Strategy Inc (MSTR) Earnings Call Transcript

Absolutely. So it became pretty clear. It's really about transparency first. So if you look at the results here, the most often mentioned requirements or activities that data and analytics teams were expected to do were really creating reports or providing additional data for data analysis or changing content in existing reports or analysis environments, also with more integration of external data. So obviously, it was really about transparency, providing transparency to the business leaders. Providing new data in -- maybe in data as it is or in reports. And we saw many examples of that, that companies were obviously very interested in the liquidity, but also in other things, especially the employment-related measures they had to take were often not shown in existing reports. So here is a good example where new reports and new data needed to be distributed. On the other hand, we saw many examples of the supply chain situation that was really of high, high interest, obviously. And here also, we saw a lot of questions that were typically not being asked before. So data analytics teams were assembling here and really trying to get answers and data to provide answers to these questions. Also looking at forecasting and scenario building, we were obviously in the interesting situation that, I would say, in almost no company, the plan was right or the plan was now working anymore. So basically, everyone had to reforecast and replan, and this is also shown in the data here. So data analytics teams were supporting that. And scenario building, I mean, is still today, it's one of the main tasks we need to do as companies in -- yes, to be able to actually understand, okay, what if -- what happens if certain scenarios come into effect. One last thing here is what struck me is the advanced analytics answers because it's really about the basics here. It's about reporting, it's about simple data analysis. It's really not so much about advanced analytics AI right now. So we thought quite interesting, despite the big hype around this topic for the last years, when it comes down to reacting to such a crisis, it was really first getting the, let's say, the basics, the reporting and data analysis in order and adapting that, and it was really not about advanced analytics and AI, which I found was quite interesting here.

Yes. The next question we asked was about the investments or measures that could have helped companies to cope better with the crisis. So questions like that are an instrument to find out what companies think are their current deficits and also to tell us where are areas for improvement, though this is the kind of time machine question and also the results tell us a lot about what companies could have done differently.

Exactly. And first of all, again, very interesting, 62% of companies said, we were not well positioned. So a bit less than 40% said, yes, we were well positioned. So that's a pretty clear majority of companies saying, well, there would have been measures or activities that would have helped us to come better through this crisis in data and analytics. And #1 here, data quality. I mean that's nothing new, really. But again, highlighting the importance, if data becomes an asset, if data is really crucial, then you also, obviously, look at the quality you have. And again, availability of data coming in #2 here is also, I think, pretty clear that we see these deficiencies for quite a long time. And they surface really here when it comes to the crisis. But it's not only these 2. If you look at the distribution of answers here, it's pretty evenly. It's nothing -- it's not that certain measures or omissions really stick out. But on the other hand, it's really a broad range. And it's quite a lot of different types of measures that companies are highlighting here as something they should have done. And we would assume that a lot of companies here are starting to look at these topics also as a reaction to the crisis. And once the immediate response is over and once everything is really settling down again to being a bit more normal, then I would expect that a lot of these topics are actually being addressed because we should think that it's a good idea to be better prepared when the next unexpected things are happening.

All right. So after taking a look at the current situation and the current experience of companies in that situation of the COVID-19 pandemic, with our last question, we wanted to take a future outlook, and we wanted to know what effect companies expect the crisis to have on their data and analytics division.

Yes. Let's look ahead. First of all, obviously, will there be effects on the data analytics division or not. And here, pretty clear again, it's 60% that say, yes, there will be changes. There will be effects in the future. And if we look at what expects, then they are really -- there's one really sticking out here; that's that the value and importance of BI analytics in the company will become clearer. So analytics -- data and analytics has shown to be crucial to understand what's happening in the crisis, to steer the company or companies in such uncertain times. And this is the expectancy here of our participants, that this value will become clear, and also, obviously, help in understanding how important data and analytics is in the company. And secondly, the transformation to a data-driven enterprise will accelerate. And here again, we see this, let's say, in general, that we think that a lot of companies will accelerate their general digitalization or digitization journey, but in data analytics, we would expect the same, so really becoming a data-driven enterprise. And again, the value of data has become quite clear over the last month that companies will also accelerate here in their transformation to become more or really data-driven in a way. If we look at investments, so the expectancy on which investments will be taken, I think it's quite interesting. It's -- again, it's a broad range, but it's also an interesting order. So #1 will be here BI and analytics use rules; and if we just look at the tools, #2 will be planning and forecasting systems; #3, the data analytics infrastructure; and then again, on the #4, advanced analytics AI. So again, same picture here. Companies see the need to invest into new tools, firstly, BI analytics, but also planning and forecasting. And then we see that the move to the cloud will accelerate. I mean this is already going on anyway, but with -- very clearly with everyone in home office, obviously, cloud-based systems have a very big advantage in terms of accessibility and also elasticity and scalability. So this is what we are seeing in terms of investments, that companies also expect to at least have some investments in upgrading or modernizing their tools, but also their infrastructure. Yes. With that, I think we can come to a summary, what we have seen in our study here. First of all, we definitely saw significant challenges and changes also for data analytics in the enterprise. And first of all, it was about the employment situation. And data analytics company -- teams also had to deal with that, and that obviously also reduced their capabilities in delivering projects. And I think this is one of the reasons why a lot of these projects have been stopped or delayed. So more than half of companies said they had to do that and basically saw themselves pretty severely affected by COVID-19. I think also it's an interesting number of this very high number of teams that needed to implement short-term requirements. So data analytics was really one of the key management tools, in a way, that helped the management to understand what's happening, but also to steer the companies. And by the way, not only in the past tense, but still today. I mean it's not over yet. So it's pretty clear that even today, data analytics teams are implementing these types of requirements to get, again, better data, better understanding of the business. And then talking about or looking at the maybe omissions, what are the experiences, where would have investments been very beneficial to be able to better cope with the crisis. And here, we saw this wide span of different activities or measures that would have been good or that hopefully will be tackled in the future. Starting with data-related measures like increasing or improving data quality, but it will also be about organizational measures, for example, tearing down the data silos or similar things. And lastly, it's -- there's also an opportunity in the crisis. 60% expect major changes for data analytics in their companies, but to the better, in a way, higher appreciation, better understanding maybe of management, but maybe as of everyone, how important data analytics is and also this faster transformation to a data-driven enterprise, which many companies already embarked on that journey, but this is really accelerating it again because the importance has become clear. And with that, we are happy to answer your questions in the chat. But with that, I will hand it back, and I'm really interested to see what MicroStrategy is doing to react to these challenges we have just seen.

Great. Thank you very much, Carsten. And very much thank you, Annika. I mean you have some very insightful things in that research. Again, my name is Rob Davis, and I lead the Solution Management organization here at MicroStrategy. It's my job to work with institutes like the BARC institute to understand what's happening in the market and get those reflected in what we're doing here at MicroStrategy. At MicroStrategy, our vision is intelligence everywhere. And this really goes a lot to what Carsten and Annika were talking about, which is speeding the transition of enterprises to be being true data-driven organizations. And we believe in innovative ways to do that. How do you actually get data into the hands of more people in the enterprise so it becomes part of their day-to-day business process? And also, how do you provide a business intelligence platform that leverages the investments an enterprise has already made in databases, in data management, in data dictionaries, in semantics that allows them to react more quickly and in a more agile manner to changes like we've seen in the pandemic? So when we at MicroStrategy have thought about what we've seen as the effects on our customers due to the pandemic, they reflect very much the same results that what BARC found in the survey. The first thing is our customers want to evolve quickly. Their analytics systems are feeling stressed because they have to do things more quickly and respond to situations that used to take weeks to respond to, but now they need to do in days. Think about situations in the financial industry, for example, where new lending parameters, new lending rules on the part of governments are requiring reporting for compliance for that very, very quickly; or situations where companies need to have new liquidity rules and need to report on that and show compliance very, very quickly. So companies require modern and agile analytics that allows their business process to respond to new requirements very, very quickly and with low cost. The second really big change that we've seen due to the pandemic is remote work. No longer do you have the luxury of sitting in your office, deciding you have a question about the data and going down the hall to ask your friend, the data scientist, what he thinks about it. You're stuck at home, the data scientist is stuck at home. You might have access to the same databases and the same data, but you're not sure if you're looking at the same thing, you're not sure if you're looking at the same results. So in order to respond to situations like the pandemic, companies also require an open analytics platform that allows people to get their own insight, that allows them to collaborate with each other easily, that allows them to create guided analytics, that allow people to get more than one answer from a given business intelligence report. And also, a single version of the truth, usually expressed as a semantic graph, that makes sure everyone is looking at and responding to the same data. In addition, since everyone is working remotely now, employees require much more education and support to be able to use data tools more effectively. And we need, as vendors, to be looking at how we can be supporting those individual rules -- roles rather, as they need that more education and support. And the third thing, and Carsten really talked about this quite a bit, is cost control. You're seeing things that we thought were the leading edge of the new analytics, big AI investments, big machine learning investments probably not happening now, and people want to concentrate more on the basics. How do I enable the reduction of the number of data silos that I have? How do you enable people to take action on data much more easily without having to start a whole new project to enhance your existing business applications? Or how do you reduce the cost of your infrastructure by having a cloud implementation that really takes advantage of the competitive market in cloud vendors right now? So those are the 3 things. And when we think about it at MicroStrategy, we're really attacking that in a certain way. We're attacking that in 3 different ways that we look at data. So first of all, we realize that 97% of real-time decisions are data deprived. In the industry, even today, people go out there and they'll say, well, it's going to take me too long to log into a BI tool and find the report I need and drill down to what I need to look at. I'd rather just do what I did last week or just ask the same guy I always ask about that particular business question. And it doesn't help me make a better decision in context. So how do we fix that? 3 ways. We look at modern analytics, we look at an open platform and we look at an enterprise-scale platform. So first, I'm going to talk about modern analytics and what that means here at MicroStrategy. And modern analytics is all about how do you get data and decision-making tools into the hands of more people so that they can make decisions with data almost invisibly, in context, in the tools they use every day and without incurring a lot of cost to upgrade existing business applications. And our main thrust at MicroStrategy to achieve that is a technology called HyperIntelligence. And HyperIntelligence is really a Zero-Click technology. It embeds analytics, it embeds decision making, and it embeds action in the applications that everybody uses already: in your email, in Salesforce on the web, in Workday on the web, any application that already exists and allows you to embed data in that coming from any source across your enterprise. The second way that we ensured democratization of data is through mobile application, so native mobile applications on iOS and Android; responsive design, meaning you can design a dashboard or a report and have it appear perfectly on a mobile device; and also custom mobile apps. But also one of the things Carsten and Annika were talking about is how do we make it faster for BI practitioners to create content that is very useful to everyone. And for that, we have the Dossier capability which is creating real storytelling with data and allowing people to very quickly create dashboards and reports that answer multiple business questions. So let's look at a few examples of that and how I think they respond to some of the concerns and some of the changes that Carsten and Annika were talking about. So let's introduce the concept of HyperIntelligence. And this is all based on something called the HyperIntelligence card. Now I will say I date myself a little bit when I explain them this way. But a HyperIntelligence card is the equivalent of the old 3.5 by 5 inch index cards that everyone used to have on their desk, right? And something -- and again, I'll date myself by using this word, the Rolodex. And so if you wanted to get information on someone you were going to call for your next sales opportunity, for example, you spin your Rolodex and so you found that person's cards. And on that card, it would be everything you needed to know about them. It would have their phone number, it would have the last time you talked to them, it would have their birthday. It would have everything you needed to know. And the HyperIntelligence card is really the digital equivalent of that. It can be about any noun that you have in your business. It can be about a person. It can be about a customer. It can be about a product, it can be about an opportunity, and it can contain data coming from any data source in your enterprise, funneled through the semantic graph of MicroStrategy, meaning that I can be sitting in Salesforce, hover over an opportunity, for example, and see information coming from both Salesforce, coming from my case management system, coming from my employee management system, all put in one place, allowing me to make a very fast decision on what I should do next in that opportunity. And these can be deployed anywhere. So let's just take a look at that for a moment. So here, I'm in my e-mail, for example, and I get an e-mail with a list of customer names. And I see the HyperIntelligence cards coming up on the right-hand side, meaning someone can just send me an e-mail asking about a bunch of customers. And I can see what the state of those customers is in terms of their support level, in terms of the opportunities I have in real time. Similarly, I can be in Salesforce here, and I can hover over any one of these particular account names that I have in Salesforce. And I see exactly the same HyperIntelligence cards coming up that show the exact information about those accounts. I can also switch to opportunities which are differently color-coded that show me the opportunities I have, or I can look at the account managers from my employee system right there within Salesforce. And again, this information doesn't necessarily have to come from Salesforce. It can come from any curated source through the MicroStrategy semantic graph. Here we have an example in our employee directory at MicroStrategy, where, again, I can hover over people in Workday and see those people's performance, any HR information that I want to see about them. So again, those cards can have any bit of information you need about your business and be deployable anywhere. And we think about the power of this and exactly what Carsten and Annika were talking about and cost control. Well, today, there's a number of different business applications across the enterprise. And each one of those delivers a certain amount of intelligence to your employees, right? They're all very siloed. Salesforce talks about opportunities, you have Workday talking about HR and performance, you might have a case management system talking about data, about customer satisfaction. Most companies now have a customer experience system as well, talking about sentiments and so forth. And all of them only show a small piece of the whole picture. When you add BI tools to that, especially the legacy BI tools, certainly, it's possible to build dashboards and reports that bring together some of those sources and give you more value and more information. But what HyperIntelligence lets you do is bring all of that information from all those business applications and BI tools together so you have the maximal amount of information and intelligence, and then deploy that on top of all of your BI tools in all of your business applications. Meaning that you have a no-code solution to bring additional value to all of your business applications. So this affects a couple of things, right? We talked about being able to do basic things faster, which is bringing more data to people in context. And we talked about being able to do that at a relatively low cost, and HyperIntelligence delivers that with no code and no need to do surgery on your business applications. But it goes a little bit deeper than that. I already talked about how the HyperIntelligence card can be based on any enterprise data that is accessible from the MicroStrategy semantic graph. We've now added triggers and wings so you can take action directly from the HyperIntelligence card to any enterprise application that we're supporting, which means I can be out there in the field, have a HyperIntelligence card on my mobile device, see that, for example, the risk analysis of the claim that I'm looking at in the insurance industry is such that I should deny it immediately and immediately be go -- be able to go into my enterprise application and disallow that claim in one click, meaning that my entire workflow that could have taken minutes before, now takes only seconds to complete. So again, it's a way, in a very low-cost way, to do a couple of things, bring data to more people in a more relevant way but also shorten the time it takes for people to do their jobs so they can concentrate on higher-value tasks rather than those mundane tasks that they have to do every single day. Now let's take a look at a little bit at the Dossier capability. So I talked about being able to rapidly create dashboards that answer one -- more than one business question. And this is an example of our dashboard creation capability now in MicroStrategy 2020. And we call this basically free form, which means you're almost creating a dashboard as you would a PowerPoint presentation. You can bring in, for example, here, sales information for this fictitious movie store. You can bring a line chart or a spark line to show you exactly what that sales is. You can have a scrolling sales number by month in the middle of the visualization here and have a ring chart that shows the sales by geography, for example. I'm also able to bring in other visualizations very quickly with a simple drag and drop of elements coming from the MicroStrategy semantic graph. I can change the colors very easily, change the formatting very easily and make a very rich, visually rich that is, a dashboard that answers lots of business questions. I can bring in GIFs and bring in different pictures to make it more visually compelling. But then the magic really happens. I can start to bring in some filters on both attributes and metrics that allow the end user to select what they want to look at and get just the answer they need in context from that particular dashboard. So here is an example. You see that I'm going to add a filter on the type of film that is being bought or rented from this organization. And I can also add other filters that allow them to look at this stuff in real time. And when I go to how that's going to look, then when this is presented on a mobile device or on the web to the end user, they then have a very rich way of looking at the data that allows them to look at the state, allows them to look at the product line that they want to look at and get just the answer they need, and the report creator only had to create one thing. But even more than that, the HyperIntelligence I showed you earlier, you can link to these Dossiers. So you can get the one piece of data you need in your HyperIntelligence card and then link to more detail in the dashboard and get that deeper amount of data. And since it's all based on a single semantic graph, it all flows through perfectly, and everyone sees that single version of truth. So that's how it's presented at the end user. And I think those innovations really help with making businesses more agile and helping to reduce costs and focus the time of business analytics practitioners on higher-value things. But it's also important to think about how does business intelligence and how do analytics fit into the ecosystem of everything else that's happening in data around the enterprise. And that's really the investment that MicroStrategy has made in our open architecture. We realize that analytics and business intelligence is only one part of it. And I found it very interesting that Carsten and Annika were talking about the fact that AI projects are not the ones being funded at the highest priority at the moment, but business planning is, for example. And figuring out, for example, how to get the business plans reloaded and get those plans into the BI tools so people can track actuals against them is one of the most important things that are happening right now. So again, getting back to cost reduction and allowing people to really benefit from the analytics structure that they already have in place. Our Federated Analytics strategy is really one of the most important things there. We decided in MicroStrategy 2019 and 2020 to open up our platform and our semantic graph to all business intelligence tools are out there -- that are out there. So if you have pockets of Power BI or Tableau or Qlik in your enterprise, they can also benefit from that single version of the truth coming from MicroStrategy and leverage those in the tools that people are already using today. Similarly for Excel and Microsoft Office, So many people use that, the original BI tool, I always call it, and you can access again MicroStrategy's governed single version of the truth from Excel. And we've opened up connectivity to Jupyter and RStudio, so existing data science algorithms and methods can be then subsumed into the business intelligence platform and leveraged in business process of everyone today. Another important thing that we've done is opened up the entire platform in REST APIs, which means companies doing embedded analytics, companies looking to put little bits of Dossiers into their web pages, for example, or into Office can do so very easily and leverage work that has already been done. So again, it allows people to concentrate on creating new content or modifying content to respond to the business needs of the organization without having to worry about how they're then going to present that or how they're going to embed it into the tools that people are already using and the web pages that people are already looking at. And finally, a big part of cost optimization for organizations today is how they're going to deploy all this. And MicroStrategy is perhaps the only company that has exactly the same technical stack whether you're on-premise, whether you're in managed cloud, whether you're in AWS or Azure, we run on Windows and Linux. And thus, you can make a choice and, in fact, change over time between those different platforms to best cost optimize and performance optimize your implementation. So let's just look at a little bit more about what the open platform means. So I often think about who are the constituent clients of a business intelligence platform. There's the analysts, of course, there's business users and there's developers that are trying to make custom applications with data. And what we've done with the Federated Analytics strategy is to open up the MicroStrategy semantic graph to all of those people. So again, if you're an analyst using MicroStrategy tools like HyperIntelligence or Dossier that I showed you before, but even if you're using Power BI, Tableau or Qlik, you have access to exactly the same performing cubes, exactly the same single-governed version of the truth as everyone else does. Similarly, if you're using Excel and developers who are creating custom mobile and custom embedded applications. We also have, through exactly the same APIs, links with Jupyter and RStudio. And in fact, through custom APIs with planning applications as well that allow those applications to take in data from a semantic graph, ensuring that their methods and models are being trained with exactly the same governed data that everyone else is looking at, but then to push back that machine learning enhanced or planning enhanced data back to the semantic graph so it can be incorporated into all of the reports, Dossiers and HyperIntelligence cards that everyone across the organization is using. So really, it closes the loop between the high-end producer of data and insights and the consumer of insights, no matter what tool they're using. The cloud, of course, as I said before, is another topic. So we have a very fast to deploy cloud. In fact, we can provide a full managed environment, if you wish. It's very secure. We reduce the risk and safeguard your data with all of the built-in security methodologies that I'll talk about in a couple of slides. And of course, it's very economical, as I said before, because with the simple backup and restore operation, you can move between on-prem, different cloud vendors or between the cloud vendors themselves in order to optimize your costs. Then we get down to really what the DNA of the whole thing is. And really what the best argument is, to reduce cost and to ensure that you can spend time in the enterprise working on greater insight as opposed to rearchitecting all of your systems. And really, the basis of this in enterprise functionality is the MicroStrategy semantic graph. MicroStrategy so strongly believes that you have purchased all of the data management systems you have for a reason. You have the data lake for a reason. You might have a fast legacy OLAP system for a reason. You might be investing in a modern acceleration technology for a reason as well. But you're using all of these things together. And it's very rare that a company will get to a situation where they have made the investments all at the right time to get on a single source of truth in a single data warehouse for a single data management technology. What MicroStrategy allows you to do is to hook up all of those data sources together in a single version of the truth in a single data dictionary across all of them and to play that data where it lies, which means you get benefit and return on investment from all of those data management systems you already have, and you can let your analysts get at providing more insight from the data. We also concentrate a lot on performance. A big part of that is making up for differences in database performance characteristics. For example, if you're connected to a fast Snowflake connection, for example, we're just going to pass straight through and pull that data through directly through that connectivity. If you're connected to a legacy data warehouse that's a little bit slower, you have the option in MicroStrategy to put that through a cube in our platform to make it faster as well. So you were able to really look at the business process that people are acting on and think about what the best way to provide the highest performance application is. And I talked about this a little bit more earlier. We do have uncompromising enterprise security. So full row level, column level, object level security, all the access control lists that you need to make sure that you can comply with all regulations about sensitive data, and also the latest cloud security certifications and everything you need to make sure that you can build a process to be compliant with government regulation, which is so important to be able to do in an agile fashion in today's environment, as we learned earlier. We talked a little bit about education and how important it is in the home working environment for people to be enabled to start to use data and analytics more effectively and to answer their own business questions and to be able to work with data more effectively. And we have now comprehensive learning paths across a number of job roles and a full course outline, a full curriculum for them that allow you to become certified in any of these roles across the MicroStrategy platform. And finally, and perhaps most importantly, we do now have a functionality called Expert.Now that allows you with a single click on our website to contact an expert in any area of the MicroStrategy technology, whether you're in the office or whether you're sitting at home, and be connected immediately to a web conference with that person to get an answer to your specific question. So we really think the more we can offer these functionalities in real time, the more we can allow people to concentrate on having conversations with the data rather than learning a tool or figuring out how to make their infrastructure better, the more we can do the things that Carsten and Annika were talking about, which is bringing faster value to their organization and allowing them to concentrate on value adds rather than their day-to-day tasks. So again, I'd really like to thank Carsten and Annika for today's session. I think it was excellent. Some very important trends that we're seeing there. It will be interesting, I think, Carsten, to look at those again in 6 to 8 months and see how they're evolving and see if we are seeing a different direction or whether we're seeing the same path go. But I think what's interesting is some of these will be long-lasting, won't they? We will actually be seeing things that will last longer than the end of the pandemic.

Absolutely. I totally agree.

Yes. So again, I think your feedback is very important. Please do enter your questions in the Q&A window. I really appreciate all of you attending today. Again, I'd like to thank Carsten and Annika, and I look forward to answering your questions off-line from the Q&A window. So thank you again. And have a very good day.