Modern data architectures as a risk management strategy

The data modernization trend

Data architecture is the high-level strategy an organization uses to store, process, secure, integrate, transform, analyze and discard its data. At the highest level, architectures are distinguished by whether they use centralized, decentralized or hybrid architecture.

With the exception of young organizations that have built data architectures from scratch using modern designs, data architectures are often more aspirational than fully realized. In practice, most businesses operate with default data architectures that emerged incrementally and don't align well with any single model.

Data modernization addresses this gap by updating architectures to reflect more effective approaches and technologies. As Gartner notes, data modernization offers a way to "increase value and reduce costs" and to "deliver innovative digital transformation products and competitive IT services at optimum cost," in IDC's assessment.

Data modernization's role in enterprise risk management

For businesses with inefficient or unscalable architectures, data modernization offers a path forward that extends beyond faster processing or lower data management costs.

The most important reason to modernize data architecture is to supercharge enterprise risk management. Data has become the foundation for virtually everything in a typical organization. By extension, data architecture now plays a vital role in risk management across industries.

Cybersecurity

An organization's ability to minimize exposure to cybersecurity vulnerabilities and breaches hinges on the data architecture it chooses.

Architectures with weak governance controls make it harder to manage access to sensitive information. In contrast, architectures that enforce clear access boundaries – ensuring data is available only to authorized users, when needed and in the appropriate form -- face an overall lower risk of data exfiltration, breaches and phishing attacks.

From a risk perspective, the question to consider isn't whether data is centralized or distributed, structured or unstructured, but whether consistent security and access policies are enforced wherever data resides.

Resilience

Business resilience -- the ability to avoid operational disruption and recover quickly when disruptions occur -- also depends heavily on data architecture.

Decentralized or distributed data architectures, such as data marts, tend to improve resilience by spreading data across the organization and reducing single points of failure that could bring all business operations to a halt.

However, decentralization strengthens resilience only when supported by efficient processes and policies that ensure timely access to required data. Otherwise, operations may slow down or fail because teams can't access the data they need to work efficiently.

Governance

Governance refers to the practices and rules organizations use to manage assets and operations, and while it extends beyond data, data provides a practical foundation for establishing a strong culture of governance.

Modern data architectures with clear, consistently enforced governance policies and controls help set the foundation for governance across all facets of the business.

No single data architecture is inherently better than others. The key is to ensure that your data platforms and infrastructure include reliable governance standards that define who can access data, who can modify it and how long it should be stored.

Compliance

Compliance, like governance, extends beyond data, but a strong compliance posture often starts with data compliance.

Data compliance involves identifying relevant regulations and ensuring that your data architecture enforces their requirements. For example, data sovereignty rules might require certain types of data to remain within specific jurisdictions, while other regulations might require encryption by default.

By embedding compliance requirements directly into data architecture, organizations can reduce exposure to regulatory gaps that may lead to compliance shortcomings and establish a solid foundation for other compliance practices beyond the realm of data.

Financial risk management

Mitigating financial risks also becomes easier with an effective data architecture in place. This is partly because failures in areas such as cybersecurity and compliance often carry direct financial consequences when they are not managed properly. It also reflects the role architecture plays in supporting operational efficiency, faster time-to-market and stronger margins.

In practice, organizations that manage their data securely and efficiently are less prone to financial waste and disruption.

A practical approach to modernizing data and reducing risk

Data modernization as the foundation for enterprise risk mitigation is far from theoretical. Given the complexity of most organizations' data assets, data processes and data management needs, achieving data modernization can be challenging. However, there are actionable steps business leaders can take to streamline the process:

• Assess the existing data architecture. Leaders should comprehensively analyze the data architecture they currently have in place, including where data originates, how it's processed and who can access it.
• Survey stakeholders about data challenges. Asking employees about challenges they face in accessing, transforming or processing data can go far identifying ways in which data architectures can be improved. For instance, a survey might reveal that data access is currently too centralized and restricted, and that a more flexible approach could improve availability.
• Build a risk map. Risk maps are visual representations of where risks lie within an organization, and can help leaders understand which risks they are struggling to mitigate. And while it's unlikely that data architecture changes alone will solve all risks, a risk map is likely to highlight areas where changes to data infrastructure and processes can improve risk outcomes.
• Establish an architecture governance board. Architecture governance boards, also known as architecture review boards, bring together stakeholders from across the organization to review proposed infrastructure or process changes. Regular assessment helps make data modernization a standard, ongoing practice rather than a one-off initiative requiring direct executive involvement. Forrester has suggested that AI agents can assist in the architecture review process to streamline operations and turn boards into decision accelerators.

Chris Tozzi is a freelance writer, research adviser, and professor of IT and society who has previously worked as a journalist and Linux systems administrator.