Originating Author: Jeff Kelly. Originally published on Wikibon.org.

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.

Figure 1 - Source: Wikibon 2014

Figure 2 - Source: Wikibon 2014

Figure 3 - Source: Wikibon 2014

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.
• The products and services related to Big Data continued to mature from a features perspective in 2013, further spurring adoption. 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.
• Big Data technologies also took important steps towards greater enterprise-grade capabilities in 2013, critical for mass enterprise adoption. These steps included better privacy, security and governance capabilities, as well as improved backup & recovery and high-availability for Hadoop specifically.
• Partnerships also played an important role in maturing the Big Data landscape in 2013. 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.

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:

• 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.
• A lack of polished Big Data applications designed to solve specific business problems.

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:

Based on the above definition, Wikibon includes the following products and services under the umbrella of Big Data:

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

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