Thought Leaders in Big Data: Interview with Sandy Steier, CEO of 1010data (Part 5)

Sramana Mitra: What is one of the most significant representative use cases outside of finance?

Sandy Steier: We will use retail, where there are a couple of interesting use cases. The first is an internal kind of data analysis. Retailers produce a fair amount of data – as we all know – every time a cash register beeps, that information goes somewhere. For a reasonably large retailer, that can be tens of billions of items sold over a period of two years, for example. >>>

Thought Leaders in Big Data: Interview with Tim Minahan, Network Strategy SVP for Ariba (Part 1)

Tim Minahan is senior vice president of network strategy and global marketing for Ariba, host of the largest business network on the planet. Tim has more than 20 years of experience in enterprise technology research and marketing. In this interview, Tim gives us insights into how Ariba helps businesses of all sizes connect with each other and explains in detail how the Ariba network operates on a global scale.

Sramana Mitra: Tim, let’s start with setting some context. Our audience knows Ariba. We also know that you have been acquired by SAP in the recent years. We covered Ariba in the past in quite a bit of detail. Could you set some context about what you are doing? >>>

Thought Leaders in Big Data: Interview with Sandy Steier, CEO of 1010data (Part 4)

Sramana Mitra: You made a comment that some of this data [consists of] regular large data repositories, but some of it falls in the domain of big data. Would you elaborate on that? What do you define as big data?

Sandy Steier: I think defining big data is a serious challenge, because I don’t think there is a good definition of big data and everyone has their own definition, which really means there isn’t one. What I meant in that sentence was big data in the volume sense, because there are billions and billions of records. >>>

Thought Leaders in Big Data: Interview with Tim Minahan, Network Strategy VP for Ariba (Part 2)

Srmana Mitra: I would like to do three or four interesting use cases of how your buyers and sellers are leveraging that data or how you are prompting them to leverage that data.

Tim Minahan: If you consider your personal life, you have heard a bit about the social graph, which maps not only your personal conductivity or sentiments, but also across that of your network. When you attain a certain volume and critical mass when it comes to business networks, you begin to map out what we would call a commerce graph. This graph is able to track all of the transactional relationships, performance and other community-generated information across your entire value chain and across every member of the network. Where this becomes interesting is in a host of different ways.

Let me give one example on the front end. I am a new buyer or even a new seller, and I am looking to discover a new business opportunity. Today folks can come to the Ariba network and say, “Hey, I am a buyer and I am looking for a plastic confection molder that is ISO 9000 certified, in the Midwest and within 100 miles of Chicago.” Up will pop  a directory of suppliers that have those capabilities, but here is where it becomes interesting. When you begin to harvest 15 years of transactional relationship and community-generated information, we begin to provide that as enriched data to the supplier profile. Now you don’t only know the supplier’s capability, but you also know they have been on the network for eight years, they have been electronically enabled to transact around purchase orders and change orders, but not invoices and payments. They have been invited to 10 RFPs last year and won seven of them, and they are doing business with 50 other buyers in the network. So, you get further qualifying information – quality information from the community itself. Those buyers that have used them give them a rating of four stars out of five.

That is the core information. The thing about big data is not only being able to pull in that type of information within your own environment, but to marry it with new information to make even more informed decisions. So we partnered with outside information providers like Dun and Bradstreet. Then when the buyer says, “I like these three suppliers,” they can get a predictive risk score on the suppliers to make a further qualifying selection. That is just one example of the power of big data in a network environment.

Let me give you another example. It is in the form of “how do you harvest that transactional information”? Once you have automated a business process like purchases, sales orders or invoices that were once very manual, involved lots of people and lots of paper. It took weeks to get the data into the system so you could manage it. Once you automated that and made it completely touchless, like we have with the network, you could begin to make not only more informed decisions but new types of decisions and processes. I give you the example of invoicing. We automated invoicing for some of the world’s largest companies and suppliers. In doing so, we have not only created greater visibility and transparency into that process, but you have also helped sellers getting paid faster and buyers lowering their accounts payable. Because the buyers and sellers know on day one that it is OK to pay, they can begin to apply an analytic to that and make recommendations and decisions about, “What if I pay you earlier?” Buyers can begin to manage their payables on a sliding scale, saying, “I know I am going to pay you on day 30, but if I pay you today would you give me an additional 1% discount?” The buyer likes it because they can lower their cost structure and put some of the working capital they set on the side to work for them and get some interest on it. The supplier likes it because in that case they can be in charge of their own destiny – they can accelerate their own payments.

Let me give you an example. There is one very large entertainment company that was using both invoicing and what we call dynamic discounting concept. They are a supplier with a fast growing mid-market company called Media Fly. Media Fly is a mobile marketing provider. They were asked to invoice this customer over the network. When exposed to this dynamic discounting, the CFO can now look at the receivables in real time and say, “I am going to even hire a new developer so we can take on this new business. And to do that I want to accelerate these three receivables – I want to get paid today, rather than 45 days from now.” They began to be able to not only better manage their working capital, but importantly for a lot of your listeners, because they are fast-growing startups, they may delay taking down another round of financing and diluting their initial investors more, because they can now control their working capital that much more efficiently.

Thought Leaders in Big Data: Interview with Sandy Steier, CEO of 1010data (Part 3)

Sramana Mitra: More than they needed to.

Sandy Steier: One of the things that now everyone knows is that they are very complicated securities. So complicated in fact, that in some cases they are too complicated to be safely traded. In those days they weren’t as bad as today; nevertheless, they required a lot of analysis, and they continue to require a lot of analysis. The kinds of analysis you do on them, besides various kinds of mathematical modeling, incorporates all sorts of market information. >>>

Thought Leaders in Big Data: Interview with Jim Swift, CEO of Cortera (Part 5)

Sramana Mitra: Given that you are now versed in that B2B analytics world – you have chosen a good side problem and instead of going to the B2C space, where there is more active big data work going on – what are some open problems that you see that you can draw the attention of entrepreneurs to?

Jim Swift: Sometimes it is hard to organize how you attack problems in the analytics space. Here is how I find it useful to think about, and this is where in each of these layers there are levels of innovation where entrepreneurs are going to be able to create interesting businesses and useful solutions. >>>

Thought Leaders in Big Data: Interview with Sandy Steier, CEO of 1010data (Part 2)

Sramana Mitra: So it is both NYSE branding and NYSE data and you are providing the framework for analyzing and manipulating that data, but it is their data and their brand.

Sandy Steier: That is correct. >>>

Thought Leaders in Big Data: Interview with Jim Swift, CEO of Cortera (Part 4)

Sramana Mitra: That is very interesting. Talk to me a little bit about the big data angle of this. What kinds of data scales and technology stacks are you dealing with?

Jim Swift: There are several different components to it. I can’t give you a perfect definition of big data. Whenever the data starts to get a little unruly for the problems you are trying to solve, and it is really testing the skills of your organization and the limits of the technology (the speed), I think that is when you start getting into big data. I have seen it in several different points back in the credit card industry in the 1990s. We were doing things that the technology just couldn’t support. We were not able to take a full file analysis. We had to take samples out of our complete databases so that we could run real-time stats and views on them. Then you would try to take a representative sample, take the results and you would try to project the counts and the trends back against the full population. But we didn’t have the horsepower or the storage reality to be able to do it on the false sample. >>>