Big Data Vendor Revenue and Market Forecast 2013-2017
From Wikibon
Contents |
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. That represents a growth rate of 58% over the previous year.
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%. 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.
Big Data Growth Drivers
Several important growth drivers fueled the Big Data market in 2013. They include:
- 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.
Big Data Adoption Barriers
While the Big Data market experienced healthy growth in 2013 thanks to maturing technology and vendor support, barriers to adoption in the enterprise remain. While not an exhaustive list, these barriers include:
Big Data Vendor Revenue
As part of its market-sizing efforts, Wikibon tracked and/or modeled the 2013 Big Data revenue of more than 70 vendors. 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.
The complete list is below:
Vendor | Big Data Revenue | Total Revenue | Big Data Revenue as % of Total Revenue | % Big Data Hardware Revenue | % Big Data Software Revenue | % Big Data Services Revenue |
IBM | $1,368 | $99,751 | 1% | 31% | 27% | 42% |
HP | $869 | $114,100 | 1% | 42% | 14% | 44% |
Dell | $652 | $54,550 | 1% | 85% | 0% | 15% |
SAP | $545 | $22,900 | 2% | 0% | 76% | 24% |
Teradata | $518 | $2,665 | 19% | 36% | 30% | 34% |
Oracle | $491 | $37,552 | 1% | 28% | 37% | 36% |
SAS Institute | $480 | $3,020 | 16% | 0% | 68% | 32% |
Palantir | $418 | $418 | 100% | 0% | 50% | 50% |
Accenture | $415 | $30,606 | 1% | 0% | 0% | 100% |
PWC | $312 | $32,580 | 1% | 0% | 0% | 100% |
Deloitte | $305 | $33,050 | 1% | 0% | 0% | 100% |
Pivotal | $300 | $300 | 100% | 15% | 50% | 35% |
Cisco Systems | $295 | $50,200 | 1% | 72% | 12% | 16% |
Splunk | $283 | $283 | 100% | 0% | 71% | 29% |
Microsoft | $280 | $83,200 | 0% | 0% | 63% | 37% |
Amazon | $275 | $70,000 | 1% | 0% | 0% | 100% |
Hitachi | $260 | $89,999 | 1% | 0% | 0% | 100% |
CSC | $188 | $14,200 | 1% | 0% | 0% | 100% |
CenturyLink | $175 | $13,757 | 1% | 0% | 0% | 100% |
$175 | $59,767 | 1% | 0% | 0% | 100% | |
Fusion-io | $173 | $401 | 43% | 90% | 0% | 10% |
NetApp | $167 | $6,450 | 3% | 73% | 0% | 27% |
Intel | $165 | $52,708 | 1% | 66% | 21% | 13% |
EMC | $165 | $23,222 | 1% | 74% | 0% | 26% |
Mu Sigma | $160 | $160 | 100% | 0% | 0% | 100% |
TCS | $157 | $11,570 | 1% | 0% | 0% | 100% |
Microstrategy | $144 | $576 | 25% | 0% | 68% | 32% |
Actian | $138 | $138 | 100% | 0% | 73% | 27% |
Booz Allen Hamilton | $125 | $5,850 | 2% | 0% | 0% | 100% |
Opera Solutions | $124 | $124 | 100% | 0% | 0% | 100% |
Red Hat | $109 | $1,437 | 8% | 0% | 78% | 22% |
Capgemini | $104 | $13,639 | 1% | 0% | 0% | 100% |
Informatica | $98 | $948 | 10% | 0% | 82% | 18% |
MarkLogic | $96 | $96 | 100% | 0% | 79% | 21% |
General Electric | $80 | $146,000 | 1% | 0% | 75% | 25% |
VMware | $80 | $5,950 | 1% | 0% | 79% | 21% |
Syncsort | $75 | $75 | 100% | 0% | 97% | 3% |
Cloudera | $73 | $73 | 100% | 0% | 53% | 47% |
SGI | $65 | $667 | 10% | 85% | 0% | 15% |
MongoDB | $62 | $62 | 100% | 0% | 56% | 44% |
Hortonworks | $55 | $55 | 100% | 0% | 73% | 27% |
DDN | $54 | $315 | 17% | 81% | 0% | 19% |
Guavus | $54 | $54 | 100% | 0% | 61% | 39% |
Alteryx | $48 | $48 | 100% | 0% | 90% | 10% |
1010data | $45 | $45 | 100% | 0% | 0% | 100% |
Rackspace | $42 | $1,520 | 3% | 0% | 0% | 100% |
TIBCO | $36 | $1,069 | 3% | 0% | 64% | 36% |
MapR | $35 | $35 | 100% | 0% | 77% | 23% |
Tableau Software | $33 | $206 | 16% | 0% | 76% | 24% |
Qlik | $30 | $467 | 6% | 0% | 73% | 27% |
Attivio | $29 | $29 | 100% | 0% | 62% | 38% |
Juniper | $28 | $4,669 | 1% | 82% | 0% | 18% |
DataStax | $26 | $26 | 100% | 0% | 85% | 15% |
GoodData | $26 | $78 | 33% | 0% | 0% | 100% |
Attunity | $23 | $30 | 77% | 0% | 74% | 26% |
Fractal Analytics | $19 | $27 | 70% | 0% | 0% | 100% |
Pentaho | $18 | $38 | 45% | 0% | 76% | 24% |
Datameer | $17 | $17 | 100% | 0% | 82% | 18% |
Couchbase | $17 | $17 | 100% | 0% | 71% | 29% |
Basho | $16 | $16 | 100% | 0% | 76% | 24% |
Kognitio | $15 | $15 | 100% | 0% | 47% | 53% |
Sumo Logic | $14 | $14 | 100% | 0% | 0% | 100% |
Jaspersoft | $14 | $34 | 41% | 0% | 64% | 36% |
SiSense | $14 | $14 | 100% | 0% | 79% | 21% |
Talend | $14 | $57 | 25% | 71% | 0% | 29% |
Actuate | $13 | $140 | 9% | 0% | 69% | 31% |
Revolution Analytics | $12 | $12 | 100% | 0% | 67% | 33% |
Aerospike | $12 | $12 | 100% | 0% | 92% | 8% |
Neo Technologies | $12 | $12 | 100% | 0% | 67% | 33% |
Digital Reasoning | $11 | $11 | 100% | 0% | 64% | 36% |
Tresata | $10 | $10 | 100% | 0% | 0% | 100% |
Rainstor | $10 | $10 | 100% | 0% | 70% | 30% |
Think Big Analytics | $10 | $10 | 100% | 0% | 0% | 100% |
ODM | $3,800 | n/a | n/a | 100% | 0% | 0% |
Other | $3,030 | n/a | n/a | 27% | 20% | 53% |
Total | $18,607 | n/a | n/a | 38% | 22% | 40% |
Methodology
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. We also held extensive discussions with former employees of Big Data companies to further calibrate our models.
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.
Big Data Definitions
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. Wikibon’s definition of Big Data contains two equally important parts.
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. 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.
Based on the above definition, Wikibon includes the following products and services under the umbrella of Big Data:
2013 Big Data Market Highlights and Trends
2013 was an important year in the evolution of Big Data technology. 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.
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. 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. Developers now have the structural underpinnings to build real-time and streaming data applications, interactive SQL-style query applications, graph analytic apps, and more.
YARN is critical to the future of Hadoop. 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.
YARN was an indirect growth driver for the Big Data market in 2013. As stated above, in 2013 vendors began to crystalize their visions for Big Data in the enterprise. The pending arrival of YARN, among other technology advances, enabled vendors to credibly position Hadoop at the center of their Big Data plans.
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. These included:
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. 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. Other key developments in the Big Data market in 2013 included:
Wikibon’s Big Data Market Forecast
Wikibon forecasts Big Data market growth to slow slightly in 2014 to 53%, reaching $28.5 billion for the year. 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 market matures through 2017 and beyond, Wikibon expects Big Data applications and cloud-based services to play an increasingly important role. 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.
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. This is still a work-in-progress for many vendors, despite progress that was made in 2013.
Finally, Wikibon believes the biggest growth inhibitors for the Big Data market are security and privacy concerns. The NSA revelations clearly illustrate that data security and privacy are hot button topics for both the American and international public. 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. 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. As Wikibon has urged before, it is critical for the industry to be proactive and address these concerns sooner rather than later. It makes good business sense to do so, but is also the responsible thing to do.
Below is Wikibon’s Big Data market forecast broken down by market component through 2017.
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. Vendors would be wise to remember that it is such business value, not technology features per se, that will drive revenue in this market. 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.