In the age of big data, IT managers are increasingly tasked with taking more comprehensive views of their organizations' data. This requires them to evaluate not just the buy-in costs of data management, but also the potential costs and risks of managing data incorrectly. It also forces them to focus on various aspects of the data lifecycle, from the time data is first created until it outlives its usefulness.
Shrinking Costs By Shrinking the Data Footprint Data is prone to proliferate. This fact becomes even more daunting when organizations must accommodate big data along with their traditional data sets. The cost to manage, store and protect a trickle of information rises exponentially as that trickle turns into an ever-rising flood.
One challenge that increases data management costs is what many call dark data, which refers to structured and semi-structured files that do not easily fit into database systems. Just look at most organizations' servers and shared drives - it is common to see the same files saved multiple times in different locations. Then, there are also files that are created once and left dormant for years without being accessed. Are they still important? Maybe not, but maybe the organization does need to keep them to meet legal or regulatory compliance needs.
Many of an organization's files can and should be archived and offloaded to a less-costly storage medium; many more should be completely deleted. Often called digital hoarding' or data bloat,' the practice of keeping unnecessary data in enterprise storage or on servers puts an added strain on an organization's power, facilities, staffing, maintenance and acquisition costs.
Why pay for more primary storage than necessary? TechTarget notes that as much as 40 percent of an organization's data could be archived, while another 30 percent could be deleted outright. By taking the time to archive and clean house, organizations can better manage their storage costs and regain valuable space. Beyond efforts to separate the data wheat from the chaff, data management deduplication software can also be used to safely remove duplicate data sets.
Seeing Double: The Soft Costs of Dirty Data Hard dollar costs are one thing, but the soft costs of managing data are quite another. These costs are hard to quantify, yet no less important. TalentIQ estimates that up to 20 percent of corporate data is dirty, meaning it is either duplicate, incorrect altogether or a combination of the two. The source gives an example of an applicant tracking or customer relationship management system with 10 million profiles, and estimates the cleanup cost of 2 million questionable profiles to be between $40 million and $100 million.
TalentIQ notes that there can be other significant impacts of duplicate data, as well, such as damaging a company's reputation or its relationships with customers. If used for marketing, for instance, duplicate profiles could lead to inaccurate market segmentation. In sales departments, it might lead salespeople to inadvertently call the same customer multiple times. Whether organizations incur soft or hard costs of data management, neither scenario is particularly efficient or cost-effective.
Costs in the Data Life Cycle and Its Surrounding Ecosystem Another TechTarget article describes the process of calculating the real cost of data storage. In short, it is about calculating what it takes to store a single piece of data over its entire lifetime. Included in this analysis is not just the underlying storage but the ecosystem of information services involved in the lifecycle cost of data storage. Cost areas include data migration, data protection, archiving and long-term retention, and disaster recovery.
Another way organizations might look at how much it costs to manage the data life cycle is to break data into its various costs: The cost to store it, the cost to access it, the cost to secure it, the cost to protect it, the cost to archive it, the cost to migrate it, as well as the cost to extract competitive differentiation and meaning from it. These costs can be tough to calculate, so to make this exercise more digestible and uncover new ways to reduce costs, look for experts versed in best practices and methodologies who can help the organization effectively reduce its own data management burdens.
To learn more about reducing data storage costs, download the Hidden Costs of Data Management eBook now.
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In the age of big data, IT managers are increasingly tasked with taking more comprehensive views of their organizations' data. This requires them to evaluate not just the buy-in costs of data management, but also the potential costs and risks of managing data incorrectly. It also forces them to focus on various aspects of the data lifecycle, from the time data is first created until it outlives its usefulness.
