But in every case, these examples of data mashups of transactional data are dealing with the customers. They are making decisions. Let me go back to the telco example. They may be answering the billing questions. Then they might be answering a technical support issue and then going into an up-sell, cross-sell mode to offer the customer something more.
Depending on the context, the data services platform is able to present different views to the front-end system. So the actual claim, the order the transaction might actually continue to flow through transactional systems meaning they work through SOA type middleware, but in presenting the context behind making such a decision, the customer agent or operational person needs a lot of information. Sometimes, it's not even an agent.
It's a self-service customer portal where the customer comes in and wants to check the status of their orders, check out what are the products that are available. The intelligence of the data services platform in combining multiple external and internal data sources and presenting them at the right moment is very valuable in actually offloading back-office work from the agent to self service portals, but also in making the agent very productive.
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So this is a very typical use case, and we have implemented multiple systems where data virtualization is used in the support of call centers, customer self-service portals, agent portals, single customer views, etc. Obviously the way we do that is combine different data sources, create a base model, create derived models, and then represent those derived models as objects based on topic or issue or context, and then allow the front-end BI application to access them using multiple different dashboards or reports. The benefits, again, are pretty phenomenal in that it can reduce first call resolution, improve the customer experience, reduce the amount of time it takes to serve a customer, and it also provide ways to increase retention or provide new value to the customer.
Another, I mentioned there were two predominant cases. One is this idea of providing data to these single view systems of various types. The second is to extend business process management itself to the Web. A lot of BPM today, and this is almost unseen, hidden in organizations, stops when you hit a browser based application that you have not integrated yet.
People then have to enter information, extract information, go back and forth between screens. One example of this is a telco retailer. They have their own client systems, but every time they take orders for some of their partners, they have to go in and do the activation on the extranet, get the results from the extranet, put it back in the client system, complete the order so on and so forth. All this is happening while there is a queue lining up in your retail store, in a very high cost location.
How do you automate that process to improve throughput? If you had options to connect to every telco provider to backend systems, great, you would take that. But in effect, to be agile, you are adding partners at the rate of maybe 10 a month, such as navigation partners, content partners, game partners, etc. All of this activation activity needs to happen must faster, and that’s where Web automation comes into play.
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In this case they have used data virtualization particularly for Web-based data integration, the capability to automate this data integration, and tie that into their own BPM and BI systems. In that process they have found that once they have been able to extract information about the customers profiles from these other systems such as game sites or music sites where they are downloading ring tones or what have you, they can look at your music preferences, and they can suggest ring tones.