This sponsored post is produced by Structure.
Big data is becoming part of the real world and every company’s DNA — not just the domain of data scientists and the enterprise. Learn how the world’s leading companies, big and small, are using it to evolve their businesses for the 21st century at Structure Data, March 9 and 10, in San Francisco.
Special offer to VentureBeat readers: Use promo code “VB_VIP” and click here to get a 30 percent discount on your ticket!
The cycles that accompany advances in computing are fairly predictable. Technology starts off in a lab setting understood by only a brilliant few, then moves down to an informed and passionate early-adopter community, finally making its way to the mainstream once the kinks have been worked out and the interface refined for those without computer science degrees.
We’re witnessing that latter transfer right now in the field of data analysis. That may be old news to many tech companies but it’s starting to have its breakthrough moment outside of Silicon Valley.
Data scientists such as Peter Lee of Microsoft and entrepreneurs such as Ann Johnson of Interana are bringing advanced data science to people and companies who can’t afford to invest in their own data science efforts through a series of APIs and other services that democratize access to data research tools.
Just like public infrastructure cloud services allowed hundreds of startups to grow and thrive without having to invest in building their own infrastructure, easier access to cutting-edge data science could improve a wide range of products and services across many industries that aren’t blessed with data science brilliance.
This is just one of the topics we plan to explore at Structure Data, scheduled for March 9th and 10th at the UCSF Mission Bay conference center in San Francisco’s Dogpatch neighborhood. Over the years, Structure Data has tried to showcase and warn of the coming importance of data analysis in making business decisions, and that certainly won’t change this year. But what is changing this year is the realization that machine learning and artificial intelligence are getting sophisticated enough to be used by regular folks (within the software development and marketing worlds, anyway).
Data is a wonderful and tricky thing. Assuming you’re measuring correctly, harnessing data within an organization can unlock benefits you would have never discovered otherwise with older analysis tools. There’s a reason why companies like Google, Microsoft, Baidu, and others are pouring hundreds of millions of dollars into artificial intelligence research and development: they’ve already learned the power of data in the development of their products and services, and the companies that get out in front of the next wave of innovation in the data world will have an advantage.
However, seizing control of your corporate data is not easy for companies that have traditionally depended on more basic forms of data analysis. In a conversation earlier this year with Slack’s Josh Wills (who will be speaking at Structure Data 2016), he noted that, one way or another, “data is coming for your business.”
For companies that already have an established data culture — think banks and other financial institutions — adapting to new trends in data analysis is relatively easy, if expensive. But other kinds of companies who have never managed their businesses based on sophisticated data — such as the taxi industry — might be crushed by the rise of more nimble competitors who live and breathe data and who are furiously working to improve their capabilities.
That is, unless the machine learning intelligence of the big powers was available more widely, in formats that allow people working in sales, marketing, and other non-technical departments to reach insights on their own without the need for a dedicated machine-learning research project.
This is exactly what Interana is doing: “We’re targeting people who are analytical and care about numbers but aren’t necessarily technical,” CTO and co-founder Bobby Johnson told Structure Data curator Derrick Harris back in 2014 when the company launched.
Johnson’s wife, co-founder, and Interana CEO Ann Johnson will join us at this year’s event to give us an update on how that work is progressing.
Unlocking access to sophisticated machine-learning APIs will also give social researchers a chance to discover ways in which companies are using sophisticated data analysis tools for troubling reasons. Case in point: the product offered by Castlight Healthcare that allows companies to gather data on who might get pregnant within their employee ranks by noticing when women stop filling birth-control prescriptions.
Sure, there are probably some pre-natal health-care related services that could be provided with this data, but in an era where women have to fight for equal pay and can easily be “mommy tracked,” there’s somebody out there who might take this data and use it as a heads-up to reassign women who might be trying to get pregnant. Allowing researchers in other fields to understand how machine learning and artificial intelligence work will help society determine the proper response to software and services developed by machine learning algorithms.
And democratized access to these technologies will even help data scientists themselves. While modern data infrastructure may be old hat at places like Google or Facebook, data scientists at newer companies face the prospect of having to build data infrastructure from scratch. What worked for Google might not work for the new company, but if all you know is one way of doing things, that’s likely what you’ll build.
This should make for fascinating conversation at Structure Data 2016.
Tom Krazit is Executive Editor at Structure.
Remember: Special offer to VentureBeat readers: Use promo code “VB_VIP” and click here to get a 30 percent discount on your ticket!
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