Chris Carter: We are very fortunate to have very strong staff members who have focused on that within not only the SAP or big data ecosystem, but on the healthcare system. They know how to benchmark certain statistics. I didn’t even know the linen side or the cafeteria side to the story. They brought that in to be able to combine and paint a picture that everybody was able to see. To me, it was quite revolutionary because I didn’t think of it. They thought about it on a daily basis because they had dealt with it in the healthcare realm.
Sramana Mitra: How many hospital clients do you have right now for which you are doing this? Are you aggressively going after the hospitals?
CC: We are very aggressive in going after hospitals. It can be a smaller hospital, up to organizations with multiple hospitals and clinics. Currently we have four hospital systems that are under contract with us, and we have started to move forward with them and put together solutions.
SM: That is great. One of the observations I am hearing from a lot of people who have come through this Thought Leaders series on our blog is that there is a battle going on in big data right now between these horizontal and vertical heuristics, whether it is a horizontal functionality like facility management and energy saving in the context of facilities’ management or verticals like hospitals. It is in the specific heuristics of how to make a certain use case that delivers specific financial results. That is where the big promise of big data is being realized. It is great to hear the kind of value you provide in healthcare. Could you share another vertical with us?
CC: We are very good in healthcare, finance, and retail. Finance happens to be one of my favorites because if I look at the financial markets in the U.S. compared to the financial markets abroad, the ones abroad are light years ahead of where we are in the U.S.
When I am talking about is taking the financial information of a particular individual – let’s say I am an individual who is dealing on the stock market, and I want to help other individuals get into it, but I don’t know much about who they are and what they are. If you don’t have that information, how can you help them? How can you help them if you don’t know what their net worth is, what they invested in, what their preferences are, what portfolios they currently have, etc.? We are creating that back to the big picture of our structured piece, where we structure all that together in one nice, clean picture. You are looking at a real-time financial painting or even enterprise risk management for a particular organization. When I look at a real-time risk management piece, I am really looking at whether those organizations are mired in myth and that their processes need to happen on a periodic basis.
SM: Where are you seeing the use case specifically? Is it in the domain of wealth management for individual high net accounts, in the risk management of financial institutions, etc.?
CC: We have a few customers on the East Coast that wanted to do a couple of things. They wanted to look at it in a risk management way for their high net worth clients. They also wanted to be able to do more revenue and resource solutions. One of our customers came forth and had an issue where they were barely making the month end closes on their financial institution.
Because they had acquired so many different banks and entities within the last 10 years, with taking all the data from those different entities, they were barely able to make those closes and to get that data. Of course, it is a highly regulated requirement to have that data available for the SEC and other organizations that regulate the financial markets. We took a subset of that and said, “If you want to do a month close, bring in the data and bring it to Hana, because Hana will bring it into itself as a in-memory database. That data will reside there, and all you are doing then is updating the cash that is coming in. You are able to run the reporting on all the different financial institutions you have acquired over the last 10 years, have that data available, structured and unstructured, and run your month end.”
Thus, what used to take them almost 30 days before took them fewer than 10 days after. They got it down to nine days, depending on year’s end.