Informatica Perspectives

A key benefit of implementing an Application Information Lifecycle Management (ILM) project is to reduce the amount of structured data in the data center.  Application ILM is a combination of a strategy and process that assesses information based on its business value and aligns the technology it resides on.  This process assures that the data center does not over allocate IT resources if the business doesn’t need it.  And likewise, if the business can provide detailed requirements for what it needs for its data, the IT department has a better idea of its technology forecasting needs.  Application ILM is a capacity planner’s friend.

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One aspect of an Information Lifecycle Management (ILM) project that often gets overlooked is deleting data. Once information has reached the end of its usefulness, delete it. It is the single-most cost effective task you can execute on an ILM project. If you don’t have the data, you don’t have to store it, manage it, or worry about it getting into the wrong hands. Delete it.

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Continuing with the Data as Asset series, in this posting I explore the negative side and what can happen when data becomes a liability. Physical assets such as buildings, equipment, or even money, can become a liability if not managed properly; buildings can become unsafe to work in, machinery can be dangerous to operate, and business investments can turn into money-sinks. Similarly, data and information systems can also be assets that provide economic value to the enterprise or they can be liabilities that destroy value or put the business at risk if not managed well. Here are three common scenarios. [Read more]

As the market continues to consolidate and companies sell off assets, not only are the physical assets separated and sold, so are the digital assets – or liabilities. John Schmidt covered it in one of his recent blogs.

When the information to be separated and sold resides in a database, you need to understand the data model and determine what master data, or reference data, and transactions belong to the new company.  In the case of separating master data this may involve understanding the relationships between multiple systems to make sure that when the data is moved into the new company, it maintains context, is accurate and complete.  With transactional data, you need to know the tables and rows that comprise a complete transaction so that when data is moved, no orphaned rows are left behind.

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No question about it, we face an information tsunami. And many organizations do not yet have a coherent strategy to deal with all the new data that is surging their enterprises. Worse yet, when applications fail or slow down, they attempt to fix the problem by throwing more hardware at it – rather than dealing with the root cause of the issue – too much data. Most organizations are only just beginning to take the necessary steps to address these challenges and keep massive amounts of data from overwhelming their enterprise applications.

As part of my work with Unisphere Research/Information Today Inc., I recently had the opportunity to team with Informatica's Application ILM team to develop and publish a survey that explored ILM awareness and implementations among 277 managers and professionals affiliated with the Oracle Applications Users Group (OAUG).  (Full survey report available for download here.) [Read more]

This is my first blog for Perspectives and I wanted to talk about one of last week's announcements -Informatica introduced the first ever cloud archiving service optimized for databases.   Since then, I've had a number of questions from customers and analysts about what exactly makes it optimized?   Certainly, a number of vendors have the ability to land data in the cloud, so how is this different?  Let me capture the highlights:

• The data is massively compressed before transmission-close to 60:1. That can take the upload time down from days or weeks to hours or minutes. No need to constantly mail DVDs to Amazon to get data on S3. [Read more]

It is that time of year for some to reflect on the past or ponder the future.  If part of your end of year ritual includes cleaning out a cluttered closet or room in the house, consider the same ritual for the data in your databases.

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In December, 2005 Sun Microsystems conducted an interview with Bill Inmon, the father of the data warehouse concept.  He said, “ILM keeps a data warehouse from costing huge amounts of money and maintains good performance consistently throughout the data warehouse environment.”  Four years later, the average size of a data warehouse has increased by 200%, surpassing the multi-terabyte size benchmark. 

With these mammoth databases comes an increase in cost to manage them and a potential deterioration in performance.   It is common practice to leverage techniques like indexing and database partitioning to address query performance issues with very large databases but those techniques do not address challenges associated with the raw volumes of data.
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One of the challenges I face as a database archiving implementer and practitioner is where to store legacy database data after an application is retired.  Typical requirements include:

• Business wants to be able to access data should they need it to support ad hoc operational needs.
• Legal wants to be able to access data should they need it for litigation.
• IT wants to get rid of the data because it is costly to maintain the old application and technology it resides on.
• Executives want a solution that doesn’t create another silo of technology.
• Records Managers want to make sure data is purged when the retention period matures to maintain compliance.

The announcement of the CommVault and Informatica partnership addresses many of these requirements in one swoop.

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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.

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