Tag Archives: Data Retention
According to an article written by Mark Brunelli interviewing James Kobielus of Forrester Research: Forrester’s Kobielus: It’s time for a Hadoop standards body, Hadoop is still a bit immature and needs adoption of standards. Mr. Kobielus goes on to indicate that when implementing Hadoop, “whether it’s through a data warehouse or Hadoop cluster, you’re talking about petabytes or multiple hundreds of terabytes worth of storage.” Hadoop, while designed to access these large data volumes (which can include social media data), does nothing to manage retention of that data. (more…)
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: (more…)
At the beginning of an Information Lifecycle Management (ILM) project for my client’s Application and Data warehouse databases, my dialog begins with Records Management and the executive team to assess their ILM and Data Governance maturity. These questions were briefly mentioned in my previous blog. Here is some background on why the answers can dictate an ILM project’s success.
Are data retention schedules defined and are they assigned to a business owner?
Data targeted for ILM needs a business owner who is accountable and responsible for the data lifecycle – including defining when the data can be archived or deleted. If data retention schedules do not exist or aren’t enforced, data volumes grow uncontrollably causing problems in the data center. IT then owns the problem but isn’t able to address the solution unless business tells them what data can go where. If data needs to be retained for longer periods of time, the business needs to provide IT with access requirements so they can properly design a database archive.