We’re getting excited for our Data Leadership Conference, which is set for April 28 in New York City! The conference will feature an expert panel where Haile Owusu (Mashable), Claudia Perlich (Dstillery), and Kirk Borne (Booz Allen Hamilton) will share practical insights on how to build data capabilities within complex organizations.
We’ll discuss questions like:
- What skills should organizations combine in data science teams?
- Why should organizations invest in experiments that may fail?
- How can organizations operationalize a rigorous, scientific method when answering questions with data?
- How should proofs of concept be structured to ensure they provide value?
- How do organizations know they are asking the right questions with data?
- What are the first steps to breaking down data silos and democratizing access across the organization?
- What can data scientists do to spread the mindset and practices of data science to other departments resistant to these practices?
- And many more!
To get started thinking about these questions, have a look at Ten Signs of Data Science Maturity, a new (free!) O’Reilly Book by Kirk Borne and Peter Guerra. The book provides a great introduction to criteria organizations can use to evaluate their “data capabilities,” or cultural qualities that “support and make the most of data science.” Using clear, accessible (and often witty) prose, the authors cover issues like data democratization, agile data product development, executing on the scientific method, and valuing the importance of visualizations and storytelling.
And come meet your peers across industries to discuss how to put these principles into practice April 28!
More from the Blog
Apr 11 2016
We’re excited to introduce the latest report and prototype from our machine intelligence R&D group! In this iteration, we explore summarization, or neural network techniques for making unstructured text data computable. Making language computable has been a goal of computer science research for decades. Historically, it has been a challenge to merely collect and store data. But it’s now so...
Apr 21 2016
Analyzing unstructured text data such as news, emails, chats, narrative prose, legal documents, or transcribed speech is an extremely tough problem. Thanks to massive leaps in data engineering, we can just about store and retrieve this torrent of information. But we can’t yet conduct the kind of rich and fast analyses that we take for granted with structured, quantitative data. Our newly...
Jul 22 2019
by — We discussed this research as part of our virtual event on Wednesday, July 24th; you can watch the replay here! Convolutional Neural Networks (CNNs or ConvNets) excel at learning meaningful representations of features and concepts within images. These capabilities make CNNs extremely valuable for solving problems in the image analysis domain. We can automatically identify defects in manufactur...