Lean Data Management is a new approach to managing your data growth. It uses the “Lean” concept that originated with Toyota car manufacturing in the 1990’s. The “Lean” concept is based on maximizing efficiency, eliminating waste and providing more value to the customer. (See Informatica’s lean integration solutions as well as John Schmidt’s 10Weeks to Lean Integration blog series.)
As technology has evolved, industries consolidated, and corporations have grown, these organizations are faced with explosive data volumes called “Big Data”. Big Data is all the different types of data that are supported by IT organizations. Applying the “Lean” concept to managing application data will help you reduce the size of your Big Data by archiving live production databases, subsetting non-production databases and archiving/retiring legacy and redundant applications. Informatica’s Lean Data Management approach to reducing Big Data is an effective, comprehensive approach to addressing the challenges created by Big Data. It’s time to Make Big Data Small with Lean Data Management.
Here are the top 5 benefits for Making Big Data Small:
1. Minimize hardware and software footprint
- Using data archiving to remove data from live production databases/data warehouses will substantially reduce the footprint for production which sits on the most expensive infrastructure.
- Implementing an ongoing data archiving schedule based on retention policies virtually eliminates production growth.
- Using data archive to remove data from redundant/legacy systems to an optimized, highly compressed, accessible archive file allows complete retirement of software and infrastructure for those applications. This greatly reduces overhead associated with maintaining these redundant and legacy systems.
2. Reduce system downtime and meet or exceed tough Service Level Agreements (SLAs)
- Implementing data archiving in Big Data production environments and/or data warehouses reduces production data resulting in substantially reduced backup, disaster recovery and batch windows.
- System crashes are greatly reduced if not eliminated and disaster recovery windows greatly reduced with less data in production as a result of using data archive.
3. Improve application performance
- Data Archiving can eliminate performance degradation resulting from explosive data growth in production environments.
- Both reporting and batch processes will run much faster as a result of data archiving.
4. Decrease time for upgrades and modifications
- Utilizing Subset in non-production environments means shorter testing cycles due to reduced size of development and test environments. Development and Testing cycles are substantially shorter due to enhanced performance and shortened refresh windows.
- Utilizing data archive in production reduces the size of production resulting in a shorter upgrade window which mitigates the risk of downtime.
5. Enables you to shift your budget to proactive projects vs. spending it on maintenance
- Using data archiving results in lower costs associated with addressing explosive data growth challenges in production environments which means more budget for other proactive IT initiatives, such as modernization.
- Implementing subset in non-production environments greatly reduces the overall footprint of non-production databases, requiring less infrastructure to support development and testing initiatives. The resulting cost savings can be reallocated to more frequent releases on more proactive, cost saving or revenue generating projects.
Take a look at the video below to see and hear me talk to Chris Boorman, Informatica CMO about the benefits.