Ontology Systems Helps Companies In Search of Data Agility

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The Semantic Web Blog earlier this month covered the news that Ontology Systems is updating its Ontology semantic search platform to Version 4.0, which was previewed last week at Mobile World Congress in Barcelona. Following that, we had an opportunity to catch up with CTO and co-founder Leo Zancani to learn more about the upcoming version of the platform that features new search capabilities under the Project Rothko code name banner, as well as the just-released Version 3.7, and how the telco and financial sectors its technology is focused on can leverage it.

The company’s Ontology platform connects its Ontology Intelligence 360 and Ontology Integrity Manager solutions. The former builds dependency models of business entities by looking at data in existing enterprise systems, and the latter is a data integrity solution that measures and monitors in an ongoing way the data alignment among various different systems that talk about the same thing. Those two products, Zancani explains, “depend on quite finely modeled data, so there are quite strict semantic models inside them. Data that is taken from existing systems populates those strict models, and customers are interested in then using the data in those models to drive business processes,” he says. Due in Version 4.0 that should debut in April, “Rothko adds the capability on the side that says, that’s great, but there also is value in the data you don’t want to or need to or can’t afford to model right now, and you can access that with a much more direct search capability.”

What’s the call for this two-tiered search approach in the verticals Ontology Systems is focused on? Take the telco sector, where the company founders have a long history. The industry, says Zancani, is in crisis now, as vendors like Apple and Google eat its lunch, and as the fallout from major consolidation among telco players makes traditional data integration economically untenable.

“The primary constraint on their ability to become competitive again, and the reason it’s in crisis, is because it is insufficiently agile around its data,” he says. “If you had to sum up what semantic technologies like RDF bring to the table, it’s data agility. It provides this whole new way of looking at and interconnecting data that doesn’t depend on the existence of an upfront design on how those dependencies and links will work. By not depending on such a design things become more plastic.”

Its Ontology system to date with its SaaS (software-as-a-service) approach has been the first phase of helping solve that agility problem in a more affordable way. Its success with telcos at that level, Zancani says, has opened a door to more strategic discussions about whether there’s a better way of thinking about data. “Telcos historically engineered everything to huge levels of reliaibility and availability, but do you need to do that all the time everywhere to achieve your goals as an enterprise? We are banging this drum that says, no you don’t,” he says. With the technology Project Rothko brings to the picture, you can, he says, produce good enough answers quicker, which is more valuable than a perfect answer later. “There is an actual case for you to take a search-based approach to your data estate,” he says. “True, it won’t immediately produce an app that directly implements one of your business processes that you are used to seeing, but instead produces a tool that democratizes access to the data estate within your organization so that more people can see more of your data. That makes it more likely you can extract value from that data.”

That matters to telcos because what differentiates Vodafone from Google or Apple, he says, is that telcos know what the network is doing, and what the users are doing. That’s a secret weapon that telcos can use themselves to build services their new-age disruptor-competitors can’t, or even provide those competitors with some insights, for a price. “But you need to get at that information and right now you can’t – or if you can the project to do that the traditional way lasts five years by which time the data is irrelevant. You need a better, faster way to get at data in a good enough way to derive value from it, by selling it to over-the-top players or building service platforms that are superior to theirs,” he says.

Think of Project Rothko as offering you essentially a more traditional semantic search engine than the company’s existing solutions, but doing it leveraging a shared ontology to make the connection between data you haven’t modeled and data that you have, at no additional cost. “So, in the context of a telco, it sells services that are implemented by network elements and sold to customers who use handsets,” he says, “and by searching the data and presenting it to the user in the context of that domain ontology, they can make sense of it without specific money spent on elaborating that data into the model.”

The banking industry is already more at home with the idea of semantic technology and ontologies and how they can bring value to helping deal with the scale of data it is wrangling with. Ontology is coming at that industry with the idea of using its platform for risk aggregation and compliance. A strategic goal for the company this year is to establish itself firmly as a player in the financial sector, too.

When it comes to enterprise data sources in both the financial and telco arena, Ontology is focused on complex structured applications, databases, files, and spread sheets. But there’s ad-hoc data to be considered too. What’s that? As Zancani explains it, it’s the private data stores in Excel or big tabulated text files that individuals keep as workarounds to big systems that don’t move as fast as they need them to. “There is some really key information in those workarounds, so we’ve invested a fair bit in making it as easy as possible to import data from anywhere, including those ad hoc data sources,” he says. Rothko also brings, to an extent, the ability to deal with unstructured data. It’s not to the point of figuring out what an entire document is about but on how to manage the fact that in what is notionally an unstructured text field in, say, a notes function in a CRM system, there may be structured information.

Says Zancani, “It’s all about the data, that’s where the value is and where the hard problems are. And that’s what is making this so relevant.”

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