On this episode of The Analytics Edge (sponsored by NetSpring), Asana CIO Saket Srivastava explores how the future of work will be impacted by technologies like self-service analytics and Generative AI. Saket shares Asana’s research findings that show 50-55% of our time is spent working on work versus more productive output, and delves into Asana’s vision for improving work efficiency with AI. He also discusses how data leaders should be thinking about the consumerization of enterprise software and the data needed to drive product-led growth, shadow IT as business teams continue acquiring self-service applications, and the time-to-value of warehouse-native apps.
On this episode of The Analytics Edge (sponsored by NetSpring), Asana's CIO, Saket Srivastava, delves into the profound impact of emerging technologies like self-service analytics and Generative AI on the future of work.
Saket unveils Asana's research findings, revealing that a significant 50-55% of our time is allocated to operational tasks rather than yielding productive output. He articulates Asana's visionary approach towards enhancing work efficiency through AI integration.
In addition, Saket explores imperative considerations for data leaders, emphasizing the importance of anticipating the consumerization of enterprise software and the requisite data strategies fueling product-led growth (PLG). Furthermore, he sheds light on the burgeoning challenge of shadow IT as business teams increasingly adopt self-service applications, alongside insights into the time-to-value dynamics of warehouse-native apps.
Saket Srivastava is currently the CIO at Asana, boasting over 20 years of extensive expertise in data and analytics. With a deep proficiency in enterprise applications and infrastructure, Saket excels in forging executive partners to implement transformative solutions. His strategic focus encompasses driving efficiencies in ERP, CRM, HRIS, and IT Operations. Saket is a recognized thought leader, renowned for building and guiding large teams. His work expands internationally across the insurance, energy, healthcare, and banking industries.
Key Quote:
“Our product managers and our growth managers rely heavily on data to see how customers are using our platform. How frequently are they using? What capabilities are they using? Which capabilities are resonating more or less with them? There’s enough self-service that happens, but there are times they rely on data scientists to build models and experimentation. That informs our product roadmap.”- Saket Srivastava
(Segment 1) (1:12) Challenges
(1:43) Saket’s career journey
(2:45) Recommended tech investments
(3:58) Leveraging data at Asana
(6:40) Consumerization of enterprise software
(9:33) Asana's vision for the Future of Work
(14:35) Product analytics at Asana
(16:48) Self-service & the challenges of shadow IT
(22:30) Data warehouse strategy
(Segment 2) (23:43) Solutions
(24:33) Warehouse-native apps
(Segment 3) Business Opportunities
(29:01) Generative AI at Asana
(34:45) Priorities for the year ahead
(Segment 4) (36:31) Takeaways
Announcer: [00:00:00] Hello and welcome to The Analytics Edge, sponsored by NetSpring.
Tom: The Analytics Edge is a podcast about real world stories of innovation. We're here to explore how data driven insights can help you make better business decisions. I'm your host, Thomas Dong, VP of Marketing at NetSpring. And for today's episode, my co host is Vijay Ganesan, co-founder and CEO at NetSpring.
Thank you for joining me on the show today, Vijay.
Vijay: Great to be here, Tom. Really excited about this episode. Saket has got a really deep and extensive experience in this space and Asana, obviously everybody knows it's a great brand, great product, so I'm very eager to hear his opinions.
Tom: Today's topic is self service analytics and our guest is Saket Srivastava, CIO at Asana.
A software company that helps teams orchestrate and organize their work. With international experience spanning Europe, Asia, and North America, and experience across multiple verticals, from energy to banking, [00:01:00] hospitality, and high tech, Socket has broad perspectives and an impressive record of delivering cutting edge data and analytics solutions.
Socket, we're delighted to have you with us today. Welcome.
Saket: Thanks a lot, Thomas. Nice to meet you, Vijay. That was some generous introduction. Looking forward to this conversation.
Vijay: Yeah, great to have you on the show, Saqib. Looking forward to this.
Tom: So Asana's business model combines aspects of both product led and sales led growth, making data critical to how Asana makes product and sales decisions.
Supporting the business with self service tools to unlock insights from that data is a key aspect of the digital employee experience he is responsible for as CIO of Asana. It's how he personally drives productivity, agility, and growth for the company. Socket, let's start with how your 20 plus year career journey is taking you to your current role as the CIO of Asana.
Saket: Sure. So, I've been at Asana for about a year and a half now, and I've been a CIO at other places as well. A long, tenured IT career. Started my [00:02:00] journey with an undergrad, and Post grad in computer sciences, worked for very large companies, started with more professional services, transitioned into IT leadership roles as well.
And so I've been with companies like General Electric, IBM, Fujitsu, Symantec, and some very modern companies like Square, Nowblock Guidewire. And now Asana. And just enjoy the work that we do here at Asana, motivated by its mission and looking to move the company forward.
Tom: Recently, you defined the role of a CIO as first, involving the prioritization of impactful tech investments, and secondly, balancing organizational efficiency with growth.
What are some of those tech investments every CIO needs to be thinking about
Saket: today, and why? So we're going through a very interesting phase with this macroeconomic climate that we are in. And every CIO that I talk to, engage with, including obviously my priorities that I'm navigating at this point in time, it's really a lot about How can [00:03:00] we drive efficient growth?
How can we focus on growth, but by removing the surplus, the excess that we have, and pivoting and redirecting all of that towards the growth initiatives. It's also all about the CIO function not acting as a back end function, an engine in the back. But more in the front, as a business strategist, someone who has a seat at the table, driving direction and strategy for the company.
And for all of that, data is front and center. So, that's how I sort of look at my role, and those are things that I'm prioritizing.
Vijay: So, Zakir, you mentioned Data being very central and data as a key challenge. And Asana, from what I can tell, you're a very data driven organization. Talk to us about that a little bit.
What makes you data driven. How have you become successful at being data, being a data driven organization?
Saket: How are you leveraging data in your business? So Asana [00:04:00] is a consumerized enterprise software company. For, for very long, we at least in, as IT leaders, the enterprise software that we've been experienced with are softwares that do the job, but No one really loves it.
No one really likes it. Asana is different, and there are some companies like this, right? Which means that these are companies that individuals and teams and functions try out and they love, and then they sort of, their usage expands broader within the enterprise. So that's sort of the product led motion, wherein the product itself sort of acts as a sales vehicle for the company.
So So obviously, when a product is speaking for itself in many ways and acting as a sales vehicle, on our end, we're collecting a ton of data as well. We're collecting a ton of data around how we, what we do [00:05:00] is adding value to the customer. What can we do more that will add value to the customer? So from a product and a customer standpoint, that's clearly a focus from a data standpoint.
Understanding How our customers are using a product and thinking about how our customers will get more value from our product. From an internal standpoint also, collecting data and from the different parts of the business and seeing how can that be leveraged, the insights that we gain from that, how can that be leveraged to again serve our customers best.
through serving our business and functions, giving them the insights and the capabilities so that they can serve our customers best. So I do feel that Asana is a data heavy, data rich, data mature company, wherein all our key decisions rest on foundations of data and insights and analytics that we built over the years.
Vijay: Saket, [00:06:00] you mentioned something interesting, consumerized enterprise software company, or consumer oriented enterprise software company, and this is something that we hear from a lot of people, we are an enterprise software company, but we want the same experience that people have in consumer applications, and that's a great question.
Becoming almost a requirement to be successful, and Asana has done a great job about that, on that. And this is something that's top of mind for a lot of enterprise software companies. How do I make my product just as appealing as a TikTok or any other consumer app that people are so used to? So, what advice would you give to somebody who's looking to do what you folks have done at Asana.
Saket: Well, it's something that inspired me because a lot of this has been done before my time at Asana and so that's certainly inspired me to consider joining Asana. But really, I think the way the company's mission is, that's really where it starts, right? So Asana's mission is all about helping humanity thrive [00:07:00] by enabling world's teams to be more, to work more effortlessly together, right?
And it's a lofty mission, right? And as missions should be. So really when you start from a customer standpoint on how can you enable, empower, benefit the customer, and if everything that you do is from that lens, I think that's how it really starts. Wherein, you're truly trying to understand the customer pain point and how you're going to serve.
And, and, and, and, and improve the customer's day in a life of if you, if you start from that mindset, I think you're starting on the right foot. Clearly, there's a ton more for you to do. And it's sort of obvious as, as you're saying that every company. Aspires to do that or needs to do that. As a CIO who looks at a lot of such vendors and companies, I am still very surprised that a lot of companies are not able to get that.
So, and there are some that, that are, [00:08:00] and those are clearly standing out. And, and to me, really, it's about Workers today Millennial, Gen Z, all of these workers today, they've, they experience in their day to day personal lives, An experience through the, the companies or products that they, they experience a very different experience.
And, and when they come to their work, they, they don't get that experience. So the companies that are able to sort of give them that kind of experience are going to be successful. And, and, and really, as I said, mentioned earlier, it starts from that customer lens. Trying to build that empathy, trying to understand truly what the customer is trying to do and then applying that design thinking, design mindset to help solve for it.
Vijay:So you're saying it has to be part of the mission of the company, the vision for the product has to be sort of,
Saket: it starts from there, right? Totally. I mean, I think it starts from there and then everything that you do from there on that customer empathy, [00:09:00] that customer appreciation. It really starts from there
Tom:.Right. And that empathy you're talking about with respect to Asana's work management platform, it's really about streamlining the work processes of these teams, optimizing their productivity. Can you tell us a little bit more about the vision that you have in the platform that you're building, you know, centered around, you know, many of these buzzwords you're hearing today, like automation and AI.
Saket: Totally. Again, this was One big reason why I decided to come join Asana in this journey, because again, as an IT leader, as a CIO, I've had the experience and a lot of my peers will share this experience as well, where from a, from a function that we have. We're tasked to run large cross functional programs, and we're at a vantage point wherein we understand what every function within the company is trying to do.
Anytime you have to [00:10:00] run or execute large cross functional programs, it is hard. It is really hard. When things are managed and done just within one function, I think it's still manageable. But when things start going across functions, it becomes really hard. And increasingly we've done some research around this area.
We find that increasingly more and more work is done cross functionally. That's where platforms like Asana stand tall, right? In the past, when, as I mentioned, when we've had to do these large transformations, it's been very hard and the chances of success is rather low. Also, being able to, not being able to connect the strategic goal with the initiatives or for even for the workers, the work that they do with what's moving the needle for the company.
These are the, these are the reasons why I think someone should consider an Asana like platform. It's, it's, it's a work management platform wherein you're able to connect [00:11:00] your strategic goals for the company with the actual initiatives and the work. That needs to make those, that strategic goal come true.
And also for the people who are actually doing the work, how does their work ladder up to the strategic goals of the company? And then obviously there's the automation around workflows and stuff, program management, portfolio management. These are hard problems to solve. There are many players trying to do that.
I'm just inspired and motivated by how Asana is going about doing that.
Vijay: There's interesting parallels to what you said in analytics, where we talk about this all the time, where you know, oftentimes in, in, in product analytics in particular, where a product manager may be looking at a feature usage, for example, right?
And it's a very narrow siloed view. It doesn't capture what impact this has on the top level business metrics, right? It's often disconnected. And so similar to what you're saying, the, the foot soldier that's working on certain tasks has to know how this impacts Sort of the, you [00:12:00] know, the top level metrics that the CEO is looking at, right?
And so, so there's a lot of parallels. And the other thing you said about going across department, you know, across different different functions in the company you know, and what we're saying, and you probably are saying this to us. I know in product led companies, SaaS companies deep understanding of business metrics around product usage, customer behavior.
It's not just for one function. It's for everybody, right? Every function needs to care about a product, marketing, support customer success. All of them have to be looking at it. And and, and I think the beauty of Asana is it's all of that happens in one single platform. It is, it's a cohesive single platform.
Saket: And when you have. All of that data on one platform, the kind of intelligence that you're again able to provide back to the customer. So Asana, like obviously every other company, is doubling down on their AI capabilities. And so now we have Asana Intelligence, wherein we're able to successfully show to the customer [00:13:00] how their teams are collaborating, how, where they are.
Over coordinating, over collaborating, where they're under collaborating, where they're right size collab, there's right size collaboration. And those insights are very compelling for them to drive greater productivity as well.
Tom: I love that term right size collaboration.
Saket: Yeah, I mean, again, to that point, Thomas, there's a fair bit of research that Asana does, and there's a work innovation lab that we have.
And they talk about collaboration, over collaboration is bad, under collaboration is bad, and there's a right amount of collaboration that's necessary for teams to be highly productive by performing. And if you've not seen that, I'd encourage you to see that.
Tom: Yeah, there's certainly a crossover to that concept in the analytics world.
I actually want to double down on that a little bit here. I would love to understand how at Asana, let's say a product manager or growth manager, if they want to understand the impact of let's say onboarding of a new feature and how [00:14:00] that impacts a business level metric like revenue and subscriptions from, let's say, Asana's premium to your business tier.
What does that process look like today in terms of like over collaborating or under collaborating? Do you have data engineers or analysts who are you know, sought out to, Build new reports, or do you have your product and growth teams able to self serve any of this analytics on their own?
Saket: I think it's a combination but certainly we, our product managers, our growth managers, rely heavily on data.
To see data on how our customers are using our platform, how frequently are they using, what capabilities are they using, right? Which capabilities are resonating more with them and which are resonating less with them? And that sort of informs in large part how we sort of decide to go about our product roadmap as well.
And, and, and yes, so they There's enough self service that happens, but there are times when they rely on data scientists to build models and experimentation and all of that stuff to [00:15:00] to see what's working and what's not working.
Tom: And what are some of the tools that they use? Are they, you know, in building these in SQL?
Or obviously, Tableau is a very popular self service visualization tool. What's in their toolkit today?
Saket: So it's a combination of build and buy on our end as well. In terms of the tools that we use, we certainly use Tableau. across the board. We've got Snowflake that, that we use for enterprise data.
There's there are a bunch of other tools there, flow and stuff for orchestration and stuff. And then we've got data engineers, we've got analytics teams, we've got the traditional sort of BI kind of talent as well. You've got data scientists who are understanding product uses. There are data scientists who are trying to understand and guide how we run our business as well.
So there's sort of a well thought out way of how we leverage data to drive the company forward.
Tom: So it sounds like you have a very deep stack of which some self service capabilities are [00:16:00] provided. Now this presents, you know, a lot of debates and debates that have happened over the years with CIOs, right, this concept of shadow IT, right, as you have your business units out there procuring their own software for their own needs.
What are some of the challenges of shadow IT and in your opinion, how should a data leader or CIO like yourself think about effectively supporting all of these tools that are proliferating out in the line of business?
Saket: There's a reason why shadow IT happens, right? I'm not the one to say that shadow IT is just bad, right?
There's a, if there's a gap that needs, that's not being met. Someone needs to go solve for that problem, right? If the CIO function or the IT function or the data function is not solving for the need, absolutely someone needs to go and solve for that problem. But again, as a CIO function, it's important that we provide the necessary guardrails where things just don't go haywire, right?
From a security standpoint, from a compliance standpoint, from a privacy standpoint, we are the ones who need to start thinking about that and [00:17:00] provide that guidance to our teams. Also especially around data, Data governance is huge. Data quality is huge. If you allow things to just mushroom on their own without any sort of central governance, then that data could just become so messy for you that one team's talking about something else and another team looking at the same definitions, getting some other numbers, right?
So creating the right level of data governance and as I mentioned, those checks and balances around the security compliance I think is the important step. And I'm not the one to say that everything should be centralized. There are certainly needs where things should be decentralized and enough self service made available so that people who can build on their own and that certainly gives you greater velocity as a company.
So we're not the ones to say that let's just rein everything in. But just do put in enough controls and checks.
Vijay: So what you just said, Saket, has a lot [00:18:00] of relevance to the explosion of SAS tools today, right? SAS took off two decades ago and, and the first generation of SAS business applications were vertically integrated.
You get everything, right, from your database all the way to the front end user interface. anD it's, it's, it's useful, I just, it's a one stop shop, I, you know, marketing team signs up for this service, and it's completely self sufficient, and they get the job done. But then, there's hundreds of these things in an enterprise, even a small startup like us, we use probably like, you know, Tom alone uses probably 20 different SaaS tools in marketing, and a company like Asana probably has hundreds of,
Saket: Thomas is running his own shadow IT.
Tom: Right.
Vijay: So, you know, you've got typically, you know, marketing, web analytics, product analytics, you know, it tends to be business controlled, business managed and they're very useful tools, but there is just an explosion of these. And when you want to do analytics across all of these, it's a, it's a challenge.
There's also the data [00:19:00] governance, privacy, security challenges. So, How should data leaders be thinking about this? Because this is just reality. There are hundreds of SaaS services that a company needs to
Saket: operate. Yeah, I do believe I concur with you Vijay there that best of breed, there is a, there's a place for best of breed.
There was a need for best of breed. The platforms of yesteryears could not serve the niche needs of some use cases and functions. And that's why Best of Breed came about. But we all might have sort of over indexed and gone towards the other extreme in this journey of Best of Breed. So I believe that a central function like the CIO, the data officer, the digital officer needs to have control in limiting that sprawl.
It's important to standardize on a few platforms. And yet, look [00:20:00] for areas where you need to go towards best of breed because that provides you a strategic differentiation and an advantage that the platforms that you've centralized on is not providing. But it can just be that anyone and everyone is deciding to use a tool of their own because because that's what appeals to them.
So again, a function like ours should provide enough control. And guidance and education to tell people, hey, you're going out looking for something here. There is something already available in our suite and in our, in our offerings that you should consider as opposed to just going somewhere else.
Because the challenge is, as you highlighted, Vijay, It is many fold, right? Your data is now siloed. You, you need to start thinking of integrating these so that if you're not integrating these, there's, there's so much swivel sharing that someone needs to do just to get a complete answer on anything, right?
If you're trying to, just for an example, if you're trying to get [00:21:00] an understanding of a customer or an account or a user. If that complete end to end, let's just say a customer's 360 sits across multiple tools, then you're having to swivel share as a user across multiple. That's, that's a time suck. That's, that's a productivity kill, right?
And then so that's where in Thinking through from a platform mindset is important. And yet, if there's a need for best of breed, then and a real need for best of breed, you might want to consider that. That's how I think about it.
Vijay: Yeah. Great. You mentioned Snowflake. So clearly cloud data warehouses are emerging as a single source of truth, or at least for a large part of being a single source of truth for, for data.
How are you looking at your data warehouse strategy and in terms of being The single source of truth where things come together, like what you mentioned, if I want to get a 360 degree view of a customer and that data is fragmented in seven different systems, potentially, if everything is in the data warehouse, I could get a better visibility
Saket: [00:22:00].I actually think about data warehouse for more from a single version of truth, if you will. I look at those operational systems, right, where data is being captured. Some of that needs to like, like a customer master or a product master, I would expect one of my operational systems to perhaps act as that.
And then you bring all of those different elements of that asset in the data warehouse so that you have a fuller view. And that's how we use it, use it as well. Right. So we've got. pipelines that feed data from several of these systems tools into a data warehouse, Snowflake in our case, and then we create models on top of that, and then we visualize for the different use cases.
But just the full view of, say, a customer or a user. It's certainly maintained and managed within our data warehouse as well.
Vijay: Let's talk a little bit about warehouse native apps. So, you mentioned homegrown applications that you've built internally. And, you know, pretty [00:23:00] much every large enterprise has a lot of homegrown custom applications that are built on built internally.
But one interesting trend is a lot of these things are built, being built directly on the data warehouse. We've got technologies today that data warehouse vendors are coming out with where it's easy to build applications on top of on top of the data warehouse. It's, it's, it's this concept of bringing the apps to the data versus Moving the data to the application, right?
And if you look at analytics, if you look at BI systems, for example, they are, you can think of them as built on top of the, of the data warehouse. So the activation should probably be done directly from the warehouse and so on. And I know that's how product analytics, we're thinking of product analytics the same way that it's just built right on top of the data warehouse.
So what are your thoughts on the emergence of this warehouse native app concept?
Saket: There's certainly a lot of promise to it. I mean, we're early in our experimentation in this space as well. But we recently rolled out a capability around [00:24:00] prospecting for our sales people, wherein we built an application directly off of.
Snowflake. And what stood out was the rapid prototype iteration that we were able to do and the time to value. So, so this required less of piping data into different systems and less of building those integrations and stuff. The data's already there. You're building an application on top of that, and the feedback that we got from the field was was overwhelmingly positive.
So that's a good. Quick prototype that we put out there. I Think that shows us that there's promise in this and we want to kind of lean in more heavily into this. But I'd love to hear from you if you're seeing more of this because this is how you're sort of building your platform on as well.
Vijay: I think this is the trend we're seeing, you know, this idea that you know, you, you, you bring everything into the data warehouse.
And historically, there is a large class of data that never came to the warehouse, right? It was only like mission [00:25:00] critical data that had an impact on, say, you know, Wall Street reporting or, you know, very critical functions of the company. Only those things came to the warehouse and that too in an aggregated fashion.
But today with Snowflake, you can bring in petabyte scale data, you know, some of the customers we have are bringing in. Trillion row event datasets into Snowflake, and it's possible today because all you pay is for storage in S3, and that's really, really cheap, and then you, because of the elasticity that these cloud data warehouses offer, you only pay for what you touch, what you compute, right?
And then to your point about Quick time to value, right? You know, the minute you start building pipelines and ETL and reverse ETL and data going off from five different places it just takes forever. And it's very fragile. Whereas if you're building something like a native application right on top of.
Where the data lives and even the application itself is living in the context of that data warehouse, like, you know, [00:26:00] if you're thinking of things like Streamlet and on Snowflake, basically, it's running, essentially, it's running in the database, right? So, so, so the ease of manageability, the the, You know, the security, privacy issues that don't arise because you're not moving the data.
And then the time to value, and that's really the key, what you said, time to value is what the phenomenal improvement in time to value, right? And that's really how we're architecting our analytics offering.
Saket: Yeah, in our case we were able to shift delivery from months to weeks, and that really sat well with The users who got to gain value from this way sooner.
Tom: Yeah, I think the biggest thing here is we were talking just about Shadow IT a moment ago here. When we're talking warehouse native apps, this is necessarily a conversation that happens between the data teams as well as the business teams, right? Because you now have a business application that can be endorsed by the data teams because they've been the ones making the [00:27:00] investment in the data warehouse.
And Snowflake now has this massive directory of applications that are connected apps, or Snowflake connected apps. I wonder if there's a world eventually where CIOs can have that same approved vendor list of warehouse native apps that the data team endorses
Saket:. Absolutely, I can certainly see that. Bottom line is, if there's value, if there's security, Appropriate controls, role based access controls, privacy, all of that stuff is taken care of.
And faster time to value that we're talking about, then why not? I'm familiar with this connected apps kind of concept. I've seen other CIOs show me some of the work that they've done. I can absolutely see other CIOs and IT leaders warming up to that.
Vijay: Saket, we wanted to double click on something you mentioned briefly around bringing more intelligence to your product with AI.
Obviously, generative AI is [00:28:00] top of mind for everybody and every CIO, CDO, CTO has generative AI initiatives. How are you thinking about it at Asana?
Saket: I think there are two perspectives to this. One, there's just a whole lot of talk around generative AI, right? There's, it's overwhelming to some extent, but it's a game changer as well, right?
So, we at Asana, I don't know if we should call ourselves fortunate, but The foundations on which Asana is based on are our architecture, what we call the work graph data model, lends itself beautifully to building, creating AI capabilities on top of that. And not every company can say that. So, I, maybe I shouldn't say fortunate, it was well thought through by our co founder Dustin Moskowitz and the people who were here earlier.
So, it sets us up well. to make our investments and move forward in this AI journey. So how it helps is we'll be able to, [00:29:00] again, bring AI capabilities faster to our customers because of how our data model is structured. And we've recently launched, as I mentioned, Asana Intelligence, and there's just a ton more work that's happening in the space.
And this could be anything around how can we add more productivity and velocity to our customers. Our approach to AI has been more human centered, where we're not saying that AI is going to replace humans, but at the end of the day, the accountability sits with humans, right? And AI is here to sort of be that co pilot, that assistant to the human.
So that's from a product standpoint. From an internal technology leader perspective. I'm staying curious, I'm staying hungry, I'm listening to everything that I'm seeing around that my partners and vendors are doing. Because I don't want to be going ahead and solving for all of those use cases myself.
Where need be, I will. But I also want to lean into my partners who who are also [00:30:00] investing in generative AI. to be brought into their products as well.
Vijay: You mentioned the advantage you have with your WordGraph data model. That's very interesting because anybody today can put out a nice demo with generative AI, right?
There's, there's, it's become a commodity now in the sense that I can Leverage models that are available out there, LLMs, and I can, I can put a nice interface and make an impressive demo. But, but the companies that are going to really, really make it big with the generative AI are the ones that have some distinct advantage, like you described, where that foundationally there are elements in the modeling and in the way you structure the application and the data model and the way you're able to provide better context to these PROMs for for more Effective intelligence, and those are the ones that are probably going to win, so I thought that point you made about the data model advantage you have is very interesting.
Saket: Vijay, the amount of waste that happens in an [00:31:00] employee's work time is surprisingly very high. Again, the research that we've done, we find that an average worker spends 50 55 percent of their time doing work about work, right? And If you have an AI assistant that's able to meaningfully sort of reduce that waste for you, which is how we're thinking about our AI assistant, Asana Intelligence, then it's a game changer, right?
There's just so much more that an individual and teams and companies are able to then do because now they have this Asana Intelligence, which has all the data. that the work data that's being captured on the platform and it's able to provide you with insights and guidance. It's able to now make your tactical, even more intelligent things easier for you.
And then you're able to spend your time doing more strategic stuff.
Vijay: [00:32:00] And 55 percent is work, about work. That's a staggering number. And
Saket: I was surprised, but then if you kind of give it a little more thought, then I'd probably not be as surprised.
Tom: That makes sense. Half our time is just mundane work just to get the work done.
Saket: Yeah, that's why there is A big need. So Asana is really trying to create a new category around collaborative work management. Right? This is more than project portfolio program management. This is about overall productivity gains that an enterprise can get by managing their work on a platform like Asana.
I'm Obviously biased, but a believer now. And our customers tell us how meaningfully they've benefited when they leverage a platform like Asana.
Tom: Yeah, that makes absolute sense. The future of work is certainly much more collaborative and with AI and automation to eliminate the unnecessary work is really going to be a game changer.
And I can [00:33:00] certainly attests to my, my, my own personal need to be able to focus on more of the strategic versus all the day to day execution tasks. All right, well, you've shared many of your predictions here today, and I know that you've written extensively and selflessly, provided advice on top strategies for CIOs.
You have a very strong peer network. I thought maybe we would end with you providing, you know, from a data and analytics leaders perspective, you know, how should we all be thinking about the months if not years ahead?
Saket: So data analytics is something that's been on a tier for the last several years, right?
I mean, there's just so much demand, so much need that you've seen so much innovation happen in the space as well, right? There's clearly a lot of attention that the VC community, that the startups do all as well, right? Have, have provided to this space and we're clearly in a much better place. There's more democratization of data.
There's easier access and [00:34:00] tools for end users to go build on top of and not just have to rely on teams like ours to come to us and ask for. For reports and data and all of that stuff, right? So from that perspective I see the data, the journey to continue, right? With this generative AI, massive amounts of data and how can that all sort of be computed and provide guidance to users continues to happen.
I am just excited to be sort of on this journey and see how We are, as data leaders, we are able to solve more real problems faster and drive companies direction in a more meaningful way.
Tom: Great. Thanks so much for joining us today, Saket.
Saket: Thank you for having me, both of you. It's been fun talking to both of you.
Tom: Alright, that was a really fascinating conversation with Saket. Obviously, he's got very broad perspectives across industries and geographies, and he's been a CIO multiple times over. What were some of your key takeaways from [00:35:00] today's conversation, Vijay?
Vijay: One interesting thing that I that got me thinking was this idea of building warehouse native apps.
You know, he was talking about experimentation that they did building in house application for a sitting directly on top of Snowflake and probably using the Streamlet technology Snowflake has. And I think that's That's an interesting trend. I think we're going to see more of this and it's good validation that this is sort of how CIOs are thinking about it.
And you brought up a great point about potentially being in a world where the CIOs will only approve certified warehouse native applications to be used by the business. Because it comes with certain guarantees of security, privacy, you know. No data copies and efficiency and governed self service access.
So, so I thought his mention of the work that they're doing is a sign of a [00:36:00] trend in the market.
Tom: Yeah, in fact, that, you know, really dovetails nicely into this concept that every CIO has to think about. Build versus buy, best of breed. And I thought he gave a very interesting perspective that you need to have a bit of a platform bias to this, right?
We've over indexed on the, the, the best of breed and we have proliferation of so many different point solutions out there. If you can take a step back and standardize in a few platforms. Snowflake, Data Warehouse, probably being one of them as a new foundation for things, then you can use your best of breed investments for that strategic differentiation.
I thought that was a really valuable piece of advice that he left our viewers with.
Vijay: Yeah, and he talked about time to value, which is really, ultimately, that's the key, right? Just data movement you have to do and more you can do directly on top of the system that holds the data, the faster time to value.
And that's really key for enterprises. You need [00:37:00] quick time to value for your business. One other thing you know, what he said about 55 percent of what people do every day, you know, is wasted, right? Every knowledge worker's Spends, you know, half their time doing work about work. So, so I think that's the area, obviously, generative AI is going to have a huge impact and and Asana seems to be well set up to take advantage of that.
Tom: Alright, that concludes today's show. Thank you for joining us and feel free to reach out to Vijay or I on LinkedIn or Twitter with any questions or ideas for future episodes. Until next time, goodbye.Announcer: [00:00:00] Hello and welcome to The Analytics Edge, sponsored by NetSpring.
Tom: The Analytics Edge is a podcast about real world stories of innovation. We're here to explore how data driven insights can help you make better business decisions. I'm your host, Thomas Dong, VP of Marketing at NetSpring. And for today's episode, my co host is Vijay Ganesan, co-founder and CEO at NetSpring.
Thank you for joining me on the show today, Vijay.
Vijay: Great to be here, Tom. Really excited about this episode. Saket has got a really deep and extensive experience in this space and Asana, obviously everybody knows it's a great brand, great product, so I'm very eager to hear his opinions.
Tom: Today's topic is self service analytics and our guest is Saket Srivastava, CIO at Asana.
A software company that helps teams orchestrate and organize their work. With international experience spanning Europe, Asia, and North America, and experience across multiple verticals, from energy to banking, [00:01:00] hospitality, and high tech, Socket has broad perspectives and an impressive record of delivering cutting edge data and analytics solutions.
Socket, we're delighted to have you with us today. Welcome.
Saket: Thanks a lot, Thomas. Nice to meet you, Vijay. That was some generous introduction. Looking forward to this conversation.
Vijay: Yeah, great to have you on the show, Saqib. Looking forward to this.
Tom: So Asana's business model combines aspects of both product led and sales led growth, making data critical to how Asana makes product and sales decisions.
Supporting the business with self service tools to unlock insights from that data is a key aspect of the digital employee experience he is responsible for as CIO of Asana. It's how he personally drives productivity, agility, and growth for the company. Socket, let's start with how your 20 plus year career journey is taking you to your current role as the CIO of Asana.
Saket: Sure. So, I've been at Asana for about a year and a half now, and I've been a CIO at other places as well. A long, tenured IT career. Started my [00:02:00] journey with an undergrad, and Post grad in computer sciences, worked for very large companies, started with more professional services, transitioned into IT leadership roles as well.
And so I've been with companies like General Electric, IBM, Fujitsu, Symantec, and some very modern companies like Square, Nowblock Guidewire. And now Asana. And just enjoy the work that we do here at Asana, motivated by its mission and looking to move the company forward.
Tom: Recently, you defined the role of a CIO as first, involving the prioritization of impactful tech investments, and secondly, balancing organizational efficiency with growth.
What are some of those tech investments every CIO needs to be thinking about
Saket: today, and why? So we're going through a very interesting phase with this macroeconomic climate that we are in. And every CIO that I talk to, engage with, including obviously my priorities that I'm navigating at this point in time, it's really a lot about How can [00:03:00] we drive efficient growth?
How can we focus on growth, but by removing the surplus, the excess that we have, and pivoting and redirecting all of that towards the growth initiatives. It's also all about the CIO function not acting as a back end function, an engine in the back. But more in the front, as a business strategist, someone who has a seat at the table, driving direction and strategy for the company.
And for all of that, data is front and center. So, that's how I sort of look at my role, and those are things that I'm prioritizing.
Vijay: So, Zakir, you mentioned Data being very central and data as a key challenge. And Asana, from what I can tell, you're a very data driven organization. Talk to us about that a little bit.
What makes you data driven. How have you become successful at being data, being a data driven organization?
Saket: How are you leveraging data in your business? So Asana [00:04:00] is a consumerized enterprise software company. For, for very long, we at least in, as IT leaders, the enterprise software that we've been experienced with are softwares that do the job, but No one really loves it.
No one really likes it. Asana is different, and there are some companies like this, right? Which means that these are companies that individuals and teams and functions try out and they love, and then they sort of, their usage expands broader within the enterprise. So that's sort of the product led motion, wherein the product itself sort of acts as a sales vehicle for the company.
So So obviously, when a product is speaking for itself in many ways and acting as a sales vehicle, on our end, we're collecting a ton of data as well. We're collecting a ton of data around how we, what we do [00:05:00] is adding value to the customer. What can we do more that will add value to the customer? So from a product and a customer standpoint, that's clearly a focus from a data standpoint.
Understanding How our customers are using a product and thinking about how our customers will get more value from our product. From an internal standpoint also, collecting data and from the different parts of the business and seeing how can that be leveraged, the insights that we gain from that, how can that be leveraged to again serve our customers best.
through serving our business and functions, giving them the insights and the capabilities so that they can serve our customers best. So I do feel that Asana is a data heavy, data rich, data mature company, wherein all our key decisions rest on foundations of data and insights and analytics that we built over the years.
Vijay: Saket, [00:06:00] you mentioned something interesting, consumerized enterprise software company, or consumer oriented enterprise software company, and this is something that we hear from a lot of people, we are an enterprise software company, but we want the same experience that people have in consumer applications, and that's a great question.
Becoming almost a requirement to be successful, and Asana has done a great job about that, on that. And this is something that's top of mind for a lot of enterprise software companies. How do I make my product just as appealing as a TikTok or any other consumer app that people are so used to? So, what advice would you give to somebody who's looking to do what you folks have done at Asana.
Saket: Well, it's something that inspired me because a lot of this has been done before my time at Asana and so that's certainly inspired me to consider joining Asana. But really, I think the way the company's mission is, that's really where it starts, right? So Asana's mission is all about helping humanity thrive [00:07:00] by enabling world's teams to be more, to work more effortlessly together, right?
And it's a lofty mission, right? And as missions should be. So really when you start from a customer standpoint on how can you enable, empower, benefit the customer, and if everything that you do is from that lens, I think that's how it really starts. Wherein, you're truly trying to understand the customer pain point and how you're going to serve.
And, and, and, and, and improve the customer's day in a life of if you, if you start from that mindset, I think you're starting on the right foot. Clearly, there's a ton more for you to do. And it's sort of obvious as, as you're saying that every company. Aspires to do that or needs to do that. As a CIO who looks at a lot of such vendors and companies, I am still very surprised that a lot of companies are not able to get that.
So, and there are some that, that are, [00:08:00] and those are clearly standing out. And, and to me, really, it's about Workers today Millennial, Gen Z, all of these workers today, they've, they experience in their day to day personal lives, An experience through the, the companies or products that they, they experience a very different experience.
And, and when they come to their work, they, they don't get that experience. So the companies that are able to sort of give them that kind of experience are going to be successful. And, and, and really, as I said, mentioned earlier, it starts from that customer lens. Trying to build that empathy, trying to understand truly what the customer is trying to do and then applying that design thinking, design mindset to help solve for it.
Vijay:So you're saying it has to be part of the mission of the company, the vision for the product has to be sort of,
Saket: it starts from there, right? Totally. I mean, I think it starts from there and then everything that you do from there on that customer empathy, [00:09:00] that customer appreciation. It really starts from there
Tom:.Right. And that empathy you're talking about with respect to Asana's work management platform, it's really about streamlining the work processes of these teams, optimizing their productivity. Can you tell us a little bit more about the vision that you have in the platform that you're building, you know, centered around, you know, many of these buzzwords you're hearing today, like automation and AI.
Saket: Totally. Again, this was One big reason why I decided to come join Asana in this journey, because again, as an IT leader, as a CIO, I've had the experience and a lot of my peers will share this experience as well, where from a, from a function that we have. We're tasked to run large cross functional programs, and we're at a vantage point wherein we understand what every function within the company is trying to do.
Anytime you have to [00:10:00] run or execute large cross functional programs, it is hard. It is really hard. When things are managed and done just within one function, I think it's still manageable. But when things start going across functions, it becomes really hard. And increasingly we've done some research around this area.
We find that increasingly more and more work is done cross functionally. That's where platforms like Asana stand tall, right? In the past, when, as I mentioned, when we've had to do these large transformations, it's been very hard and the chances of success is rather low. Also, being able to, not being able to connect the strategic goal with the initiatives or for even for the workers, the work that they do with what's moving the needle for the company.
These are the, these are the reasons why I think someone should consider an Asana like platform. It's, it's, it's a work management platform wherein you're able to connect [00:11:00] your strategic goals for the company with the actual initiatives and the work. That needs to make those, that strategic goal come true.
And also for the people who are actually doing the work, how does their work ladder up to the strategic goals of the company? And then obviously there's the automation around workflows and stuff, program management, portfolio management. These are hard problems to solve. There are many players trying to do that.
I'm just inspired and motivated by how Asana is going about doing that.
Vijay: There's interesting parallels to what you said in analytics, where we talk about this all the time, where you know, oftentimes in, in, in product analytics in particular, where a product manager may be looking at a feature usage, for example, right?
And it's a very narrow siloed view. It doesn't capture what impact this has on the top level business metrics, right? It's often disconnected. And so similar to what you're saying, the, the foot soldier that's working on certain tasks has to know how this impacts Sort of the, you [00:12:00] know, the top level metrics that the CEO is looking at, right?
And so, so there's a lot of parallels. And the other thing you said about going across department, you know, across different different functions in the company you know, and what we're saying, and you probably are saying this to us. I know in product led companies, SaaS companies deep understanding of business metrics around product usage, customer behavior.
It's not just for one function. It's for everybody, right? Every function needs to care about a product, marketing, support customer success. All of them have to be looking at it. And and, and I think the beauty of Asana is it's all of that happens in one single platform. It is, it's a cohesive single platform.
Saket: And when you have. All of that data on one platform, the kind of intelligence that you're again able to provide back to the customer. So Asana, like obviously every other company, is doubling down on their AI capabilities. And so now we have Asana Intelligence, wherein we're able to successfully show to the customer [00:13:00] how their teams are collaborating, how, where they are.
Over coordinating, over collaborating, where they're under collaborating, where they're right size collab, there's right size collaboration. And those insights are very compelling for them to drive greater productivity as well.
Tom: I love that term right size collaboration.
Saket: Yeah, I mean, again, to that point, Thomas, there's a fair bit of research that Asana does, and there's a work innovation lab that we have.
And they talk about collaboration, over collaboration is bad, under collaboration is bad, and there's a right amount of collaboration that's necessary for teams to be highly productive by performing. And if you've not seen that, I'd encourage you to see that.
Tom: Yeah, there's certainly a crossover to that concept in the analytics world.
I actually want to double down on that a little bit here. I would love to understand how at Asana, let's say a product manager or growth manager, if they want to understand the impact of let's say onboarding of a new feature and how [00:14:00] that impacts a business level metric like revenue and subscriptions from, let's say, Asana's premium to your business tier.
What does that process look like today in terms of like over collaborating or under collaborating? Do you have data engineers or analysts who are you know, sought out to, Build new reports, or do you have your product and growth teams able to self serve any of this analytics on their own?
Saket: I think it's a combination but certainly we, our product managers, our growth managers, rely heavily on data.
To see data on how our customers are using our platform, how frequently are they using, what capabilities are they using, right? Which capabilities are resonating more with them and which are resonating less with them? And that sort of informs in large part how we sort of decide to go about our product roadmap as well.
And, and, and yes, so they There's enough self service that happens, but there are times when they rely on data scientists to build models and experimentation and all of that stuff to [00:15:00] to see what's working and what's not working.
Tom: And what are some of the tools that they use? Are they, you know, in building these in SQL?
Or obviously, Tableau is a very popular self service visualization tool. What's in their toolkit today?
Saket: So it's a combination of build and buy on our end as well. In terms of the tools that we use, we certainly use Tableau. across the board. We've got Snowflake that, that we use for enterprise data.
There's there are a bunch of other tools there, flow and stuff for orchestration and stuff. And then we've got data engineers, we've got analytics teams, we've got the traditional sort of BI kind of talent as well. You've got data scientists who are understanding product uses. There are data scientists who are trying to understand and guide how we run our business as well.
So there's sort of a well thought out way of how we leverage data to drive the company forward.
Tom: So it sounds like you have a very deep stack of which some self service capabilities are [00:16:00] provided. Now this presents, you know, a lot of debates and debates that have happened over the years with CIOs, right, this concept of shadow IT, right, as you have your business units out there procuring their own software for their own needs.
What are some of the challenges of shadow IT and in your opinion, how should a data leader or CIO like yourself think about effectively supporting all of these tools that are proliferating out in the line of business?
Saket: There's a reason why shadow IT happens, right? I'm not the one to say that shadow IT is just bad, right?
There's a, if there's a gap that needs, that's not being met. Someone needs to go solve for that problem, right? If the CIO function or the IT function or the data function is not solving for the need, absolutely someone needs to go and solve for that problem. But again, as a CIO function, it's important that we provide the necessary guardrails where things just don't go haywire, right?
From a security standpoint, from a compliance standpoint, from a privacy standpoint, we are the ones who need to start thinking about that and [00:17:00] provide that guidance to our teams. Also especially around data, Data governance is huge. Data quality is huge. If you allow things to just mushroom on their own without any sort of central governance, then that data could just become so messy for you that one team's talking about something else and another team looking at the same definitions, getting some other numbers, right?
So creating the right level of data governance and as I mentioned, those checks and balances around the security compliance I think is the important step. And I'm not the one to say that everything should be centralized. There are certainly needs where things should be decentralized and enough self service made available so that people who can build on their own and that certainly gives you greater velocity as a company.
So we're not the ones to say that let's just rein everything in. But just do put in enough controls and checks.
Vijay: So what you just said, Saket, has a lot [00:18:00] of relevance to the explosion of SAS tools today, right? SAS took off two decades ago and, and the first generation of SAS business applications were vertically integrated.
You get everything, right, from your database all the way to the front end user interface. anD it's, it's, it's useful, I just, it's a one stop shop, I, you know, marketing team signs up for this service, and it's completely self sufficient, and they get the job done. But then, there's hundreds of these things in an enterprise, even a small startup like us, we use probably like, you know, Tom alone uses probably 20 different SaaS tools in marketing, and a company like Asana probably has hundreds of,
Saket: Thomas is running his own shadow IT.
Tom: Right.
Vijay: So, you know, you've got typically, you know, marketing, web analytics, product analytics, you know, it tends to be business controlled, business managed and they're very useful tools, but there is just an explosion of these. And when you want to do analytics across all of these, it's a, it's a challenge.
There's also the data [00:19:00] governance, privacy, security challenges. So, How should data leaders be thinking about this? Because this is just reality. There are hundreds of SaaS services that a company needs to
Saket: operate. Yeah, I do believe I concur with you Vijay there that best of breed, there is a, there's a place for best of breed.
There was a need for best of breed. The platforms of yesteryears could not serve the niche needs of some use cases and functions. And that's why Best of Breed came about. But we all might have sort of over indexed and gone towards the other extreme in this journey of Best of Breed. So I believe that a central function like the CIO, the data officer, the digital officer needs to have control in limiting that sprawl.
It's important to standardize on a few platforms. And yet, look [00:20:00] for areas where you need to go towards best of breed because that provides you a strategic differentiation and an advantage that the platforms that you've centralized on is not providing. But it can just be that anyone and everyone is deciding to use a tool of their own because because that's what appeals to them.
So again, a function like ours should provide enough control. And guidance and education to tell people, hey, you're going out looking for something here. There is something already available in our suite and in our, in our offerings that you should consider as opposed to just going somewhere else.
Because the challenge is, as you highlighted, Vijay, It is many fold, right? Your data is now siloed. You, you need to start thinking of integrating these so that if you're not integrating these, there's, there's so much swivel sharing that someone needs to do just to get a complete answer on anything, right?
If you're trying to, just for an example, if you're trying to get [00:21:00] an understanding of a customer or an account or a user. If that complete end to end, let's just say a customer's 360 sits across multiple tools, then you're having to swivel share as a user across multiple. That's, that's a time suck. That's, that's a productivity kill, right?
And then so that's where in Thinking through from a platform mindset is important. And yet, if there's a need for best of breed, then and a real need for best of breed, you might want to consider that. That's how I think about it.
Vijay: Yeah. Great. You mentioned Snowflake. So clearly cloud data warehouses are emerging as a single source of truth, or at least for a large part of being a single source of truth for, for data.
How are you looking at your data warehouse strategy and in terms of being The single source of truth where things come together, like what you mentioned, if I want to get a 360 degree view of a customer and that data is fragmented in seven different systems, potentially, if everything is in the data warehouse, I could get a better visibility
Saket: [00:22:00].I actually think about data warehouse for more from a single version of truth, if you will. I look at those operational systems, right, where data is being captured. Some of that needs to like, like a customer master or a product master, I would expect one of my operational systems to perhaps act as that.
And then you bring all of those different elements of that asset in the data warehouse so that you have a fuller view. And that's how we use it, use it as well. Right. So we've got. pipelines that feed data from several of these systems tools into a data warehouse, Snowflake in our case, and then we create models on top of that, and then we visualize for the different use cases.
But just the full view of, say, a customer or a user. It's certainly maintained and managed within our data warehouse as well.
Vijay: Let's talk a little bit about warehouse native apps. So, you mentioned homegrown applications that you've built internally. And, you know, pretty [00:23:00] much every large enterprise has a lot of homegrown custom applications that are built on built internally.
But one interesting trend is a lot of these things are built, being built directly on the data warehouse. We've got technologies today that data warehouse vendors are coming out with where it's easy to build applications on top of on top of the data warehouse. It's, it's, it's this concept of bringing the apps to the data versus Moving the data to the application, right?
And if you look at analytics, if you look at BI systems, for example, they are, you can think of them as built on top of the, of the data warehouse. So the activation should probably be done directly from the warehouse and so on. And I know that's how product analytics, we're thinking of product analytics the same way that it's just built right on top of the data warehouse.
So what are your thoughts on the emergence of this warehouse native app concept?
Saket: There's certainly a lot of promise to it. I mean, we're early in our experimentation in this space as well. But we recently rolled out a capability around [00:24:00] prospecting for our sales people, wherein we built an application directly off of.
Snowflake. And what stood out was the rapid prototype iteration that we were able to do and the time to value. So, so this required less of piping data into different systems and less of building those integrations and stuff. The data's already there. You're building an application on top of that, and the feedback that we got from the field was was overwhelmingly positive.
So that's a good. Quick prototype that we put out there. I Think that shows us that there's promise in this and we want to kind of lean in more heavily into this. But I'd love to hear from you if you're seeing more of this because this is how you're sort of building your platform on as well.
Vijay: I think this is the trend we're seeing, you know, this idea that you know, you, you, you bring everything into the data warehouse.
And historically, there is a large class of data that never came to the warehouse, right? It was only like mission [00:25:00] critical data that had an impact on, say, you know, Wall Street reporting or, you know, very critical functions of the company. Only those things came to the warehouse and that too in an aggregated fashion.
But today with Snowflake, you can bring in petabyte scale data, you know, some of the customers we have are bringing in. Trillion row event datasets into Snowflake, and it's possible today because all you pay is for storage in S3, and that's really, really cheap, and then you, because of the elasticity that these cloud data warehouses offer, you only pay for what you touch, what you compute, right?
And then to your point about Quick time to value, right? You know, the minute you start building pipelines and ETL and reverse ETL and data going off from five different places it just takes forever. And it's very fragile. Whereas if you're building something like a native application right on top of.
Where the data lives and even the application itself is living in the context of that data warehouse, like, you know, [00:26:00] if you're thinking of things like Streamlet and on Snowflake, basically, it's running, essentially, it's running in the database, right? So, so, so the ease of manageability, the the, You know, the security, privacy issues that don't arise because you're not moving the data.
And then the time to value, and that's really the key, what you said, time to value is what the phenomenal improvement in time to value, right? And that's really how we're architecting our analytics offering.
Saket: Yeah, in our case we were able to shift delivery from months to weeks, and that really sat well with The users who got to gain value from this way sooner.
Tom: Yeah, I think the biggest thing here is we were talking just about Shadow IT a moment ago here. When we're talking warehouse native apps, this is necessarily a conversation that happens between the data teams as well as the business teams, right? Because you now have a business application that can be endorsed by the data teams because they've been the ones making the [00:27:00] investment in the data warehouse.
And Snowflake now has this massive directory of applications that are connected apps, or Snowflake connected apps. I wonder if there's a world eventually where CIOs can have that same approved vendor list of warehouse native apps that the data team endorses
Saket:. Absolutely, I can certainly see that. Bottom line is, if there's value, if there's security, Appropriate controls, role based access controls, privacy, all of that stuff is taken care of.
And faster time to value that we're talking about, then why not? I'm familiar with this connected apps kind of concept. I've seen other CIOs show me some of the work that they've done. I can absolutely see other CIOs and IT leaders warming up to that.
Vijay: Saket, we wanted to double click on something you mentioned briefly around bringing more intelligence to your product with AI.
Obviously, generative AI is [00:28:00] top of mind for everybody and every CIO, CDO, CTO has generative AI initiatives. How are you thinking about it at Asana?
Saket: I think there are two perspectives to this. One, there's just a whole lot of talk around generative AI, right? There's, it's overwhelming to some extent, but it's a game changer as well, right?
So, we at Asana, I don't know if we should call ourselves fortunate, but The foundations on which Asana is based on are our architecture, what we call the work graph data model, lends itself beautifully to building, creating AI capabilities on top of that. And not every company can say that. So, I, maybe I shouldn't say fortunate, it was well thought through by our co founder Dustin Moskowitz and the people who were here earlier.
So, it sets us up well. to make our investments and move forward in this AI journey. So how it helps is we'll be able to, [00:29:00] again, bring AI capabilities faster to our customers because of how our data model is structured. And we've recently launched, as I mentioned, Asana Intelligence, and there's just a ton more work that's happening in the space.
And this could be anything around how can we add more productivity and velocity to our customers. Our approach to AI has been more human centered, where we're not saying that AI is going to replace humans, but at the end of the day, the accountability sits with humans, right? And AI is here to sort of be that co pilot, that assistant to the human.
So that's from a product standpoint. From an internal technology leader perspective. I'm staying curious, I'm staying hungry, I'm listening to everything that I'm seeing around that my partners and vendors are doing. Because I don't want to be going ahead and solving for all of those use cases myself.
Where need be, I will. But I also want to lean into my partners who who are also [00:30:00] investing in generative AI. to be brought into their products as well.
Vijay: You mentioned the advantage you have with your WordGraph data model. That's very interesting because anybody today can put out a nice demo with generative AI, right?
There's, there's, it's become a commodity now in the sense that I can Leverage models that are available out there, LLMs, and I can, I can put a nice interface and make an impressive demo. But, but the companies that are going to really, really make it big with the generative AI are the ones that have some distinct advantage, like you described, where that foundationally there are elements in the modeling and in the way you structure the application and the data model and the way you're able to provide better context to these PROMs for for more Effective intelligence, and those are the ones that are probably going to win, so I thought that point you made about the data model advantage you have is very interesting.
Saket: Vijay, the amount of waste that happens in an [00:31:00] employee's work time is surprisingly very high. Again, the research that we've done, we find that an average worker spends 50 55 percent of their time doing work about work, right? And If you have an AI assistant that's able to meaningfully sort of reduce that waste for you, which is how we're thinking about our AI assistant, Asana Intelligence, then it's a game changer, right?
There's just so much more that an individual and teams and companies are able to then do because now they have this Asana Intelligence, which has all the data. that the work data that's being captured on the platform and it's able to provide you with insights and guidance. It's able to now make your tactical, even more intelligent things easier for you.
And then you're able to spend your time doing more strategic stuff.
Vijay: [00:32:00] And 55 percent is work, about work. That's a staggering number. And
Saket: I was surprised, but then if you kind of give it a little more thought, then I'd probably not be as surprised.
Tom: That makes sense. Half our time is just mundane work just to get the work done.
Saket: Yeah, that's why there is A big need. So Asana is really trying to create a new category around collaborative work management. Right? This is more than project portfolio program management. This is about overall productivity gains that an enterprise can get by managing their work on a platform like Asana.
I'm Obviously biased, but a believer now. And our customers tell us how meaningfully they've benefited when they leverage a platform like Asana.
Tom: Yeah, that makes absolute sense. The future of work is certainly much more collaborative and with AI and automation to eliminate the unnecessary work is really going to be a game changer.
And I can [00:33:00] certainly attests to my, my, my own personal need to be able to focus on more of the strategic versus all the day to day execution tasks. All right, well, you've shared many of your predictions here today, and I know that you've written extensively and selflessly, provided advice on top strategies for CIOs.
You have a very strong peer network. I thought maybe we would end with you providing, you know, from a data and analytics leaders perspective, you know, how should we all be thinking about the months if not years ahead?
Saket: So data analytics is something that's been on a tier for the last several years, right?
I mean, there's just so much demand, so much need that you've seen so much innovation happen in the space as well, right? There's clearly a lot of attention that the VC community, that the startups do all as well, right? Have, have provided to this space and we're clearly in a much better place. There's more democratization of data.
There's easier access and [00:34:00] tools for end users to go build on top of and not just have to rely on teams like ours to come to us and ask for. For reports and data and all of that stuff, right? So from that perspective I see the data, the journey to continue, right? With this generative AI, massive amounts of data and how can that all sort of be computed and provide guidance to users continues to happen.
I am just excited to be sort of on this journey and see how We are, as data leaders, we are able to solve more real problems faster and drive companies direction in a more meaningful way.
Tom: Great. Thanks so much for joining us today, Saket.
Saket: Thank you for having me, both of you. It's been fun talking to both of you.
Tom: Alright, that was a really fascinating conversation with Saket. Obviously, he's got very broad perspectives across industries and geographies, and he's been a CIO multiple times over. What were some of your key takeaways from [00:35:00] today's conversation, Vijay?
Vijay: One interesting thing that I that got me thinking was this idea of building warehouse native apps.
You know, he was talking about experimentation that they did building in house application for a sitting directly on top of Snowflake and probably using the Streamlet technology Snowflake has. And I think that's That's an interesting trend. I think we're going to see more of this and it's good validation that this is sort of how CIOs are thinking about it.
And you brought up a great point about potentially being in a world where the CIOs will only approve certified warehouse native applications to be used by the business. Because it comes with certain guarantees of security, privacy, you know. No data copies and efficiency and governed self service access.
So, so I thought his mention of the work that they're doing is a sign of a [00:36:00] trend in the market.
Tom: Yeah, in fact, that, you know, really dovetails nicely into this concept that every CIO has to think about. Build versus buy, best of breed. And I thought he gave a very interesting perspective that you need to have a bit of a platform bias to this, right?
We've over indexed on the, the, the best of breed and we have proliferation of so many different point solutions out there. If you can take a step back and standardize in a few platforms. Snowflake, Data Warehouse, probably being one of them as a new foundation for things, then you can use your best of breed investments for that strategic differentiation.
I thought that was a really valuable piece of advice that he left our viewers with.
Vijay: Yeah, and he talked about time to value, which is really, ultimately, that's the key, right? Just data movement you have to do and more you can do directly on top of the system that holds the data, the faster time to value.
And that's really key for enterprises. You need [00:37:00] quick time to value for your business. One other thing you know, what he said about 55 percent of what people do every day, you know, is wasted, right? Every knowledge worker's Spends, you know, half their time doing work about work. So, so I think that's the area, obviously, generative AI is going to have a huge impact and and Asana seems to be well set up to take advantage of that.
Tom: Alright, that concludes today's show. Thank you for joining us and feel free to reach out to Vijay or I on LinkedIn or Twitter with any questions or ideas for future episodes. Until next time, goodbye.