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Turn Your Big Data Customers into Big Data Revenue

June 06, 2017

Turn Your Big Data Customers into Big Data Revenue

Your big data sales prospects have a common problem – turning unorganized data sources into actionable business intelligence. They want an oracle that will answer their business questions and they are coming to you to build it. Your challenge is how to turn their big data problem into big data revenue.

Big data revenue is generated from three basic sources: hardware, software, and services. In 2013, sales of big data services made up 40 percent of the overall market, hardware made up 38 percent, and software 22 percent. When you consider that IDC predicts the overall size of the big data market is predicted to reach $16.1 billion in 2014, that’s $6.4 billion for services, $6.1 billion for hardware, and $3.5 billion for hardware. That’s a lot of potential big data revenue.

So you can profit from hardware, software, or services. The approach to any big data project is basically the same – you create a vast data pool, write software to categorize and manage that data pool, and then analyze the data for actionable insights. So if you think of big data as an end-to-end process, resellers can create value through architecture development, software programming, and analytics services.

Hardware Revenue

Big data requires big hardware, or at least a different approach to data center design to deliver more capacity. You need more computing power for analytics, and data and processing is typically spread across multiple servers. Adding new clusters of nodes and servers could be part of your big data revenue.

Since big data is still in its infancy it still has growing pains. Much of the storage management technology introduced over the past few years – archive, snapshot, high availability, etc. – is largely irrelevant in big data projects. Data storage and data processing are typically handled on the same server, and Hadoop programmers assume you are dealing with raw data storage and pure I/O to deliver what you need.

That being said, the growing demand for data storage is another source of big data revenue. The storage market is strong and, according to Aberdeen Group, IT is doubling storage capacity every 24 to 30 months. And if you are combining data storage and server capacity your revenue possibilities grow accordingly.

And of course there is more demand for cloud storage. Virtualization and a reluctance on the part of big data newcomers to invest before they can prove big data’s value means a lot of the storage and processing is moving to the cloud.

Software Programming Revenue

In addition to the hardware, customers will need big data applications. Any big data platform is going to need an application development framework, including a development lifecycle and a means of executing big data applications. You may be called on to provide such a framework, including development, execution, testing, and debugging new application code.

That means you will need to offer Hadoop for hire – experts who understand Apache Hadoop and can create custom applications that integrate the structured and unstructured data sources. It also means you will need to provide a programming model, development tools, system configuration and management tools, and other software support.

Of course, software consulting is a very specialized resource, especially for big data since Hadoop programmers are still relatively scarce. If you have the resources to offer software development and support it could be offered at a premium and become a significant part of your big data review.


Big data is only valuable when it delivers insight, so you need to turn the software into analytics. Analytic experts aren’t programmers, but data scientists and statisticians who understand how to interpret the data output.

The real value of big data comes from identifying trends and outliers that can guide business strategy. The data will tell you which product performs better, or what sales partner is more efficient, but you still need the human element to tell you why. Without answering “why” you don’t have actionable insight.

For example, social media is become a popular unstructured data source to drive decision-making about consumers. Monitoring Facebook activity using big data analytics can tell you a lot about the demographics and psychographics of your customers. But you have to monitor conversations for context so get qualified data to make sense of the quantified data.

If you understand the big data needs of your customers you can identify your best opportunity for generating big data revenue, whether through hardware, software, analytic services, or providing end-to-end support. Where do you think VARs offer the most value in a big data engagement?