Copy Data Management: The Challenges, Benefits and How Silk Simplifies the Process

As your business grows and your data needs expand, more and more teams become consumers of your data. What was once the domain of ITOps has expanded to CloudOps, data teams, generative AI tools and agents, and various lines of business needing to consume and process data. While you may have teams or individuals charged with data governance and data-quality management, the subset of that responsibility, copy data management (CDM), is often an afterthought.

In 2014, Storage Switzerland predicted that most enterprises would need 20 copies of their data to meet business needs. Businesses often struggled with managing the copies of that data, and ensuring it was accurate, up-to-date, and had the appropriate data masking for their security needs. Over the past decade – with the rise of real-time analytics, recommendation engines, and AI  – this data need has grown exponentially.

What Is Copy Data Management?

Copy data management is the practice of creating, using, and managing copies of data from a common source. A mature CDM practice enables an enterprise to maintain security of data; provides accurate, testable data to development and QA teams; ensures regulatory compliance; and enables analytics and AI tools without impacting production performance.

Who Benefits from Copy Data Management?

Any organization with consumers of production data – analytics applications and teams, compliance requirements, regular backups, developers and testers who need valid data – benefits from a documented and governed CDM practice. With a strong CDM practice, you can create, manage, maintain, and retire copies of data, while saving time, decreasing costs, and minimizing risks for your enterprise.

AI and Analytics

AI agents, tools, and development teams are intensive consumers of data – and the recency and validity of that data is imperative to ensuring you can deliver accurate and actionable results.

In many cases, it is important to mask sensitive data – including PII – to ensure your AI and analytics tools have only relevant data, while protecting the privacy of your users and customers. Your CDM practice should include the ability to use the masking tool(s) chosen by your organization, so any SaaS applications used for AI and analytics do not have sensitive data incorporated into dashboards, LLMs, or other data sets.

Regulatory and Audit Compliance

Organizations with regulatory oversight often need to comply with auditor requests for point-in-time data, to understand what data was available to users on a certain date, track transaction volume, or validate data security.

SaaS providers additionally benefit from CDM when complying with a SOC 2 audit, which requires that auditors verify appropriate controls are in place to ensure the security, standards, and policies for removal of that data. Your CDM documentation can highlight your governance and security practices, streamlining the audit process and reducing the risk of an adverse opinion.

Backups, Recovery

The original purpose for a copy of production data was to back up data and ensure that data was available for disaster recovery purposes. While CDM needs have increased, the original need remains – and has expanded to require different kinds of copies, more frequent creation, and unmasked data.

FinOps

If your organization has a FinOps practice, the commercials around data copies are critical. A FinOps professional wants to ensure you are using best practices to ensure data copies aren’t taking up valuable space in the cloud – and aren’t kept longer than necessary.

Dev/Test

Agile development teams are often the most overlooked consumers of data copies. Ramping up new developers and testers with accurate data can take days or even weeks, impacting the team’s velocity. So, they often opt to compromise by using old, outdated, or mock data, to meet their sprint deadlines. An effective CDM practice can enable more effective and efficient testing of new features, ensuring releases are accurately vetted before external users see them.

How Has Copy Data Management Evolved?

The days of only 20 copies are long gone, but an increase in demand is not the only change in the CDM world. Enterprises are more focused on internal control needs – including those impacting SOC and SOC2 audits – as well as risk mitigation, cloud costs, data masking, and formal retirement policies.

For many enterprises, CDM has matured into a practice or a mandate for creating that practice. FinOps organizations often require copy data management to streamline costs and reduce risks.

Types of Copies

One important aspect of CDM is defining the types of copies needed by various teams. Every organization is different, but the main types of copies are:

• Full clones of data with no masking, often used for backup, rollback, and recovery purposes
• Snapshots of a point in time, to enable troubleshooting
• Masked copies used for AI, analytics, development, and quality assurance

What Are the Challenges of Copy Data Management?

Managing the lifecycle, type, and process for making copies takes time and money. Enterprises often encounter resistance to CDM practices because there are not enough people to create and manage copies, or to follow up on retirement policies. Copies are often stored in environments – shared drives, data lakes, local drives, SaaS environments, and more – that make management difficult, if not impossible.

Sensitive and Protected Data

Data sets often include data that should not be distributed to or accessible by the full organization. Personally identifiable information (PII); credit card and other payment information; medical, education, employment, and purchase history; and other types of data are often protected by regulatory agencies and need to be masked or redacted from copies. Not doing so can put the enterprise at risk, leading to fines and reputation loss. Companies regularly lock down copy capabilities due to this complexity, reducing the effectiveness of teams who can benefit from valid, current copies of data.

Time and Effort

Creating, maintaining, and refreshing data copies is often the responsibility of database administrators (DBAs) who are stretched for time and don’t always have access to the data destinations as new technology is being tested and introduced.  Those who do have access are not always well versed in the organization’s data-governance requirements. These challenges are especially apparent when organizations are innovating on short timetables, like with AI tools. See our previous post, The Hidden Cost of Agentic AI, for AI-specific challenges.

Real-time Needs

New technology often pushes the limits of CDM practices at an organization. What was once appropriate for monthly or weekly data refreshes, now requires real-time data to make business decisions, enable AI, and support sales efforts. But many organizations don’t have the infrastructure to support these needs. Accurate, usable data copies can take hours or days to generate, rendering them inaccurate as soon as they are created.

Copy Data Management Costs

Data copies take up space and, for organizations that host their data in the cloud, space equals money. Some organizations restrict certain types of copy creation by giving access to production data, but that practice can reduce performance and introduce other risks. Other organizations opt to store data copies in lower-cost cloud environments, but that doesn’t eliminate costs completely – and can introduce complexities that add time and effort to the creation of copies.

Overcoming Copy Data Management Challenges

These challenges are what inspired Silk Echo, which enable organizations to make near-instantaneous, zero-cost copies of data that meet specific CDM governance guidelines – all while distributing the ability to create these copies to roles outside the DBA function. This democratizes the creation of copies, frees up DBAs for more business-critical functions, activates innovation, and lowers cloud costs.

How To Streamline and Improve Your Copy Data Management Practice

Building and improving your CDM practice requires the right infrastructure and empowered and knowledgeable practitioners.

Establish Governance Principles

A critical aspect to a CDM practice is to have documented processes, roles, and guidelines. As CDM needs have grown, what was once permissible as an ad-hoc set of activities, now needs structure, definitions, and scalable tools.

Democratize Copy Creation

Depending on a small team of DBAs – or even a single individual – to manage copy data creates unnecessary bottlenecks and increases enterprise risk. With the appropriate roles, tools, and guidelines in place, data copies can be created by the consumers of the data, without introducing risk or increasing cloud storage costs. Reducing the friction involved with CDM can free up teams to focus on innovation and business outcomes, rather than unnecessary bureaucracy.

How Silk Can Help You Achieve Your Copy Data Management Goals

Silk’s software and support offerings were designed for CDM needs. With Silk, full copies are free, roles allow for data consumers to manage data, and our software works seamlessly with data-masking tools – all while reducing your overall cloud spend. If you are experiencing any of the challenges listed in this post – or are just starting to document your CDM needs – reach out and let us help you supercharge your data.