Big Data
Viewpoints
2023
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April:
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March:
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February:
2022
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December/January:
2022: The Year in Review
Look for These Developments in 2023 -
November:
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October:
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September:
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August:
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July:
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June:
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May:
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April:
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February:
2021
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December/January:
2021: The Year in Review
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November:
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October:
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September:
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March:
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February:
Social Media's 9/11 Moment?
Possible Future: The Data Divide
Archived Viewpoints
2020
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December/January:
2020: The Year in Review
Look for These Developments in 2021 -
November:
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October:
Possible Future: Automated Privacy
Possible Future: Big Brother -
September:
Where Data Regulation Meets Geopolitics
Big Picture: Regulatory Factors Shaping Big Data -
August:
Data Pipelines and Warehousing in the Cloud
Big Picture: The Rise of the Cloud -
July:
The Data Pandemic
Bigger Picture: Building a Data-Driven Culture -
June:
The Pandemic Crisis: Scenarios for the Future of AI and Automation
Scenarios Presentation: The Pandemic Crisis: Scenarios for the Future of Technology Development
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May:
The Pandemic Crisis: Key Forces That Will Shape the Future of AI and Automation
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April:
After the GDPR, What Is Next for the European Union?
Big Picture: Bias in Big Data -
March:
Update: Big Data in the Investment Industry
Big Picture: Big Data in Finance -
February:
2019
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December/January:
2019: The Year in Review
Look for These Developments in 2020 -
November:
Fractured Privacy Regulations in the United States
Deepfakes and Synthetic-Media Technology -
October:
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September:
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August:
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July:
Industrial IoT Platforms
Consolidation in the Analytics Market -
June:
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May:
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April:
Integrating Big Data into Decision Making
Big Data and Smart Grids -
March:
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February:
2018
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December/January:
2018: The Year in Review
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November:
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October:
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July:
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May:
Reflections on Cambridge Analytica and Facebook
Data-Driven Materials Development -
April:
Victory for the Incumbents: Big Data in Agriculture
Automating Recruitment -
March:
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February:
Quantum Computing for Big Data
Spectre, Meltdown, and Big Data
2017
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March:
Predictive Maintenance in Practice
Virtual Reality for Data Visualization -
February:
The Challenge of Digital Transformation
AI: The Next Phase for Financial Data
2016
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December/January:
2016: The Year in Review
Look for These Developments in 2017 -
November:
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October:
Data-Driven Manufacturing
Big Data as an Enabler for Strategic IT -
September:
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August:
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July:
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June:
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May:
Device-Based Deep Learning
Big Data for Building Brand Loyalty and Trust -
April:
Changing Data Laws in Europe
Big-Data Adoption: Avoiding Analysis Paralysis -
March:
Alluxio: Accelerating the Storage Layer
Big Data in High-Stakes Decisions -
February:
Spark: The Analytics Operating System
Data Integrity through Blockchains
2015
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December/January:
2015: The Year in Review
Look for These Developments in 2016 -
November:
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October:
People Analytics: Caution Required
Designed by Big Data, Fabricated by 3D Printing -
September:
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August:
Lessons from Big Data in Life Sciences
Opportunities from New Data Sources -
July:
Truth Detection
Watson Health Cloud: Challenges and Potential -
June:
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May:
Understanding Visual Data
Building Big-Data Capability: A GE Case Study -
April:
Industry 4.0 Shifts the Competitive Landscape
Real-Time Big Data -
March:
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February:
2014
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December/January:
2014: The Year in Review
Look for These Developments in 2015 -
November:
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October:
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September:
Big Pharma Shares Data
Privacy Fears Topple Big-Data Education Company -
August:
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July:
About Big Data
Big data refers to a collection of data sets whose volume, velocity, and variety overpower conventional relational databases. Big-data sets are large, change quickly, and contain many data types; for example, a big-data set may contain a mix of text, audio, video, and location traces. Big data has no specific size, because some organizations have the capacity to deal with greater amounts of data than others do before needing to invest in new data approaches. For example, large retailers and major financial institutions have been working with very large data sets for many years. Also, because of improvements in data storage, processing power, and communications, the "normal" size of data increases from one year to the next. In addition to exceeding the capacity of conventional systems, big data may enable novel solutions to previously inaccessible or difficult-to-solve problems. For example, during recent years, data-driven approaches have enabled rapid progress in machine translation—progress that far outpaces researchers' previous attempts to build purely rule-based machine-translation systems.
The digitization of many areas of life and the proliferation of digital devices are accelerating data growth. Estimates vary, but a reasonable consensus is that the amount of data in the world is now growing by 40% to 50% every year. Current sources of big data include financial transactions, medical records, social-network updates, GPS traces, surveillance images, energy-use telemetry, search indexes, genomic sequences, geophysical profiles, and astronomical observations. The topic of big data generates interest because stakeholders see opportunity to monetize flows of information, gain insights that are hidden in large data sets, build predictive models, combat information overload, and present highly relevant information to consumers, businesses, and governments. Challenges relating to big data include protecting individuals' privacy, ensuring information quality, sharing data between organizations, finding people with the skills necessary to analyze big data, and crafting technology architectures that can scale up.
Although some big data initiatives have been slower to pay off than organizations hoped, the growth of data and the accompanying increases in the power of statistical algorithms are very real phenomena that will transform many areas of business and society in the next ten years and beyond. For example, big data could help industrial companies optimize their processes and implement predictive maintenance, help health-care organizations create personalized treatments, and transform advertising from an intrusion into welcome advice. In addition, and in combination with artificial intelligence, big-data technologies could also automate decision making (analytics software already automates some online pricing, financial trading, and other processes). Whether big data will yield all these benefits is uncertain, but growth in data volume, velocity, and variety in the next ten years is almost inevitable. The key question is whether companies, governments, and individuals will be able to harness the opportunities that these data create.