Sramana Mitra: Let’s do a couple more use cases from different industries.
Bob Renner: Let’s go all the way over to distribution. Two of the largest distributors in the US of a variety of industrial supplies as well as paper products leverage Liaison’s technology to pull in information about the products that they then package and sell, normalize that, and synchronize that data to their ERP systems. In one case, one of those two very large distributors has a fairly common ERP model. The other one, a direct competitor, has a geographically distributed ERP model. Both of these companies use Liaison’s cloud-based integration and data management platform as the authoritative data source for all of their products that they pull in and also a variety of attributes related to those products.
You can see that this is a very different use case. The dimensions that are used to describe the data that we’re storing, which are all structured data is quite different in terms of its ontology and taxonomy vis a vis a longitudinal patient record, which I’ve described in the first example. To Liaison, this is all about acquiring data from a variety of sources that are very generally incompatible without using our system to provide compatibility. Our common data models that we can implement across a variety of use cases allows for disparate data to then be viewed through a common lens. Then, it becomes much more useful to the end user in that way.
Sramana Mitra: Let’s do one more and then we’ll switch to other topics.
Bob Renner: Let me talk about a use case that’s a little bit different but uses the same platform and essentially uses it in a different way or for a different use case. Today, one of the things that’s very important, as I mentioned in the first two examples, is what I call operational data. The first one is health data, but it’s in a way operational because it’s for administering care for an individual patient. The second one is managing a wide and deep portfolio of disparate products that need to be assembled in different ways and delivered.
We’re also using our platform to pull-in unique new data sources including social media and marry the social media data with information that’s more classically used either in supply chain or other aspects of managing your business. What that can give you is new views and insights into your products such as sentiment analysis. You may look at your sales. How many units of XYZ you sold? You may try to correlate that with point of sale data and things like geography and demographics, but it doesn’t give you a lot of insight as to what people are saying about your products.