Do you know the five main points of big data entrepreneurship?

It is not only difficult to develop infrastructure technology products, but also difficult to sell them, especially big data infrastructure tools such as Hadoop, NoSQL database and stream processing system. Customers need a lot of training and education, and paid Yona Valley needs a lot of support and timely follow-up product development.

This means that a lot of financial support is needed. For example, Greenplum received an investment of $654.38 billion in 2065.438+00, but it was still not enough to complete all the work, and finally it had to be sold to EMC. Nowadays, the most famous big data startups have more money, such as Cloudera. Big data startups in infrastructure usually need millions of dollars in seed money to start, but the road to A round of financing is extremely difficult.

Emerging big data startups must also compete with some well-known companies that have even cooperated with customers, such as Cloudera, Hortonworks, 10gen, Amazon AWS, IBM, Oracle and so on.

On the other hand, big data application entrepreneurship is relatively simple, including vertical industry applications and general big data applications such as data visualization. Because the value of these big data applications is more intuitive for customers, closer to the business, and less friction when entering the enterprise IT system.

2. Cloud computing is a friend.

Whether you sell big data infrastructure or applications, cloud computing is a more effective business carrier. Choosing cloud computing is not only hosting in the cloud, but more importantly, providing services to customers through cloud computing. You will have more control, and optimizing your operation with limited resources will make you understand the products more thoroughly.

Cloud computing also reduces the cost and threshold for potential users to try out products. From NewRelic to Amazon AWS, they all benefit from the cloud computing+big data model.

3. Developers are friends

If you are mainly engaged in big data analysis, such as ClearStory, Platfora or CRM marketing applications, data analysts are your friends. In either case, the best way is to develop and market around the target audience, which are mainly developers and marketers. CIO is not a good target audience!

Paying attention to the CIO instead of the developer will often lead to difficulties when you actually sign the contract. The tactics around developer marketing are adopted by many cloud computing startups and pure big data software companies, such as Splunk and Tableau.

For example, Infochimps and Continuity products are similar (both are forced to go to the cloud and land in the user data center), but Continuity is completely oriented to developers, which means that more technical fans can be accumulated.

4. Push data scientists to the center of the front desk.

This is both a marketing strategy and a sales strategy. Data scientists are the people who can best demonstrate the power of data and platforms, and they are also the most popular speakers at the conference.

But big data scientists also need to choose content carefully. Now that everyone has accepted Hadoop and NoSQL, there is no need to talk about popular science such as 4V at every meeting. As for how to configure and integrate the big data system, it can only attract a small audience, unless your project is very large.

There are many reasons why Cloudera is more famous than its competitors, but Jeff hammerbacher is definitely a pivotal figure. Don't talk about the value and architecture of big data. From the standpoint of the audience, talk about what analysis can be done and how to do it.

5. How important open source is depends on yourself.

Almost all big data companies rely on open source software, some are "borrowed", such as Hadoop, Storm and various databases, some are developed by themselves, and some are mixed models, such as some functional applications added on HBase. These open source projects are so popular because of the power of the community.

Open source is by no means as easy as it looks. This doesn't mean that you can give back to the community by putting some code on Github. The purpose of open source is to gather people who use the same code into a community and constantly improve the code. This is related to attracting developers mentioned in the third point. Only when more users and developers are interested in you and spend time and energy on your product can you finally pay for it.

Countless startups open source their code, but those companies that can really promote projects and build communities can stand out. For example, Neo4j Technology's Secondary, Concurrent's Casading, 10gen's MongoDB, and even public-oriented companies like Twitter have opened up projects such as Storm and Mesos.

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