Big data hype (and reality)
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Presentation on "BIG DATA HYPE (AND REALITY)" made as a task for the internship on "DATA ANALYTICS WITH MANAGERIAL APPLICATIONS" under Professor Sameer Mathur, IIM Lucknow. Submitted by TARANG JAIN,DTU
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Big data hype (and reality)
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This presentation analyzes the HBR Article on "Big Data Hype (and Reality)" by Gregory Piatetsky-Shapiro. It emphasizes on the slow improvement of the technology, but in the end provides the areas where big data is useful.
Analysis of "Big data hype and reality - Gregory Piatetsky-Shapiro"
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Big data hype
1. The document discusses the hype around big data and its limitations for prediction.
2. While big data has led to some improved predictions in areas like movie recommendations, customer churn, and ad click-through rates, the accuracy remains limited, with most predictions being incorrect.
3. Managers should not rely entirely on big data and recognize its predictive gains are small, requiring combination with other methods.
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Predicting, managing and profiting from new technologies is one of the most important challenges that business leaders face.
It requires them to integrate a hugely diverse range of perspectives in a meaningful way: they must balance the insights of technology specialists with those of consumer experts, they must understand the related technologies that will determine a new launch’s success, and they must predict the moves and motivations of all of the players behind those technologies.
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Gli amministratori delgati di molte grandi aziende sono oramai concordi nell'affermare che la tecnologia è il più importante fattore esterno che può impattare sul business. Ma non tutte le innovazioni si rivelano "innovative" come sembrano inizialmente. I Leader devono saper distinguere fra innovazioni dirompenti e "distrattori" di risorse. L'innovazione supportata dall'ecosistema può evidentemente fare la differenza: come riconoscerla?
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Big data hype (and reality)
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Big data hype and reality
This document discusses the potential and limitations of big data analytics for predicting consumer behavior. It notes that while big data offers unprecedented insights into consumer actions and attitudes, human behavior remains inconsistent, impulsive, and dynamic, limiting the ability to completely predict outcomes. Marginal gains may be possible, but breakthroughs will be elusive as long as human behavior is unpredictable. It argues that while big data analytics can improve predictions in some domains like web advertising and improve services from companies like Google, predicting something like who will buy a candy bar remains very difficult due to the role of human whims.
Big data hype (and reality)
Big data provides an unprecedented opportunity to predict consumer behavior through the longitudinal and cross-sectional analysis of vast time series data. However, the inherent randomness of human behavior poses a limiting factor, and while marginal gains can be made through big data, breakthroughs may remain elusive as long as human behavior stays inconsistent, impulsive, and dynamic. The biggest impact of big data will be creating new areas like personalized medicine, improved customer service, and powering artificial intelligence through vast data analysis to understand and anticipate human behavior.
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Big data has received a lot of hype but its potential for improving predictions of human behavior is limited. While marginal gains can be made, major breakthroughs are unlikely because human behavior remains inconsistent, impulsive, and dynamic. The biggest impacts of big data will be in creating new areas and applications, such as location-based services, healthcare, and artificial intelligence. Managers should recognize that data cannot fully predict changing human behavior and ensure their teams use analytics appropriately.
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Big data hype (and reality)
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2. While big data has led to some improved predictions in areas like movie recommendations, customer churn, and ad click-through rates, the accuracy remains limited, with most predictions being incorrect.
3. Managers should not rely entirely on big data and recognize its predictive gains are small, requiring combination with other methods.
Demystify the future - see beyond the hype
Predicting, managing and profiting from new technologies is one of the most important challenges that business leaders face.
It requires them to integrate a hugely diverse range of perspectives in a meaningful way: they must balance the insights of technology specialists with those of consumer experts, they must understand the related technologies that will determine a new launch’s success, and they must predict the moves and motivations of all of the players behind those technologies.
Demystify the future: see beyond the hype
Gli amministratori delgati di molte grandi aziende sono oramai concordi nell'affermare che la tecnologia è il più importante fattore esterno che può impattare sul business. Ma non tutte le innovazioni si rivelano "innovative" come sembrano inizialmente. I Leader devono saper distinguere fra innovazioni dirompenti e "distrattori" di risorse. L'innovazione supportata dall'ecosistema può evidentemente fare la differenza: come riconoscerla?
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http://www.specialistspeakers.com/?p=8367
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PCOS corelations and management through Ayurveda.
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
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Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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Big data hype (and reality)
- 2. BIG DATA OFFERS UNPRECEDENTED AWARENESS OF PHENOMENA — PARTICULARLY OF CONSUMERS’ ACTIONS AND ATTITUDES WHAT IS BIG DATA?
- 4. KEY INSIGHTS
- 5. DECISION-MAKERS WANT TO UNDERSTAND THE PATTERNS IN THE PAST AND THE PRESENT IN ORDER TO ANTICIPATE WHAT IS MOST LIKELY TO HAPPEN IN THE FUTURE.
- 6. LET US LOOK AT SOME CASE STUDIES:
- 7. #1. FILM RATINGS •NETFLIX ROUTINELY SERVES UP PERSONALIZED RECOMMENDATIONS TO CUSTOMERS BASED ON THEIR FEEDBACK ON FILMS THEY’VE ALREADY VIEWED. THIS IS A PREDICTION CHALLENGE. •THE ERROR OF NETFLIX’S ALGORITHM WAS ABOUT 0.95 (USING A ROOT-MEAN-SQUARE ERROR, OR RMSE, MEASURE), MEANING THAT ITS PREDICTIONS TENDED TO BE OFF BY ALMOST A FULL “STAR.” •WITH THREE YEARS OF EFFORT BY SOME OF THE WORLD’S BEST DATA MINING SCIENTISTS, THE AVERAGE PREDICTION OF HOW A VIEWER WOULD RATE A FILM IMPROVED BY LESS THAN 0.1 STAR.
- 8. #2. CUSTOMER ATTRITION •IF PREDICTIVE ANALYTICS DRAWING ON BIG DATA COULD ACCURATELY POINT TO WHO IN PARTICULAR WAS ABOUT TO JUMP SHIP, DIRECT MARKETING DOLLARS COULD BE EFFICIENTLY DEPLOYED TO INTERVENE, PERHAPS BY OFFERING THOSE WAVERING CUSTOMERS NEW BENEFITS OR DISCOUNTS. •THERE IS A LIMITING FACTOR TO PREDICTION ACCURACY FOR CONSUMER BEHAVIOR SUCH AS CHURN.
- 9. #3. WEB ADVERTISING •THE AVERAGE CTR% FOR DISPLAY ADS HAS BEEN REPORTED AS LOW AS 0.1-0.2%. •BEHAVIOURAL AND TARGETED ADVERTISING HAVE BEEN ABLE TO IMPROVE ON THAT SIGNIFICANTLY, WITH RESEARCHERS REPORTING UP TO SEVEN-FOLD IMPROVEMENTS. •BUT STILL, SEVEN-FOLD IMPROVEMENT FROM 0.2% AMOUNTS TO 1.4% — MEANING THAT TODAY’S BEST TARGETED ADVERTISING IS IGNORED 98.6% OF THE TIME.
- 10. RELEVANCE TO MANAGERS IN INDIA
- 11. The randomness inherent in human behavior is the limiting factor to consumer modelling success. Marginal gains can perhaps be made thanks to big data, but breakthroughs will be elusive as long as human behavior remains inconsistent, impulsive, dynamic, and subtle. But when an activity is driven by consumers’ whims, no amount of ingenuity can produce the ability to know what will happen.
- 13. Big data analytics can improve predictions, but the biggest effects of big data will be in creating wholly new areas. The success of facebook, twitter, and linkedin social networks depends on their scale, and big data tools and analytics will be required for them to keep growing. Big data will see its biggest and most important application in the realm of artificial intelligence.
- 14. So you should expect big data to have big impact. And you can bet that it will help machines interact more usefully with our unstructured, changing, and sometimes downright confused human ways. But if you’re counting on it to make people much more predictable, you’re expecting too much.