Going From Disparate Data to BI

Track: Product Presentations

Audience Level: High Level/Technical view

Time: Tuesday, November 15 11:00

Author: Phil Storey, Datawatch Corp.

Keywords: XML, Data Interchange, Legacy Data conversion, Content Conversion, BI, Report Mining


The proliferation of useful enterprise applications presents an unintended consequence - the generation of corporate data that can be revisited for operational insight if managed, stored, integrated, filtered and distributed intelligently. And while these applications generate data in different silos, technologies including ODBC, OLE DB, XML and RSS enable enterprises to make the leap from simple, stratified content management to corporate business intelligence.

However, getting from data generation to BI is complicated by myriad data formats, the sheer amounts of data involved, data integrity, and the poor state of application integration. Data ETL (Extraction, Transformation and Loading) is a critical first step, enabling IT staff to grab only the data required from each application, transform it into the optimal final application format, and efficiently combine and load it into favored end-user applications that range from spreadsheets to multidimensional cubes to PDF files.

Ideally, ETL processes require no programming or otherwise laborious upfront process development, are able to be reused for different departmental applications, and can leverage the benefits of the increasingly popular XML and RSS technologies. Datawatch's Monarch Data Pump is the ideal ETL server for this scenario, and can automate many of these complex and ongoing processes.

This session will provide a best practices overview, and specific customer business intelligence case studies from healthcare, financial services, and government IT.