Big Data Analytics in a Minute with Sisense Ceo Amit Bendov

DAVID FEINLEIB: Hi, this is Dave Feinleib with another episode of Big Data TV brought to you by BigDataLandscape.com, and I’m here today with Amit Bendov of SiSense. Welcome to the show. AMIT BENDOV: Thanks David, nice to be here. DF: So, your company is in the business analytics, business intelligence space, generally speaking. Tell us a little about the company and what you do AB: Well, SiSense makes Prism software that anyone can download from the website and get to Big Data analytics in a matter of minutes. Unlike some of the traditional solutions that are hefty, expensive, complicated and only a large company can afford, with Prism, any company, small, large, or medium-size can get from data to analytics in a matter of a few minutes, at a fraction of the cost of what it used to cost to analyze big data. DF: And does that also work in the cloud? Is it just on the desktop? How does it all work? AB: It works wherever you want it to work. It can work on-premise, on cloud, on a laptop, same price, we don’t care. In fact, at Strata in New York last month, we demonstrated how to analyze a terabyte of data on a 750 bytes laptop, believe it or not. So that was pretty amazing and people were lining up to see the little miracle. DF: Yeah, pretty remarkable stuff. What is it that makes that possible? What’s the technical innovation? AB: Well, this is both deep and broad technological development. We’ve been in development for over 6 years. Basically it’s the next generation of BI. First there is a database, the traditional BI solutions, then there is the in-memory solution, SiSense is already a third generation that combines a lot of different things to get that kind of performance, but first, it’s in-memory, but also works with a disk. So, with in-memory processing or some of the lower-end in-memory solutions, you’re limited to how much data can fit in to physical RAM. With SiSense, we definitely use the physical RAM but we have two terabytes of disk and 64 gigs of RAM, you’re fine. Second, it’s a columnar database, it works 50 to 100 times faster than a relational database, which is what most of the traditional tools use. Very strong compression. Last, but not least, very strong hardware optimization, it was designed from scratch for 64-bit parallel processing on standard, commodity hardware so you don’t have to buy anything that is dedicated, custom-designed hardware. Whatever you can buy at a shop, you can probably use right away. DF: So, a real use of commodity hardware to provide really fast data analytics across really broad data sets. AB: Absolutely. DF: You joined the company as CEO. What are some of the things that attracted you to the company and got you involved? AB: I’ve had two successful companies before that and when I looked at SiSense, first: everybody knows it’s a huge market. Basically infinite in size, a lot of money has been changing hands and it just keeps growing. The team here is phenomenal, really sharp, most of them are technical and their energy is unbelievable. Hundreds of customers are happy, they’re using, they’re renewing. Software as a service model so  if we’re not good, customers don’t renew. That was phenomenal. The technology is so ahead of the curve that it’s unbelievable. Really, the amount of data that we can process – no tools in the area can even approach what we’re doing here. Basically everything is scaled and geared-up for scaling up, and it’s ready to go on fire. DF: That’s great. Talk about a customer use case or two. What are people actually using this for? AB: Well, it runs the whole gamut. It’s unbelievable how many types of customers we have. Some of the world’s largest brands, like Target, who is this for in-store theft analysis for stores across the United States. We have people in NASA who are using it. We have Merck as a customer. Lots of large companies and also lots of small companies, a lot of the hottest startup companies like Wix and Uber. A lot of online companies, everyone that has large amounts of data, and they don’t need to be big companies. Often people think that you need to be a Walmart or a Facebook to have Big Data, but just two guys and a dog doing mobile analytics applications – there you go: billions of impressions. DF: Yeah. So, a company like Wix or a company like Uber, what are they using it for? AB: Well, let’s take Wix for example. They have millions of websites. Wix, if you don’t know, helps small businesses and individuals build websites very easily. So they have millions of websites which in turn, they have, who knows, hundreds of millions of visitors to each one of these websites. So they can’t analyze traffic, conversion, who’s using what, what are the trends, what are the patterns? That’s a lot of data to crunch, and that’s how they use it, pretty much. Dozens of employees of the company are looking at dashboards day in, day out. DF: That’s great. That’s a really interesting use case, and they have so many websites, so I can imagine that’s a great use case. So, in terms of price point, what does a product like this cost and what would a customer expect to spend on this kind of product? AB: Well, it’s certainly a fraction of what it traditionally used to for BI solutions for a number of reasons. First: commodity hardware. So, you want to run some of the Oracle and SAP things, even just the hardware and the basic software comes to hundreds of thousands of dollars. Second, you don’t need any consulting, because basically the software is up and running in a matter of hours, maybe a couple of days, but that’s it. So you can do it yourself without hiring IT developers. Third, we’re a software as a service pricing model, whether you use it on premise or in a cloud, which is not so common in the industry. Most vendors, they charge you a pretty hefty amount up front and then you may end up being stuck with shelfware. With SiSense, you always keep us on our toes. So it’s an annual subscription, it starts as little as 5,000 dollars a year, depending on how many users, usually in the tens of thousands, and sometimes hundreds of thousands for larger companies. That’s the model. DF: What a great way to stay aligned with your customers. It sounds like you’re really aligned with them, so that’s a great thing. AB: Absolutely. DF: Opus Capital and Genesis Partners invested in SiSense. Talk a little about the funding and what you’re doing with the capital and then when you plan to raise money again, if you do. AB: Well, they both invested 10 million dollars to date in the company. We’re using it, obviously, to develop the product for almost 2 years, we’re perfecting the product. With very little marketing the company more than tripled sales for 2 consecutive years, with very little marketing. Hundreds of customers in 49 countries. Now, we’re scaling up, and basically investing more in marketing and sales and expansion and growing the support team. So, that’s the major use of the money. We’re probably going to do another round sometime next year. We’re good for now, but probably the first half of next year, we’re going to be raising more. DF: Let’s wrap up by talking about the future of big data. What are some of your thoughts on where this whole space is headed? AB: Oh, I mean, where to we start? It’s limitless. I think that more companies are realizing that they’ve always had Big Data, they just didn’t know it was called Big Data. And they have a lot of data in the attic and now the technology is there and they can make use of it. I think that our mission is to make big data available to all companies, not just the large companies. If you look at the number of case studies out there it’s really very limited. There’s always Facebook and Target, and the big boys. But we see that big data is coming not just for the Fortune 1,000 or the Fortune 1,000,000, lots of companies can do it and take advantage. Basically, using very simple software and hardware, you don’t need to run 80 clusters to analyze Big Data, I think that’s overrated. I think in-memory, we’re starting to see the limitations, that’s what we personally are tackling to go beyond in-memory. Provide the same kind of performance, but not being bound by the physical limits of the memory. And overall, I think companies have identified that this is really an edge and a weapon that they can use in a competitive landscape and it’s going to become pretty much the standard. If you don’t understand your data, it will be hard for you to compete in the market. DF: That’s a great insight on Big Data. Well, so great having you. SiSense is a super interesting product and offerings, and thanks for being on the show. AB: Hey, my pleasure. Thanks, David.