The need for business intelligence analytics means organizations have to speed up their orchestration of data in the data warehouse while scaling the process to growing volumes of data and all the while maintaining data quality and reducing the potential for errors. Timing your data upload, especially with disparate data sources, is challenging. You want to be sure your data is complete and up-to-date so your business users have the right information available at the right time to make important business decisions. No matter how complex your businesses process are, you can define a successful data management strategy with IBM® Workload Automation and IBM Netezza®.
Business scenario
A communications company offers fixed telephone and mobile communications services to its customers. The marketing team uses IBM Netezza to plan and forecast the impact of cost changes to the plans they offer their customers. They compare customer usage profiles before and after such changes to measure their success. To succeed, the company manages and processes large volumes of data from databases, some even remote, and integrates all of the data into a single unit. This gives them a single, unified view of every transaction in its network.
Before implementing the Workload Automation on Cloud solution, staff had to rely on a summary report, delivered three times a day to ensure the information was complete and up-to-date, to understand customer trends and activities.
Today, with the new solution, they can control exactly how and when the data transfer and upload processes should run. They can apply time-based as well as event-based scheduling dependencies. Staff can perform large and complex queries at any time and rest assured that the data is always current. A combination of Workload Automation on Cloud applications are used to transfer customer usage data to a given location, and then only when that data is actually present, proceed to upload it to the Netezza database. Any delays in collecting data from remote data sources automatically trigger a pause on the loading of data until it has been successfully collected and processed.The company no longer needs to prepare summary reports three times a day. The timeframe for query results is now very predictable, and running analytics on large data volumes has been made simpler and faster.