The Death of Big Data and the Emergence of the Multi-Cloud Era

RIP Era of Big Data
April 1, 2006 – June 5, 2019

The Era of Big Data passed away on June 5, 2019 with the announcement of Tom Reilly’s upcoming resignation from Cloudera and subsequent market capitalization drop. Coupled with MapR’s recent announcement intending to shut down in late June, which will be dependent on whether MapR can find a buyer to continue operations, June of 2019 accentuated that the initial Era of Hadoop-driven Big Data has come to an end. Big Data will be remembered for its role in enabling the beginning of social media dominance, its role in fundamentally changing the mindset of enterprises in working with multiple orders of magnitude increases in data volume, and in clarifying the value of analytic data, data quality, and data governance for the ongoing valuation of data as an enterprise asset.

As I give a eulogy of sorts to the Era of Big Data, I do want to emphasize that Big Data technologies are not actually “dead,” but that the initial generation of Hadoop-based Big Data has reached a point of maturity where its role in enterprise data is established. Big Data is no longer part of the breathless hype cycle of infinite growth, but is now an established technology.
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Todd Maddox Explains Why Extended Reality (xR) Technologies Will Disrupt Corporate L&D

Research Fellow Todd Maddox, Ph.D. has just published a new Analyst Insight: Leveraging Learning Science: Why Extended Reality (xR) is Poised to Disrupt Corporate Learning and Development.

In this Analyst Insight, Todd Maddox, Ph.D. provides guidance on why Augmented and Virtual Reality are set to disrupt corporate learning. This report focuses on a learning science evaluation of the potential for extended reality (xR) technologies to disrupt corporate L&D and show how xR technologies have the potential to improve the quality and quantity of training, to accelerate learning and enhance retention in all aspects of corporate learning to provide the following benefits:

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Why Extended Reality (xR) is Poised to Disrupt Corporate Learning and Development – Part IV: xR Behavioral Skills Applications, and Recommendations

Note: If you missed Parts I, II, and III of this blog series, catch up and read

This is part of a four-blog series exploring the psychology and brain science behind the potential for extended reality tools to disrupt corporate Learning & Development.

xR and Behavioral Skills Learning: Whereas hard skills learning involves knowing what to do, behavioral skills learning involve knowing how to do it. People (aka soft) skills, such as the ability to communicate, collaborate, and lead effectively, or to show empathy and to embrace diversity, are behavioral skills. Similarly, technical skills, such as the ability to learning how to use new software, to upskill to a new software release, or to use and maintain a piece of hardware or equipment, are behavioral skills.

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28 Hours as an Industry Analyst at Strata Data 2017

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Companies Mentioned: Aberdeen Group, Actian, Alation, Arcadia Data, Attunity, BMC, Cambridge Semantics, Cloudera, Databricks, Dataiku, DataKitchen, Datameer, Datarobot, Domino Data Lab, EMA, HPE, Hurwitz and Associates, IBM, Informatica, Kogentix, LogTrust, Looker, < MesoSphere, Micro Focus, Microstrategy, Ovum, Paxata, Podium Data, Qubole, SAP, Snowflake, Strata Data, Tableau, Tamr, Tellius, Trifacta.

Last week, I attended Strata Data Conference at the Javitz Center in New York City to catch up with a wide variety of data science and machine learning users, enablers, and thought leaders. In the process, I had the opportunity to listen to some fantastic keynotes and to chat with 30+ companies looking for solutions, 30+ vendors presenting at the show, and attend with a number of luminary industry analysts and thought leaders including Ovum’s Tony Baer, EMA’s John Myers, Aberdeen Group’s Mike Lock, and Hurwitz & Associates’ Judith Hurwitz.

From this whirwind tour of executives, I took a lot of takeaways from the keynotes and vendors that I can share and from end users that I unfortunately have to keep confidential. To give you an idea of what an industry analyst notes, following are a short summary of takeaways I took from the keynotes and from each vendor that I spoke to:

Keynotes: The key themes that really got my attention is the idea that AI requires ethics, brought up by Joanna Bryson, and that all data is biased, which danah boyd discussed. This idea that data and machine learning have their own weaknesses that require human intervention, training, and guidance is incredibly important. Over the past decade, technologists have put their trust in Big Data and the idea that data will provide answers, only to find that a naive and “unbiased” analysis of data has its own biases. Context and human perspective are inherent to translating data into value: this does not change just because our analytic and data training tools are increasingly nuanced and intelligent in nature.

Behind the hype of data science, Big Data, analytic modeling, robotic process automation, DevOps, DataOps, and artifical intelligence is this fundamental need to understand that data, algorithms, and technology all have inherent biases as the following tweet shows:

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