Kumar Srivastava
star Advisor
IDG Contributor Network
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Opinions expressed by ICN authors are their own.
Kumar Srivastava has spent his career building big data and analytics products as part of a diverse and broad application set such as social networking, online security, identity, reputation and trust management, online fraud and abuse, online search and advertising, digital platforms, mobile applications and monetization services.
Kumar has worked with organizations of all sizes, from Fortune 50 companies to midsize businesses and small startups, and he has been involved in research at Columbia University and has led products that have been honored as industry-defining by customers and analysts.
Srivastava holds several patents and regularly writes thought leadership articles about the convergence of big data, analytics, cloud, mobile and digital and its impact on and opportunity for entrepreneurship and innovation. He has been published in Forbes, Wired, Entrepreneur and other publications, and he wrote a book on APIs and platform product management.
The opinions expressed in this blog are those of Kumar Srivastava and do not necessarily represent those of IDG Communications Inc. or its parent, subsidiary or affiliated companies.
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Let's face it: downtimes are not only frequent, but expected. What is your company doing to to ensure speedy recovery and restoration of service when the inevitable occurs?
Planning for disaster recovery
How do leaders of enterprises plan for outages to minimize the impact on the users of all the individual service providers running their services on the enterprises' platforms?
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Container technology has a direct impact on the agility of a software development team and consequently have seen a huge increase in interest, adoption and usage.
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As image and data quality goes up – driven by better capture, transfer, processing and compressing technologies – the ability to store and process these images and video data easily becomes a competitive advantage.
Disaster recovery in the age of data and AI
To ensure that data is not lost, and outages are recovered from swiftly and efficiently, enterprises need to invest in high levels of redundancy in their infrastructure.
Developing leaders for managing AI-driven businesses
A long-term approach and investment is required to train the next generation of business leaders who can leverage the state of the art to make the best decisions for their customers, users, employees, stakeholders and shareholders.
To be AI-first, move beyond managing data warehouses
Enterprises need to define a path to data-maturity and that requires reshaping and reorganizing their data storage, description, maintenance and value generation processes, procedures, tooling and functions.
Changing the definition of software testing in the AI era
AI-driven software requires an updated approach to software testing.
The 'A' in AI needs to be for accountability
AI needs to be accountable.
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