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Databricks recently announced that it is accelerating efforts to drive growth in the Asia Pacific & Japan (APJ) region with the launch of the company’s Singapore office as well as appointment of industry veteran Jason Bissell as General Manager & Senior Vice President.

Founded by the creators of Apache Spark, Databricks’ cloud-based Unified Analytics platform was designed to provide a simplified approach for data science and data engineering teams to make data-driven business decisions based on big data analytics and artificial intelligence (AI) and accelerate innovation.

In November, Deloitte ranked the company 18^th in its top 500 list of the fastest growing technology, media, telecommunications, life sciences and energy tech companies in North America. In this region, however, Databricks has yet to become a prominent name but that could change over the next few of years as AI increases its foothold in APJ.

DTA managed to get in contact with Mr. Bissell and asked him to share his views on Databricks’ expansion in the region and the opportunities that lie here.

The following is the full transcript of the email interview.

DTA Editor: Why is Databricks is targeting the Singapore and the APJ market. Why now, and what is the opportunity?

Jason Bissell: Databricks works with global enterprises which makes a global expansion a natural next step. Asian companies are looking to be at the forefront of innovation and are investing in AI, but like many organisations, few are succeeding. The primary reasons behind these challenges are that organisations face data-related problems like silos and inconsistent datasets, as well as significant organisational friction like lack of collaboration between data scientists and data engineers. Databricks’ Unified Analytics Platform is a solution for these challenges – the approach of unifying data science and data engineering across the machine learning lifecycle will conquer the AI dilemma.

DTA Editor: The press release mentioned briefly about the ‘AI dilemma’. Can you explain what this is in further detail and how Databricks can help organisations in the region overcome this dilemma?

Jason Bissell: Large amounts of reliable data that data scientists can iterate on is the key to AI success. But organisational silos between data science and engineering cripples the iterative model development process. And to make matters worse the divide between today’s data and AI technologies increases complexity throughout the lifecycle further slowing down AI. Unified analytics addresses this AI dilemma by providing an end-to-end analytics platform that unifies big data and AI, while fostering better collaboration between data science and engineering teams.

DTA Editor: You also said, “The organisations that succeed in unifying their data at scale with the best AI technologies will be the ones that succeed with AI.” With so much hype around AI, how do companies determine which AI technologies would best serve them?

Jason Bissell: With unified analytics, companies do not need to decide. Unified Analytics solutions provide collaboration capabilities for data scientists and data engineers to work effectively across the entire development-to-production lifecycle.

DTA Editor: Can you please tell us more about how Databricks works with AWS, Microsoft, Snowflake and RStudio and why Databricks is committed to open source.

Jason Bissell: Only one in three artificial intelligence projects are successful today because enterprises are struggling with data-related problems like silos and inconsistent data sets. To address these challenges, Databricks develops partnerships to accelerate innovation and streamline integration for customers doing big data analytics and machine learning. Our partnerships with companies like Snowflake and RStudio, allow users to leverage data-driven solutions on one platform.

Our alliance with Microsoft is a major milestone for the growth of Databricks’ Unified Analytics Platform. There’s a large base of Microsoft Azure customers looking for a high-performance analytics platform based on Apache Spark – and Databricks is already the leading Cloud platform for Apache Spark. These organisations will be able to simplify Big Data and AI with Azure Databricks.

The concept of open source software has been around for many years. And for good reason, as it’s shown to significantly improve product quality, speed innovation and drive widespread adoption through engagement with communities of passionate developers. There are no markets in which open source is more critical than big data and machine learning. By open sourcing big data and ML projects, the community allows us to bring limited resources together to spur development and support the greater good, enabling a broader set of organisations to drive innovation.

To read the full press release of Databricks’ recent announcement, click here.

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