AiThority Interview with Buno Pati, CEO at Infoworks

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How do you stay updated on AI? How do you interact and integrate with AI products and solutions?

I have been deeply involved with AI since my doctoral studies in the 1980s and early 1990s. For the past five years, at Centerview Capital, I have focused on investments in Data Analytics, AI, and Cybersecurity, which has given me a great deal of visibility into the most recent wave of developments in the field and new commercial applications of AI.

As CEO of Infoworks, the topic of AI is of great interest to our customers and it is one of our most frequently encountered customer use cases.  AI is data-hungry and demands speed and agility. Experimentation and rapid iteration is central to discovering the models and applications that deliver the most value to the business. Through automation and end-to-end integration of functionality, the Infoworks Enterprise Data Operations and Orchestration (EDO2) system delivers data to AI (and other analytic) applications with the agility, speed, and flexibility required for our customers to succeed with AI.

On a personal note Like most consumers, I am surrounded by a growing suite of AI that I interact with on a daily basis. However, I find that most consumer AI products still have some distance to go before they can be relied on to do the right thing with great certainty. More experimentation and iterations are required.

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How did you start in this space? What inspired you to lead Infoworks? 

My journey with Infoworks started in 2014 when I met Amar Arsikere, our co-founder, Chief Product Officer, and CTO. He told me of his work at Google, where he led the creation of the first data warehouse on Bigtable six years prior a daunting and large-scale task that required a great deal of automation. He also told me of a similar effort that he later led at Zynga. His hypothesis was simple  while Google may have been the only company that needed such an automated enterprise-wide data platform at the time he built it, he believed that every business would eventually need a similar system to compete effectively in an increasingly digital world.

I joined the Board of Infoworks as Chairman, we obtained initial funding, and set out to build our product, working with one of the largest healthcare companies in the world. In 2016, as Infoworks continued to win Fortune 500 customers, my partners and I at Centerview Capital decided to lead the second round of financing even though the company was at an earlier stage than our primary investment focus.

We quickly realized that Infoworks was in the rare and enviable position of having the right product to address a very large market need, at exactly the right time; so six months ago, as the company entered a high-growth phase, I had the honor of being asked to run the company as CEO and my partner Dave Dorman, who was formerly the Chairman and CEO of AT&T, joined the company as Chairman.

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What are the major differences between serving as a board member and as a CEO? What do you prefer more?

Over the past 20 years, after taking my first company public and running it as CEO for 8 years, I have found myself on quite a few boards. As a board member, I have always viewed my primary job (excluding fiduciary and governance responsibilities) as helping management make good decisions about strategy, organizational issues, dealing with challenging situations, and other topics where an experienced perspective that is not “muddied” by the day-to-day operational activities can be helpful. I have also found that I often can add value as a mentor, coach, and sounding board for the CEO. As CEO, I look to my Board to fulfill the same functions, as it is sometimes challenging for me to step back from the day-to-day activities and take a fresh perspective. I cannot say that I prefer one role over the other, but I can say that I am thrilled to be the CEO of Infoworks.

How do your products achieve end-to-end data operations and orchestration? 

The most common issue that we see data and analytics teams facing, is an inability to keep up with the deluge of new analytics use cases arising from the accelerating pace of business. This issue is largely rooted in the use of legacy methodologies and point tools that require a persistently growing team of skilled data engineers and developers to onboard data and create analytic pipelines to deliver data to applications.

There are many “first-generation” Big data software tools on the market that solve point problems associated with on-premise or cloud big data environments. The problem is that they force businesses to figure out how to make it all work together. These hand-coded approaches with stitched-together point tools have failed to deliver the required level of agility, speed, and flexibility.

The Infoworks Enterprise Data Operations and Orchestration (EDO2) system was designed from the ground up with a far more holistic view, based on Amar Arsikere’s experience building the systems at Google and Zynga. The core design principles were: i) deep automation ii) end-to-end-integration of functionality, and iii) abstraction from specific big data infrastructure (e.g. Hadoop, AWS, Azure, GCP).  With our holistic approach and core design principles, we enable our customers to keep pace with the demands of business, while dramatically lowering the demands on specialized talent. We deliver a code-free environment where our customers can configure inbound data flow, design and create sophisticated data pipelines, and launch new analytics use cases in a manner that reduces the time-to-value by as much as 100x and requires one-tenth the resources.

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How do your solutions automate the processing of Big data? 

The workflow and tasks associated with Big data analytics are complex. These include ingesting data, incrementally updating the data, organizing and managing data and metadata, creating analytic pipelines, optimizing pipelines and models for query performance, delivering data to applications, and orchestrating and governing all of these activities. Data scientists currently spend about 80% of their time on these tasks and only about 20% of their time on developing applications to further growth and competitive advantage for the business

By automating the bulk of these time-consuming tasks Infoworks enables data scientists to focus their talents on creating value for the business.

What are the biggest challenges and opportunities for AI companies in dealing with rising technology prices? 

Amongst the biggest challenges faced by AI companies is the challenge of acquiring the right talent to create valuable AI products. The talent required is very scarce. In fact, according to Glassdoor, a data scientist is the #1 in-demand position in the United States.  If this scarce talent, once found, spends 80% of their time acquiring and preparing data, the cost of development and the time-to-market both suffer greatly. Automation, such as we offer in our Enterprise Data Operations and Orchestration (EDO2) system, is a big opportunity for AI companies to accelerate time-to-market and reduce the cost of development.

Where do you see AI/Machine Learning and other smart technologies heading beyond 2025? 

I believe that the pervasiveness of AI and Machine Learning in our lives, both at home and in our businesses, will continue to grow at an accelerated pace and lead to noticeable societal change. In comparison to my early days in the field in the 1980s, we now have the support of adequate computing power, data availability, and network connectivity to derive meaningful value from AI and ML. While we are seeing emerging and impressive applications of AI such as self-driving cars and personal digital assistants emerge, it is almost certain that most, and likely the most impactful, applications of AI are yet to be discovered. With increasingly agile and automated data operations, we will see an accelerated pace of discovery and innovation in the field.

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What AI start-ups and labs are you keenly following? 

As a venture investor, I spent years following every AI start-up that I could find. As such, it would be difficult to make a short list. However, I believe that AI start-ups that are narrowly focused on specific and well-defined problems in an industry vertical have an opportunity to make the greatest impact in the short term.  Healthcare and cybersecurity are great examples of where I believe AI and ML solutions can have a significant short-term impact.

What technologies within AI and computing are you interested in? 

One of the technologies that I find particularly interesting within the world of AI and computing is the development of specialized processors for AI and Machine Learning. There are a number of startups and larger companies focused on this development. Given the limited set of operations required and the speed at which those operations must be performed, it seems natural that such specialized processors will emerge and expand the scope of AI applications beyond the capabilities of general purpose processors.

What’s your smartest work-related shortcut or productivity hack?

My smartest work-related shortcut and productivity hack is neither a shortcut nor a hack. The secret lies in hiring a great team and fostering a great culture. Talented and capable people working together in an energized and empowered culture contribute more to productivity – both personal and corporate, than any shortcut or hack that I know.

What would have been your alternate career choice if you were not working in the field of technology?

If I were not working in technology, my alternate career choice would have been as a university professor specializing in ancient history. I have always been fascinated by how little we know about our past. I also truly enjoy teaching, as I learned while I was on the faculty of Harvard University early in my career. A combination of history and teaching would have been a natural alternative for me.

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Tag the one person in the industry whose answers to these questions you would love to read. 

Marvin Minsky, Professor at MIT. He helped create the field of AI over a long and distinguished career and helped coin the term Artificial Intelligence in the 1950s. Unfortunately, he passed away in 2016 and so I will have to imagine what his answers would have been.

Thank you, Buno! That was fun and hope to see you back on AiThority soon.