Businesses must eliminate the unnecessary energy costs of data processing

Between 70% and 90% of the data organizations collect is considered “dark data” — that is, data that is unquantified and untapped. Dark data isn’t turned into insights and business opportunities, yet it still results in unnecessary energy costs. Considering data is growing at an exponential rate, these unsustainable data processing practices are a growing problem. More than 90% of the world’s data has been generated in the last two years alone, with data sprawling across more devices, applications and cloud platforms and in more formats. 

The exponential growth in data has the potential to result in increased energy demand and carbon emissions, which experts agree is significantly derailing global net-zero and 1.5°C ambitions. Companies must adopt a greener approach to data management in order to reduce storage requirements, generate energy savings and help meet global and local sustainability goals.

By identifying and removing unnecessary data, including dark data, redundant, obsolete and trivial (ROT) data, and data outside retention service-level agreements (SLAs), businesses can eliminate storage waste and reduce their overall data storage requirements. In other words, less storage translates into less energy consumption and CO₂ emissions.

Reduce carbon intensity

Data centers are essential in propelling our digital world forward — supporting everything from video conferencing to smart cities — but they also require an exceptional amount of energy to operate. With no argument for ridding our world of datastores, we must instead focus on how to manage data in order to reduce their carbon intensity so that they can operate more efficiently, consume less energy and emit less CO₂.

Sustainable data management calls on companies to operate a more environmentally friendly cloud service and leverage edge computing by facilitating data movement and automation from any location. Average on-premises to cloud migrations can drive 65% energy reduction and carbon emission reduction of 84%.

A data intelligence and automation platform can help companies identify and remove unnecessary data, including dark data, ROT data and data outside retention SLAs, to eliminate storage waste and reduce overall data storage requirements. We’ve seen clients reduce their storage footprint by up to 40% simply by deploying data intelligence and automation technology to transform their “data chaos” into intelligent information.

Increase storage sustainability

Data centers already generate the same amount of carbon emissions as global airlines. This must be a wake-up call to the global enterprise, policymakers and the public. Sustainable data storage must be implemented soon, as we know our world’s data consumption is growing exponentially.

Many organizations have what we call a “data swamp,” or an unmanaged data lake, that provides little to no business value. Data swamps occur so often because most data owners and IT departments can barely keep up just with storing data, let alone creating suitable data quality and data governance measures. Moreover, identifying the data that needs special attention is a highly manual process that often requires special skills, leading to compromising data security, compliance and data protection. High-value data is then mixed up with trash data making data analytics projects more difficult, time-consuming and expensive.

However, automated data intelligence platforms can discover and classify all of a company’s data no matter where it lives, giving IT back the power to better visualize and analyze the data that matters most in order to make more sustainable data storage decisions.

Data processing: Optimize data migrations

Cloud suppliers usually have a higher level of CPU utilization compared to individual companies, so they can compute more without increasing power consumption. A data intelligence and automation platform simplifies data offload to the cloud to increase storage sustainability, providing companies with a seamless process for cloud data migration. Companies can reduce schedules, costs, risks and the complexities associated with moving data by ensuring only useful datasets are moved through automated cloud migration.

The more unstructured data a company has, the bigger the data footprint. Companies can go green by identifying redundant, outdated and trivial data. Unaccounted for data is detrimental to the environment as it takes up space on servers and slows down processing. Companies buy disk after disk to support data growth, but come five years from now, they will run out of space and will have accumulated an abundance of disks. This process is expensive and unsustainable, increases security risks and often costing companies millions.

Companies must adopt a greener approach to data management to reduce storage requirements, generate energy savings and help meet internal and external sustainability goals.

Adrian Knapp is the CEO and founder of Aparavi

Originally appeared on: TheSpuzz