Datameer Blog

AI, Analytics and IoT, Oh My! What big growth potential you have!

The healthcare industry is expected to undergo tremendous change and growth due to the overwhelming amount of data available today to help organizations make better, more informed and timely decisions. Patients and medical practitioners are poised…

GDPR – There is a Silver Lining

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…

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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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While IDC forecasts tremendous payback from cloud investments and spending is estimated to grow at more than six times (17%) the rate of general IT spending (4%) through 2020, adoption forecasts are a little less clear — McKinsey reports that it hovers around 20 percent. Suffice it to say enterprises are taking their time, and for good reason, but it also means they’re stuck with environments and processes that are less than efficient until they adequately plan and execute their cloud transformation. It also means they’re giving the competition an advantage and risking higher support costs when business units go out on their own and buy cloud services.

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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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Digital transformation is disrupting the insurance industry with the digitization of underwriting at the heart of this change. Digitalization of underwriting can also enable a retail-style to reach new demographics that often prefer a more virtual experience.

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Over the past weeks, I’ve been covering some of the newest big data use cases we’re seeing emerge this year. If you haven’t already read parts 1-3 on blockchain, MiFID II and healthcare precision management, you may want to check them out or download all five use cases here. This blog will cover Personalized Omni-Channel Experience in Retail.

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