While the concept of information lifecycle management (ILM) has been around for more than a decade, the practices and products that enterprises use to address it have evolved significantly over time. Yesterday’s ILM is today’s intelligent data lifecycle management automation, as detailed here.
Despite how the approach may have evolved, the fundamental considerations of lifecycle management haven’t changed. Data is born, put to work, and retired to a remote location before it eventually meets its end of life. Sadly, data is continually copied and moved, and multiple versions created without tracking or understanding the real value of the data. Moreover, your data is now fragmented across on-premises data centers, cloud storage, and endpoints.
To reduce the costs of ever-increasing volumes of data while simultaneously transforming it into relevant, accessible business information, organizations must ask the right questions about their data. The SPA (State, Placement, and Availability) model is a great place to start.
State refers to several attributes, including:
- Is the data physical or digital?
- Is it changing or fixed?
- Is it structured or unstructured?
- What format is it: audio, video, image, text, transactions?
- What is its value or importance?
- What is its security or access level?
- What policies apply to it?
Placement refers to where it is stored:
- Online or offline?
- Primary, secondary, or other?
- What does that placement cost, and what are the egress fees?
- What policies dictate where it is stored?
- What policies dictate its removal from that place, such as deletion or aging out?
Availability is its level of requirement:
- What applications need to access it?
- What users need to access it?
- What performance level?
- What redundancy or replication level?
As organizations struggle to overcome the ever-increasing volume of data that is being shared, created, and copied, an automated approach to data intelligence is needed to properly ensure that data enjoys SPA days throughout its lifecycle. Without understanding the value of its information stores at any given moment, businesses will continue to overspend on storage of old, irrelevant data and be susceptible to increase in risk.
One critical note about the SPA approach is that it must be constantly revisited, because that the state, placement, and availability of information changes over time. A spreadsheet that is in high demand today may rarely or never again be accessed a month or two from now. This now-inactive file may be requested for research or reference purposes, of course, or contain information that is valuable to another application, like business analytics, but it likely won’t be edited or changed.
The lifecycle of surveillance video footage may find it routinely being moved to offsite storage with the expectation that it will never be viewed again. However, its lifecycle requirements change considerably when one day there’s a request from law enforcement to provide all footage captured from midnight to 4 a.m. the week of September 18.
The Aparavi Platform takes data management to the next level by offering organizations a simple way to lower their costs, minimize risks, and achieve greater insight to their data. Aparavi’s cloud-based platform finds, automates, governs, and consolidates distributed data to tackle growing data demands. With more than 140 industry-specific classification policies built in, Aparavi automates data governance and compliance to remove human error and reduce time by dramatically improving the time to find required information. Efficient data lifecycle management puts organizations in control of their data.
Rather than having a data pool that merely exists, ensure that your business-critical information truly lives by visiting and re-visiting the SPA discipline often. Only then will you be able to maximize your cost savings while improving performance, efficiency, and availability of data for a competitive advantage.