API management is a necessary but boring practice. As developers make use of a mix of public cloud, purchased or open source libraries, and homegrown services, the number of APIs used by developers quickly renders pouring through documentation impractical.
Microservices, usually accessed via RESTFul APIs, cause API calls to rapidly proliferate. Even modest-sized microservices-based systems experience API overload quickly. Agile development can exacerbate the problem of understanding and using APIs. The rapid pace of Agile, especially Scrum, leaves little time for proper documentation of APIs. Documentation often takes a back seat to continuous deployment. Continue reading “As API Management Problem Grows, Informatica Jumps into the Market”
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: Continue reading “28 Hours as an Industry Analyst at Strata Data”
Amalgam Insights (AI) recently attended Informatica World 2017, where executives, partners, and customers provided backing for Informatica’s ability to support “The Disruptive Power of Data,” (an Informatica-trademarked phrase) as well as its positioning as the Enterprise Cloud Data Management leader.
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