Entrepreneurs in the field of data integration would like to read this interview to get a sense of the state of the union, and identify open opportunities in the field.
Sramana Mitra: Bob, tell us about Liaison Technologies and yourself.
Bob Renner: I’ll tell you a little bit about myself first and then I’ll dive into Liaison. My background is in the technology space. I call myself a technologist by trade. I joined Liaison Technologies almost 15 years ago as a Chief Technology Officer. Then, I moved up from CTO to CEO after being with the company for a couple of years.
Bridging to what Liaison does and what drew me to the company, the company was a next generation integration and data management company in the cloud before the cloud was really even defined as such. What we’ve been doing and continue to do is provide a utility infrastructure for moving, managing, and transforming all types of data.
I think what makes Liaison a little bit unique is that that type of activity has gone on for years but Liaison is really agnostic as to the type of data they move, manage, and transform. We have a spectrum of everything from clinical trials data which is very different from transactional supply chain data. We also have marketing and financial services type of data. It’s an interesting broad spectrum of use cases. We can expose our services through a variety of different vertical markets as well as different use cases.
Sramana Mitra: Let’s do some use cases. Maybe pick two or three customers from different verticals and tell us how those customers are using your technology.
Bob Renner: One that comes to mind that I think is very illustrative is in healthcare. As you may know, the whole healthcare industry has become much more digitized and automated in the last five years. Part of that is through our big push driven from government-funded initiatives to digitize patient health records, and also moving from paying for services to a model where you pay for outcomes and results as a result of the care that’s given to a patient.
One good example of how Liaison brings all of our services and solutions together is this particular client of ours that manages, what I call, transitions in care. When a patient has a procedure done in a hospital and they leave a hospital, they have a regimen that might include prescriptions, monitoring and management of their treatment over a course of a period of time, which if followed diligently, drastically reduces the possibility of a readmission through the emergency room within 30 to 60 days from the procedure.
The way Liaison would help facilitate that is we take inputs and data signals from monitoring devices that monitor the patient’s health. We take inputs from a patient’s as well as a physician’s portal. Also, we take inputs from call centers that would be part of that transition as they leave the facility after the procedure. Liaison’s technology and infrastructure is the integration platform that pulls all these disparate data together, harmonizes it into a common data model, and then presents it in a way that’s cohesive and consistent so that you can, in a simple way, understand how the patient has been adhering to the regimen. That’s one interesting example of how Liaison’s technology is brought together to really solve a real world problem.
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.
Bob Renner: In one example and use case, we married our sales data with Twitter feeds so that we can access the API’s. We pull the data in, normalize and correlate it, and we created a dashboard that allowed our clients to look at sentiment. We were able to dimension that along with the sales data. We product a very different, simultaneous view of geography, volume of sales, and demographics of who they’re selling to and then generalize sentiment about how people are talking about the data from social media standpoint. The interesting part of that is it uses some forms of natural language processing and parsing to take free-form data and turn it into structured data from the Twitter feeds and marry it up to other structured data.
Sramana Mitra: Who provides that unstructured data to structure data transformation technology?
Bob Renner: That was our technology using generally available applications for parsing that data.
Sramana Mitra: In each of these cases, obviously you’re dealing with disparate types of systems that you are integrating. Could you simplify the different types of systems that you’re working across or bringing together?
Bob Renner: We have never met a system that we didn’t like or we couldn’t integrate to. We, literally, have tens of thousands of different clients with a variety of hundred thousand different permutations and combinations of systems that we interface with. The strength of the Liaison platform is that our semantic integration technology has four issued US patents.
We are built on the premise that we believe standardization at the end point is not only unlikely, but it’s impractical. A lot of what we do and the strength of our technology is doing that data transformation, translation, and adaption to these systems. That’s one of the first things that we relied on in our development. It was the premise that we needed a ubiquitous tool that could translate and transform, and adapt to any system or data.
Sramana Mitra: If you look the data integration world today, we are working with a lot of different types of systems and data. Unstructured data presence in business today is much higher than it used to be because of social media. Give me a couple of really hairy integration problems that you see out there that have not been solved yet.
Bob Renner: You mentioned unstructured data. I think everybody gravitates towards that. To be honest with you, a lot of the structured data problems are yet to be solved on a large scale. What I mean by that is I think the combination of accessing authoritative data – profiling the data to determine whether there are redundancies, which data source for common or overlapping data is, in fact, the best authoritative data source. I think sorting so that you’re giving the best possible and the most up-to-date view of the accessible structured data is a pretty hairy problem generically. We’ve actually been faced with that at many clients. Trying to pull out one specific example might be difficult. But I’d say generally, inside an enterprise or ecosystem, what’s surprising to me is the amount of redundant data and the challenge to profile that data and determine which source should be used in your final product.
Sramana Mitra: You’re saying that the structured data integration problem at scale is still an open problem? I’m looking for problems that are unsolved out there, not problems that you’ve solved already.
Bob Renner: I think scale is an unsolved problem at this point. A very difficult problem that you have to solve elegantly is cross-domain master data management (MDM). I think you’ve got a few products and solutions and implementation within a given domain and a less complex domain had been adequately handled, but I think cross-domain master data management is a problem that continues to be very use-case specific, and generally, there have not been a lot of solutions.
Sramana Mitra: I think I know what you’re talking about. Let’s do a use case example that is a cross-domain master data management example.
Bob Renner: Forget about the cloud part of it. Let’s start out within an enterprise. If you’re trying to reconcile in to a single data set with concept of a customer and the concept of a product, what you have right now is an environment that has surfaced because of the different taxonomy and ontologies where you have products that are optimized for a customer. In some cases, optimized for employee or product.
In the more complex world of healthcare, you have registries. You don’t even have what I would call master data management. They’re registries that are optimized by therapeutic area. When you want to look at in the same tool or through the same view, it’s a very difficult problem. Often, our clients and other people have to deploy multiple solutions and then try to join those together through a third-part product.
Sramana Mitra: Is there anything else that is worth discussing that you would like to discuss?
Bob Renner: It’s fairly well-written on at this point but I think it’s something that Liaison has been focused on for quite some time – the convergence of technologies that have been separated. I’ve talked about one which is the fine grain multi-domain MDM, which really needs to converge in order to provide a reasonable view of not just cross-discipline data, but also the convergence of data management including big data techniques and others with the integration substrate.
What we think is a powerful trend and I think it’s picked up on not only by a lot of vendors, but a lot of customers, is really starting to unify the integration platform with the data management platform. I think that’s a trend if you look forward from an entrepreneurial standpoint. You’ll start to see products come to market that are not separated to B2B integration, MDM, big data. These things are all going to be unified platform going forward.
Sramana Mitra: Thank you for your time.
Liaison Technologies is a global data management and integration company. It provides innovative solutions to integrate, transform, harmonize, manage and secure critical business data on-premise or in the cloud. With a comprehensive array of business-to-business and application-to-application integration and data transformation services, as well as on-premise and cloud-based data security solutions, Liaison’s practitioners implement data management infrastructures adapted to each client’s specific business requirements. Headquartered in Atlanta, Liaison has offices in the Netherlands, Finland, Sweden and the United Kingdom. For more information, visit www.liaison.com.
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