Data Integraion is the best way to transform your data into asset. However, multiples factors(internals and externals) will determine the success level on your DI initiatives. What are these factors ?
How do you evaluate each one of these factors ?
The goal is to help diagnose the strengths and weaknesses of current Data Integration level in order to develop remediation and enhancement strategies and to provide insights applicable to Data Integration initiatives.
The keys factors that are predictors of DI level, include:
Organizational information orientation (the degree to which the organization's culture values the use of Data for decison making),
IT Strategies and Orientations
Organisation's position vs competition (Technology, human Capital,....)
Data Governance and stewarship
Data Integration tools features,
mor kane 06000220B62 Posts
Re: Data Integration Diagnostics2009-12-02T20:39:16ZThis is the accepted answer. This is the accepted answer.Data Lifecycle: regulation and conformity will have an impact on your archiving activities
The corporate accounting scandals of the past few years have caused an onslaught of new laws to be written. These laws place regulations on how businesses are to treat their sensitive, business-critical data. Additionally, older laws that have been on the books are being enforced more rigorously than in the past. Basically, government regulations are being adopted to ensure that corporations are "doing the right thing" with their data. One of the things that is being mandated by these regulations is longer data retention periods. Indeed, the number one driver of data management initiatives is likely to be government regulations. The growing number of regulations and the need for organizations to be in compliance is driving data retention. Regulations such as the Sarbanes-Oxley Act, HIPAA and BASEL II are some of the laws governing how long data must be retained. Moreover, industry analysts have estimated that there are over 150 federal and state laws that dictate how long data must be retained.
To comply with these laws corporations must re-evaluate their established methods and policies for managing and retaining data. What worked in the past to retain data for a few years will no longer be sufficient over a much longer period. Data is the life blood of any organization, but too much data can slow application and reporting performance and tax IT resources. Utilizing database archiving to actively manage the growth of application data is key to managing data for its value to the organization.
As a result, the trend toward Data Lifecycle Management (DLM) is giving rise to a class of solutions designed to help companies meet these goals, and database archiving is a critical component in any DLM implementation.
Reducing Storage Costs
Data has a lifecycle is not really new. This lifecycle begins when data or information is acquired and ends when it is no longer needed and can be deleted. Over time, as the information is accessed less frequently, its business value decreases. DLM has evolved to define an approach for managing and storing information on the most cost-effective storage medium over time, based on its business value and access requirements. At first glance, DLM shares many similarities with the more familiar concept of Hierarchical Storage Management, which allows for automatically managing information and moving it to a higher or lower performance storage medium based on access rates. However, while HSM is best suited for managing files, DLMis designed to manage all types of data and provides a framework for data and storage management.
Understanding Data Retention Requirements
To comply with regulatory requirements. ask yourself a couple of questions
After the data retention period has expired, when can data be deleted?
Are there business reasons for keeping data beyond the time required to comply with regulatory data retention requirements?
What is the risk if this data is archived and cannot be retrieved in a timely manner?
How often will access to this data be required ?
What type of media will be used for storage (tape, magneto-optical, compact disk, etc.)?
Is technology obsolescence a consideration?
Is off-site storage required?
Which applications are affected and how are they impacted?
Are there application requirements for accessing older data?
After the data has been analyzed, the next step is to select the appropriate solution(s) to help achieve these objectives.
Before developing an DLM strategy, you must know what data you have. This process provides a comprehensive understanding of what the data is and how it is used, as well as its data retention and storage requirements.