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.

This segment is part 2 in the series : Thought Leaders in Big Data: Interview with Tim Minahan, Network Strategy VP for Ariba
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