This Month in Data Science: January 2016

January 29, 2016 Paul M. Davis

This month in data science for January 2016To kick off the first month of the New Year, we are beginning to see how people are moving data science from “hype, hype, hype” to a more pragmatic outlook. In this month’s round-up, we cover ways to organize data science teams, where AI is taking financial traders, the personalization holy grail, wearables and advanced analytics, the view of data at Davos, how data science can’t predict hit products yet, and related Pivotal news.

Organizing Data Science Teams for Strong ROI

Data is quickly growing alongside advanced analytics, but how do you design the structure, roles, and approaches to build a winning organization? Professional sports teams know that great members alone don’t necessarily win championships—greater cohesion is needed. This article recommends a combination of data analyst, data management, quality assurance, data engineering, data science, and data science product management to win, saying the right model will improve ROI and output to realize the full potential of data science investments.

The Artificially Intelligent Hedge Fund

Though algorithmic trading and software-based modeling has existed for some time, we are entering a new era in trading. Companies like Aidyia, Sentient, Two Sigma, Renaissance, Bridgewater, and Point72 are relying on artificial intelligence to automate trades without human intervention. This goes beyond the commonly found quant models and moves into systems that learn and predict. One of the systems was developed with an ex-member of the Siri team, who explains its use of genetic computation.

After Decades, Is True Personalization Finally Here?


Since the database marketing field emerged in the 1980s, marketers have sought personalization, and, according to this article’s cited research, only 31 percent say they use customer data to provide better experiences while only 5% of marketers have advanced to a predictive understanding of the customer journey. However, it looks like 2016 will be the year thought leading marketers achieve this holy grail. Proof points and perspectives are shared by Lyft, Shopkick, Poshmark, Smule, and Walmart at the recent 2016 Mobile Resolutions Conference.

The Future Of Wearables According To Jawbone’s Data Science Leadership

Wearable technology has already begun to change how we live, and data science is playing a big role in the future. This QCon SF interview with Jawbone’s data science leader, covers the current state and future outlook of Jawbone’s focus areas—fitness trackers, sleep research, personalized coaching, healthcare, data science, and machine learning. His team is becoming quite important, considering their CEO recently said the company is thinking of itself as a software and data company, not just a hardware company.

Straight From Davos: The World Economic Forum’s View On Big Data

From this attendee’s viewpoint, “It feels like data is no longer a fad, but a minimum qualification. Everyone wanted to be more data literate.” He goes on to cover two of the stand-out themes. First, one CEO wanted his company to stop using data and really start understanding the customers and the problems at hand—providing a foundation for context to trump insights. Second, one thoughtful venture capitalist argued, “Data is an asset and has option value.”

According To Netflix CEO Reed Hastings, Informed Intuition Is The Mantra

While Netflix is known as a heavy investor and believer in big data and advanced analytics, their leadership team sees informed intuition as the best approach. He goes on to explain how they manage their products like an investment portfolio, saying, “It’s a simple fact that data science isn’t sophisticated enough to predict whether a product will be a hit.”

Our Top 5 Predictions For Big Data in 2016

In this post, Pivotal’s Stacey Schneider summarizes what our big data team sees coming in 2016 and why it is on the way. What is covered? Real-time analytics, artificial intelligence, standards productivity in the Hadoop ecosystem, strategic roles for open source and data, and disruption by the incumbents. The area of big data and data science continues to emerge in the era of digital transformation.

Jeff Kelly On Your Need For An Open Source Playbook In 2016

Former Wikibon analyst turned Pivotal evangelist, Jeff Kelley, cites research on open source software’s takeover in corporate America. He then focuses in on Hadoop and big data to explain why open source is critical to big data success—it’s superior software, it’s got the smartest people behind it, and it’s anti- vendor-lock-in approach is loved by many.

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