We know data is omnipresent in our world; that’s no secret. By now, most are familiar with the “Vs” of big data – volume, variety, velocity and veracity. Organizations are finding innovative ways to harness big data for competitive advantage. But if you stop and think about it, what we are really
The telecommunications industry has dealt with big data for years, for example tracking individual wires into peoples’ homes and places of business. Detailed billing to track time and duration of each telephone call, even for local billing in the 1980s, introduced a huge influx of data to manage
Recently, various publications including the Times of India, the Wall Street Journal, Reuters and CNET (just to name a few) have been abuzz with news of IBM’s partnership with international automotive supplier Continental. On September 10, at the Frankfurt International Motor Show, IBM and
And the winner is..... InfoSphere Streams!
IBM has been around for a long time, in fact over 100 years. Throughout the decades, IBM has changed the way we live, work and play. Radically new innovations have transformed our world. For example, IBM’s hierarchical database was invented for the Apollo
During a recent conference, I had the privilege of speaking with clients from many different organizations about their big data challenges. Most were very excited and just starting down the path of harnessing its power. Tempering this excitement was a concern about the complexity of big data
Data management has undergone significant change ever since the introduction of online transaction processing systems (OLTP) some 50 years ago. The level of change during this period, however, has not been uniform. There have been times when data management technologies and products became
We’ve got a new zone on developerWorks, dedicated to big data and to architects and developers looking to build analytics applications to derive insight from that data. It turns out developerWorks was already covering big data to some extent, just not in a classic developerWorks “zone” format. And
Big data has its discontents. The backlash is a necessary reality-check in an otherwise vibrant arena. Often in this industry, when a technology is vogue, the hype can interfere with rational decision making, both among users and among solution providers.
Big data tends to focus on extreme scale.
The University of California, Los Angeles (UCLA), announced their work to develop a bedside early-warning system for brain pressure in traumatic brain injury patients. At the core of this system is InfoSphere Streams, which can ingest and analyze, in real-time, huge volumes of fast-moving data –
Cyberspace is today’s new battleground, and cyber security continues to be a top imperative for both enterprises and governments. This is where big data comes in. IBM® Security QRadar® uses big data capabilities to help keep pace with advanced threats and prevent attacks before they happen. It
“The hardest part of any journey is taking that first step.” - Unknown
No, Tony Robbins is not guest blogging today. And it’s a shame, too, because if he were, he would come up with plenty of inspiring quotes designed to help you overcome the perils of a big data journey. He’d motivate you to build
Can you tell that this:
Is the same as this:
This is a very simple application, and I still had to shrink the source code down to fit on the page.
What do you think is easier: typing in a few hundred characters or dragging a box onto a graph and dragging lines to connect it up? Do you know all
Closing the big data talent gap requires tackling the problem from both sides: the people and the technology. Adequately training the data scientists of tomorrow is an obvious and necessary step, but what about the non-data scientists? And what about the technology side? What can we do to make the
What if you could learn what’s happening as it’s happening, and use what you learn to change what happens next? Sounds like a futuristic sci-fi movie itself, doesn’t it? But it’s not only possible now, it’s happening - even in an industry that hasn't changed much in 50 years.
In Part I of this series, we looked at the key considerations for an analytic enterprise to stay competitive in today’s world, and in Part II we discussed how those translated into imperatives for a supporting big data platform. In Part III we covered how IBM applied those considerations and