DATA AGILITY
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AGGREGATION ON-DEMAND
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Data fragmentation is a costly problem. Information scattered across silos can affect performance, compromise security and increase operational risk, making regulatory compliance a challenge. The disjointed nature of enterprise data in its many formats also makes adapting to changes in data structure a difficult task. And that makes it hard for businesses to stay competitive. Implementing a centralized data storage or warehouse solution, even with low cost technologies like Hadoop or NoSQL is often impossible given the geography and variety of modern data sources. Besides if data silos are the problem, another silo is probably not the solution. So the focus of enterprise architecture is shifting from data capture and management to enabling data agility.
Agility is all about how fast you can extract value from mountains of data stored in disparate systems and how quickly information can be turned into action. All that raw data will also need some common structure before it can be analyzed. After all, just because you can get data into Hadoop easily doesn't mean an analyst can easily get it out. That means some data preparation must happen before it can be used by data crunching applications and tools.
Stream processing and schema-on-read capabilities make data more nimble, allowing users to aggregate information without the need for creating additional copies. This reduces data sprawl and eliminates the people and process bottleneck of traditional, ETL-centric data preparation. And combining all this capability into a single, scale-able data layer makes implementation a snap while reducing cost. |
Data aggregation on-demand is a transformative technology. It's goal is to present all critical assets and resources as a single, always-on view that is contextually relevant to the observer. This greatly simplifies data-driven decision making, improving one's ability to mitigate risk, track performance and identify threats or opportunities on a global level.
This 'schema-first' approach, sometimes called 'Schema on Write' required complex models to be developed for organizing data. As systems evolved and business requirements changed, enterprise data became fragmented while data schema became more rigid. It became impossible to re-organize critical data in a timley fashion, resulting in major impact to the business.
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