Category Archive

Big Data Perspectives

DataOps: New Term, Similar Concepts

Componentization, containers and the cloud have all converged to usher in a new era focused on “Ops.” It started with DevOps, which according to Wikipedia is defined as: “a software engineering culture and practice that aims…

Regulatory Compliance as a Strategic Weapon – conclusion

Last week we looked at The Ever Evolving Regulatory Environment and the need for a regulatory compliance architecture. If you didn’t see the first blog post on the topic, read it here. Part 4: Meeting Critical…

Regulatory Compliance as a Strategic Weapon

Guest post by Philip Gilligan, Sand Hill East LLP We have seen an extraordinary array of regulations in the last 8 to 10 years, globally as well as specifically in the US and EU. Starting with…

Healthcare and Artificial Intelligence: Saving Lives and Costs

Last week we looked at how the healthcare industry (and vendors) would enjoy financial advantages of using Artificial Intelligence (AI), computer vision, IoT and more. There’s no doubt AI is changing the world in ways both…

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It feels like 1999 all over again, when Y2K was looming and IT pros were pulling consecutive all-nighters to get ready for it. GDPR, which is a set of rules under which the EU strengthens data protection for approximately 750 million people, is more than cleaning email lists and landing pages.

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Last week we looked at industry trends that have contributed to the creation of market demand for public cloud solutions, what’s in the cloud analytics stack and Amazon integration pairs. If you missed it, check out part one first.

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Today’s leading cloud platforms include numerous components for storing, processing and analyzing large volumes of data.  All the basics are there: storage, analysis and processing, streaming data processing, data pipelining, data warehousing, BI and even AI. But while it’s great to have all those raw components, how do you tie them together into a comprehensive architecture? While the parts are great, they still must be assembled into the whole.

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The May 25, 2018 deadline for the General Data Protection Regulation (GDPR) is almost upon us. And the question many in management are asking is: Are we ready?

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Having spent some time in the retail business, specifically apparel, and at a company that focused on helping e-retailers, I have an appreciation for the challenges these organizations face. With the entire retail world turned upside down from the pure-play e-commerce giants like Amazon, many incumbents are trying to transform themselves in this new digital era.

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The insurance industry, in particular the property and casualty, life and annuity, and re-insurance sectors, is fraught with very interesting data and analytics challenges. While there is vast potential for big data and advanced analytics such as Artificial Intelligence (AI) and Machine Learning (ML), data challenges often hold insurance carriers back from fully exploiting this potential.

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Many CEOs see Artificial Intelligence (AI) and Machine Learning (ML) as a key component to gaining competitive advance in their respective marketplaces. A 2017 survey of Fortune 500 CEO’s found that 81% of the respondents listed AI/Machine Learning as a “critical area of investment”, ranking just behind Cloud and Mobile Computing (91% and 86% respectively).

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As always, one always learns something new at the Gartner Data and Analytics Summit (the 2018 North America version held last week in Grapevine, Texas). I attended a fascinating session with two of Gartner’s most knowledgeable analysts – Mark Beyer and Adam Ronthal – on modern data architectures. In this case, it was not so much learning something new, but rather being reminded of a concept I had used before.

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