Data can deliver an important strategic advantage — if organizations can make it simple and safe to consume and share. However, legacy systems and processes that are slow to change means this is often easier said than done.
To realize the full potential of data-driven modernization, automated policy enforcement is crucial. In fact, automated policy enforcement is the backbone of modern data operations (DataOps), which enables self-service analytics and expedites digital transformations by removing data access bottlenecks.
Any organization that aims to scale operations, regardless of size, industry, or geography, stands to save time, money, and resources by removing error-prone manual processes from their data ecosystems. In this blog, we’ll explore why automated policy enforcement offers a clear advantage in today’s fast-paced world, and what it means for the modern data stack.
Automated policy enforcement is the process of applying data access, governance, and/or privacy policies that grant or restrict data accessibility without manual human intervention. This requires establishing predefined access rules, authoring accompanying policies, and integrating a tool that is capable of automatic enforcement across your data platforms and applications.
Automation is a key aspect of today’s cloud infrastructure security due to the adoption of multiple platforms and in a single ecosystem, as well as increasingly decentralized data architectures. Eliminating manual processes eases the management burden across these otherwise complex systems without sacrificing security.
Although the upfront work needed to enable automated policy enforcement may seem daunting and time intensive, the benefits are far greater. Without the risk of human error – not to mention the time it takes to manually comb through each and every access request – data consumers are able to securely access data faster and increase productivity. Some of the other benefits of automated policy enforcement include:
With data, it’s all about deriving new analytic-driven insights, whether you’re building artificial intelligence (AI)-based innovation into customer experiences or improving decision-making for national security missions. But this also requires having a handle on what data is in your possession to begin with.
Many organizations turn to data catalogs to manage this. Data catalogs are useful, but they are primarily “lists,” or inventories of data assets with built-in workflows to empower data stewards to manage them. While data catalogs provide a front door to data, they don’t reduce the operational overhead associated with safely and efficiently enabling data consumers to access it for analytics.
Given the volume, variety, and increasing velocity of data being created, a front door is not enough. With the cloud’s ubiquity, any organization that wants to accelerate transformation and become truly data-centric must employ tools that reduce operational overhead and eliminate bottlenecks.
At the same time, the tools must ensure data access policies are enforced and auditable, regardless of where the data resides. To that end, the entire data supply chain must be automated in order to make data inventories and data catalogs more operationally valuable.
To more effectively use data to drive decisions via advanced analytics, you need an integrated, automation-driven architecture where data owners ensure that users have the right access to the right data at the right time.
The paradigm shift is like the evolution of e-commerce. Early on, payment platforms were not fully integrated into the shopping platforms, and consumers waited days for a package to be delivered. Now, most consumers prefer e-commerce since it is a completely streamlined, automated, self-service process. We can go online, click a few buttons, and in a brief period – sometimes less than two hours – have goods delivered to our door.
Similarly, automated data operations make it easier for users to operationalize the right data quickly and repeatedly, driving faster digital transformations and insights.
In addition to policy enforcement, automating capabilities such as sensitive data discovery, dynamic attribute-based access control (ABAC), and data monitoring streamline processes that would otherwise be time- and labor-intensive. By permitting or restricting data access based on assigned user, object, action, and environmental attributes, an ABAC policy provides more proactive, flexible, and scalable access control than traditional, RBAC (role-based access control) models. In today’s world of increasing regulations, policies written in plain language can be audited on-demand, increasing transparency without slowing down approval workflows.
For a leading multinational bank, automating policy enforcement and leveraging dynamic access controls saved $50M in resources and enabled self-service data access for more than 5,000 users in just six months. Over time, it’s clear how implementing this one tactic has an impact that drives real business results.
The lesson is this: If you really want a more data-centric organization, you must make it easy for consumers to use data by removing bottlenecks in the data supply chain through automation. This means ensuring data policies are automatically enforced so they can open data up for analysis significantly faster, empowering data users via self-service data access.
A data security platform that offers built-in automation – in addition to data discovery, dynamic access control, and data monitoring – is the most simple, streamlined way to establish efficient, self-service, and secure data workflows.
Once these steps are solved, all existing data investments – data processing platforms; business intelligence tools; Extract, Transform, Load (ETL) and data movement tools; and AI development tools – will be able to deliver scalable, timely impacts to your organization’s objectives.
To read more about data supply chains, download 451 Research’s Data Governance & Management Report.
Protect your data, more efficiently.
