If you haven’t heart of Arduino or Raspberry Pi, then you need to get up to speed urgently. Arduino is revolutionizing hardware and gadget innovations. It is a do-it-your-self-hardware-kit that allows you to build complex systems by stacking up components like GPRS/3G, NFC, etc. Raspberry Pi is an ARM GNU/Linux box for $25.
However Kickstarter just funded the next generation of both projects:
The Parallella Super Computer (alternative link) for $99. A 64 Cores computer on a small board for an affordable price and very low power consumption. Imagine stacking a 100 parallella’s in a box. There is already a parallel programming competition set-up.
Both projects are open hardware and open source project, hence expect hobbyists to come up with lots of cool ideas…
Data Scientist is going to be the sexiest job of the 21st century. However do we really need a new army of Data Scientists or is there an alternative? There might be and it is called data democracy.
What is data democracy?
Data democracy allows all people to have access to all data insight. In an enterprise, data democracy is about enabling knowledge workers to share insights. To avoid the construction of data silos. To democratize tools that enable each co-worker to become a data scientist without needing a PhD in statistics, mathematics, etc. Visual tools that allow “Excel-users” to use Neural Networks, Support Vector Machines, Random Forests, etc. to make predictions, to classify or cluster data, etc. But without the need to understand the underlying computer learning algorithms into great detail. A sort of corporate RapidMiner that scales.
At the same time we also need better visualization tools. Everybody should be able to create infographics easily. Tools that allow ordinary people to create stunning data visualizations that go beyond the boring reports.
Finally we need better tools to find and share data insights. We need a “Databook”. A Facebook to easily find the data insight you need. A tool that allows you to distribute your predictions about next quarter’s sales and to compare them with the predictions of others.
In summary, we need the data scientists of this world to focus on making access to data insight available to every knowledge worker. Simplify instead of algorithmify! Enable everybody to be a data scientist…