Big Data Vendor Revenue and Market Forecast 2012-2017

From Wikibon

Revision as of 03:38, 20 February 2013

Contributing authors: Jeff Kelly, David Floyer, Dave Vellante, Stu Miniman

Introduction

The hype surrounding Big Data, which showed no signs of abating in 2012, now has big dollars backing it up. Factory revenue generated by the sale of Big Data-related hardware, software and services took a major step forward in 2012, growing by 59% over 20111.

The total Big Data market reached $11.4 billion in 2012, ahead of Wikibon’s 2011 forecast and putting it on pace to exceed $47 billion by 2017. That translates to a 37% compound annual growth rate over that period.

Growth Drivers and Adoption Barriers

The growth rate of Big Data revenue in 2012 was due to a number of factors, including:

• An increased awareness of the benefits of Big Data as applied to industries beyond the web, most notably financial services, pharmaceuticals and retail;
• The maturation of Big Data software such as Hadoop, NoSQL data stores, in-memory analytic engines and massively parallel processing analytic databases;
• Increasingly sophisticated professional services practices that assist enterprises in practically applying Big Data hardware and software to business use cases;
• Increased investment in Big Data infrastructure by massive web properties – most notable Google, Facebook, and Amazon – and government agencies for intelligence and counter-terrorism purposes.

In the enterprise space in particular, the combination of (1) a better understanding of the use cases for Big Data and (2) more mature product and service offerings resulted in a significant percentage of Big Data early adopters graduating to large-scale, production-level deployments from small, proof-of-concept projects. This evolution naturally required increased investment in Big Data hardware, software and services. Feedback from the Wikibon community included multiple reports of $100 million+ deals from both government and commercial buyers.

Additionally, a number of enterprises previously reluctant to undertake Big Data projects due to fuzzy ROI, lack of specific business use case and/or concerns over product and services maturity began exploring Big Data in their organizations with small pilot projects, their concerns assuaged by the market potential underscored by the growth factors listed above.

The Big Data market is still within the confines of the early adopter phase and is poised for significant growth. For the Big Data market to reach its full potential, enterprises and vendors must overcome a number of obstacles. While a detailed discussion of these obstacles is outside the purview of this report, they are worth noting. They include:

• The well-publicized lack of analytic specialists and Data Scientists armed with both the technical skill and business acumen to derive insights from large, multi-structured data sets merged from disparate sources;
• A lack of understanding among enterprises on how to organize Big Data staff to best identify business requirements for Big Data projects, and effectively communicate insights gleaned from Big Data to the business;
• Organizational resistance to adopting Big Data analytics-driven decision-making in replace of “gut instinct”-style decision-making.
• Vendor marketing overly focused on “speeds-and-feeds,” product features and “Big Data-washing” rather than laying out a vision for Big Data in the enterprise, articulating a path to achieve this vision and the potential for Big Data to disrupt well-established vertical markets.
• Development of Big Data platforms and tools by vendors that eschew open frameworks in favor of closed, locked-down solutions. This will limit interoperability with competing and complimentary products and reduce customer choice.
• A lack of best practices and related technologies for managing Big Data as a corporate asset, including data quality, data governance and security platforms and tools;
• A dearth of Big Data application development tools and services that allow existing developers to build and customize Big Data applications using common and popular application development languages and processes.

Big Data Vendor Revenue

As part of its market-sizing efforts, Wikibon tracked and/or modeled the 2012 Big Data revenue of over 60 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:

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 it was necessary for Wikibon to triangulate many types of information to determine our 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:

• Hadoop software and related hardware;
• NoSQL database software and related hardware;
• Next-generation data warehouses/analytic database software and related hardware;
• Non-Hadoop Big Data platforms, software and related hardware;
• In-memory – both DRAM and flash – databases as applied to Big Data workloads;
• Data integration and data quality platforms and tools as applied to Big Data deployments;
• Advanced analytics and data science platforms and tools;
• Application development platforms and tools as applied to Big Data use cases;
• Business intelligence and data visualization platforms and tools as applied to Big Data use cases;
• Analytic and transactional applications as applied to Big Data use cases;
• Big Data support, training, and professional services.

Key Findings: 2012 Big Data Market Highlights and Trends

Following are key findings, market highlights and trends for the Big Data market in 2012.

• Market-leader IBM offers by far the largest product and services portfolio by both breadth and depth. The company also supports its Big Data practice with a well-crafted, high-level marketing campaign focused around its Smarter Planet initiative that often includes illustrations of real-world Big Data deployments.

The biggest criticism of IBM from practitioners is that the company’s portfolio is in fact so wide and deep as to cause confusion. IBM combats this confusion by initiating many Big Data customer engagements through its professional services division. A challenge and area of focus for IBM moving forward is to continue to articulate its Big Data vision in a way that focuses on industry solutions and not point products.

• HP achieved second-place status in the overall Big Data market by revenue in 2012. It did so mostly thanks to revenue derived from Big Data-related services, followed by sales of hardware to support Big Data deployments. HP by its sheer size is in a position to impact and participate in a number of Big Data deployments.