The stages of enterprise IoT adoption

To learn more about data culture and meet experts & executives leading data initiatives, join us at the Strata Business Summit in New York on Sept 26-28, 2017. It will take a long time, and a great deal of money, to achieve the full dream of the Internet of Things (IoT)—a world whose sensors and machines are connected to the Internet as fluidly as our phones and PCs are today. That’s why it’s important to plot out intermediate steps in IoT adoption. Companies that launch into the IoT without careful strategic planning are likely to find themselves on an expensive adventure as they connect disparate endpoints like medical devices and industrial machinery and ingest vast quantities of data. At the Strata Business Summit in March, Accenture Labs managing director Teresa Tung cited a World Economic Forum report on the Industrial Internet to outline…
Original Post: The stages of enterprise IoT adoption

Architecting actionable insights

This is an excerpt from Bas Geerdink’s talk “Hadoop and Spark at ING” from Strata + Hadoop World New York 2016. Visit Safari to view the entire presentation. To learn more about business cases for data in finance, check out the financial services sessions at the Strata Data Conference in New York, September 25-28, 2017. Registration is now open. Article image: Idea sketch. (source: Pexels).
Original Post: Architecting actionable insights

The business advantages of embedding analytics into applications

Money tree. (source: Tanya Hart on Flickr).For more on embedding BI directly into software, download a free copy of “Delivering Embedded Analytics in Modern Applications,” by Federico Castanedo and Andy Oram. Imagine, if you will, that you’re a product manager recently tasked with adding some whiz-bang visual analytics to your software, web site, or mobile app. Your first instinct may be to build your own solution as part of your existing product. However, you have to ask yourself if you have the team resources to accomplish the chore, or if you would rather have your team work to keep your core product fast and functional. Luckily, there is another option, one that leverages embedded analytics. With that in mind, let’s take a deeper dive into the world of embedded analytics and determine why you might choose it, who is doing…
Original Post: The business advantages of embedding analytics into applications

Jupyter Digest: 15 May 2017

Jupyter Digest. Got a project you think we’d be interested in? Submit a link. TSFRESH. TSFRESH is a “time series feature extraction based on scalable hypothesis tests.” In layman’s terms, it finds interesting things on a time-series chart for you automatically. The notebooks folder has Jupyter examples that show how to use it in your work, like this one that uses accelerometer data to figure out when you’re walking, climbing stairs, or just doing nothing at all. (Submitted anonymously.) 100 Days of Algorithms. Tomáš Bouda (@coells on GitHub) compiles a nice list of examples that illustrate a host of different algorithms with Python. If a title like “Day 14 – huffman codes.ipynb” lights you up, then you’re gonna love this (times 100). Also, bravo for not calling it “50 algorithms to whiteboard before you die.” How JupyterHub tamed big science:…
Original Post: Jupyter Digest: 15 May 2017

Language understanding remains one of AI’s grand challenges

Yada, yada. (source: Pixabay).David Ferrucci will deliver a keynote at the O’Reilly Artificial Intelligence Conference in NYC, June 26-29, 2017. His colleague Jennifer Chu-Caroll will also give a talk, “Beyond the state of the art in reading comprehension,” at the same conference. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with David Ferrucci, founder of Elemental Cognition and senior technologist at Bridgewater Associates. Ferrucci served as principal investigator of IBM’s DeepQA project and led the Watson team that became champion of the Jeopardy! quiz show. Elemental Cognition (EC) is a research group focused on building an AI system that will be equipped with state-of-the-art natural language understanding technologies. Ferrucci envisions that…
Original Post: Language understanding remains one of AI’s grand challenges

What Facebook learned when it opened its data to every employee

Library books. (source: Pixabay).To learn more about data culture, meet experts, and interact with other executives leading data initiatives, join us at the Strata Business Summit in London on May 24-25, 2017. Data executives might spend most of their time on technical and vendor management, but their work ultimately comes down to the task of building an effective data culture. Reorienting a company around data-driven decision-making takes more than just software tools; it also involves training your employees to understand data essentials, establishing processes that safeguard data and clarify its ownership, working with line-of-business managers to set expectations and goals, and generally striking the right balance between risk-taking and caution. Since the first Strata conference six years ago, O’Reilly has identified data as an important driver of value in every industry. In this series, we’ll revisit advice to data executives…
Original Post: What Facebook learned when it opened its data to every employee

The Internet of Things Market

Geometrical abstract (source: O’Reilly). For more on building strategies and data-driven business models, check out the Strata Business Summit at Strata Data London, May 22-25, 2017. This is the full version of “The Internet of Things Market,” by Aman Naimat. A downloadable version is available as well. Introduction The Internet of Things (IoT) is that brilliant kid who’s grown up but still stuck at home. Will 2017 be the year that IoT finally moves out and gets a job? This report presents the current market for IoT, including top companies and industries adopting IoT, based on a data-driven methodology. Unlike previous work from McKinsey and other analysts, this report presents an approach for measuring the market based on big data rather than models or human-entered surveys. While we cannot predict the future, we believe that the following is the most accurate…
Original Post: The Internet of Things Market

Jupyter Digest: 8 May 2017

Jupyter Digest. Got a project you think we’d be interested in? Submit a link. Docker Stacks. An array of “opinionated stacks of ready-to-run Jupyter applications in Docker.” Basically, it’s a collection of layered Dockerfiles that progress from a minimal notebook through a basic setup for data science all the way to a full PySpark setup. If you’re sick of struggling with trying to configure your machine—or worse, someone else’s machine—this is an absolutely invaluable resource. Jupyter Themes. Tired of your notebooks looking like something designed by a bunch of Python programmers and scientists, and not designers? (Because, in fact, that’s exactly what they are?) Then Kyle Dunovan’s (@dunovank) jupyter-themes project is for you. With just a simple pip install and a single command, you can set up shop next to all those be-bearded Sublimesters in Brooklyn and they’ll never know…
Original Post: Jupyter Digest: 8 May 2017

Data preparation in the age of deep learning

Fan Hui vs. AlphaGo – Game 1. (source: Alzinous on Wikimedia Commons).Registration is now open for the O’Reilly Artificial Intelligence Conference in NYC, June 26-29, 2017. Early price ends May 12. Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the Data Show, I spoke with Lukas Biewald, co-founder and chief data scientist at CrowdFlower. In a previous episode we covered how the rise of deep learning is fueling the need for large labeled data sets and high-performance computing systems. CrowdFlower has a service that many leading companies have come to rely on to provide them with labeled data sets to train machine learning models. As deep learning models get larger and more complex, they require training…
Original Post: Data preparation in the age of deep learning

Tips for managing metadata in a data lake

Petrie polygon graph of the eight-dimensional cube. (source: Watchduck (a.k.a. Tilman Piesk) on Wikimedia Commons).To learn more about architecting a data lake to leverage metadata and integrate with existing metadata tools, read the free O’Reilly report, Understanding Metadata: Create the Foundation for a Scalable Data Architecture, by Federico Castanedo and Scott Gidley. Modern data architectures promise broader access to more and different types of data in order to enable an increasing number of data consumers to employ data for business-critical use cases. Examples of such use cases include product development, personalized customer experience, fraud detection, regulatory compliance, and data monetization. Data-focused enterprises must explore several key questions, including what, exactly, is a “modern data architecture”? How can we ensure what we build successfully supports our business strategy? And how do we make our system agile enough to scale and accommodate…
Original Post: Tips for managing metadata in a data lake