Predictive analytics is the sweet spot where machine learning meets the enterprise. Analytics & predictive algorithms fused together mean we can now gains insight into how future patterns and trends may develop. This type of technology has ramifications across industries, but many are left clueless about the development and applications in this field. But never fear- we’re here to help. These 10 influencers provide a fascinating and hassle-free entry point into the world of predictive analytics.

As always, a note on our methodology: we discovered and ranked influencers based on Twitter activity around “#PredictiveAnalytics” and “Predictive Analytics”, using Keyhole, FollowerWonk, Klout, and a little of our own algorithmic magic to compile the sources. The list contains influencers from a diverse range of backgrounds, including media moguls, serial entrepreneurs and academics. Still have a burning passion to tell us who our formula missed? Be sure to let us know in the comments.

Top 100 Big Data Influencers on Twitter KDNuggets1. Gregory Piatestsky (@kdnuggets)
Gregory is the President of KDNuggets, who have been covering innovations in the fields of analytics, data mining and big data for almost 20 years, so it’s no surprise to see his name come up on this list. He recently published some fantastic analysis on Gartner’s Advanced Analytics Magic Quadrant– in short, Microsoft has made leaps and bounds in this field, Alpine Data Labs & Alteryx are on the rise, and SAS continues to lead the field.

10 Predictive Analytics Influencers You Need to Know vv 2. Vineet Vashishta (@v_vashishta)
Vineet is a consultant based out of San Francisco, with almost 20 years experience in data & statistical analysis. His consultancy firm now specialises in predictive analytics & data science. Recent predictive analytics tweets from Vineet include this fantastic article on “the ethical blindess” of algorithms & predictive models, and how to make your predicitve analytics strategy more pervasive. 

10 Predictive Analytics Influencers You Need to Know AK 3. Aki Kakko (@akikakko)
Boasting over 32,000 Twitter followers, it’s little wonder that Aki Kakko made the final list. Aki is a serial entrepreneur, who is currently a Co-Founder & Head of Product for Joberate, a predictive analytics-fuelled HR tool which mines publically-available digital footprints to match the right candidates to companies. His Twitter is also a treasure trove of HR insights.

10 Predictive Analytics Influencers You Need to Know MG4. Mike Gualtieri (@mgualtieri)
I have fond memories of watching Mike’s “What is a Data Scientist?” tutorial back in my early days of researching data science, and can firmly attest that Mike Gualtieri deserves his place on this list. As Forrester’s analyst for Big Data, Hadoop, Spark & Predictive Analytics, Mike has his finger firmly on the pulse of news & insights from across the data science sphere, which he shares both on Twitter and on his Forrester blog. His Twitter bio also states he enjoys “improvisational swing dancing” with his wife- always good to know.

10 Predictive Analytics Influencers You Need to Know MW5. Michael Wu PhD (@Mich8elWu)
Dr. Michael Wu is the Chief Scientist at Lithium, where he uses complex data-driven methodologies to analyse the social web. As well as predictive social analytics, Michael is also passionate about gamification, analytics and data mining. Recent awesome predictive analytics projects he’s shared include predicting the Oscars and an app that can predict the future price of flights. 

10 Internet of Things Influencers You Need to Know TE6. Timo Elliott (@timoelliott)
Recently also making an appearance in our Internet of Things Influencers list, it appears our formula loves Timo’s tweets. Timo is an Innovation Evangelist for SAP, and well-versed on all things business analytics and future-facing tech. As well as tweeting about the recent release of SAP Predictive Analytics 2.0, he’s also shared this fantastic list of predictive analytics success stories– well worth a read.

10 Predictive Analytics Influencers You Need to Know BB7. Brian Burke (@Adv_NFL_Stats)
A niche player, but a significant one. Brian Burke runs, which has been providing realtime predictive NFL analytics since 2009. Recent analytical highlights he’s shared include his own piece on the value of a good analytics programme, ESPN’s rankings of how well teams have adapted to the analytical age, and this piece on probablistic valuation on player’s contracts.

10 Predictive Analytics Influencers You Need to Know DP8. Dirk van den Poel (@dirkvandenpoel)
Dirk is A Professor of Marketing Analytics, Analytical CRM, Predictive Analytics & Big Data at Ghent University in Belgium. Dirk’s course focuses on using R, Hadoop, Spark and Python to delve in to the analytics life cycle, with a particular focus on predictive analytics for CRM. He regularly tweets about his classes, European lectures and events, and the odd entertaining image about being a Professor.

10 Predictive Analytics Influencers You Need to Know DE9. Dave Elkington (@DaveElkington)
Dave is the founder of Inside Sales, a company who offer a sales acceleration with no less than 4 predictive analytics products. The most famous of these products is NeuralView, a self-learning predictive analytics engine which claims to be able to boost sales by an impressive 30%. The company celebrated their 10th birthday last year, so it’s safe to say Elkington is a knowledgeable source on the evolution of the fast-paced predictive analytics industry.

10 Predictive Analytics Influencers You Need to Know RL10. Ronald van Loon (@Ronald_vanLoon)
Ronald is the Director of Business Development for Adversitement, who specialise in on & offline customer journeys. Their work involves reducing churn by predicting which customers may soon jump ship, and predicting buyer intent to offer more sophisticated and relevant product recommendations. Recent content shares from Ronald include Using Decision Modeling to Make Predictive Analytics More Pervasive, and this insightful piece from NextGov on why most companies don’t need real-time analytics- yet.

(Featured image credit: William Warby, via Flickr. Because everyone loves a bit of Back to the Future.)

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  • Meta Brown

    Eileen, if your list of predictive analytics influencers includes only men, you ought to get out and meet some of the many women in analytics. You can find some names to get started in my Meta’s Binder Fulla Women in Analytics, Over 160 profiles of women in analytics are already posted there, and I will be posting profiles of 262 female analytics book authors soon.

    • Daniel Gray

      Thanks for the comment Meta, and the interesting resource you linked.

      As Eileen mentioned in the article intro, the list is based off quantifiable data rather than opinion to provide an impartial measure of influence. It might not have provided the most interesting list, but it does achieve what it set out to do.

      That said, you’ve certainly highlighted a hole in our coverage that we’ll aim to rectify in the near future. Thanks for the feedback.

      • Meta Brown


        You must be joking. No good data analyst believes that activity around a couple of twitter hashtags represents an “impartial measure of influence”. You’re measuring a certain type of popularity, but that’s not the same as which voices are important in this, or any, profession.

        • Daniel Gray

          You make a very good case. Would you be interested in constructing a similar list with a more thoroughly analytical approach?

          • Meta Brown

            No. I wrote years ago (The STEM Profession that Women Dominate about the many data sources data indicating that nearly half the analytics profession is female. That work is done.

            Since then, I have turned my attention to sharing information about the many accomplished women in this field, and the biases that prevent them from enjoying recognition and rewards they deserve. I have invested many hours of my life profiling and promoting women in analytics, and will continue to do so. I hope that you and others will take advantage of those profiles, and use them as a starting point to diversify the analysts highlighted in your writing.

  • Vineet Vashishta

    I’d like to recommend readers check out Maryam Danes-Kajouri @DKajouri and Rachel Clinton @RachClinton. They’re both excellent sources for predictive analytics on Twitter.

    • Meta Brown

      Vineet, thanks! I’m looking forward to learning about Maryam Danes-Kajouri (and I already follow Rachel Clinton).

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  • Heads up, Brian Burke’s Twitter handle is listed incorrectly, it should be @bburkeESPN.

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