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CIO Influence Interviews Cloud Data Management

CIO Influence Interview with Ed Anuff, Chief Product Officer at DataStax

CIO Influence Interview with Ed Anuff, Chief Product Officer at DataStax

“DataStax helps enterprises leverage the power of real-time data and quickly build smart, high-growth applications at unlimited scale, on any cloud.”

Please tell us a little bit about your journey in the data management industry and what inspired you to start at DataStax.

I came into the world of data management through the lens of all things applications: From building applications to customer engagement systems, I’ve had the opportunity to create innovative consumer and enterprise products for the past 25 years. My background in application development is a bit different when compared to people who came into this industry from the analytics/reporting side of things, so I’m able to provide a unique perspective that is much more focused on the user experience.

Prior to DataStax, I was the Director of Product Management at Google and Senior Vice President, Product Strategy at Apigee. I’ve worked with Chet Kapoor, DataStax CEO, at both previous roles and believe in his leadership abilities. Together, I knew we could build a tremendous cloud product at DataStax that organizations look to so they can leverage the power of real-time data across their business.

What is DataStax and what are your core offerings? What does your product roadmap look like for cloud and open source software makers? 

DataStax is the real-time data and AI company. DataStax helps enterprises leverage the power of  real-time data and quickly build smart, high-growth applications at unlimited scale, on any cloud. Hundreds of the world’s leading enterprises, including Verizon, Audi, ESL Gaming and many more rely on DataStax to unleash the power of real-time data to win new markets and change industries.

DataStax offers an open data stack that enables real-time applications by unifying operational data-at-rest and streaming data-in-motion. One core component is the Astra DB cloud database, a serverless, multi-cloud database service built on Apache Cassandra that is optimized for real-time applications that require large data volume, low latency, and flexible data models. Astra DB makes it easy to deploy and manage databases that scale automatically with demand, on any cloud provider a business chooses. It eliminates operational overhead, the biggest obstacle to using Apache Cassandra, the open-source NoSQL database behind the largest applications in the world, including Netflix and Instagram.

In June 2022, DataStax announced the general availability (GA) of Astra Streaming, an advanced, fully-managed messaging and event streaming service built on the open source Apache Pulsar to continue advancing its company mission.

Recently, DataStax announced a major acquisition in the AI/ML space with Kaskada, a machine learning company that will add AI capabilities to their database offering for the first time. Bringing this technology to the DataStax Real-Time Data Cloud, gives organizations a single environment for building real-time applications that have instant access to data with AI-driven insights – a must have for delivering effective recommendations, forecasts and risk assessments for transactions.

Additionally in February 2023, DataStax launched Astra Block, a web3 developer feature,  that allows companies to load the entire Ethereum Data set into Astra DB with the click of a button –  helping developers build applications for Web3 and crypto innovations.

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The last two years have accelerated digital transformation for businesses of all sizes and stature. What has been the biggest lesson for you that helped you stay on top of your product innovation goals? Would you like to share your pandemic experience on how you managed to continue your product development works during these uncertain times?

Prior to the COVID-19 pandemic, DataStax was already a decentralized company and operating as a distributed workforce, so the pandemic didn’t pose significant challenges when it came to staying on top of our product innovation goals. Resiliency was already built into our system which allowed us to continue innovating and building products during uncertain times. The pandemic was a reinforcement of the benefits of a distributed workforce. Collaborating in a distributed fashion isn’t something that should only be enforced during uncertain times. It should be something that companies always do and have embedded into their business model.

I highly encourage businesses to revamp their offices to appeal to a hybrid workforce and operate as a  distributed company. This approach is what ultimately allowed our team at DataStax to quickly adjust to the realities of work during the pandemic and continue achieving our product development goals. Distributed work empowers employees to work together from wherever they’re most productive, whether it’s on-site, off-site, or a mix of both. The key focus is to build a culture of collaboration that reduces friction and enables faster execution – in an effort to best serve customers.

There is so much buzz around AutoML and AIOps in the data management landscape. How do you stay abreast of the latest trends and developments in AI and ML?

AI/ML is a very deep and complicated topic that is evolving quickly. I can’t emphasize enough how important it is for leaders within this industry to stay vigilant with learning and keeping abreast of the latest developments and trends. It’s going to take a lot more than just reading a recent New York Times article about ChatGPT to keep up with this constantly evolving industry area.

A great news resource is Techmemes or Hacker News; these tend to be the main places where a lot of information on the latest developments flow through. I also suggest attending events and conferences or online courses offered through platforms like Coursera to stay up to date with what’s happening in the world of AI/ML development.

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Which AI product in the recent months made the most profound impact on your business vision? What do you think about the future of generative AI and semi-supervised ML algorithms for data architecture?

The AI product that made the most profound impact on DataStax’s business vision is our real-time machine learning (ML) capabilities delivered via our recent acquisition of Kaskada to enable rapid delivery of AI-driven applications at scale.

Most machine learning initiatives don’t deliver the results that businesses need because the process is manual, complex and frustrating. Compounding this problem, many models underperform because they lack the relevance and context of real-time data. The addition of Kaskada to DataStax’s portfolio of cloud services—which today includes the massively scalable Astra DB database-as-a-service built on Apache Cassandra and event streaming with Astra Streaming— give organizations a single environment to easily and cost-effectively deliver applications infused with real-time AI, using an advanced ML/AI model proven by industry leaders such as Netflix and Uber.

What is the future of multi-cloud database-as-a-service in the era of AI and RPA?

Thinking about where your data sits in relation to where your AI sits is going to drive a lot of future conversations within the industry. Data has gravity, – particularly as we think of multi-cloud and hybrid, and the reality is that data is very expensive to move. When you think about where your data sits – whether it’s in the cloud or the data center – you’re going to be driven by the following question: “Can I bring AI capabilities to where my data currently sits or will I need to bring the data to where I can deploy AI?” When planning for where to store data, AI must also be part of the equation.

Another big question will be whether or not you can use ML from hyperscaler cloud providers. This will drive a lot of future decisions as companies will have to identify if they can use a database-as-a-service (DBaaS) option that allows for flexibility or if they’ll have to use services like AWS and Google Cloud, where they lose the optionality of where they can store data.

In conclusion: Mult-cloud data will require multi-cloud AI.

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An event/ conference or podcast that you have subscribed to consume information about the B2B technology industry: If invited, would you like to be part of a podcast episode on Martech Tech/ IT/ Cloud / AI research?

An event I highly recommend attending is Apache Cassandra’s upcoming Summit which should be amazing because there will be exclusive insight into the next generation of features and cutting-edge use cases. I also definitely recommend listening to Sam Ramji’s Open Source Data podcast – it’s a good one!

Thank you, Ed! That was fun and we hope to see you back on soon.

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DataStax is the real-time AI company. With DataStax, any enterprise can mobilize real-time data and quickly build smart, high-growth applications at unlimited scale, on any cloud.

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