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Ari Kaplan, Head of Technical Evangelism, Databricks

Describe your career to date

It has been enlightening to have a career spanning major paradigms of enterprise data: databases, the internet, mobile business software, moneyball analytics, lakehouses, and now generative artificial intelligence (genAI) and data intelligence platforms. 

 I am largely known for being one of the early pioneers of the sports analytics industry, being part of the inspiration of Moneyball, and creating the Chicago Cubs analytics department. Sports analytics is such a great example of how data has changed an industry – both technology and the culture around it.  

My career started as a student at Caltech, where I got a fellowship to come up with improved ways to evaluate talent with data. Fred Claire, Dodger’s GM, gave me my first opportunity as a teenager. After graduating I joined Oracle in their earlier days and eventually became President of the worldwide Oracle users group at the time Oracle acquired MySQL, Java, and Peoplesoft.  

Up next was the mobile revolution, where I co-founded one of the first mobile business software companies, Expand Beyond, raising $16 million in funding, enabling mobile data and database management before there was an iPhone or Blackberry.  

AutoML was the next paradigm, and at DataRobot I traveled the world with McLaren’s Formula 1 race team, doing amazing AI use cases.  

Now I am head of technical evangelism at Databricks, with its game-changing genAI and data intelligence platform, built on the success of creating the lakehouse architecture on open-source. 

What challenges do you see for data in the year ahead that will have an impact on you and on the industry as a whole?  

Companies are now realizing that the quality genAI models and machine learning productions they strive for still depend on having quality data. Many businesses are still foundationally struggling with siloed data, cloud migrations, and lack of ease to tap into unstructured data lakes – where great lengths are taken to collect data assets, yet 95% is not even accessed.  

Other companies are struggling with data coming in so fast that it is ungoverned, unable to handle streaming, or to scale without breaking their bank. And everyone needs better intelligence from their data. 

How do you see data literacy developing across a) your network and b) the data industry generally? 

My network is full of learners who understand data literacy is key to business and career survival. Those who are better equipped to understand the complexities of structured and unstructured data, find signals in the noise, and tell the story so that it can affect real-life, are those who will succeed.  

The data industry increasingly embraces democratization. It used to take a PhD in computer science to be able to run machine learning predictions, but now there are automation platforms. If a company can get incredible power when 100 times the number of employees can generate more intelligent data-driven insights. 

How do you see the industry preparing for AI adoption and change management? 

Most companies are experimenting with genAI, but few are in production. For those that succeed, AI adoption leads to fundamental improvements in business efficiencies, human interaction, and new innovations. Change is accelerating, and companies need to prepare and manage their change. They need to balance their innovation of art of the possible with their realism of defining tangible business goals, time prioritization, and resources.  

All industries have a justified skepticism of large language models hallucinating, leaking data, and limited transparency. Companies are preparing by adopting platforms that can govern both the large language models and underlying data. 

Ari Kaplan
has been included in:
  • 100 Influencers 2024 (USA)