In the last one decade, cloud application delivery has become extremely important but undeniably complex, sometimes getting out of direct control. This has become a roadblock towards achieving total self-service automation within budget control principles.
According to a report by the IDX, 69% of enterprises believe that they are overspending on the cloud and the lack of automation is the number one reason they cite. It all boils down to data governance because that’s what essentially, well, governs who can access what, where, and for how long.
This makes data governance not just essential but crucial for self-service automation. Naturally, the question arises as to what is the foundation upon which it rests.
Hybrid Cloud Self-service Automation
Since the cloud is not a singular destination, it must be adaptable to change and not averse to evolution. Self-service automation provides the necessary agility which enables the end users to provision their applications into the right cloud based on their needs, whether they want a public cloud or a private cloud – a truly hybrid synergy becomes the need of the hour.
However, governance becomes even more important with such cross-cloud environments where the control needs to be more poignant and strong.
Data Governance: The Foundation of Hybrid Cloud Self-service Automation
As mentioned above, data governance is key to better comprehend the value that a cloud provides which makes it the most important, foundational need of the cloud environment, especially a hybrid cloud or multi-cloud.
Having said that, developing and implementing a common governance model that’s adaptable to the various requirements and complexities in such environments is a challenge in itself. Therefore, there has to be a shared control plane that enables centralized governance across clouds and other associated technologies.
Most companies fall short with data governance when they treat it as just another tool in their cloud arsenal – it’s so much more than that. It includes all the required integrations into the existing technologies that organizations have deployed over the years along with any operational links that enable collaboration among them, across the lifecycle of an application.
With a foundational data governance framework in place, businesses can assign and manage the applicable multitenancy, role-based access controls, and policies.
Principles of Good Governance
Data governance isn’t confined to data. In fact, it blankets the people, the processes, and the technology that surrounds the data. As such, there’s a need for auditable compliance for these three areas that are well-defined and agreed-upon. When done correctly, this could help organizations make data work for them.
Moreover, organizations need to think macro, not micro. They must consider the entire data governance lifecycle instead of monitoring in siloes. Although this could prove to be overwhelming, especially for the small and medium enterprises, it’s also extremely important and worthy of detailed attention.
Some of the key areas organizations must focus on includes:
- Data Discovery & Assessment
- Classification of Sensitive Data
- Data Catalog Maintenance
- Data Sensitivity Level Assessment
- Documentation of Data Quality
- Defining & Assignment of Access Rights
- Regular Audits for Evaluation of Security Health
- Enabling Encryption & Other Additional Data Protection Methods
With these guiding principles, organizations are able to create a highly effective data governance strategy that enables them to achieve control over their data assets and maintain total visibility. This translates into a culture that’s data-driven, helping organizations make better decisions, improve risk management, and most importantly, maintain regulatory compliance as per industry standards.
