Sign up for our newsletter and get the latest big data news and analysis.

Creating a Data-Based and Client-Focused Culture

In the world of social media, increasingly quick-moving communication amid rapidly growing data, a company’s culture defines its reputation, and its reputation often defines its success – or failure. Clarity Insights shares how companies can create a data-based and client-focused culture from the top down.

Reaching the Next Level of Open Data Maturity – Arriving at Open Data 3.0

In this special guest feature, Adnan Mahmud, Founder and CEO at LiveStories, discusses the next phase of development in the open data movement, what he calls “data 3.0,” which focuses on increasing the usability of large public databases.

The Difference Between Data Science and Data Analytics

In this contributed article, tech writer Rick Delgado, examines the differences between the terms: data science and data analytics, where people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. Although they may sound similar, the terms are often quite different and have differing implications for business.

Enterprise Applications of the R Language (EARL) Conference – San Francisco

It’s the home of big name companies like Google, Salesforce, Twitter, Uber, Airbnb and Yelp. San Francisco is the perfect place to celebrate and share the power of R. The Enterprise Applications of the R Language (EARL) Conference is happening in San Francisco on June 5-7, 2017.

BlueData, HPE Combined Solution Works to Tackle Big-Data-as-a-Service Challenges

Big-Data-as-a-Service is growing in popularity and evolving to better suit the needs of today’s businesses. A recent white paper from BlueData describes a new solution for Big-Data-as-a-Service combining the BlueData EPIC software platform with the HPE Elastic Platform for Big Data Analytics.

7 Steps From Raw Data to Insight

Data scientists generally ascribe to the “machine learning process” which is seen as a roadmap to follow when working on a data science project. The infographic at the end of this article provides a detailed work flow that it is general enough to encompass pretty much any data science project.

Trifacta Reveals Spring ’17 Release to Accelerate Expansion of Data Wrangling Projects in Large Scale Enterprise Environments

Trifacta, the global leader in data wrangling, announced the Spring ’17 Wrangler Enterprise release, accelerating expansion of data wrangling projects in production environments through advancements in self-service scheduling, sharing and sampling capabilities. With the Spring ’17 release, Trifacta now provides enhanced features that meet the growing expectations for deploying data wrangling solutions at enterprise-wide scale.

TickSmith Releases a Python Tool for the New Generation of Financial Data Scientists

TickSmith, a leader in Big Data applications, released an open-source Python API feature to obtain data from its flagship TickVault big data platform.  Based on Hadoop technology, TickVault processes, stores, and analyzes massive amounts of capital market data. The addition of the Python API  toolkit to TickVault provides data scientists fine-grained access to historical exchange […]

Interview: Jennifer Marsman, Principal Software Development Engineer at Microsoft

In this podcast interview, I caught up with Jennifer Marsman, Principal Software Development Engineer at Microsoft, to find out what it’s like to be a data scientist at Microsoft and get her take on the upward trajectory of AI and deep learning that we’re seeing in the industry today.

DataScience.com Rolls Out Extensive Update to Enterprise-Ready Data Science Platform

DataScience.com has rolled out extensive new features to accommodate the requirements of enterprise data science teams including deployment of  the platform on-premise and across multiple public cloud providers. The latest release expands enterprise support for data scientist’s tools of choice,  such as Jupyter and RStudio, in addition to support for Python, R, Spark, and SAS. […]