Agile Data Management: Four Critical Use Cases

Data is the foundation of business intelligence

Data abounds, but how easy is it to get information from it? Enterprises awash in data often struggle to answer basic business questions because they can’t supply data to the people who need it, when they need it. To tackle the problem, they’ll invest millions of dollars in complex data warehousing projects and ETL systems that require highly trained IT personnel. However, generating agile data management results can take months, frustrating the business side of the house that needs information now.

Organizing, prepping, transforming and provisioning useful data shouldn’t be difficult. In fact, it isn’t: if you’re using the right tool. And that’s what agile data management is all about.

Traditional, schema-based extract, transform, and load (ETL) systems for data integration and analysis do an excellent job of processing large amounts of known, structured data to answer predictable business needs. But many needs aren’t predictable.  Traditional ETL systems aren’t designed to meet the needs of people with urgent business questions whose demands for ad-hoc, slice-and-dice analysis of multiple, disparate data sources are part of today’s “gotta-know-it-now” data analysis tsunami. An agile data management platform meets those needs with on-the-spot, flexible, granular data analysis that informs both tactical business decisions and strategic planning. An agile data management platform also performs data integration, data quality management, enrichment and business analysis, such as correlating, filtering, sorting and statistical analysis.

These capabilities offer a powerful, flexible alternative to traditional ETL in situations where you can’t define business requirements at the outset, or when conditions are constantly changing, situations where you’re dealing with:

• Exploring external data
• Changing data sources
• Adjusting business logic
• Changing data fields

This whitepaper explores agile data management activities required for you to create new data sets for reporting and analysis. The success of that effort relies on timely data management: acquiring, integrating, filtering and analyzing the data from several angles.