Research Hub > Overcoming Data Lifecycle Management Hurdles

November 25, 2020

Article
3 min

Overcoming Data Lifecycle Management Hurdles

Automation and tiered storage can help organizations trim costs while still meeting performance and compliance requirements.

Bill Snell

data-management
So. Much. Data.

According to some estimates, 90 percent of the world’s data was created in the past two years, and growth is only accelerating. While storage costs have dropped as data volumes have ballooned, organizations are still running into trouble when they don’t optimize their data storage environments for factors such as cost and regulatory compliance.

Learn how CDW can help you optimize your data storage.

By embracing solid data lifecycle management practices, you can overcome major challenges — such as cost, retrieval, disaster recovery and management — and keep up with the rapid pace of your data’s growth.

Tiering Data to Better Manage Costs

Too often, companies keep most or all of their cold and archival data on the same tier of storage they use for production environments. Data management best practices call for segmenting data into “hot,” “warm” and “cold” groups, with infrequently used data moved to lower storage tiers. This can save organizations substantial sums of money without significantly affecting performance.

Automation can play a valuable role here. For instance, IT shops might set up data storage environments to automatically move files that haven’t been accessed for 90 days to “warm” or “cold” tiers, helping organizations avoid paying a premium when they don’t need to.

The public cloud is often an attractive option for organizations looking to cut data storage costs. While the cloud isn’t always a great fit for primary production environments, it offers ready access to a number of less expensive storage options for secondary environments. These lower-tier cloud storage options can help you manage your costs while still keeping data accessible.

Simplifying Data Retrieval with Object Storage

It’s relatively simple to move older data to less expensive storage environments. However, you also need to plan to keep data as accessible as possible for stakeholders who may still need to occasionally retrieve it for work tasks. I’ve found that object storage is often a much better fit than traditional file storage for these secondary storage environments.

File storage and object storage are sometimes compared to keeping cars in parking garages. With file storage, you need to know everything about a file (or car) — including the make, model, color and which garage it’s parked in — to retrieve it; meanwhile, with object storage, you can retrieve what you need with some simple metadata (or, in our analogy, a valet ticket).

Planning for Effective Disaster Recovery

Even sophisticated IT and business leaders tend to conflate data backup and disaster recovery. It’s one thing to have all of your data backed up at a secondary location. It’s another to have a thorough runbook with detailed plans for bringing your systems back up in a timely manner after a major cyberattack or natural disaster. You should ensure that your organization has robust disaster recovery tools and systems in place, and you should test them regularly.

Managing Data for Optimal Business Outcomes

Ideally, data should be more than just something that you need to organize and optimize for cost; it should create ongoing business value. Increasingly, artificial intelligence and machine learning solutions are valuable tools for organizations across industries looking to leverage their information in new ways — helping them to predict changes in market conditions, better segment their customers and enhance information security. By looking for such opportunities, you can move beyond legacy processes and help set the pace for your industry.