What news media companies need to know about Big Data 2.0



Greg Doufas, vice president of data science and audience intelligence with The Globe and Mail, outlined for delegates of the INMA World Congress four major components to think about when it comes to Big Data: infrastructure and technology, data itself, talent, and value objectives.

These components then relate to four separate practices: transparency, experimentation, relationship management, and innovation.

Through these methods, data can be used to help understand and better accommodate the consumer. With experimentation, for example, new approaches can be tried without having to wait.

“The ability to change the form factor could take months, sometimes years,” Doufas said. “Here, we can experiment in real time.”

Laura Evans, vice president of audience development and data science for Scripps Networks Interactive, discussed the importance of data scientists and the power of understanding data through her definition of data science.

“Inherently a process, not a person,” Evans said. “Piece by piece taking data apart … and developing reports around it.”

This leads to a multi-step process that includes understanding what the most important goal is, working with a data warehouse, and reviewing and analysing incoming data.

Through this process comes lessons learned by the company. One of the lessons learned involves the way consumers interact or don’t interact with what they are seeing: “What they didn’t click is as important as what they did.” 

Irene Lee, director of data and nnalytics for Refinery29, discussed the different ways the company follows data and makes changes to posts and content accordingly. With publishing about 50 articles a day, understanding how that content is received and understood is important to Lee and her position in relation to other groups producing more content.

“We don’t have a lot of content when we compare ourselves to the other media companies that are here,” Lee said.

Lee uses data as input into an editorial calendar, which will help decide how many articles should be written and what the subject of those articles should be — all based on data. After that process is complete, content strategy people look at it again to see if anything was missed or if anything has changed.

With using data in the future, Lee is hoping to be able to add personalisation to the mix, where people would be able to receive content that they wanted to receive through their e-mail.

“Knowing what readers want to read and when they want to read it is very important,” Lee said.


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