Delta-V Awards - 2016 Edition - 15 to 11

If you're just joining us now, please see the previous post for winner numbers 20 to 16. We'll continue to recognize the top 20 companies that accelerated the big data and analytics marketplace this year with a countdown from 15 to 11 here:

Topics: Big Data Data Management Data Analytics

Delta-V Awards - 2016 Edition - 20 to 16

It's that time again. Time to reflect on the year that was. And what a year it was for big data and analytics, with many massive advances in the technologies that power our understanding of the world around us. I don't know about you, but I certainly need a better understanding of the world. Thankfully, the industry continues to deliver innovations and gives me plenty of choices for recognizing those companies whose efforts are changing the velocity of insights. An introduction to the Delta-V awards and past winners can be found here. 

Topics: Big Data Data Management Data Analytics

Why Machine Learning is the Future of Big Data

Just as big data has emerged to heavily disrupt traditional databases and data warehouses, machine learning will be the next big wave of advancement in data management. Why, you ask? There is a simple, one word answer for you, "economics." They say any innovation has to be 10x better, faster, or cheaper to overcome the inertia of a traditional approach in IT. Apache Hadoop made it at least 10x less expensive to house data by distributing it across commodity hardware using open source software. Of course, there were (and still are) some rough edges and hidden costs, but this was compelling enough to get significant market traction versus legacy hardware and software. Weirdly, the core utility of Hadoop distributions has morphed towards being utilized mostly as a storage layer, with an ecosystem of other tools building analytics value above it.

Topics: Big Data Data Management Data Analytics machine learning

IT Operations Analytics from the Source


One of the top uses of big data today is IT operations analytics. This makes sense. By nature, IT components are designed to log all of their many status messages, and this information is generated with debug, tracking, and audit purposes in mind. The aggregate output, however, can be a logistical problem in itself. For each device, some poor sysadmin has to decide what level of logging is desired, and then live with the consequences of that decision. Set the logging threshold to "errors only" and important context will be missing when it's time to diagnose an issue. Set the logging criteria to "everything" and staggering amounts of data will be generated, often too much to process, and certainly much of little or no value. Limit the time period to an hour or a day, and the key information may have been overwritten by the time it's needed, and then the problem will have to be recreated.

Topics: Big Data Data Management Data Analytics IT operations analytics

Getting It with Big Data

Long time readers will know I'm passionate about customer service and fussy about bad marketing. Nothing gets my goat faster than the call center agent who can't or won't help answer a question because it's off script. Except maybe the marketing department that clearly has no clue who I am.

Topics: Big Data Data Analytics

Data Analytics for Everyone at IBM Edge

We live in an unsettling time for enterprise storage technology. It may not be too much of a stretch to say that the future of storage technology is more uncertain now than it has been in the past 10 or 15 years. Emerging technology trends such as the cloud and big data analytics, along with the storage innovations of software-defined storage (SDS) and solid-state are transforming the information technology landscape as we speak.

While multiple, if not all, technology providers have identified this transition, execution strategies differ. Some providers focus on the infrastructure, designing the best storage technology and features to prepare for the pending onslaught of data. Other providers focus on the expanded ecosystem, understanding that organizations may need technology in multiple locations, on- and off-premises. IBM takes these ideas one step further, not just focusing on bridging the old world of IT to the new, but striving to help businesses do more by layering advanced data analytics in an effort to achieve new and valuable insight.

Topics: Data Analytics IBM Edge

Video Blog: Swimming in the Data Lake

Big Data is quickly evolving from being a buzzword to becoming a critical IT initiative. Companies are starting to realize the benefits of analytics, identifying actionable insight and achieving competitive advantage.

The debate is no longer about whether big data analytics provide value; it is about how to design the right architecture to maximize the potential of analytics. Some solutions recommend higher performing storage closely tied to compute, others recommend larger pools, or data lakes, of disparate data that can collect content from multiple sources. In the end, it is a question of whether to move the data to the analytics, or move the analytics to the data.

Topics: Storage Big Data Data Analytics

Big Data 2015 Predictions: Right So Far!

At the start of the year, about 6 weeks way back, I made a number of predictions about the big data, business intelligence, and analytics market. See my previous posts herehere, and hereWhile it may seem early to measure the results for the current year, from this collection of recent vendor announcements it sure looks like the accuracy will be proven out! 

Topics: Big Data Data Analytics business intelligence

How to Choose the Right Big Data Infrastructure

I get asked all the time about the "best" option for deploying big data. Most people are curious about the impact of choices between:

Topics: Big Data Data Analytics

AWS re:Invent(ing) IT, Business Models and Marketing

AWS held its 3rd annual re:Invent user conference with 13,500 attendees in Las Vegas. While the event had similarities to other IT conferences that included an expo floor, keynotes, and sessions I was fascinated by, AWS has the potential to disrupt the way businesses consume IT, IT organizational structure, high margin IT infrastructure business models, and product marketing. 

Topics: Cloud Computing cloud Data Management marketing Data Analytics