ; Considerations in the use of direct and indirect asset measurement -- Precision in data: new possibilities for mass customization and location-based services : New sensors and mobile phone systems enable precision in location-based data capture ; Social networks enable measuring the previously immeasurable ; Precision in measuring human performance is here now ; Precision agriculture is changing decision-making in powerful ways ; Precision medicine and genomics enable personalized care ; High precision in customer data leads to mass customization ; Digital platforms enable increased precision in data capture ; Precision in data is critical to unraveling complexity -- Data fusion: combining data to produce economic value : Data availability in the real estate industry ; Zillow: a real estate innovator ; History of Zillow: data opens opportunities ; Zillow focuses on data fusion and data productization ; Zillow's data product innovations ; Success with data breeds competition and innovation ; Data comes in all forms ; Lessons from Zillow ; Mint.com transforms personal finance ; Fusing of data at Mint.com creates novel data views for users and vendors ; Lessons from Mint.com --

Part II. Success in leveraging Big Data : Strategies for monetizing Big Data : Keep the data proprietary ; Data strategy: trade data to business partners for shared benefits ; Data strategy: sell the data product (to a host of possible clients) ; Stat strategy make the data available (and even free) to many users ; Novel data creation in advertisement on digital platforms ; Origins of the marketplace ; Overview of data strategies and monetization strategies ; Multi-sided business models form to monetize data from digital platforms ; LinkedIn.com creates Big Data ; Lessons from LinkedIn on multi-sided business models -- Monetizing Big Data through productization and data inverting : The origins of Netflix as a disruptive innovator ; Blockbuster: a history ; Netflix cultivates Big Data on customer preferences ; Netflix forms a data exchange with customers ; Big Data and analytics enable Netflix's success ; The future of Netflix: data wars have begun ; Lessons from Netflix -- Impact of analytics and Big Data on corporate culture and recruitment : The rise of the data scientist ; A portrait of a data scientist ; Graduate programs in data science are available ; Benefits of functionally assigned analytical teams ; Challenges of functionally assigned analytical teams ; Benefits of centralized analytic teams ; A new organizational model: chief data scientist ; Maximizing the impact of data scientists -- Stimulating innovation through Big Data : Leveraging and re-leverage data dynamically ; New data fuels innovation ; Digital platforms enable innovation ; New data and digital platforms can change markets ; Innovation in health care ; Innovation through data requires a data laboratory for data creation ; Nest and building new digital platforms for innovations ; Digital platforms and the Internet of things fuel innovation ; Stimulating innovation with Big Data challenges ; Experimenting with data at the enterprise -- Disrupting business models with new data from location-based services : Big Data possibilities from cellular networks ; Passive vs. active data capture in location-based services ; Leveraging location for data monetization ; Location-based services ; Trends in location-based services ; Foursquare: using customer location data to guide you where to go ; Opportunities created by leveraging location-based data ; Foursquare example: gaining precision in user location data ; San Francisco vs. New York ; Lessons from Foursquare ; Strategy implications of using location-based data -- Protecting data assets : Privacy concerns ; Tracking and monitoring ; Who owns the data?: Data ownership raises many questions ; Data ownership differs for actively shared and passively captured data ; Privacy in aggregate data views -- Future trends in Big Data : Increases in automation for data capture, creation, and use ; Cloud computing makes Big Data possible for most firms ; Flexible analytical tools make Big Data processing possible to more firms ; Mobile platforms drive location-based data and services to new levels ; Analytical talent will be in short supply due to high demand ; Aggregation of digital platforms will become more common ; Digital platforms will reduce market inefficiencies through new data ; Autonomy, not just automation, will become more mainstream -- Getting started: SIGMA framework for implementing a Big Data strategy: from Big Data to big profits : Sources of data ; Innovation ; Growth mindset Market opportunities ; Analytics ; Big Data to big profits diagnostic: scoring an enterprise with the SIGMA framework for Big Data readiness ; Getting started on the path from Big Data to big profits.

Reviews provided by Syndetics

CHOICE Review

Economists Barro (Harvard) and Lee (Korea Univ.) constructed a country-level data set on educational attainment in dozens of countries spanning the time period 1820-2010; a second data set, covering 1870-2010, tracks educational attainment by age group and gender. The authors use these data to describe the evolution of educational attainment over time--for the world as a whole and by region and the development stage of the country--and to project future education attainment. They forecast that by 2040 educational attainment will increase to 12.83 years (up from 11.94 in 2010) in advanced countries and to 10.20 years (up from 7.69 in 2010) in developing countries. Though the educational-attainment gap between developing and advanced countries will narrow by 2040, the authors forecast that the gender gap in educational attainment worldwide will disappear by 2040 and that female educational attainment will in fact be slightly higher than that of males. The authors also consider the relationship between a country's educational attainment and the growth rate of its GDP, fertility rates, and indicators of democracy. Finally, for a narrower time frame (1965-2010) they look at the relationship between GDP and the quality and quantity of a country's education. Though technical in places, the book includes easy-to-understand graphs and interesting empirical analyses. Summing Up: Recommended. Upper-division undergraduates through faculty and professionals. --Brian P McCall, University of Michigan

Author notes provided by Syndetics

<br> Russell Walker, Ph.D. is Clinical Associate Professor at the Kellogg School of Management, Northwestern University. Russell Walker has developed and taught leading executive programs on Big Data and Analytics, Strategic Data-Driven Marketing, Risk Management, and Global Leadership. He founded and teaches the popular Analytical Consulting Lab, which brings Kellogg MBAs together with real-world projects in Analytics and the use of Big Data. His is the author of the award-winning text Winning with Risk Management http://www.amazon.com/Winning-Risk-Management-Financial-Eng ineering/dp/9814383880 and had also authored many popular business cases. He received his Ph.D. from Cornell University, where he studied catastrophic risk analysis. He also holds an MS from Cornell University, an Executive MBA from the Kellogg School of Management of Northwestern University. Dr. Walker consults with firms on the topics of Big Data and Analytics, Risk Management, and International Business Strategy. He was named Top 100 Most Influential Individuals in the Big Data Landscape by Onalytica in 2016.<br>

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