Not only is it difficult to develop infrastructure technology products, it is also difficult to sell them, specifically big data infrastructure tools such as Hadoop, NoSQL databases and stream processing systems are even more difficult. Customers need a lot of training and education, and paid yonagunas need a lot of support and timely follow-up on product development efforts.

This means a lot of money, such as Greenplum, which received $100 million in investment in 2010 but still didn't have enough to get it all done and had to be sold to EMC. a few of today's most notable big data startups have raised even more money, such as Cloudera. infrastructure big data startups typically need millions of dollars in seed funding to get started, but the road to Series A funding is not as easy as it seems. infrastructure startups typically need millions of dollars in seed funding to get off the ground, but the road to Series A funding is incredibly difficult.

Emerging big data startups also have to compete with companies that already have some name recognition and even partnerships with customers, such as Cloudera, Hortonworks, 10gen, Amazon AWS, IBM, Oracle and others.

Big data application startups, in contrast, are much simpler in comparison, whether they are geared towards vertical industry applications or general-purpose big data applications like data visualization. Because the value of these big data applications is more intuitive to customers and closer to the business, there is less friction to enter enterprise IT systems.

2. The cloud is a friend

Whether you're selling big data infrastructure or applications, the cloud is a more effective business vehicle. Choosing the cloud is more than just hosting in the cloud, it's about delivering services to your customers through the cloud. You'll have more control, while running optimally on limited resources will give you a better understanding of your product.

Cloud computing also lowers the cost and barrier for potential users to try out the product, with everyone from NewRelic to Amazon AWS benefiting from the cloud + big data model.

3. Developers are friends

If you're primarily involved in big data analytics, such as ClearStory, Platfora, or CRM marketing apps, data analysts are your friends. In either case, it's best to center your development and marketing efforts around a target audience of primarily developers and marketers, with CIOs instead not being a good target audience!

Focusing on CIOs rather than developers can often lead you to run into tricky problems when it comes to actually signing up. The tactic of marketing around developers is used by many cloud startups and pure big data software companies, such as Splunk and Tableau.

Then again, Infochimps and Continuuity have similar offerings (both are forced to press down into the cloud and force land in the user's datacenter), but Continuuity is entirely geared towards developers, which means more technical fans can be amassed.

4. Putting data scientists front and center

This is both a marketing and sales strategy; data scientists are the ones who can demonstrate the power of data and platforms, and they're the most popular speakers at conferences.

But big data scientists also need to choose what to communicate carefully. Everyone accepts Hadoop and NoSQL these days, so there's no need to talk about the 4Vs and other science at every meeting. How to configure and integrate big data systems will only appeal to a small audience, unless your project is very large.

Cloudera is better known than its competitors for a number of reasons, but one of them, Jeff hammerbacher, is definitely a pivotal figure. Don't talk about the value and architecture of big data big data, put yourself in the audience's shoes and talk about exactly what analytics can be done and how.

5. How important open source is depends on you

Almost all big data companies rely on open source software, some of which is "borrowed", such as Hadoop, Storm, and various databases, some of which is self-developed, and some of which is a hybrid model, such as adding some functionality to HBase. Some of these open source projects are so popular because of the community's support. These open source projects are so popular because of the power of the community.

Open source is never as easy as it looks, and it's not like you can talk about giving back to the community just because you put some code on Github. The goal of open source is to bring together a community of people who use the same code and continually improve it. This is related to attracting developers as we mentioned in the third point. Only if more users and developers become interested in you, and spend time and effort on your product, are they likely to end up shelling out money.

Numerous startups have open-sourced their code, but it's the ones that really drive projects and build communities that stand out. Examples include Neo Technology's Neo4j, Concurrent's Casading, and 10gen's MongoDB. even public-facing companies like Twitter have open sourced projects like Storm and Mesos.

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