Wikibon is a professional community solving technology and business problems through an open source sharing of free advisory knowledge. The Big Data market as measured by vendor revenue derived from sales of related hardware, software and services reached $18.6 billion in calendar year 2013. Broken down by type, Big Data-related services revenue made up 40% of the total market, followed by hardware at 38% and software at 22%.
Both mega-IT-vendors and pure-play Big Data vendors took steps to better articulate their product & services roadmaps and larger visions for Big Data in the enterprise, creating greater confidence from enterprise buyers. The products and services related to Big Data continued to mature from a features perspective in 2013, further spurring adoption. Big Data technologies also took important steps towards greater enterprise-grade capabilities in 2013, critical for mass enterprise adoption. While the Big Data market experienced healthy growth in 2013 thanks to maturing technology and vendor support, barriers to adoption in the enterprise remain. A lack of best practices for integrating Big Data analytics into existing business processes and workflows. Concerns over security and data privacy in the wake of numerous high-profile data breaches and the ongoing NSA scandal. Continued “Big Data Washing” by legacy IT vendors leading to confusion among enterprise buyers and practitioners, as well as “political” factors that make it difficult for enterprise buyers to engage new vendors. A still volatile and fast developing market of competing Big Data vendors and, though to a lesser degree in 2013, competing technologies and frameworks.
Regarding methodology, the Big Data market size, forecast, and related market-share data was determined based on extensive research of public revenue figures, media reports, interviews with vendors, venture capitalists and resellers regarding customer pipelines, product roadmaps, and feedback from the Wikibon community of IT practitioners. Many vendors were not able or willing to provide exact figures regarding their Big Data revenue, and because many of the vendors are privately held, Wikibon had to triangulate many types of information to determine its final figures. Information types used to estimate revenue of private Big Data vendors included supply-side data collection, number of employees, number of customers, size of average customer engagement, amount of venture capital raised, and age of vendor.
It is critically important to understand how Wikibon defines Big Data as it relates to the market size overall and to revenue estimates for specific vendors in particular.
First, from a technology perspective, Wikibon defines Big Data as those data sets whose size, type, and speed-of-creation make them impractical to process and analyze with traditional database technologies and related tools in a cost- or time-effective way. Second, Wikibon believes Big Data requires practitioners to embrace an exploratory and experimental mindset regarding data and analytics, one that replaces gut instinct with data-driven decision-making, and exchanges stubbornness for a willingness to question long-held assumptions. Cloud-based Big Data services including infrastructure, platform and software delivers as a service.
YARN, or Yet Another Resource Negotiator, has been in the works for more than three years and made its official debut in October 2013 as part of Hadoop 2.0. Complimenting YARN were a number of moves by Hadoop and non-Hadoop vendors to better integrate the open source Big Data framework with existing data management infrastructure and legacy databases.
While each of these releases and features is still relatively immature, they served to bolster confidence in Hadoop and related Big Data technologies as a core part of the modern data architecture.
Market leader IBM, while slowing in overall Big Data revenue growth, did not sit on its lead. From the number two positions in terms of Big Data revenue, HP likewise took important steps to improve its Big Data portfolio in 2013, particularly its software portfolio. Professional services made up the largest segment of the Big Data market in 2013, with IBM, Accenture and HP leading in total professional services revenue for the year. There were a number of important improvements to Hadoop’s enterprise-grade capabilities around security, privacy and reliability in 2013.
Speaking of General Electric, the concept of the Industrial Internet came to the fore in 2013. Wikibon forecasts Big Data market growth to slow slightly in 2014 to 53%, reaching $28.5 billion for the year.
As the market matures through 2017 and beyond, Wikibon expects Big Data applications and cloud-based services to play an increasingly important role.
While Wikibon believes over the long term Big Data practitioners will generate significantly more value than Big Data vendors, there is significant opportunity for those vendors that can deliver Big Data solutions that speak to business rather than technical value. Finally, Wikibon believes the biggest growth inhibitors for the Big Data market are security and privacy concerns. Action Item: The value of Big Data is in its potential to help practitioners make better strategic and tactical decisions, run more streamlined and efficient organizations, and deliver better products and services to customers. As Big Data analytics will require broader and deeper data sources to model for predictive and prescriptive business insights, Is there a forecast that estimates the income value and growth potential for primary data providers? What's the reason for the difference between that and the IBM value given here (other than the difference in year)? Created page with '====Introduction==== The Big Data market as measured by vendor revenue derived from sales of related hardware, software and services reached $18.6 billion in calendar year 2013. Last year IBM established the OpenPower consortium and opened up Power8 processor specifications and architecture to its partners. For years the Power architecture and the Power processors remained proprietary IBM technologies used for high-end servers and mainframes. IBM licenses the Power architecture and some other essential technologies to its allies from the OPC.


IBM, Google, Tyan, Nvidia and Mellanox are the founding members of the OpenPower consortium.
Since the Power technology is new for everyone outside IBM, the company faces an uphill battle in getting server makers to move to Power, according to Nathan Brookwood, principal analyst at Insight 64. Nonetheless, thanks to the fact that so many companies have already joined the OpenPower consortium, the Power technology may have a chance.
KitGuru Says: Keeping in mind that several large consumers of servers – Amazon, Google, Facebook, Microsoft, etc. While some small businesses are likely to continue buying hardware, look for the pace of cloud migration to accelerate as more and more business applications become cloud-ready. They’ve heard the benefits of cloud server hosting: flexibility, cost savings and access to data for employees wherever they are. For some companies — startups, boutique operations, virtual organizations or those with employees dispersed around the globe – there seems to be little question that a cloud based-environment is the way to go. And, with the emergence of the trend toward BYOD (bring-your-own-device), these SMBs are wise to consider that their employees use mobile apps on their smartphone and rely on cloud data to make them more productive.
But for the majority of SMBs considering a move to the cloud, the answer isn’t always so straightforward.
At that point, they must do the numbers and compare the cost of moving to the cloud versus buying new hardware plus the associated costs of internal IT and maintenance. Such a breakdown is due in part to the open source nature of much Big Data software and related business models of Big Data vendors, as well as the need for professional services to help enterprises identify Big Data uses cases, architect solutions and maintain performance. These include the advent of YARN, which lays the foundation for Hadoop as a true multi-application framework, and the continued evolution of cloud-based Big Data services for large-scale analytics and application development. These steps included better privacy, security and governance capabilities, as well as improved backup & recovery and high-availability for Hadoop specifically. Of particular importance are a number of reseller agreements and technical partnerships between Big Data vendors and non-Big Data vendors, the results of which that make it easier for practitioners to adopt and integrate Big Data technologies. This list includes both Big Data pure-plays – those vendors that derive close to if not all their revenue from the sale of Big Data products and services – and vendors for whom Big Data sales is just one of multiple revenue streams. We also held extensive discussions with former employees of Big Data companies to further calibrate our models. Projects whose processes are informed by this mindset meet Wikibon’s definition of Big Data, even in cases where some of the tools and technology involved may not. The concept of Hadoop-based Big Data analytics and applications moving beyond MapReduce-style batch analytics existed before 2013, but this was the year that the structural foundation to such a transition was laid in the form of YARN.
While the technical architecture of YARN is outside the purview of this report, the important point is that YARN enables Hadoop to operate as a true multi-application framework. It ensures that Hadoop will not be relegated to backroom data science projects but will take a prominent (and potentially starring) role in the modern data architecture. As stated above, in 2013 vendors began to crystalize their visions for Big Data in the enterprise. This confidence translated into significant investment by Fortune 1000 enterprises in 2013, though the fruits of these investments won’t be enjoyed until 2014 and beyond. The company released several important Big Data-related products and services in 2013, including PureData System for Hadoop, BLU Acceleration, BigSQL and the Watson Developer Cloud. Namely, the company unveiled HAVEn, a modular reference architecture designed to integrate (and make more consumable to enterprise customers) Hadoop, Vertica, Autonomy, enterprise security and application development. Amazon Web Services released Kinesis, a streaming data framework for real-time applications, and RedShift, its large-scale data warehousing service.
These include WANdisco’s Non-Stop NameNode for high availability that removes Hadoop’s single-point-of-failure, IBM’s InfoSphere Data Privacy for Hadoop offering that de-identifies sensitive data in Hadoop, and Sqrrl Enterprise that enables cell-level security in Hadoop via Accumulo. In addition to analyzing data from individual pieces of equipment, the larger goal of an Industrial Internet (sometimes called the Internet of Things) is to orchestrate multiple industrial applications to work intelligently in order to optimize entire operational environments.
Tableau Software went public in 2013 and racked up impressive customer wins, revenue growth and partnership announcements in its first few months as a publicly traded company. Looking ahead, the Big Data market is currently on pace to top $50 billion in 2017, which translates to a 38% compound annual growth rate over the six year period from 2011, the first year Wikibon sized the Big Data market, to 2017. As the underlying infrastructure solidifies, Wikibon believes mainstream and late-adopters will look to service providers to deliver polished applications and services that sit on top the hardened Big Data infrastructure and target specific, high-value business challenges. The NSA revelations clearly illustrate that data security and privacy are hot button topics for both the American and international public.
Vendors would be wise to remember that it is such business value, not technology features per se, that will drive revenue in this market. He loves to write about Big Data and the Internet of Things, and explore how these technologies are evolving and helping businesses to become more agile.
The goal of the organization is to create an eco-system of Power8-based servers designed for future data centers and cloud computing. As the world is moving away from proprietary solutions to industry-standard x86 servers based on processors from Intel, it became impossible for IBM to popularize Power itself.
Samsung, Altera, Micron, SK Hynix, Hitachi and many others are also members of the organization. Server manufacturers have invested a lot in x86 hardware and software, hence, they are pretty reluctant to move to anything new.


Self-reliance runs deep, which is why many small to midsize businesses (SMBs) still manage their own servers. What’s behind the decision to remain with an in-house solution or embrace cloud server hosting? The same goes for companies with high internal IT costs or those requiring more compute power. These applications simply didn’t exist a few years ago, and their growth and benefits are undeniable.
Smart owners and managers of SMBs who have existing applications running on old servers must weigh their options.
Before investing in new equipment, they must determine the length of time need to achieve ROI on those servers – and compare that to the monthly cost of cloud server hosting.
Developers now have the structural underpinnings to build real-time and streaming data applications, interactive SQL-style query applications, graph analytic apps, and more. The pending arrival of YARN, among other technology advances, enabled vendors to credibly position Hadoop at the center of their Big Data plans. The company also acquired SoftLayer, which will play an important role in IBM’s Big Data from the cloud strategy. While Big Data technologies continued rapid maturation in 2013 (as explained above), enterprise practitioners still require significant help from professional services organizations to identify use cases, design and deploy systems, and integrate the technology and output (read: analytic insights) into business processes and workflows. Meanwhile, Hortonworks and Microsoft released HDInsights, which delivers HDP on Microsoft’s Azure cloud. Industrial Internet will play an increasingly important role in the world of Big Data analytics. This, despite the public’s relative lack of understanding about just how much personal data is available on the web and how it often unwittingly provides this data with the click of a button. In order to propel the Big Data market forward and entice early mainstream adopters, Big Data vendors must align not just their marketing messages but product roadmaps to this reality. This applies to both the Standard Edition (which is a new edition also announced earlier this month) and Enterprise Edition. But this is a good illustration of the intersection of these areas with Big Data and how they all have an important role in modern approaches to managing enterprises and governments. I'm particularly interested in understanding the "component(s)" (i.e services) associated with each big data vendor. Before joining SiliconANGLE, Mike was an editor at Argophilia Travel News, an occassional contributer to The Epoch Times, and has also dabbled in SEO and social media marketing. As a result, it formed the OpenPower consortium (OPC) with the aim to build advanced server, networking, storage and GPU-acceleration technology aimed at delivering more choice, control and flexibility to developers of next-generation, hyperscale and cloud data centers. ARM architecture (which is available for licensing too and which can power custom system-on-chips) will probably be capable to address only low-end server space, whereas Power can offer high performance and an opportunity to customize.
That said, the times, they are a-changin’ and, increasingly, SMBs are looking to move to the cloud; to cloud server hosting. An organization reaches that fork in the road because it has pushed its servers to the end of their useful lives and now need to do something about it.
They’ve may have even read that some SMBs are losing money as a result of ineffective IT management, and that they could avoid that fate by using a cloud server. Increasingly, the numbers favor of the cloud — and numbers are just part of the equation.
It is still very early days for Big Data in the cloud, however, with the vast majority of current use cases focused on test-and-development, not production deployments. The current ire directed at the NSA is likely to turn its attention to the commercial sector in 2014, as the public comes to better understand how social networks, retailers, banks and other businesses are using its data. IBM priced the BigInsights by storage capacity (RVU,TB), but the other Hadoop players price their SW by nodes, which would be reasonable, why IBM chooses storage price metric? He usually bases himself in Bangkok, Thailand, though he can often be found roaming through the jungles or chilling on a beach. IBM is not concerned that non-IBM Power-based machines will eventually be able to compete against its own high-end System-Z and other servers. While cloud service providers (CSPs) don’t provide identical services at a uniform cost, most SMBs can query various CSPs to determine the average cost per month per user to move their existing applications to cloud servers.
Savvy SMBs are recognizing that their incumbent (that is, not-yet-virtualized) applications will become out of date all too soon.
The future for Big Data deployments is clearly hybrid, with cloud and on-premise deployments living (hopefully) in harmony. As Wikibon has urged before, it is critical for the industry to be proactive and address these concerns sooner rather than later.
And when that day arrives, their best option may well be to migrate to cloud-based applications.



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