The Six Five at Google Cloud Next ’22 with June Yang and Sudhir Hasbe, Google Cloud

The Six Five “On The Road” at Google Cloud Next 2022. Hosts Daniel Newman and Patrick Moorhead are joined by June Yang, VP, Cloud AI & Industry Solutions and Sudhir Hasbe, Sr. Director, Product Management, Data Analytics, Google Cloud. Their conversation focuses on Google Cloud’s Data Cloud and its ability to drive innovation through the use of a unified, open, and intelligent platform.

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Patrick Moorhead: Hi, this is Pat Moorhead. We are on the road here at Google Cloud Next in New York City at the brand new Pier 57 building. It’s exceptional and awesome and I might even want to come work here but not this year. I am also here with my co-host, Daniel Newman of Futurum Research. Daniel, how are you my friend?

Daniel Newman: I’m doing great. Just got back from the Keynote and super excited. It is really nice here, Pat.

Patrick Moorhead: It is. We were talking about like, “Is this a country club or a place to come work?”

Daniel Newman: I think that’s kind of the idea, right? It’s a little…

Patrick Moorhead: I think it is.

Daniel Newman: …bit of both. As we came out of the Keynote, there was sushi, there was salmon. The meals look delicious. I stopped and got a latte. I guess, though, we always talk about culture. You and I do a lot of research. We find in our research that companies that want to drive innovation tend to have great cultures. And I got to say, I don’t know if you ever get hangry, but I know that when I’m satiated, I tend to do my best work.

Patrick Moorhead: Daniel, we got to get focused here. Let’s introduce our guests here. June and Sudhir from Google Cloud, representing data NAI. Rather than me go in and pretend I know exactly what you do. Maybe, Sudhir, we’ll start with you on. What do you do for Google Cloud?

Sudhir Hasbe: I’m Sudhir Hasbe. I run product management for all of our data analytics services. So, everything from “how do we get data into data Cloud,” “how do you process it with Spark as well as different engine,” “how do you analyze it with products like BigQuery?” “How do you govern it with governance and management, business intelligence or anything?” “How do you visualize it?” “How do you get value out of it?” That’s the whole portfolio that I manage.

Patrick Moorhead: Yeah, I saw some big announcements today. That’s great. How about you, June?

June Yang: Hi everyone. My name is June Yang. I run our Google Cloud AI and Industry Solutions portfolio. It’s a very expansive portfolio. We have products that’s geared towards the data scientist and machine learning engineers. We have products that’s really geared towards developers. We also have products that’s really towards the business users, and so really trying to use AI and make AI more accessible to all of our customers big or small.

Patrick Moorhead: You look awful familiar to somebody I saw on stage about 15 minutes ago, so okay.

June Yang: Yes. I was part of a Thomas’ Keynote.

Patrick Moorhead: Now, congratulations on your announcements as well.

June Yang: Yeah, we’re very excited about that.

Daniel Newman: She did a great job. I was actually in her breakout before and I came up and I saw her here and I’m like, “I’ve seen you twice already today.” Now, we have the honor and the chance…

Patrick Moorhead: I sat next to her at dinner last night, and she had to tell me that she was on our show.

Daniel Newman: We are in the presence of celebrities.

Patrick Moorhead: I know.

Daniel Newman: But in serious, you had some really great announcements, some stuff that was very excited. You’ll see some of my initial analysis has already gone out into my Twitter stream. That’s modern, if anything, right? We can’t even wait to write a report. We just start tweeting it right away.

Patrick Moorhead: We’ll write the boring PDFs. We’ll get there.

Daniel Newman: But I love some of the stuff with Looker. Very exciting. I just can instantly start to see everything moving from citizen developer all the way through very complex data science, but it’s really all about making the world better, making companies more efficient, driving operations experiences. So, just give us that high level, why Google’s data cloud?

Sudhir Hasbe: I think there are three key things that we focus on. So, every organization is going through digital transformation and as they’re going through that, they need lot of different capabilities. So, we are focused on three key areas to differentiate. First is comprehensive, but unified. So, you need lot of capabilities, but how do you bring them together into a unified platform, so our customers don’t have to stitch these things together. Second is openness. Openness across, not just open formats, open APIs, but also open to being in an environment where you have a lot of different parties like multi-cloud. We believe data is going to be in multiple clouds for people. How do we enable those use cases or hybrid use cases? So, being open to that and also being open to partnering with, in some cases our competitors as well as our partner ecosystem. So, our customers get the end-to-end value. And third, of course, intelligence.

June Yang: So, continue the open theme a little bit, right? Open is something that’s important to all of Google Cloud and certainly within the data and AI portfolio as well. There’s just a lot of innovations out there and we want to make sure we give the flexibility to the customers. So, in the Google AI for example, we support all the open frameworks, the tensor flow, high torch, et cetera. And of course we’re also big contributors to many of the open source projects there as well. And coming to the intelligence pillar, intelligence to us, it really means “how do we get more intelligence out of the data you have,” whether you’re applying analytics, whether you’re applying AI to be able to process these data.

And then Google has done really the last 20 years of research and really solving a lot of those hard problems. Sudhir mentioned some of this earlier, right? Computer vision, natural language processing conversation and so forth. And we have benefited a great deal from these technologies. So, now we want to bring a lot of these technologies and make that available to enterprises as well, so they can start enjoying the benefit and really make AI and harness the power of AI work for them.

Patrick Moorhead: Yeah. The openness is super important long term. We have five generations here in NIT, even people who remember getting kind of locked into a certain vendor over time. And on AI, it seems like everybody knows how to make it complex, but the hard part is how to make it simple. It’s kind of like the three bears, which is not too difficult, not too easy where it doesn’t necessarily add value. It’s kind of like right in the middle of that. And I’m so glad to hear Google finally talking about leverage from the other part of the company. There have been years where I was like, “No, we’re not going to talk about that.”

But since TK came in and put… I don’t think anybody asks you about ads or anything like that. You can finally talk about your leverage strategy because it makes sense. You’re investing hundreds and millions, if not billions, in RND that why not scale? And sure application of AI and analytics is going to be different whether it’s consumer or business, but the raw technology is the same and the security it needs to be the same as it does across companies. So, I appreciate that, I appreciate that.

June Yang: Yeah, that’s something that’s really important to us. This is area we work very closely with Google Research, work with Google Deep Mind and really figure out how do we take the best of what Google has done and what makes sense for us to commercialize and make this available to the rest of our organization.

Patrick Moorhead: So, I heard you talk about being the most complete data platform out there. And listen, you’re running the businesses out there. This isn’t just some holistic big marketing claim. Can you talk about the proof behind it?

Sudhir Hasbe: So, if you think about complete and unified is the key thing for us, right? It’s of course, “how do you do end-to-end use cases for organizations?” If you think about today’s challenge for organizations is, you have all these different systems that they need to go out and get data in, process it, analyze it, business intelligence and AI. The whole ecosystem becomes pretty complex in organizations and they need to then start stitching it together. And the amount of effort they put in that is significant. So, for us, actually, as June was saying, we leveraged some of the core foundational technology that was built in Google to scale to all these billion user applications that we have built over time. And that allows us to build differentiated product, wherein, we can build out of the box integrations on things. We use the same substrate from a storage perspective for our operational databases, analytical systems and AI platform, so all the data gets shared across all of that for a customer.

And so we are able to build these tight integrations between operational analytical systems, real time synchronization that you have. We just launched our CDC service, which is data stream, where we can start synchronizing more seamlessly and all. So, that’s one aspect of it: “How do we leverage this?” And so you heard about our announcement on Big Lake. “So, what is Big Lake?” Big Lake is fundamentally, break the silos between warehouses, which is, let’s say, BigQuery Lakes that maybe an object stores, like GCs. And you have a single, now API framework or storage engine, that breaks all these silos and then expanding it into S3 and Azure, AWS as well as in Azure. So, you have a single substrate for all of that. So I think…

Patrick Moorhead: Dare I call it a data fabric?

Sudhir Hasbe: You could. I think that I’ve been different.

June Yang: I got this line 20 years ago.

Sudhir Hasbe: So, different terms for it, but the whole idea is unification across all your data, so you can have a single environment for it. The other thing is, it’s not just one type of data, structured, semi-structured, unstructured. We are the leaders in unstructured data processing with AIML, but with now enabling BigQuery to do unstructured data, now you can use simplification, SQL-based processing on top of unstructured data. So that whole thing around “how do you get customers to use any data that have in the environment.” The second is, “how do you use any kind of workloads on it,” which is whether you’re running Spark or whether you’re running TensorFlow, whether you’re running SQL, we want you to be able to leverage a unified platform for any workload but on the same data. So, governance and management is centralized. You don’t have to take copies and copies of data. And most other platforms you take data from here, make a copy, then you do something, make another copy. What’s the big problem? How many versions of customer do you have now?

Patrick Moorhead: Well, there’s cost to move that data as well.

June Yang: Yeah, cost to store the data as well.

Sudhir Hasbe: Exactly. And the third is reach. This is where you saw the Looker announcements and how we are bringing Data Studio as Looker Studio and unifying it to reach every user in a governed and measured manner where required and self-service where required. So that’s the three angles to it, but we also believe from a unification and openness, our ecosystem. So, we basically started our data alliance with key players in the industry to define common governance, common interoperability, as well as skills gap management. We have 800 plus ISVs building on top of Data Cloud so they can share information with different companies within the environment. And we have a hundred plus technology partners who are also part of the ecosystem. So, it’s not just differentiated platform products we are building, but the ecosystem that goes around it.

Daniel Newman: So, I got to ask, a little bit off the script, but you talk about the open ecosystem and we’ve seen this tried before and it’s kind of blown up a little bit. What makes you think it’s going to work? Is it the number of partners that have committed? Is it the type of partners? Is it the ecosystem? Why, this time, is Open going to get the buy-in when it hasn’t in the past?

Sudhir Hasbe: I think the goals have to be aligned. It’s not how many partners are in the ecosystem. It’s about “Are you focused on these same problem set?” and if you agree on the problems. And so, when we defined the Alliance, our first goal was what are we trying to solve for? So one was, “Hey, governance is a problem for everybody.” And the more products you have, whether you use… We have a close partnership with Elastic and we have close partnership with Bricks and various other folks.

So, the question is, How do you have common governance across all of these platforms? Is one problem to be solved? How do you interoperate more seamlessly between these?” One of the announcements we made with Elastic was, “How do you do searches on top of data without making copies and moving data?” So, understanding those common thing, and third one that we identified was skills gap. There is a challenge, people don’t know what reference architecture should look like. How do you simplify it? So, when you have common goals, I think it’s easier to go ahead and make changes versus too many partners trying to do too many things. It doesn’t work. So, that’s why I’m confident that with Alliance, actually we will be able to go ahead and make good progress on these areas.

Patrick Moorhead: A little clarity on the all. So, it’s all types of data anywhere for any application. Did I hit the three?

Sudhir Hasbe: Yeah. So, it’s all types of data across any workloads that you’re running to reach every user that you want to have.

Patrick Moorhead: Thank you. Now, does that include data that’s on-prem on a data center?

Sudhir Hasbe: Yes, it does. And we are doing various things on data integration side, “How do we bring it?” There will be different technologies that will work in cloud versus on-prem. Yeah, for BigQuery, we are going to run it in all different clouds. Moving BigQuery on-prem, I think it’s a massive thing. We may not do that, but our open source technologies like Data Pro and all of course we are looking at how they run on-prem and stuff like that.

Patrick Moorhead: Okay, final speed round for both of you. I know you love all your children the same but June, for UAI and Sudhir, on the data side, most exciting announcement, you only get one.

June Yang: Now, you’re really putting it on spot for…

Patrick Moorhead: I have to do this.

June Yang: So, I think for me, I would talk about Translation Hub because that’s something that is really AI for everyday people. Translation Hub allows everyday people to be able to upload a document and we can translate to 135 languages and all done this in a matter of seconds. And so, you don’t have to be a data scientist, you don’t even have to be a developer. You need to know how to click a few buttons. Now, you can really think about this whole open and connecting world. Whether you’re translating things for your internal employee base to feel making them feel more connected, or to try to reach your customers through your marketing brochures and whatnot. I just think it’s such a powerful fun.

Patrick Moorhead: I took four pictures during the Keynote and that was one of them. I was like, “Now that’s something I haven’t seen before.”

Daniel Newman: May have done a little selling for you, too. We as analysts in research firms write a lot of papers and I’ve got some clients that translate these into dozens, and it’s always very difficult to get the translation, especially because of local language issues. And I send it to a client, “You’ve done 300 of these in the last year.” I’m like, “You need this.” And I literally got a response and she’s like, “I do.” And so…

June Yang: You did it yesterday, right?

Daniel Newman: Yeah. So, we’ll get it. We might have a real world opportunity.

Patrick Moorhead: Okay, Sudhir, you get one answer, that’s it. No connecting two together either.

Sudhir Hasbe: No, I will not do that. So, I was thinking whether it’s unstructured data support in BigQuery [inaudible], but I will pick a completely different one. The top of mind for every customer is governance and management. And I think that the most important announcement from us was around data quality and lineage. If you don’t know what’s your quality of your data, if you don’t understand where it’s coming from, everything on AI analytics becomes not that valuable. So, I would pick that one as the top announcement.

Patrick Moorhead: I’m glad you picked that because quite frankly that’s where point solutions fall over. A regulator comes in, it’s like “What’s protected? What’s secure? Where’s your data?” And it’s like, “I don’t know, let me check about 50 different places for these point products.” But you want to take us out, Daniel here, it’s been a great conversation.

Daniel Newman: Yeah, absolutely. And I do want to put one last caveat because I always got to get the last one. Well, I sat in your session though, and I had two of your panelists at my table and one of them was the Walmart exec and he definitely told some great stories from the stage. But all I would say is your Googler at the table asked us the question basically about are we seeing transformation? And it was really interesting to listen to someone like him who has prem and multiple clouds, he talked about having two, basically say we talk all about this.

He said, “But when you actually get under, it is a cluster.” But what he did say, this openness that you guys are talking about is the opportunity to solve so many data challenges. So June, Sudhir, thank you so much for joining us here. Thanks for coming on to this show and letting us be here at this cool new Pier 57 here in New York City. So, for everyone out there, thank you all so much for tuning in with us. We’re here at Google Cloud Next. This is The Six Five on the road for Patrick, for myself. We’ll see you all really soon.

Author Information

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.


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