Top Ten Best Practices for Data Integration
Use these guidelines to help you achieve more modern, high-value and diverse uses of DI tools and techniques.
For far too long, data management has pretty much been an afterthought for IT. Historically, it's been a lot easier to throw hardware at various problems in the form of giving each application its own dedicated server and storage resources.https://o1.qnsr.com/log/p.gif?;n=203;c=204663295;s=11915;x=7936;f=201904081034270;u=j;z=TIMESTAMP;a=20410779;e=i
But as we look at the impact of virtualization and cloud computing on the enterprise, it's clear that shared IT infrastructure is going to be the general rule of thumb from here on out. The problem this creates for IT organizations is that anything that has to be shared by definition needs to be managed.
In fact, when you peel back a lot of the objection to virtualization and cloud computing, you hear talk about security, skills and tools. But underneath those issues is an even more troubling problem: If data is now running on top of virtual machines both inside and outside the enterprise, IT organizations need enforceable policies to manage it.
Unfortunately, most IT organizations don't have an effective data management strategy. Most of them have storage systems where they allocate a portion of the storage system to a particular application. But that's not the same thing as being able to automatically tier data across different layers of compute and storage based on the value of that data to the business.
The good news, says Claus Mikkelsen, chief scientist for Hitachi Data Systems (HDS), is that storage systems are evolving into information management systems that allow IT organizations to apply polices to different classes of data. Based on those policies, the storage system will automatically organize a company's data across primary, secondary and tertiary layers of storage running on premise and in the cloud.
This could be one of the best things to happen to IT in a long time. Rather than having to minutely manage data, the whole process can be managed at a higher level of abstraction using policies that have been determined by the business value of the information being managed.
And in the unlikely event that you might not have noticed, the amount of data that needs to be managed keeps growing steadily. So no matter what an IT organization decides to do, one way or another, data management issues are going to come to a head in 2011.