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MicroStrategy Incorporated (MSTR)

Big Data Conference

August 20, 2013 12:00 pm ET


Anurag Tandon

Vihao Pham


Anurag Tandon

Hello, and welcome to this MicroStrategy webcast. My name is Anurag Tandon, and I'm responsible for marketing around Big Data and analytics capabilities of MicroStrategy. And I'm excited here to talk to you about how you can bring Big Data into the enterprise and really make it available to everybody, every employee so that they can use it for getting the great insights that are available in the Big Data.

But before we dive deep into the story, let's just do a little bit of the context setting because we hear a lot about Big Data in the press. And that's an indication that the Big Data phenomena is maturing because press, the general media, is talking about it. But too often, we hear about the Big Data topic in the context of large volumes of data, so retailers sifting through millions and billions of point-of-sale records or banks looking at transactions in the millions and billions and whatnot. And a lot of the time, those applications that are being discussed could be construed as just large-scale BI.

So as an example, MicroStrategy has been used -- MicroStrategy customers are using MicroStrategy for these large volume BI applications already in many cases. So here's, on the left, just a few examples of companies using MicroStrategy against many terabytes to even petabytes of data, which is no chump change, it's significant amount of information, and they're deploying these applications for thousands of users in many countries, many different applications focusing on vendors, things like that. So a lot of these challenges that are associated with large volumes of data have been, not -- I wouldn't say necessarily solved in most cases, but tackled. And there are many instances, many examples of successful applications in those areas already. So 2 or 3 years ago, when we started looking at Big Data and what's going on there, it was really going beyond these challenges of volume, of large number of users, of many applications and so on and so forth.

And so, let's -- when you think about defining Big Data, Gartner's definition is a decent one. There's volume. We already talked about that terabytes, petabytes and exabytes of information and, really, the need for Big Data technologies, newer technologies, because traditional databases can't handle or are already cost-prohibitive in handling these large volumes of data. But then, there's also a variety of issues, structured information versus semi-structured information versus unstructured format information, text information, voice, video, what have you. And we'll discuss this more in a little bit. And then the concept of velocity. So information that's coming in at a very fast speed in realtime were

[Audio Gap]

in realtime to take into account what's happening in the last few hours, days and whatnot.

So let's take a look at variety a little bit more. So where is this Big Data coming from? What are the different categories of sources of data? So there's, obviously, the phenomena of traditional Big Data getting big. So you have data in your company, there might be more sources of information available from the government as the governments open up more, there's data coming in from the financial sector, there's business and consumer studies and more and more of them, surveys and polls and things like that. And all of that data is useful for business operations top and bottom line performance, just traditional things that BI has been used for.

But then there are some newer sources as well that have been untapped before. So the digital exhaust from people, what does that mean? So it's data that people are involuntarily leaving behind from things like the online click stream. So not only does transaction information for e-commerce, but also what might be in these shoppers' shopping cart that's discarded or the items that people looked at in succession or what did you put in your shopping cart, this cart, and then put on instead? So all of these trends that are showing value, showing how the consumer thinks are becoming more and more useful; data and the application logs, how does a consumer interact with the application, even data about people's movement that are in store or to look at fraud, things like that, for security purposes and so on and so forth.

Then there is the Web 2.0 phenomena, which is essentially social media and the content that is being generated in social media from posts, tweets, blogs, pictures, videos and so on and so forth; content not involuntarily generated but voluntarily generated, voluntarily shared, that provides information on how the customer or consumer expresses themselves, how do they state their preferences, what words do they use, what sentiment do they express, things like that, which are valuable for customer engagement, customer service, brand management and so on. And also the things that may be useful from digital exhaust like the new revenue sources or the promotions or things like that. These can often work in conjunction. When one shows behavioral data, the other one shows stated expression of preference.

And then finally, the source that is less often used or less often thought of in this particular context, which is the Internet of Things, things around us, equipment, machinery, everything has lots and lots of sensors. This picture that you see is from the cockpit of an Airbus A380 that shows just the variety, just the numerous sensors. There is literally thousands and millions of sensors on the aircraft and cars and equipment and industrial-heavy machinery and so on and so forth. And not only just what they're recording, but the machine-to-machine sensor-to-sensor communication as well has lots and lots of data there that can be used for avoiding risk to look at quality issues, to look at when equipment is failing, what parts might be failing, to predict trends across not just one machine but thousands of machines that might be using the same sensors and so on and so forth.

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