James Dixon’s Blog

James Dixon’s thoughts on commercial open source and open source business intelligence

On “selling to developers” and “monetizing the community”

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After a decade of open source business models, the leaders of open source companies are still making fundamental mistakes.

Today’s example is provided by Peter Goldmacher, VP of Strategy and Market Development at Aerospike. In a post by Dan Woods at Forbes titled “Why Open Source Does Not A Business Make” Golmacher talks about “selling to developers”.

This is the same mistake that VCs and executives make when they use the phrase “monetizing the community”. Your customers are not the developers, unless the developers pay for the software themselves and don’t expense it. Why? Because developers typically have no budget. The litmus test for this is the question “if the developer leaves their current employer and joins another, who is my customer now?” In almost all cases the answer is “their former employer”. A simpler question is “who signed the contract, a developer personally, or a representative of the company?” The developer may still be a part of the community, but the customer remains the company that wrote the check.

The correct way to think about this is to consider developers as a group who can sell the software for you. Looking at it this way has several advantages:

  • You recognize that developers are not the final decision maker
  • You realize that developers have one set of needs, and that corporations have additional or different needs
  • You realize that you can provide tools and guidance to help developers sell your solution to management

As I state in the Beekeeper Model

Customers are corporations, the community are people. They have very different needs.

For more detail on this you can read this section of the Beekeeper Model.

It’s possible Goldmacher was paraphrased or miss-quoted about “selling to developers”. If he wasn’t this VP of Strategy might consider adjusting his strategy.

Written by James

June 26, 2015 at 2:59 am

Posted in Uncategorized

The Next Big Thing: Open Source & Big Data

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These are the main points from the Open Source & Big Data section of my Next Big Thing In Big Data presentation.

  • Open source fuels big data
  • Big data fuels large enterprise
  • Large enterprise fuels interest
  • Interest fuels adoption and contribution
  • Adoption and contribution benefits open source
  • Open source has won
  • Watch Apache projects for future standard building blocks

Please comment or ask questions

Written by James

June 23, 2015 at 5:55 pm

Posted in Uncategorized

The Next Big Thing: Big Data Use Cases

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These are the main points from Big Data Use Cases section of my Next Big Thing In Big Data presentation.

  • Use cases are important for the big data community
  • SQL on Big Data
  • Data Lakes
  • Real-Time/Streaming
  • Big data should be designed in
  • The use cases will change

Please comment or ask questions

Written by James

June 23, 2015 at 5:54 pm

Posted in Uncategorized

The Next Big Thing: Tomorrow’s Technologies of Choice

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These are the main points from Tomorrow’s Technologies of Choice section of my Next Big Thing In Big Data presentation.

  • If data is more valuable than software, we should design the software to maximize the value of the data
  • Data treatment should be designed-in
  • Old architectures don’t work for new problems
  • Scalable technology is better than big technology

Summary

  • Data is valued
  • New architectures
  • Open source platforms
  • Analysis is built in

Please comment or ask questions

Written by James

June 23, 2015 at 5:54 pm

Posted in Uncategorized

The Next Big Thing: Today’s Technologies of Choice

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These are the main points from Today’s Technologies of Choice section of my Next Big Thing In Big Data presentation.

  • Interest in Hadoop ecosystem continues to grow
  • Interest in Spark is growing rapidly
  • R is important
  • SQL on Big Data is a problem today
  • Application developers leave data analysis for version 2
  • Application architecture and big data architecture are not integrated or common

Summary

  • Hadoop ecosystem is vibrant
  • Analysis is an after-thought

Please comment or ask questions

Written by James

June 23, 2015 at 5:53 pm

Posted in Uncategorized

The Next Big Thing: Data Science & Deep Learning

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These are the main points from Data Science & Deep Learning section of my Next Big Thing In Big Data presentation.

  • Value has drifted from hardware, to software, to data
  • The more granular the data, the harder the patterns are to find

Summary

  • Value of data as a whole is increasing
  • The value of data elements is decreasing
  • Data scientists don’t scale
  • Tools are too technical
  • Correct conclusions can be hard
  • Something has to change

Please comment or ask questions

Written by James

June 23, 2015 at 5:53 pm

Posted in Uncategorized

Next Big Thing: The Data Explosion

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These are the main points from The Data Explosion section of my Next Big Thing In Big Data presentation.

  • The data explosion started in the 1960s
  • Hard drive sizes double every 2 years
  • Data expands to fill the space available
  • Most data types have a natural maximum granularity
  • We are reaching the natural limit for color, images, sound and dates
  • Business data also has a natural limit
  • Organizations are jumping from transactional level data to the natural maximum in one implementation
  • As an organization implements a big data system, the volume of its stored data “pops”
  • Few companies are popping so far
  • The explosion of hype exceeds the data explosion
  • The server, memory, storage, and processor vendors do not show a data explosion happening

Summary

  • The underlying trend is an explosion itself
  • The explosion on the explosion is only minor so far
  • Popcorn Effect – it will continue to grow

Please comment or ask questions

Written by James

June 23, 2015 at 5:50 pm

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