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The Making of a Real-Time Hero

Data warehousing has prospered in support of strategic decision-making. Now, as business intelligence expands, the world looks to the sky, not for a bird or a plane - but for a data warehouse that meets real-time, tactical demands.

Answering the Call

Novaes reports that Claro is meeting real-time BI demand by integrating information about each customer's contact point. "We are developing what we all Touch Point Server to integrate our database marketing infrastructure with that of the data warehouse. This synchronicity is what we need to support 'touch' CRM campaigns that are designed to respond appropriately to each customer's needs in every contact with the company. We are improving our hardware capacity to enable real-time answers to customers at these touch points. We are finding that business demand is causing exponential growth in our transaction data."

Using Teradata database software from NCR's Teradata Division, Claro employs an operational data store (ODS) to integrate information about customer transaction behavior, especially for use in predictive analytics. An ODS frequently helps organizations by first facilitating data cleansing and other quality activities, and second and most importantly, providing a nearer to real-time supply of accurate, structured data to users and their BI systems. "We don't have to store this data forever," Novaes, explains. "We can define what we need to store from each customer. Or, we can store different periods for different customer segments."

Claro is planning for "mixed workload" data warehousing; that is, where the system supports both strategic and tactical BI and analytics. "At the same time that the data warehouse is being asked to work with actual transaction data," Novaes says, "it is being asked to respond to several automatic reports based on data from strategic and tactical areas, including finance and marketing."

Analytics as Services

Noel-Levitz employs a services-based approach. "We meet the need for real-time BI by using Microsoft Visual Basic .Net and SAS Integration Technologies, as well as SAS/Access Interface to ODBC," says Thein. "Developers created custom Web services that enable clients to input their data, manipulate it, and then submit it for scoring. SAS Integration Technologies lays a foundation for standardized communications and effective information distribution, and SAS/Access enables SAS solutions to read, write, and update data to SQL tables."

Thein continues: "Our statisticians save client data on a SQL server and build their recruitment models with SAS Enterprise Miner. Analysts create a scoring algorithm based on variables unique to a specific institution. The algorithm is deployed via Web services, and the university, using .Net Web services created by Noel-Levitz, submits the data about prospective students. Another Web services routine writes the data to SQL tables. On the back side, integration technology picks up the data via SAS/Access, runs the scoring algorithm created in SAS Enterprise Miner, and returns the scores to the SQL tables. Then, Web services return the score — and the probability of whether a student will choose to attend — back to the end user."

Web services are a key part of evolving middleware approaches to both application and data integration, where the term "enterprise information integration" (EII) is gaining favor to describe a next-generation approach to the amalgamation of data to answer a query. If organizations can streamline (or bypass) the typically time-consuming replication and loading steps — not to mention the creation of downstream data marts and OLAP cubes — they can reduce information latency and come closer to real-time BI and data warehousing goals.

Certainly, exposing users to and involving users with as little of the data integration and query service infrastructure as possible goes a long way toward creating a real-time BI perception. Noel-Levitz offers a good example of how Web services help to meet this requirement.

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