5 steps for sustainable data management

What is sustainable data management?

"Sustainable data management refers to the responsible and ethical handling of data throughout its entire lifecycle -- from creation and collection to storage, processing and disposal -- to minimize environmental impact, maximize resource efficiency and ensure long-term value creation," said Himanshu Jain, a principal in management consultancy Kearney's digital and analytics practice.

There are many ways to approach sustainable data management, depending on an organization's business goals. Protiviti, for example, views sustainable data management as a process instead of a project, according to Matt McGivern, managing director at the management consultancy. But data governance initiatives can be a challenge. "Many roles that are relied upon, such as data owners, stewards and custodians," McGivern explained, "are part-time allocations against other already busy commitments, so ensuring these roles are given the proper attention and care is difficult."

Protiviti also focuses on the longer-term costs and risks of sustainably managing data over time, including historic data. The initial concerns for managing historic data centered on eDiscovery rule changes two decades ago concerning the risk of penalties for retaining data too long. These risks have been further quantified due to the enactment of data privacy laws, such as the GDPR and CCPA, as well as proliferating cybersecurity breaches.

"We are also seeing another trend come back from the late '90s," McGivern noted, "cost concerns over the storage of vast quantities of historic data as organizations move this [data] to the cloud and have the associated monthly recurring costs."

Businesses must have an effective data governance program in place to address challenges posed by traditional data management. "Too many of our clients call data an asset without treating it like any other asset," McGivern said. "No company would have a financial asset on their books that they didn't know the definition, the number of, the location for, or how it was being accessed or leveraged."

Yet many companies haven't completed asset inventories for all their unstructured, structured and semi-structured data. As a result, they can't guarantee they have the controls necessary for data backup and recovery, protection, usage and other core tenets of effective asset management. Businesses also need to know more about the data they're backing up to efficiently build out a strategy for what data to keep, how to use it and how to keep it safe and secure. "Historically, our infrastructure has not been designed with efficient long-term data storage or destruction in mind," McGivern acknowledged, "so many firms are having to grapple with the lack of core knowledge about their data assets as they consider the future questions for sustainable data management."

Why is sustainable data management important?

"Sustainable data management … allows the organization to benefit from the data that it has in the long run," said Krishna Prasad, chief strategy officer and CIO of digital transformation consultancy UST. This approach to data management can provide several benefits for businesses, including the following:

• Streamlined data backup and recovery processes.
• More resilient and cost-effective data ecosystem.
• Reduced risk of data loss due to a cyberattack.
• More efficient data reuse.
• Improved regulatory compliance.
• Reduced energy consumption.
• Smaller carbon footprint.

Challenges to sustainable data management

With the benefits come challenges. Enterprises need to consider possible pitfalls in the data sustainability process, Jain said, including the following:

• Initial investment costs. Transitioning to sustainable data practices often requires significant upfront investments. Upgrading existing infrastructure, implementing energy-efficient technologies and training staff on new systems can strain budgets. 
• Lack of standardization. Businesses can struggle to find universally accepted and consistent guidelines for sustainable data management. Collaborative efforts by industry sectors and regulatory bodies can help develop industry-wide standards, however.
• Resistance to change. Organizational changes to accommodate a sustainable data strategy are likely to encounter resistance. Employees accustomed to existing data management practices might be reluctant to embrace new procedures.
• Limited awareness and education. Many businesses are still unaware of the environmental impact of their data operations or lack a clear understanding of sustainable data practices. Educating stakeholders, including employees, management and customers, is essential.

How to build a sustainable data management strategy

To collect, secure and monetize data in the short and long term requires a strategy to ensure data quality, storage, processing, governance, security and privacy. This step-by-step approach to a sustainable data management strategy, Jain said, promotes data consolidation, energy-efficient infrastructure and responsible data lifecycle management.

1. Build consensus

A significant portion of collecting, storing, using, securing and destroying data depends on relevant business and technology stakeholders understanding the importance of data as well as ensuring the right processes are in place.

2. Define roles and responsibilities

Set out clear roles and responsibilities for each stakeholder to ensure proper data stewardship across the organization. Also, establish a foundation for building the data models that embrace data structures, data ownership and responsible use.

3. Conduct a thorough inventory

Data inventory helps teams understand the data's value, how it's backed up and protected, how it's being used, the risks involved and data's operational needs to ensure it's available and accessible.

4. Establish data reduction mechanisms

To reduce data footprint and associated storage costs, McGivern recommended eliminating duplicative data, moving data into offline storage, de-identifying sensitive data and removing data that's no longer needed. "I see too many clients that still have the antiquated view that 'all data is good data, so we shouldn't delete anything,'" McGivern said. That approach is no longer sustainable, given today's climate of heightened data breaches and stringent data privacy laws.

5. Solidify the data management process

Systems need to be architected and monitored to ensure data quality, backup, recovery and proper usage throughout the data lifecycle. Tools, technologies and practices, Prasad advised, should monitor data usage, secure data with access controls and encryption, and ensure that the data is stored and used in a commercially effective and responsible manner.

George Lawton is a journalist based in London. Over the last 30 years, he has written more than 3,000 stories about computers, communications, knowledge management, business, health and other areas that interest him.