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2017 State of Analytics Adoption Report

The annual State of Analytics Adoption Report by our friends at Logi Analytics provides insights for executives, product managers, and technology leaders on how broadly and deeply users are adopting business intelligence and analytics tools. The 2017 survey respondents included members of IT teams who provide analytics tools to end users, as well as the end users of BI and analytics tools.

The 5 Key Challenges to Building a Successful Data Science Lab & Data Team

In this special technology white paper, The 5 Key Challenges to Building a Successful Data Science Lab & Data Team, you’ll learn how a Data Lab establishes an effort to answer business needs by making sense of raw information. Data labs are intended to create critical mass within the organization that enables them to reach the level of innovation required for new data-driven products.

IoT Analytics – Part 3

This is the third article in a series focusing on a technology that is rising in importance to enterprise use of big data – IoT Analytics, or the analytical component of the Internet-of-Things. In this segment, we’ll provide an overview of the rise of IoT analytics.

“Above the Trend Line” – Your Industry Rumor Central for 1/16/2017

Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

NewVantage Partners Releases 5th Annual Big Data Executive Survey for 2017

NewVantage Partners, strategic advisors in big data and business innovation to Fortune 1000 businesses, has released the results of its 2017 5th Annual Big Data Executive Survey, entitled “Big Data Business Impact: Achieving Business Results through Innovation and Disruption.” The 2017 Big Data Executive Survey reports what executives from 50 Fortune 1000 firms see as the key factors driving big data adoption, investment – and success.

Putting Data Science in Production

In this special technology white paper, From Development to Production Guide – Finding the Common Ground in 9 Steps, you’ll learn how managing a successful data science project requires time, effort, and a great deal of planning. Defining the problems to solve and planning the project’s scope is just the tip of the iceberg, as team members need to fully understand all aspects of a project in order to effectively contribute.

Is the Pessimism Behind Big Data Unfounded or Legitimate?

In this contributed article, technology writer and blogger Kayla Matthews takes a high level view of big data by discussing how big data systems can help influence decision-making to reveal important insights. But it’s not always right. The algorithms and systems that big companies are using must be transparent enough that people can challenge decisions that have been made.

“Above the Trend Line” – Your Industry Rumor Central for 1/9/2017

Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Survey of Available Machine Learning Frameworks

The presentation below, “Survey of Available Machine Learning Frameworks,” is provided by Brendan Herger of CapitalOne as part of the H2O World 2015 conference. Learning a new modeling framework is time consuming, and doesn’t always pay off. However, as more feature engineering and modeling frameworks become available, its difficult not to leverage their abilities.

Monte Carlo Simulations in Ad-Lift Measurement Using Spark

In this talk from Spark Summit East 2016, Prasad Chalasani explores some of the challenges that arise in setting up scalable simulations in a specific application, and share some solutions and lessons learned along the way, in the realms of mathematics and programming.