Strategy Inc (MSTR) Earnings Call Transcript & Summary

Hello, everyone, and welcome to our webcast today. Thank you so much for joining us. My name is Jaclyn, and I'm a member of the marketing team here at MicroStrategy. Next slide. Before we get started, I just want to go through a few housekeeping notes. To participate in the Q&A session, we'd ask that you please submit your questions in the Q&A window at the bottom of the toolbar. If we don't get to your questions today, we apologize that we'll follow-up with you directly via e-mail. Next, this webinar is being recorded, so you will receive a link to the recording via e-mail in the next couple of business days. To help us improve, we'd like to ask you a few questions about your experience viewing our webinar today. So if you have the time, we would really appreciate your feedback. Check out the survey at the end of this webcast, will pop up on your window after the conclusion of the webcast. Last but not least, be sure to check us out on Twitter, @MicroStrategy, and feel free to start a conversation about our webcast today. We hope to see a lot of live tweeting. Next slide. Today, we have 3 amazing speakers: Doug Henschen from Constellation Research; Vijay Anand from MicroStrategy; and Rob Davis from MicroStrategy as well. All right, speakers. So now it's time to hear from you. Let's tell the audience a little bit about yourselves. Take it away, Doug.

Well, thank you, Jaclyn. Great to be here. Again, my name is Doug Henschen. I'm a Principal Analyst at Constellation Research. I've been here for 5 years. And I don't just cover BI and analytics in my research. My research domain is called Data-to-Decisions. So I follow that whole chain from data ingest and integration through the data platforms, both big data and data warehouse through to BI and analytics and on to data science, things like machine learning and artificial intelligence. Previously, I was an Executive Editor at InformationWeek. I was a tech journalist for 20 years at InformationWeek. And before that, I was Editor-in-Chief of Intelligent Enterprise, and I'm very glad that MicroStrategy uses that term. Intelligent Enterprise describe what organizations need to be today. So thank you, and I'll pass it off to Vijay.

Thanks, Doug. And excited, as always, to be a part of a session with you. We've worked together for a long time in this industry, seen a change over time. And at MicroStrategy, I run product marketing for the entire suite of analytics and mobility products. We think of analytics to be very comprehensive, covering technologies across self-service data discovery and all the way through predictive machine learning, big data, cloud, mobile and embedded as well. And I've got the opportunity for the last 15 years to be a very ingrained part and parcel of this industry to see how things have evolved. And we've always discussed trends every single year. And I'm looking forward to today's discussion with Doug and Rob as well. Rob, I'll hand it off to you.

Thanks, Vijay. Doug, great to be here with you today. My name is Rob Davis, and I lead the solution management team at MicroStrategy. The solution management team is really all about discovering new use cases for our customers and discovering what the next thing in business analytics is going to be. My personal passion is how people can have easier conversations with data and get access to the data that they need in order to make decisions and to drive their businesses forward. So in that context, I'm really looking forward to your view on how analytics will drive the new success playbook post-2020 and what that business reboot is going to look like. So let's get into it. Doug, I'll leave it to you.

Very good. So I'm very happy to be here today. It's post-Memorial Day here. We have a nice warm day here where I live in New Jersey. We've got the mood of the country, and I think the mood of the world is to get back to business. But it's not quite business as usual as we'll discuss. And that's why analytics is so important, why insight, foresight, analytical agility are some of the themes we'll be talking about today for this new climate. And just taking a look at our own country here from this New York Times graphic, we see that different states are at different states of returning to business. As I mentioned, in the New York metropolitan area, the conditions here in the New York metropolitan area was hard hit by the virus, but we're getting back to business, too. And the different -- there's a big difference between the conditions Upstate New York, Central New Jersey, Southern New Jersey, different policies that the governments are putting in place. A key point here is analytical granularity, the level of detail of your insight. We think that organizations that are relying heavily on aggregation up at higher levels, looking at regions of the globe versus individual countries, looking -- we think you need to get more precise, more granular. And within countries, looking at states, even counties, even in our area, we see dramatic differences between exurbs and suburbs and inner cities. And it will be important to see the patterns of business returning and what is selling and the level of interactivity we have with our customers in different states. And to be able to see these patterns, we might have a regression in different areas. So there's an importance for continuous monitoring at that granular level of detail. So this is sort of a backdrop of what businesses, your suppliers, what your business-to-business customers and indeed, each and every one of our companies sort of a backdrop of the odds of getting back to business very quickly and thriving, and in some cases, businesses are going to be hard hit. So on this vertical axis, we have resources. And that's not just the size of the company, it's the brand equity. It's the quality and agility of the leadership. It's the customer, the size and loyalty of the customer base. It's access to capital and financial health. So many factors in that resource and then also debt that can have a very corrosive effect on a business in any economy. And that's something we're already seeing. And then that -- on that horizontal axis, the impact of the pandemic. Is it going to be long term? Is it going to be short term? How severe is it? And we've already seen, obviously, travel and retail heavily impacted. Hertz declared bankruptcy this last weekend, JCPenney, Neiman Marcus, J.Crew. What do they all have in common? High debt levels and some question about leadership. And I think it's no surprise, even in a mild recession, to have seen some of these bankruptcies. At the other extreme, grocery stores never closed. Pharmacies never closed. Hardware stores never closed. Indeed, many CPG companies, consumer product goods companies, are in pretty good shape. In fact, Kimberly-Clark: first quarter, paper products, 14% increase in sales. A lot of people stockpiling. PepsiCo had a wonderful -- we had a great Super Bowl this year, really dynamic. People were enjoying that. And then soon after that, we went into lockdown and people were hoarding and stockpiling their beverages and the snacks. PepsiCo had a 7% increase in the first quarter. PayPal. Lots of organizations seeking alternative methods of payment, and PayPal has actually done quite well and is finding new customers in this climate. But the caution here, Kimberly-Clark, PepsiCo, these companies have pulled their guidance for 2020 because there's so much uncertainty. Even though they've done well, they have to be monitoring and looking at the market very granularly. Maybe in the second quarter, the third quarter, people will be working off that stockpile of stores in their garages and store rooms. So that just underscores the need for precise analytics in this recovery period and to stay in tune with the market and changes in consumer behavior. Many of the predictive models that were built on business as usual have to be rebuilt for these more uncertain times. So at Constellation Research, we've come up with our post-pandemic playbook. We think organizations have to be dealing with and understanding the political and legislative climate. They have to be looking at their business model, they have to be responding to the economy. And what they're going to be more -- much more proactive about people and process and their technology, and all in the context of this continuing debate and measurement of public health versus economic health. So diving in with politics and legislative. It might be a little tough to be proactive here as much as reactive, but we think it's important for organizations to stay in touch with what governments are doing, again, at a granular level to understand their major markets and their big city metropolitan area markets and understand what patterns emerge, see what patterns emerge in different areas of the country so they can better anticipate what's ahead in other areas. There's certainly a lot of tumult today. There have been disruptions in supply chain, there's lots of debate in society about prosperity versus public health, privacy versus public health. We think it's just a good policy for organizations, businesses to be helpful if you can to help the COVID crisis. But if you're not contributing to helping the situation, stay out of the fray of the politics. A steady hand do not damage your brand by patting yourself on the back or injecting yourself into this COVID situation. Your brand is your treasurer in this climate. The business model and economy considerations, we think there's a massive merger and acquisition climate ahead in the next 3 to 6, even 12 to 18 months for those organizations that are saddled with debt and low on resources that might fail the innovators, the companies that have been agile, the companies that are managing well, the companies that are in a good cash position. And many, many companies are in a very good cash position to take advantage of this disruption and to seize market share. We've seen certain aspects of the crisis expose weaknesses and strength, the supply chains, the global supply chains. We'll need analytics to better understand how to better balance where we're getting our goods, particularly critical goods. We saw that so clearly in the health care industry, with so much supply and production dependent on what came from China. So we'll see some shifting of that and some better planning around that. And then subscription models. We've observed that the innovative businesses that have come up with subscription models, in many cases, data-driven subscription models for their business, have had a sticky presence with their customers. They've not seen a big impact on their revenue. So these are areas where you can have more of an impact, where you definitely can be proactive. We've seen companies that are agile, digital, progressive in terms of collaboration, are having a better time of it with their leadership. They have a matrix model, they're driven by data, they're collaborating digitally. They're not doing managing by walking around. They're not doing managing by gut feel. They're using the data, they're collaborating with a matrix organization, and they've been the organizations that have been the most agile. Trust is a key currency in this climate. If you have the confidence of your customers and your employees, you're in good stead. So don't squander that confidence. Work-from-home policies. Many organizations, particularly knowledge-based industries, have done very well in adapting to work from home. The employees certainly appreciate this flexibility. And we think this is going to be one of the lingering kind of learnings and aspects and things that lives on past this crisis. And indeed, many organizations have already announced that they're going to make work-from-home permanent, or at least making a long-term commitment to it because employees will have different comfort level in returning to the office environment. So we think it's good to maintain that flexibility and learn these lessons and keep them, and don't think we're going to go back to the old way of always doing things. It's important for employees to be flexible in this climate, but the quid pro quo for employers is to invest in re-skilling and retraining. We're going to have a lot of baby boomers retiring in the next few years, so we're hoping certainly that organizations are hanging on to their talent. We think this is actually a great opportunity. Data scientists and data management experts were so scarce. If companies are rationally laying talented people off, this is a good opportunity for people to snap up good talent. And certainly, employees will trust and prize the employers that are investing in re-skilling and retraining. Any HR professional will tell you, these are troubling times. Investing in mental health support, we're seeing that broadly, more support for employees. And then safety is utmost. We've seen the leading organizations. Their first concern is the safety of their employees and then the safety of their customers. This is not just good business sense. It's -- from a liability standpoint, again, following the guidelines and avoiding liability situations. On the technology front, many organizations, as I mentioned, have done quite well in establishing continuity and resiliency in this climate, but others discovered that they had hundreds of desktop -- employees on desktop computers, and they were buying hundreds of laptops at a time and having to procure VPN and capacity, having to procure cloud scalability and network capacity and suddenly having to ensure trust and security with many working from home in a way that they weren't quite prepared to deal with. If you're still struggling with some of those issues, you can go to constellationr.com. My colleague, Dion Hinchcliffe, has lots of webinars and free videos on guidance to CIOs on these basic IT continuity, availability, durability issues. We've definitely seen the cloud-first organizations doing quite well. We've seen this -- we feel this is going to only accelerate, the move to cloud for its flexibility, for its scalability. On the other hand, we did see some organizations that were maybe flying in the face of the idea of cloud flexibility with 3-year discount levels, commitments to capacity that they now cannot use. So that's something to keep in mind, to have balanced privacy throughout this. As we have more digital interactions with customers, as we have more digital -- entirely digital interactions with our employees, we have to make sure that trust of our data is sound, that we have our security in place, that we're adhering to the privacy policies like GDPR, CCPA and others that are emerging. That's not going to go away. We can't just forget about that in these times. We're going to talk at length about analytics, but the themes are granular insight, agility, low latency. We'll talk about these at length. You got to get your fingers on the pulse of the performance of the organization in all of its aspects. AI and automation. I think some of the -- we expect that some of the more speculative, not clear goal AI initiatives, maybe they'll go to the back burner for now. But anything AI related to automation, bots, robotic process automation, anything in IT automation, if there were so many organizations that regret it not having automated things that they could have automated before this crisis, we think that will be front burner. Anything that helps with efficiency is key. I've been having this conversation with MicroStrategy. So I want to bring Vijay and Rob into this discussion on mobility. Certainly, with new ways of interacting with consumers and even B2B customers, a need for new sorts of mobility, a touchless mobile interactions.

Vijay, Rob, what can you tell us about what you're seeing? I know MicroStrategy has long been a leader in mobile BI and analytics.

Yes. Thanks for that, Doug. And just to go off on this playbook for business, MicroStrategy certainly is in a lockstep with a lot of this as well. On the people and process standpoint, we've invested in our customers by offering up free education, right? All of our tutorials are now announced to be free. And so we certainly are allowing folks who have invested in MicroStrategy, upskill themselves and become more adept at building stronger applications, fine-tuning their existing applications to react better to this climate and even take advantage of new technology that they have. Like I said, MicroStrategy has a broad spectrum of capabilities, and now is the time to understand it and see how you can apply it to your business. And from a technology standpoint, again, cloud, right? We're certainly encouraging all of our customers to move to the cloud, and we're offering free migration efforts as well to support them in this. We realize a lot of people are in tough times, and we certainly want to be a part of this, where we help them be successful because this is not going to -- we're going to outlast this for sure. And at this time, we certainly want to put them ahead. To your question around mobility, there's 2 paradigms that, in my opinion, that have driven the need for mobile. One, as people, the first thing that I look at when I wake up in the morning is my cell phone. I'm used to this sort of app-based consumer-grade apps that I use for everything, from entertainment all the way to what I need for my banking and retail even, right? And secondarily, from a mobile standpoint, it is -- since it's just so available in every single person's hand, it just makes it a lot more adoptable, right? And if you think about retail stores, we think of mobile a little differently. Of course, we support the consumption of reports and dashboards and analytics on its own within mobile devices. But we're in an age where people depend on these tools like Salesforce and Workday and other BI tools, who haven't necessarily invested in mobile apps. I don't know anyone who uses mobile for Salesforce. I don't know anyone who uses mobile for Workday. So when you get a user who wants to switch to an iPad or an Android device, you suddenly lost that adoption. And so you want to deliver on the continuity as they switch devices, and that's where it becomes critically important. The one thing that we've done exceptionally well and has worked for us from the very beginning is to think of mobile as an extension of our workflows, right? It's catered to the natural job roles and duties and responsibilities of people. So think of the retail store associate at CVS or Gucci or Guess? or whatever, right? Or think about the guy who delivers your mail from FedEx or USPS, right? Or think about the field technician that comes to fix your refrigerator. How many of these people have access to a laptop, right? It's suddenly all on a mobile device where you get to record touchpoints. You get transactional workflows where you can input detailed order parts, fix inventory and record the motions of your day within that workflow. And again, you're not looking at your mobile app and saying, "Oh, okay, I need to learn this new data-driven app", right? It's natural and it's progressive with what you need to do, and it's very workflow oriented and you click next and you're logging tasks and touchpoints, et cetera, whether you're a sales rep or a field technician, and that's where mobile has certainly been a fascinating investment for us. If you think of some of our customers like PetSmart and they've reacted to mobile where it's changed their cultures. Two years ago, they decided that managers within specific manufacturing facilities didn't need offices, right, because they're mainly walking around the shop floor, et cetera. And this is pre-COVID, right? And we've been enabling them to do that outside of their office spaces and making them empowered to continue their roles and responsibility and oh, by the way, increasing productivity, right? What would take 10 minutes, would take 3 seconds, right? So now we're not measured...

Have you seen that lots of companies have progressive use of mobility? What about that last mile, the touch list? I think you were mentioning some things about scanning.

Yes. Absolutely, right? Like -- and think about like the situation we're in, right, like contactless delivery is a thing now, right, where Nordstrom is accommodating curbside pickups, where you want to be able to scan a barcode or a tag while you're delivering to log the touchpoint. You don't need to wear gloves anymore because you're -- you've got the scanner in-built into a mobile app, which is tracking the transactions, saying that I made this delivery or this product is broken. So let me replace it right here on the shelf, right? So the ability to scan and input details and transactions have certainly made workflows a lot stronger because now it's bidirectional. It's not just me getting the insights that I need, but it's me being able to take an immediate action right there, and that's where productivity really matters and [indiscernible] .

Right. And we'll be returning to that theme of taking action with our analytic apps, not just looking at dashboards. Let me move on to our -- my post-pandemic playbook for Data-to-Decisions. As you see, there's 3 themes here, 3 areas of focus: the data management; the BI and analytics; and the planning and the immediate, the short term, the long-term priorities. Obviously, priority one. And to that point on granular data, tapping fresh authoritative granular data. And I think this crisis, for some organizations, has exposed one of the downsides of some of the self-service initiatives. And that is too many versions of the truth, too many dashboards, too many insights, conflicting versions of what is the state of the business, what is the health of the business, what is the state of everything from our cash flow and our HR and our sales, our supply chain and so on. So this is an area, a new focus of renewed investigation into what are these authoritative sources? Do we have our definitions of different dimensions of data lockdown? And do we have granular enough data? Have we been relying too much on aggregations and generalities? Can we get to more granular data in driving our insights? Easing access and governance. A lot of BI and analytics platforms help with that with data certification. These are the sanctified data sets for BI and reports, but there's a broader purview to enterprise data assets and things beyond just data sets but models, data models and predictive models. And we've seen the emergence in the last 3 years. I've done quite a bit of research on the enterprise data catalog category, where machine learning is helping to understand and see who is using which data. What are the emerging new sources of data that are popular? And then lockstep with those catalogs, our new governance capabilities that have come in with things like GDPR, the need to enforce policies, the need to enforce access controls and so on. This is at the data management layer. And then longer term, without -- definitely we're already seeing sort of a consolidation of the big data efforts that kind of catered to more modern, cloud-based data lakes, in many cases, taking advantage of low-cost object storage. We think once the necessities of the immediate and short term are handled, we'll see a continuation of those consolidation and optimization efforts, reductions in the size of the data warehouse, maybe more data warehouse is more mart-like instead of the old enterprise data warehouse, and we can rely more on these data lakes to have our hands on all data, not just highly structured data that's hard and rigid to change. We have more agile marts. On BI and analytics, again, that goal is to gain for the first time or regain your visibility into all aspects of the organization into finance, sales, HR, supply chain, we're going to need that finer-grained insight. And the other big push here, we've seen executives in these war room-like conditions pouring over the latest data. And if they were already seeing weekly reports, they want a daily reports. If they were seeing daily reports, they want intraday. If they were seeing intraday, they want hourly. There's a hunger for low latency. And when you get to lower and lower latency, there's less and less time to do the analysis. So not only more frequent, but faster analysis. These are priorities today. And we already knew about interest in moving beyond the descriptive and diagnostic into the predictive, into the descriptive and diagnostic. That's baseline. That's table stakes. We want to see forward, that's gold. We want to know what's coming. And we think augmented analytics, which we'll talk about shortly, is a way we can do this without necessarily hiring armies of data scientists. And on the planning front, we've seen something like a 30% increase in the frequency of planning. I'm talking about financial and operational planning here usually led by finance teams, so we're seeing more operational planning going on. This is around the budget and expectations. It's been very typical in the past for organizations to establish a base case, a best case and a worst case. But we're seeing that planning activity happen much more frequently, much more frequent monitoring of how the organization is doing against the plan and then having layers of these plans. So you have the grand plan, then you have the same sort of scenario planning in HR, sales, finance. And then it's important for organizations to develop action plans for these scenarios before you hit milestones that you don't want to be hitting. Things like getting out of covenant with your loan situations, that debt situation I talked about earlier or headcount or cash flow. These are all crucial points that you want to set milestones at which you trigger action. And this, again, is such an important area with how analytics is changing, triggering action, not just showing insights. And we'll talk about that more deeply.

So a question about that, Doug. I mean when I think about what it takes for an organization to really deeply take on data on a day-to-day basis to make decisions, I think about 3 things, right? You have to provide data in an easy-to-consume way to as many people in the organization as you can. Because the important thing is connecting those human brains with the data that they need to make decisions on a day-to-day basis. The second thing is that people need to know how to make decisions with the data. They need to know what to do next. Too often, we get people dashboards, right? And they're like, "Oh, that's nice. I see a bunch of data. What am I supposed to do with it?" And the third thing, and you touched on it in this slide, is they have to be able to trust what they are being shown. People will revert to making the gut decision or taking the hunch or doing what they did yesterday in absence of really trusting the data that they're making a decision on. So I see a common thread through here, and I appreciate if you comment on it. I see the easing access in governance as a way to maybe give data to more people and allow more of that connection between humans and data. There is the investing in prediction and augmentation and really building database decisions into business process and doing that in an agile manner. And I was interested in what you said about financial institutions, in particular, having all these new regulations coming on very quickly and having to bring in new data to apply to those business processes to show compliance very quickly. And finally, the whole idea about how do you base that on a fabric of trust. And so when you say ease access in governance, I'm wondering if in the short term, companies should be looking at ways to make governance faster to basically figure out a way to provide a trusted data layer without having to build a single source of truth, but maybe a single version of truth across all of their data sources.

Yes. I view this as not a -- you can't buy this. This is a discipline that organizations and muscle institutional capability that organizations have to develop. I think of just some case studies on actually 2 MicroStrategy customers, TAP Air Portugal and Stuller, a jewelry manufacturer. And both of them were good examples of having a center of excellence or an analytics center. And that sort of organization is very good at certifying and resolving on sort of authoritative data, having good data definitions and being able to help. Usually, these are centralized teams. Often, they're small. It's not a rigid thing where everything has to go through them. We've evolved from the early BI period where this centralized team had a big long queue. Now these are facilitating teams that work with the business units and the line of business teams, and they're able to facilitate, help them establish things. And in the case of TAP and Stuller, the pattern that we saw is this: okay, centralized help, but decentralized self-service, so teams could -- had access to sanctified data and they could develop dashboards and reports on their own. And then as these new analytical insights became available and became popular, then they could filter them up to the analytics team, the center of excellence, to harden them and productionalize them, add them to the data model. So they're not breaking things, but they have freedom to explore, to have access to the data and to develop the analytics that are pertinent to them. And then if it seems to have a wider role, then they can roll it up to the large organization. So that -- and I think just organizations in general that have agile methodologies, that are not waterfall, it will come in, we'll take your requirements. And by the time we develop something for you, you'll have moved on to a new requirement. It's very much having more interaction and interplay with the business, with the data experts, the data teams, that's the sort of thing I'm talking about. So very good. So on BI and analytics, really, we talked a little bit about self-service, and that was really the prevailing trend from 2005 to 2015. Self-service is pervasive today. It's not really differentiating any longer to say you provide self-service. In my research area, I'm looking at 3 areas where I think BI and analytics platforms and capabilities and the use of these platforms is maturing and differentiating organizations. And the first is cloud. And on this front, I think you need to be handling on-premises. You need to be addressing the SaaS option, if you don't have the capacity to be managing these BI environments. And then the third, that one that's emerging really is cloud portability, multi-cloud support. And this -- the hybrid and multi-cloud is needed because you have these legacy investments that aren't going to be ripped and replaced anytime soon on-premises. You've made huge investments in virtualization, so private cloud capabilities frequently that aren't going to go away soon. There are data sovereignty requirements. There are organizations, particularly I see financial services and banks, that just aren't comfortable yet. They have to prove very vigorously that the security of this prized privacy information, prized trade secrets, essentially, their prized financial data. And there's also the factor of data gravity, where data is born. We have lots of companies working and developing applications on AWS, but then there's these marketing teams that with Google AdWords and Google Analytics, have huge slots of data on the Google Cloud. And there's also the attributes that these various public clouds and the others not on this list have to offer, Azure, with its hybrid and IoT capabilities that is not AWS and seen as by retailers, for example, as a threat. And then there's the data science capabilities on the Google platform. Vijay, I was at MicroStrategy World in February. I heard more about the -- obviously, the on-premises is well known, highly scalable. Heard more about your SaaS options. You really kind of announced full equivalency on Azure as well as AWS at MicroStrategy World. Heard a bit from Tim Lang, your CTO, about plans for Kubernetes and cloud portability, multi-cloud support. Maybe you can share an update of MicroStrategy's plans for cloud.

Yes. From a cloud standpoint, MicroStrategy started a few years ago, partnering with AWS to launch our entire platform in a click of a button from start to end with AWS. We took our time to optimize it for that environment. And we did the same over the last year or 2 with Azure. We believe that these are the 2 strongest players in today's market. Of course, Google is catching up from a capability and strength standpoint. But the one thing we certainly invested on, from a capability standpoint or from a feature standpoint, is portability. We realize that strategic decisions come about for a variety of different reasons that you cannot control, whether you are AWS first or whether you changed it to a Microsoft stack for whatever reason, whether it's something you can't control like a regulatory reason or a pandemic like this. I compare cloud to the real estate market. You just cannot anymore commit for long-term relationships with vendors. On the flip side, you just have to, just because they have the infrastructure to be able to do that. And by being an open platform, where with a click of a button, you can back up an entire application that relies on terabytes of data and restore it on a different cloud provider, certainly gives our users the competitive edge where they don't lose the work, where they're able to maintain the continuity because of climatic region -- decisions that they can't control. There's another flip side as well, right? If you look at the data centers today, I was reading an interesting article about how they are impacting the climate with pollution far more than electricity is today and it's sort of going unnoticed. And I guarantee that in the next 4 to 5 years, there's going to be strong governmental impositions of data centers. And then when that happens, who knows what impositions will mean for -- and enterprises depending on specific data centers, right? And so portability becomes quickly very important for you to maintain that continuity in all of the investments you've made for the past decades, if you will. So that's something that we certainly have invested in. So customers today who are on AWS can quickly migrate to Microsoft in a click of a button. And with containerization in our road map, it gives you the ability to go to any other vendor as well, right, like Google or other cloud providers that are part of your strategic initiatives. So we certainly have prioritized openness and portability.

Yes. Well, we do see organizations bringing the analytics to the data. And I think another play -- we haven't seen moving things one cloud to another so much, but balancing and having certain workloads on some clouds because of the strengths on that cloud, other workloads on other clouds because of the strengths and favorability or the data being born on that cloud, creating a need for analytics on another cloud. But to your point, what containerization and use of Kubernetes will enable organizations to do is take learnings from one cloud, take configurations, take dashboard styles, templates, and make those very portable, even if it's being applied to a different workload, a different data set on a different cloud. So great stuff. I'm glad to see progress coming on that front. Now the second area where my research is focused on, where BI and analytics platforms are differentiating is in what many are calling augmented analytics and the technologies underlying this augmented analytics: machine learning, automation, natural language, user interfaces. We see this in data prep, with machine learning helping to spot the data that needs to be clean and helping with the data cleansing steps. We see it in discovery. We see it in helping with the -- being predictive with trending and forecasting are usually the first places where augmented capabilities come in. And we have to -- one of the challenges is having the trust and transparent, knowing what the recommendations are based on, being sure that it's not a black box. But the one that I think gets underplayed is automation. And I -- when I started researching this category 3 years ago, I call this smart analytics. The term augmented to me is about assisting the human. And particularly in this climate, I would not diminish the importance of automation. There are things that humans can't do, either because of scale or complexity. And if we have confidence in the analytics, why would we not take advantage of the machine smarts to take an action without our assistance? I think this is an important area where, particularly in this climate, anything that helps us with efficiency is going to be priced. So don't think of augmented analytics as only the machines supporting the human. In many cases, it will do super human things for us. And that's only to be invited and encouraged, in my view. So Vijay, we talked about mobile. Any updates on the natural language user interfaces that MicroStrategy is providing?

We've, over the last few years, the one significant area of investment for MicroStrategy has been around making it simpler and more intuitive so that you can drive adoption to a broader set of users. When you think about the folks who are attending to use analytics in their daily jobs, a majority of them are not necessarily data-conscious or data-literate folks, right? Maybe they don't have the skill set, maybe they just don't have the inclination to interact with data to make those decisions. And in that environment, natural language has certainly helped both from a creator standpoint and from a consumer standpoint to not only create analytics content by asking simple questions, right, whether it's typed out or whether it's asking Alexa as a voice-controlled query to get the response as a business user; or from a consumer standpoint, where I'm searching for business terms, right? Quarterly revenue, right, things like that, where you could type it out. It's become an increasingly sort of -- Google sort of world where consumer apps and business apps are blending and natural language is going to bind them. But beyond the generation of natural language, I think that's the easy part. What's difficult is to be able to monitor these search queries, to be able to mold those queries over time from a learning standpoint. And that's where I think our investment in the semantic graph has certainly come to our support and allowing our customers to be able to track and measure and learn from what their users are doing from a querying or consumption standpoint, optimize it. And in your post-pandemic book, you had a long-term strategy for AI and sort of automation, right? Automation is certainly going to be there. But I think it's going to take some time to be able to learn and deliver optimized responses. And I think generating NLG and supporting NLQ, or natural language querying, is certainly important, but I think that's easy enough. What's difficult and is time-consuming is being able to manage and absorb that data to be able to make those recommendations.

Learning is definitely the hard part, learning from these things. And I think natural language query is going to go right into -- well, we're seeing -- we're learning that this question and these terms are coming up a lot, and the system can learn that certain reports and assets seem to be satisfying the question. So that's one area where I think we'll be seeing that. Go ahead, Rob.

It's obviously the important part, right? The ability to measure what success is, is really the important part. And I think that's what's been missing from BI platforms up till now. So as we're starting to add real measurements of what people are doing with the data, what business process they instantiate, what question they answer, what trigger do they put into an underlying business system, we have a much better way to measure success and then to train these machine learning algorithms about what success is and how to get there. So it's interesting, you talked about guiding the human being with this machine learning and with automation. I think it's really measuring what the human beings are doing through the platform, and then giving that data to the machine so that they can help us automate some of those tasks.

Absolutely. That's a great point. So let's move on to the third area of my research, where it's exploring innovation in BI and analytics. And this is the area of embedding. Now a lot of times when people talk bring up the topic of embedding, they think of ISV, independent software vendors, and SaaS vendors who turn to companies like MicroStrategy, license some of their analytical capability or even white label it and kind of put it under the hood of their application. That's not what I'm talking about here. I'm talking about embedding by end user organizations. And we see pioneers and fast-following companies becoming software and service providers in their own right, and that's heightening the importance of embedded capabilities. This is sort of a 5-step maturation cycle we've seen here. The most basic way of embedding is a web page. You take an iframe or another form of code and embed a report or a dashboard or maybe a more concise, granular data visualization into a web interface. It gets more sophisticated with custom portals, where you can gather up all your analytical assets or reports, your dashboards, your visualizations, provide security and access control so you can even extend this. You can personalize -- you can customize it with your company logos and branding, you can extend it to your partners, all with secure access and granular-level access to the right data for the right people. For the end user organizations, it gets more sophisticated with commercial off-the-shelf, or COTS, and SaaS embedding offerings, whereby you have these prebuild integrations of dashboards, reports or data visualizations or KPIs within predetermined, predefined, prebuilt aspects of those applications. So it could be an SAP ERP application or salesforce.com, where you see a pretty mainstream interface that's used by many people. That's usually what distinguishes these things. These are popular interfaces, therefore, they merit the effort to do a custom embedding. In some cases, organizations are freed by the BI and analytics vendor to get access to the APIs and toolkits to do more of this by themselves. But where we see this going is the ability to deliver much more in context analytics, concise analytics at the point of decision within not just commercial off-the-shelf and SaaS applications, but within your own custom-built applications. And there again, we see that push for more granular insight, for more real-time insight and even interactivity. So you can take actions and have your actions feedback into the data loop and you can learn. And then the ultimate, really, we're seeing a lot of emerging low-code and no-code capabilities exposed to business users and teams that aren't necessarily developers to develop new applications and do this in an agile way and not have to sit -- align with IT and do that requirements gathering and have a long cycle. No, you can develop these applications on your own, and all of those values of concise analytics at the point of decision are within that capability. And ultimately, when it really matures, we'll also see the workflow triggers, predictions, recommendation features embedded in that. Again, I was at MicroStrategy World. I was pretty impressed by the progress I saw there in MicroStrategy's HyperIntelligence offering. I was at MicroStrategy World 2 years ago, and it was really pretty much just announced at that point. A couple of customers were talking about it, using it, kicking the tires. But this year, in February, clearly, a lot of progress and some very impressive presentations by the likes of Sonic and Clarín. Rob, Vijay, any updates on either adoption or -- particularly interested and if this turned out to be an agility booster in the context of the last couple of months?

Certainly, have seen close to about 250 large enterprises now using HyperIntelligence, and that's gone a long way within a year, right? It certainly has been one of our strongest-growing products. Having been here 15 years, and this is something that I can certainly -- I'm amazed by. I was at a conference, remember those when we were able to attend those in-person events, where I think there was at least 500 people in the audience. And I asked, and this was an analytics conference, by the way, where people came there to learn about analytics and were interested in analytics. And I asked the audience, how many of them that morning had run a dashboard for whatever need for their existing roles. And about 10% or maybe less percent of the audience raised their hands, so 30, 40 people, I would say, part of an audience of 500. And then I asked the audience if how many of them had read their e-mail, right? And it was a resounding 100% of the audience. So we're in a world where, clearly, where dashboards have been along -- have been around for so long, haven't necessarily driven that level of attachment or stickiness where people depend on them, although we've certainly come into a generation D or generation data world, right? We're no more Gen Y or Gen X, we're certainly defined by our vocation, and data has become a strong part of it. But we're not necessarily using it as much as we should be. So HyperIntelligence has changed that paradigm by bringing it where people are, right, where you're in your e-mail, you're on a website, you're in a Word document, you're in an Excel spreadsheet. So why not bring contextually, like you said, the data that you need so you don't wait on the people to ask the question to get an answer, but to naturally elevate their smartness or their IQ from a data standpoint, right? I'm on a Word doc, and I'm typing out a customer name, and it suddenly gets underlined and I hover over it and in 0 clicks and 0 seconds, I get a card that tells me what they're worth, what they want, what they need, right, and how much they bought, et cetera, whatever that question is that is relevant and contextual to that name. And I, as a business user who doesn't understand or want to understand data, suddenly, I'm just quickly enlightened with some facts that's help me make smarter conversations the next time I speak about that customer or to that customer. Similarly, on a mobile device, right? When I'm walking up to a meeting to meet with, let's say, someone at a company like Coke, right, who's probably a customer. And I can go into Salesforce or another app on the computer to find details about that to prepare for that meeting. Or while I'm walking up can use my iPhone to type C-O-K-E and get all the information that I need in a bite-sized card to quickly become the smartest person with real-time data that is relevant to that conversation and be able to answer questions even mid-conversation, right? So suddenly, I think HyperIntelligence has changed the paradigm by bringing analytics to more people who didn't care for it or didn't know how to use it or didn't want it. And it certainly does it in a more natural way, and that's why we're sort of seeing lots of companies using it in very clever and unique ways.

Well, I was very excited by it. I wrote a piece of research published earlier in the year about embedded analytics generally. It's a thought leadership piece. It's not about any particular vendor. It's just about the maturation we're seeing and the demand for embedded analytics among leading organizations. And I sort of lay out, this is a bit of the maturity matrix or the maturity curve I lay out and talk about in that research. I'm very glad to understand that you folks are making that report available as well as a separate report I did specifically about HyperIntelligence, making that available for download to attendees of today's webinar. So I think those are good pieces of research that are valuable, and I hope folks attending today's event will download them. So that's a good point to end on. A lot of BI and data management professionals, analytics professionals have kind of lost sight that they think it's all about insight when it's really about action and decisions. And I think I would encourage you to make that your North Star, decisions and actions. That's really why BI came about, and that's really what it should continue to be all about, driving better decisions. That's why my research domain is Data-to-Decisions, not BI and analytics or data science or data integration or any aspect. It's all about driving that data towards decisions. So to sum up, we need to be, in these days, preparing for a political and economic change. We need to adapt our people, our processes, our technology, and we need to innovate new business models. We need to continue to be business leaders. We need access to that authoritative fine-grained data. We need low-latency insight into all aspects of our business. And we need to embrace this continuous idea of continuous planning, really anticipating where our business is headed. That's where prediction is also important. And acting proactively before we have to act in a panic. And looking at analytics in ways you can differentiate your deployments these days, it's going cloud. Companies want to bring their analytics to the cloud to where their data lives, they want that flexibility. They're looking to the cloud for that scalability; certainly in data science, mind frame that's flexible; compute capacity, that low-cost storage, very attractive, very enabling. We want to consider these augmented capabilities. So you can do things like trending and forecasting without necessarily hiring a data scientist and then delivering concise in-context analytics in places where people do their work. Self-service only went so far, so we need to bring it in those places. Vijay was talking about like e-mail, like your mobile interfaces, like your day-to-day transactional applications where you're actually doing your work instead of doing the swivel chair, going off and looking at a report and considering and analyzing or going off to a dashboard and trying to make sense of it, and then going back to what decision you were trying to make, and maybe you've forgotten by then what it was. And maybe you could make sense of -- or drill down far enough to really understand what to do. And it gets back to a gut decision. No, we want to be able to deliver concise analytics in the context of the work, in the context of the decisions. So with that, I thank you very much for your time. I wish you the best of business and the best of good health. And I'll turn things back to Jaclyn.

Thank you, Doug, Vijay and Rob. That was an awesome webcast. I certainly enjoyed listening in, so thanks again. I just want to thank everyone for joining today, and we hope you are pleased with the content delivery. Just a couple of reminders. Survey will be popping up at the end of this webcast. Your feedback is so important to us, so we hope you take the time to answer some of those questions. And you will receive a recording to this webcast via e-mail in the next couple of business days. And finally, we have a lot of great webcasts coming up. Check out those on the website, as you see on the slide. And again, thanks for joining us today, and we hope to see you online again soon.