Mark Flaherty (MF): Think of this data access platform as a very rapid platform for developing agile data access. So there are really three steps. We talked about a lot of diverse data, structured, unstructured, internal, and external.
The first step is to connect and mash them up into something, a normalized view that we call a base view. The next step is to combine and integrate all of those different base views into derived views and in the process perform data matching, data quality, data transformation as well as data model transformation, and then finally publish those outputs as data services.
So the platform corresponds to that idea of having a connect, a combine and a publish layer, a connect engine that has connectors to a wide range of data sources, a Web automation engine, an indexing engine that also converts indexes to inverted indexes, combines and transforms them and publishes them. I am going to touch on each one very briefly. But you will also notice that while that can all be done virtually, you can also cache, and you can schedule preloads of the data, and you can manage them at a all levels of data security.
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So what this would look like is you would connect, create a base view, you have a lot of sources that are provided for with connectors. You would be able to define what we call automatic navigation, and extraction wrappers that go against any public Web site, Web application et cetera. You define a target data structure, and the product automatically extracts using examples that you provide the data structure on these pages and returns them to you as though it’s a relational base view.
And we even have some advanced capabilities to automatically maintain data models if those Web sites change again. When you think about unstructured data, it really doesn’t have to be Web data and PDFs and Word and email, it could also be unstructured content and databases. We can index them or if you have already got an index, we would bring those indexes together and invert them into a relational looking table so they can be joined together.
Now you see that multiple base views provide you this derived view, with that process you perform complex tasks such as cleansing, combination of data sources, managing the logical models and also managing which of these views needs to be cached versus virtual versus batch optimized. And the final step is you deliver data services in a rich variety of ways. Now you can access them using our BI tools, you can publish them as Web services, various separate formats and right out of the box directly.
InetSoft's platform provides robust data integration capabilities. It supports a wide range of data sources, including databases, spreadsheets, flat files, and web services. This flexibility allows users to easily connect and integrate data from diverse sources, enabling them to work with a wide variety of data types. The platform is designed to handle real-time data, which is crucial for organizations that require up-to-the-minute insights. This real-time access ensures that users are working with the most current information available, enabling them to make more informed decisions.
InetSoft's platform supports flexible data modeling, allowing users to adapt their data structures on-the-fly. This means that as business needs evolve or new data sources become available, the platform can accommodate these changes without significant disruption to ongoing analytics processes. InetSoft's platform is designed to handle large volumes of data and a high number of concurrent users. This scalability ensures that the platform can grow alongside an organization's data needs, without sacrificing performance.