A majority of organizations plan to increase spending on the applications, services, and infrastructure underlying big data initiatives, especially those larger in size and affiliated with the retail and health care verticals. This may be attributable to the fact that not only are many organizations that have been experimenting with big data pilot projects now moving into full-scale enterprise deployments, but also those more conservative organizations that have been waiting for more market maturity are now confident enough to proceed with their own initiatives.
Previously in this blog series we looked at business and technology goals for big data and analytics initiatives as described by the leaders of these projects at a wide variety of businesses today. The aim is to understand the state of the market and how people seek to drive value from the data naturally occurring in their businesses. While some organizations are truly on the cutting edge, others are taking a more conservative approach or are just now implementing new solutions. Today I’d like to look at some of the ways people actually measure the value of big data and analytics to their business.
I recently started a blog series based on research done by interviewing a number of leaders for big data and analytics initiatives. The first part in that blog series on leaders' perspectives on big data was specifically about business goals. This time we’re going to look a little more at the more technical objectives of big data, by which I mean technology goals.
For several years, there has been a tremendous demand for data scientists. Businesses and governments got really, really excited about all the possibilities from applying big data, and the data scientist was seen as the most critical role to make it happen.
IT spending optimism is at a three-year high, with more than half (56%) of the senior IT decision makers surveyed by ESG reporting that their organizations’ total investments in IT products, staffing, and services will grow in 2015, relative to 2014 budget levels. This equates to an average year-over-year increase of 2.82%, which jumps to nearly 6% among those with plans to boost spending. It is worth noting that only 5% anticipated a drop-off in the funds earmarked for 2015 technology purchases and projects.
Big data continues to fascinate business leaders across all industries. Yet few agree on a standard definition, much less a best approach to capitalize on the new opportunities and how to leverage new technologies. To better understand the differing perspectives, ESG undertook a series of in-depth interviews with the self-proclaimed leaders of big data initiatives at ten different companies. The goal was to establish a baseline of the most common strategies, as well as identify the philosophical and environmental differences that lead to more distinctive efforts.
In an earlier blog post, I discussed some architectural options in deploying a big data environment, including cloud vs. on-premises, and dedicated vs. shared infrastructure. In this post, I'll examine topics that may be even more divisive: open vs. proprietary software and commodity vs. purpose-built hardware. These choices seem to reflect personal philosophies as much as technological differences.
At a recent Analyst Day in San Francisco, Cloudera all but declared the company’s dominance of the core Hadoop distribution market. The case was made around three measures of success, namely: product strength, market results, and strategic alliances.
At a recent Analyst Day in San Francisco, Cloudera all but declared the company’s dominance of the core Hadoop distribution market. The case was made around three measures of success, namely:
When I was in college, my housemate Craig* justified his lack of tidiness with a theory he espoused as the "One Pile Method." In practice, this involved dumping all of his clothes, books, homework, sports equipment, and anything else he happened to be carrying right in the middle of his room upon entry. The argument was that anytime he needed anything, he knew right where to look—it had to be somewhere in that one pile. This was claimed to be highly efficient in terms of time and efforts.
The recent Strata + Hadoop World show in San Jose was again a fascinating cross-section of the larger big data and analytics market space. Watch our "man on the scene" video or read on below for some quick highlights. Note: the one-side flared collar is a fashion fad you saw here first!
The content delivery network (CDN) market largely appears to be a mature market that is dominated by Akamai due to the market’s apparent affinity for a high degree of physical network infrastructure and caching to minimize the impact of last mile issues. It’s therefore surprising to see the degree of attention focused on Instart Logic by some of Silicon Valley’s leading entrepreneurs and VCs. Instart Logic is a west coast application delivery startup with funding from Andreessen Horowitz, Greylock Partners, Kleiner Perkins Caufield & Byers, Sutter Hill Ventures, Tenaya Capital, and several notable Silicon Valley angel investors. This is the A-team when it comes to investors and a group whose attention is not easy to attract.
In order to assess IT spending priorities over the next 12-18 months, ESG recently surveyed 601 IT professionals representing midmarket (100 to 999 employees) and enterprise-class (1,000 employees or more) organizations in North America and Western Europe. All respondents were personally responsible for or familiar with their organizations’ 2014 IT spending as well as their 2015 IT budget and spending plans at either an entire organization level or at a business unit/division/branch level.
I'm a "big data" guy, in the broadest, most aggressively futuristic sense of the term. So when Tom Davenport opened the TDWI keynote saying he doesn't like the Kardashians and he doesn't like the term "big data," I was alarmed. Was this going to be another grandpa-style lecture on how business intelligence and data warehousing didn't need any of them new-fangled gizmos? You know the spiel, "Back in my day we did analytics on the way to school, for 10 miles, uphill both ways, in the snow, without shoes, and we were happy to do it!" When he started on his history of decision support analytics, I began to wonder if there there any earlier flights available out of Las Vegas that day....
In my first post of this blog series on big data deployment models, I discussed some of the fundamental choices enterprises must make and shared a somewhat tongue-in-cheek flowchart to help people think about their options on how to host a new big data environment.
Optimization is surfacing more frequently in the applications and tools markets. I was recently speaking with a vendor who competes in the content delivery network (CDN) market about their optimization technology. Consequently, I started thinking about optimization, its role in decision analytics, and how it is applied.