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Analytics For The 2019 Enterprise

Founded by the creators of unified analytics engine Apache Spark, San Francisco-headquartered Databricks provides a Unified Analytics Platform for data science teams to collaborate with data engineering and lines of business to build data products.

The Unified Analytics Platform accelerates innovation by removing silos and unifying data science, engineering and business, enabling enterprises to adopt tangible Artificial Intelligence (AI) and machine learning (ML) strategies.

“The organizations that succeed in unifying their data at scale with the best AI technologies will be the ones that succeed with AI,” says Jason Bissell, newly appointed general manager and senior vice president for Asia Pacific and Japan, Databricks.

With the year coming to an end, and businesses looking ahead to 2019 and beyond, Enterprise Innovation tapped on Bissell’s more than 25 years of experience running hyper-growth tech companies, for his views.

How do you think AI and machine learning are going to change our world in 2019 and beyond?

Bissell: The ability for AI to change the world for the better is undeniable.

Looking forward to 2019, some of the most exciting developments we see are in the healthcare industry where large volumes of genomics data, health records, medical imaging and the like are being used to help predict disease and enable clinicians to prescribe more personalized treatments based on an individual’s unique biology.

But the use cases aren’t limited to one industry.

In fact, the bigger trend I see in the year to come is an increasing number of vertical-specific solutions and libraries that incorporate the newest ML techniques to deliver on industry-specific use cases like real-time securities fraud detection, highly tailored shopping experiences or predictive maintenance on the factory floor.

What is the “99% versus the 1%” problem and how does Databricks intend to help with this?

Bissell: The impact AI is having is unprecedented, but a huge gap exists between the companies that can deliver on AI and those who cannot.

The 1% that can deliver on AI today have armies of data scientists and engineers that can sift through the complex zoo of big data and AI technologies, stitch them together and manage the infrastructure.

The other 99% of enterprises are struggling. In fact, in our recent survey, executives shared that only 1 in 3 AI projects are actually successful.

We want to flip this on its head and make the other 99% successful by making ML accessible.

First, we’ve automated much of the DevOps work of setting up infrastructure  that distracts data scientists and engineers from high value activities.

Then we unified data and AI workflows in a collaborative workspace so cross-functional teams can work together and innovate faster.

Finally, we brought together the latest ML frameworks and tools like TensorFlow and Keras into our Unified Analytics Platform so teams can build the most powerful models possible with the tools they love.

What needs to be done in Asia Pacific to allow enterprises of all sizes to benefit from AI and machine learning?

Bissell: Whether you have a team of 50 or 5, enterprises in Asia Pacific hoping to take advantage of AI and machine learning need to focus on providing their teams with infrastructure and tools that allow them to focus on high value activities like data exploration and model building.

If your teams are spending the majority of their time on managing on-prem infrastructure or setting up clusters, then you’re missing the boat on innovation.

Additionally, they should be considering a unified approach to analytics that enables teams to work together on shared datasets. A lot of productivity is lost when teams are moving data or models across disjointed tools and platforms. Not to mention the security gaps this creates when toolsets aren’t natively designed to work together.

Why is open source important for big data and machine learning?

Bissell: 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.

Delivering on ML projects is a challenge. The problems that need to be solved are complex and require a broad set of expertise across a lot of different domains. On top of that, data scientist and engineering skills are in high demand, but in low supply.

By open sourcing big data and ML projects, the community allows us to bring these limited resources together to spur development and support the greater good, enabling a broader set of organizations to drive innovation. – CT Bureau

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