The rise of analytics and big data presents a once-in-a-generation opportunity for organisations to put themselves at the cutting edge, to create a competitive advantage by developing a culture of analytical success within their organisation. Yet most seem unable to grasp the opportunity that is within their reach. Eric Hoffer
At most banks, data is stored in separate databases and data warehouses. Customer data is stored in marketing databases, fraud analyses are done on transactional data, and risk data is stored in risk data warehouses. Oftentimes even liquidity, credit, market, and operational risk data is stored separately as well. Bringing
In the first installment of this series on Hadoop, I shared a little of Hadoop's genesis, framing it within four phases of connectivity that we are moving through. I also stated my belief that Hadoop has already arrived in the mainstream, and we are currently moving from phases three of connecting people to phase four
So, you've heard the Hadoop hype and you are looking – or have already invested – into Hadoop. Maybe you have also realized some benefits from the Hadoop ecosystem. But now you want to maximize those benefits by using advanced analytics, or you might have heard about algorithms or machine learning libraries available
In the world of IT, very few new technologies emerge that are not built on what came before, combined with a new, emerging need or idea. The history of Hadoop is no exception. To understand how Hadoop came to be, we therefore need to understand what went before Hadoop that led to its creation. To understand
For many years companies have been working to increase their use of predictive analytics and to execute analytic models faster on increasingly granular and growing volumes of data. Recently, there has been a great focus on "faster" from a technology standpoint, as modelers seek to iterate quickly and fail fast on
Because every single Hadoop deployment, like the structure of a snowflake, is different. For the open source big data framework, there are different distributions from various Hadoop vendors and all implementations are, or should be, tailored for that specific organization’s needs. And given this infinite variety of Hadoop foundations, the analytics
I’ve been told I have rocks for brains before, but right now I have rocks on the brain – the kind that are millions of years old and contain precious stores of oil and gas. One reason I have petroleum on my mind is that I’ve just returned from Brazil, where
The US health care industry is always getting a bad rap. It takes heat for being too expensive or not efficient enough or just too complicated. We know we need it, and that living long and healthy lives requires it. But we also know we love to complain about it
Jill Dyché, internationally recognized speaker, author and business consultant, spends her days talking to businesses about big data – how they’re using it, challenges, successes, strategies, plans and more. What she’s hearing again and again from IT leaders is that they have to innovate with big data, move quickly and
