Avoid data waste
Strategies for sustainable data management
A guest post by Peter Wüst*
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Data that no one uses costs: resources, money and energy. But with sustainable data management, companies can minimize data loss. Cloud solutions and an awareness of the data life cycle help here.
Data is not demanding. Does it matter if they are stored in abundance on the company’s storage media? In times of energy crisis, climate change and high CO2emissions can only be a rhetorical question. Companies are therefore advised not to let their data go to waste, but to handle and use it purposefully. The most important step is to take a stand and take a closer look at what data is stored where and how. Awareness of the data life cycle is long overdue.
Data drives innovation
It is therefore necessary to fulfill two objectives: On the one hand, data should not be considered “digital waste“ Unnecessarily consuming storage capacity. On the other hand, the data that is of primary importance to the business model must always be kept up to date. Keeping key data up-to-date is essential for survival, especially in sectors where rapid technological advances have shortened half-lives. After all, business data is the driving force for innovations when the competition changes and you need to respond quickly to challenges.
Data’s ecological footprint should not be underestimated
Companies should make it clear that their data always assumes one of three states:
- data at rest and stored on fixed storage media,
- Data transferred between different systems,
- Data that is in use and resides in volatile storage such as CPU cache or RAM.
The data in the first category in particular is growing at a breathtaking rate. According to Statista, around 181 zettabytes of data will be generated annually in 2025. However, data that resides on physical drives does not just take up space. They take up maintenance resources and energy. With the current energy mix in industrialized countries, the electricity required for this means high levels of CO2– Emissions. The ecological footprint should therefore not be underestimated. To make matters worse, according to a study by Seagate and IDC, 68 percent of this data goes unused. Many of these arise, for example, in business processes or are reserved for big data applications and AI processes. Some of them are relevant, but most of them are not. Distinguishing one from the other is the starting point for sustainable data management.
With three questions about sensible data handling
What data do we need? Which ones are obsolete? What data can be stored in a resource-saving cloud storage for later use? These three questions stand at the beginning of a sensible data life cycle strategy. The consequences are significant. If data that is not used or no longer required is sorted out, a major problem in sustainable data management solves itself. Even better: What is not stored in the first place consumes no resources.
Current technologies that are particularly resource-efficient are useful here. For example, the snapshot technology, with which the necessary backups are quickly and efficiently made. A snapshot is created as an image of a data source at a specific point in time within seconds. Because these snapshots do not copy data blocks of the entire volume, they are very space efficient. Instead, only the changes are saved. To get important data fromdigital waste‘, AI routines are being used more and more frequently. In the best case, they categorize the data completely automatically and individually with the appropriate strategy.
What role does the cloud play?
A sustainability strategy is: migration to the cloud. There are several reasons for this: On-premises data centers are often not sustainable. Data stored locally therefore consumes more energy in proportion. The more indispensable the data, the more serious the consequences. On the other hand, economies of scale and the green IT approaches of the hyperscales ensure lower CO2-Footprints of their data centers.
From a purely operational point of view, locally stored data causes high costs, which are then lost elsewhere. Shrinking local data centers with less infrastructure use less electricity and thus lower costs. In addition, the capital investment in the case of new acquisitions is lower.
Strategically, local data silos can hamper the company’s innovative strength. Above all, the development of digital transformation in many companies is slowed down by the fact that important digitization paths, such as cloud computing, digital platforms, big data and the Internet of Things, are not taken. The hyperscalers are the fastest to deliver new innovations. At the same time, the company’s own IT specialists can train their cloud skills instead of upgrading the data center. With growth and constant innovation in the cloud, the company ensures its future viability.
Cloud strategies for effective data management
Not all companies are the same. Therefore, a data strategy should be tailored individually to the respective requirements. A data substance required for this includes multiple cloud strategies: starting from the local data center, a hybrid strategy with outsourcing of backup copies or less important data to the cloud can be a good start. Migration to the cloud goes one step further, which includes the possibility of a cloud-native approach.
Solutions that map different clouds and services, such as data management and optimization of delivery within one interface and on the same technology base, are ideal. We don’t want enterprise data professionals to feel like they’re jumping between different systems and applications, but to find everything in one connected environment. With the transition to the cloud, it should also be ensured that the systems used are scalable with regard to the company’s growth.
Forward-looking data management thanks to cloud analytics
As the migration to the cloud progresses, more inefficient storage silos can be gradually reduced. By the way, the fact that sustainability and cost-effectiveness are in harmony can already be calculated when planning your own data life cycle strategy. With cloud analysis, companies can reduce CO2– Show the consumption of individual components and initiate targeted savings measures. Business intelligence reporting ensures that the entire cloud infrastructure is transparent at all times. As a result, the company maintains control over its costs while reaping the benefits of being aware of the data lifecycle.
*The author: Peter Wüst is Vice President and General Manager Germany at the cloud and data-oriented software provider NetApp.