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The O'Reilly Data Show explores the opportunities and techniques driving big data and data science. Through interviews and analysis, we highlight the people putting data to work.

The O'Reilly Data Show Podcast

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In this O’Reilly Podcast, Ben Lorica talks with John Hugg, founding engineer and manager of developer outreach at VoltDB. From the deluge of today's diverse data, Hugg describes how to extract meaningful information to make things cheaper and faster.

Streaming data + stream processing = information

In this episode of the O’Reilly Podcast, O’Reilly’s Ben Lorica sat down with Ben Sharma, CEO and co-founder of Zaloni, an organization that provides enterprise data management solutions for Hadoop. They discussed real-time data processing, the changing nature of data, sentiment analysis, and the trend toward combining cloud with on-prem infrastructures.


Approaching Big Data From a Business Perspective

Amy Edmondson, professor at Harvard Business School, first identified the concept of psychological safety in work teams in 1999. Since then, she has observed how companies with a trusting workplace perform better. Psychological safety isn't about being nice, she says. It’s about giving candid feedback, openly admitting mistakes, and learning from each other. And she argues that kind of organizational culture is increasingly important in the modern economy. Edmondson is the author of the new book "The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth.”

IdeaCast

666: Creating Psychological Safety in the Workplace

Kieran Setiya, a philosophy professor at MIT, says many people experience a mid-career crisis. Some have regrets about paths not taken or serious professional missteps; others feel a sense of boredom or futility in their ongoing streams of work. The answer isn't always to find a new job or lobby for a promotion. Motivated by his own crisis, Setiya started looking for ways to cope and discovered several strategies that can help all of us shift our perspective on our careers and get out of the slump without jumping ship. He is the author of the HBR article "Managing Your Mid-Career Crisis."

669: How to Cope With a Mid-Career Crisis

Pipedrive has become the dominant player in the sales CRM market, with over 80,000 businesses using it daily. Their story to the top features persistence and overcoming a lot of negative bias towards building 'yet another CRM tool'.

In this week’s episode of This Much I Know, Carlos sits down with co-founder, Martin Henk as he shares the journey so far including challenges in product, growth, pricing and fundraising.

Tune in to hear how Pipedrive has managed to stand out in so competitive a market and why swapping out marketing hacks for brutal product simplicity has been the key to gaining customers and accelerating growth.

Links:
Martin - LinkedIn: www.linkedin.com/in/martinhenk
Martin - Twitter: twitter.com/henkmrtn
Martin - Medium: medium.com/@nokkloom
Carlos - LinkedIn: www.linkedin.com/in/carloseduardoespinal
Carlos - Twitter: twitter.com/cee
Carlos - Medium: sdca.mp/2entVR3

Pipedrive: www.pipedrive.com
Seedcamp: www.seedcamp.com

Pipedrive: Martin Henk, co-founder

In this episode of the Data Show, I spoke with Kartik Hosanagar, professor of technology and digital business, and professor of marketing at The Wharton School of the University of Pennsylvania.  Hosanagar is also the author of a newly released book, "A Human’s Guide to Machine Intelligence," an interesting tour through the recent evolution of AI applications, which draws from his extensive experience at the intersection of business and technology.

Algorithms Are Shaping Our Lives—Here's How We Wrest Back Control

In this episode of the Data Show, I spoke with Roger Chen, co-founder and CEO of Computable Labs, a startup focused on building tools for the creation of data networks and data exchanges. Chen has also served as co-chair of O'Reilly's Artificial Intelligence Conference since its inception in 2016. This conversation took place the day after Chen and his collaborators released an interesting new white paper, "Fair value and decentralized governance of data." Current-generation AI and machine learning technologies rely on large amounts of data, and to the extent they can use their large user bases to create “data silos,” large companies in large countries (like the U.S. and China) enjoy a competitive advantage. With that said, we are awash in articles about the dangers posed by these data silos. Privacy and security, disinformation, bias, and a lack of transparency and control are just some of the issues that have plagued the perceived owners of “data monopolies.”

Acquiring and Sharing High-Quality Data

In this episode of the Data Show, I speak with Cassie Kozyrkov, technical director and chief decision scientist at Google Cloud. She describes "decision intelligence" as an interdisciplinary field concerned with all aspects of decision-making, and which combines data science with the behavioral sciences. Most recently she has been focused on developing best practices that can help practitioners make safe, effective use of AI and data. Kozyrkov uses her platform to help data scientists develop skills that will enable them to connect data and AI with their organizations' core businesses.

We had a great conversation spanning many topics, including:

How data science can be more useful

The importance of the human side of data

The leadership talent shortage in data science

Is data science a bubble?

Make Data Science More Useful

In this episode of the Data Show, I speak with Alex Ratner, project lead for Stanford’s Snorkel open source project; Ratner also recently garnered a faculty position at the University of Washington and is currently working on a company supporting and extending the Snorkel project. Snorkel is a framework for building and managing training data. Based on our survey from earlier this year, labeled data remains a key bottleneck for organizations building machine learning applications and services.

Ratner was a guest on the podcast a little over two years ago when Snorkel was a relatively new project. Since then, Snorkel has added more features, expanded into computer vision use cases, and now boasts many users, including Google, Intel, IBM, and other organizations. Along with his thesis advisor professor Chris Ré of Stanford, Ratner and his collaborators have long championed the importance of building tools aimed squarely at helping teams build and manage training data. With today’s release of Snorkel version 0.9, we are a step closer to having a framework that enables the programmatic creation of training data sets.

Labeling, Transforming, and Structuring Training Data Sets for Machine Learning

In this episode of the Data Show, I speak with Kesha Williams, technical instructor at A Cloud Guru, a training company focused on cloud computing. As a full stack web developer, Williams became intrigued by machine learning and started teaching herself the ML tools on Amazon Web Services. Fast forward to today, Williams has built some well-regarded Alexa skills, mastered ML services on AWS, and has now firmly added machine learning to her developer toolkit.

Becoming a Machine Learning Practitioner

In this episode of the Data Show, I speak with Michael Mahoney, a member of RISELab, the International Computer Science Institute, and the Department of Statistics at UC Berkeley. A physicist by training, Mahoney has been at the forefront of many important problems in large-scale data analysis. On the theoretical side, his works spans algorithmic and statistical methods for matrices, graphs, regression, optimization, and related problems. On the applications side, he has contributed to systems used for internet and social media analysis, social network analysis, as well as for a host of applications in the physical and life sciences. Most recently, he has been working on deep neural networks, specifically developing theoretical methods and practical diagnostic tools that should be helpful to practitioners who use deep learning.

Understanding Deep Neural Networks

Jon Bruner and Mike Loukides talk about the classical music publishing industry and how it's being disrupted by projects like the Petrucci Music Library (http://imslp.org/) that make public-domain scores available online.

The future of classical music

Jim Stogdill, Edd Dumbill and Roger Magoulas have a conversation about in-memory databases and the impact on real time analysis. Leave questions and comments for us at http://radar.oreilly.com/2013/05/talking-about-in-memory.html

Talking about in-memory

Jon Bruner, Mike Loukides and Jim Stogdill from O'Reilly Radar muse on the return of software to the physical world.

Software and the physical world

Jon Bruner, Jim Stogdill, and Roger Magoulas talk about anthropology in big data, advances in JavaScript, searching for ancient civilizations, the importance of context, and good sites for checking the weather.

Related blog post: http://radar.oreilly.com/2013/06/radar-podcast-anthropology-big-data-and-the-importance-of-context.html

Anthropology, big data, and the importance of context

Jon Bruner, Jim Stogdill, and Roger Magoulas talk about what's been on their minds this week: the NSA's surveillance programs, what defense contractors will do with their technology as defense budgets dry up, and a Californian who isn't doing what you think he's doing with hydroponics.

For more on these topics, and links to the things we talked about, see http://radar.oreilly.com/2013/06/radar-podcast-the-internet-of-things-prism-and-defense-technology-that-goes-civilian.html.

The Internet of Things, PRISM, and defense technology that goes civilian

O'Reilly Media spreads the knowledge of innovators. At O’Reilly, a big part of our business is paying attention to what’s new and interesting in the world of technology. We have a pretty good record at having anticipated some of the big technology developments in recent history. For instance, we launched the first commercial Web site, GNN, in 1993; we organized the meeting at which the term “open source” was first adopted; we were early investors in Blogger, which helped launch the blogging revolution; and more recently, our Web 2.0 conference launched a world-wide meme. We call this predictive sense the “O’Reilly Radar.” And while we’re certainly not always right, we are, at least, good at making interesting guesses.

Our methodology is simple: we draw from the wisdom of the alpha geeks in our midst, paying attention to what’s interesting to them, amplifying these weak signals, and seeing where they fit into the innovation ecology. Add to that the original research conducted by our Research team, and you start to get a good picture of what the technology world is thinking about.