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Can Automation Save Big Data?

Click to learn more about author Amar Arsikere. “What would you think if I sang out of tune? Would you stand up and walk out on me?” The Beatles song everybody knows could also pass as the wilting rallying cry for Big Data, whose “tune” does seem out of sorts these days as more people question […]

Chicago Crime Q2, 2018 Update

Click to learn more about author Steve Miller. In my April blog, I reported an update on violent crime in Chicago from January 2001 through March 2018. This is a continuation of analysis I’ve regularly conducted, born of concern for the staggering 50+% increase in Chicago homicides in 2016 from 2015, along with an only […]

Data Profiling: The First Step to Data Science

Click to learn more about author Mark Hensley. In 2012 former Chief Data Scientist of the United States DJ Patil and Babson College Professor Thomson H. Davenport famously declared that the job of Data Scientist would be the “Sexiest job of the 21st Century.” While that may true, most Data Scientists spend the biggest chunk […]

Streamlining Real-Time Data Integration and Analytics: The Struggle is Over

Data Architects and CDOs today face a variety of demands, including delivering Real-Time Analytics, predicting customer behavior, or operationalizing Machine Learning. All of these initiatives share one critical requirement: real-time data. But to get value from real-time data, they must enable continuous collection of data, and on-the-spot integration in-flight across heterogeneous systems. For years, companies […]

Data Science Compensation Survey

Click to learn more about author Steve Miller. I recently came across an article in Forbes on the salaries of Data Scientists. The piece summarized findings from the just-published 5th-annual Burtch Works Study: Salaries of Data Scientists. The study is generally well-done, providing insight into trends and breakdowns of compensation for Data Science professionals. I […]

The Case for Agile Machine Learning

Click to learn more about author Paul Barba. Agile methodologies have become the go-to approach for Data Science projects. A central element of these methodologies is iterative development. In an Agile project, solutions and requirements are constantly evolving based on new understandings, input and feedback. Unsurprisingly, Agile project management methodologies are characterized by short development […]

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