Channeling the data avalanche. [CyberWire-X]
Proliferation of data continues to outstrip our ability to manage and secure data. The gap is growing and alarming,especially given the explosion of non-traditional smart devices generating, storing, and sharing information. As edge computing grows, more devices are generating and transmitting data than there are human beings walking the planet.
High-speed generation of data is here to stay. Are we equipped as people, as organizations, and as a global community to handle all this information? Current evidence suggests not. The International Data Corporation (IDC) predicted in its study, Data Age 2025, that enterprises will need to rely on machine learning, automation and machine-to-machine technologies to stay ahead of the information tsunami, while efficiently determining and iterating on high-value data from the source in order to drive sound business decisions.
That sounds reasonable, but many well-known names in the industry are trying - and failing - to solve this problem. The struggle lies in the pivot from “big data,” to “fast data,” the ability to extract meaningful, actionable intelligence from a sea of information, and do it quickly. Most of the solutions available are either prohibitively expensive, not scalable, or both.
In this episode of CyberWire-X, guests will discuss present and future threats posed by an unmanageable data avalanche, as well as emerging technologies that may lead public and private sector efforts through the developing crisis. Don Welch of Penn State University and Steve Winterfeld of Akamai share their insights with Rick Howard, and Egon Rinderer from sponsor Tanium offers his thoughts with Dave Bittner.
Available Results
Generated results are saved to your library for reuse and search.
Choose Template
Pick the result you want. You can review provider and model before generating.
A concise first-pass summary for understanding the episode quickly.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Repeatable methods, frameworks, mental models, processes, and systems.
A dedicated inventory of concrete resources named in the episode.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Investment theses, assets, catalysts, valuation reasoning, time horizons, and risks.
Chronologies, actors, causes, consequences, turning points, and competing historical interpretations.
Policies, proposals, stakeholders, arguments, implementation constraints, and predicted effects.
Career paths, skills, hiring signals, workplace decisions, transitions, and limitations of the advice.
Behavioral mechanisms, biases, motivation, habits, emotions, interventions, and evidence limitations.
Economic mechanisms, incentives, indicators, market structure, forecasts, and uncertainty.
Leadership principles, team systems, organizational design, culture, feedback, and failure modes.
Audience, positioning, messaging, acquisition, retention, experiments, metrics, and failed approaches.
Teaching methods, learning strategies, practice, feedback, assessment, and effectiveness evidence.
Theses, premises, arguments, objections, values, thought experiments, and unresolved questions.
Communication patterns, conflict, boundaries, expectations, repair methods, and contextual limitations.
Books, papers, authors, courses, and other learning resources mentioned in the episode.
Repeatable methods, frameworks, mental models, processes, and systems.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Memorable statements and important claims with attribution and source context.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.