Cloud Data Management Challenges and Best Practices

Data sources are increasing at an accelerating pace, and so is the volume of data. In 2021, it was reported that 79 zettabytes of data were generated across the globe. The ceaseless growth in data volume has brought with it numerous challenges, with most of the challenges associated with access management, data security, and regulatory obligations. That being said, there needs to have a certain set of practices or a system or resources that would enable organizations to streamline their privacy management, data protection, and access governance frameworks. Here, cloud data management comes into the picture.


What Is Cloud Data Management?

Cloud data management is a set of tools, resources, systems, and procedures that enable efficient management of data, stored across the cloud, either with or in place of on-premise storage infrastructure.

By the end of 2021, 67% of most enterprises’ infrastructure was based on the cloud. Organizations are adopting cloud infrastructure in huge flocks because of the various benefits it has to offer. Cloud is critical for disaster recovery and backup storage systems. Moreover, organizations are turning to the cloud so they may better optimize new products, run applications faster, and smoothly share mission-critical data with personnel stationed across the globe.

However, cloud data management is entirely on a different level than traditional data management practices since there are unique principles and standards optimized for cloud data. Similarly, there are unique challenges associated with the cloud that one wouldn’t experience with traditional data management frameworks.


Cloud Data Management Challenges

There are a number of hurdles that IT teams need to address for cost-efficient, secure, and efficient cloud data management.

Gaining visibility into the data assets residing in the cloud is one of the top challenges that IT teams usually face. The problem usually arises during the lift-and-shift phase of cloud migration where the IT teams shift their databases and assets to the cloud. Amongst those assets exist the unmanaged devices that the IT teams are not aware of. To boost productivity and save time, enterprises can use Azure cloud to deploy and manage their cloud. These dark data systems create security vulnerabilities since teams don’t have clear visibility into the asset and its security posture.

Another serious problem that has been frequently observed is the lack of visibility into the sensitive data that may be residing in either managed or unmanaged data assets or both. Veronis reported in its 2021 survey that 64% of financial services have over 1,000 sensitive files accessible by their employees. Losing sight of business-critical sensitive data could mean continuous insider threads, cybersecurity breaches, or regulatory penalties due to non-compliance.

Speaking of non-compliance, another problem that may lead to regulatory fines is the absence of a data minimization strategy. As mentioned earlier, there’s a high volume of data that organizations generate on a regular basis. However, storing personal data or sensitive personal data of users for a prolonged period can invite not only unnecessary storage expenses but also regulatory fines, especially if the data has served its purpose and should be deleted as per the data retention provisions in most data privacy laws.

The cost associated with the cloud often gets out of control. Although the cloud is treated as an inexpensive or cost-efficient tool, the growing volume of data that is stored in data lakes and data warehouses makes it more expensive.

Cloud security is yet another important challenge that needs to be addressed efficiently. Although cloud security has seen a dramatic improvement over the years, it is still up to the organizations what procedures they establish or tools they use to improve access management and thwart unauthorized access or data leaks.

Regulatory obligations are continuously being updated to ensure data subjects’ rights, freedom, and the protection of their personal data. As regulatory authorities ceaselessly update compliance frameworks, organizations must update their cloud data management processes to demonstrate compliance.


Cloud Data Management Best Practices

Cloud data management is all about ensuring data quality, keeping data integrity intact, enabling the growth of data in a controllable environment, and making sure that the data is accessible to only authorized personnel. To enable a smooth migration to the cloud and seamless cloud data management, the EDM Council has formulated a Cloud Data Management Capabilities (CDMC) framework that outlines the best practices associated with managing data in the cloud. The CDMC framework further enables organizations to assess the maturity level of their cloud data management practices.

By following the CDMC framework, you will be able to clearly define governance and controls associated with cloud data. You will have complete details on where the data resides or its lineage. You will be able to effectively oversee the management of data across its lifecycle. Let’s check out the following best practices capabilities that are outlined in the CDMC framework for cloud data management.

Identifying the data assets and associated security posture should be the topmost important concern for every organization. Unmanaged or dark data assets often top the list of cyber threat actors because of a lack of monitoring or weak security posture. It is imperative to have a controlled inventory of all the data assets residing across your on-prem and cloud storage, cataloged under relevant metadata, such as vendor version, residency, ownership, and security posture.

Sensitive data discovery and classification are considered among the most important factors that make up the security and privacy framework of any organization. Since most sensitive data rests across unstructured systems, it gets difficult for organizations to have clear visibility into all the data they own. Organizations must have an efficient data discovery and classification strategy in place to effectively catalog every bit of data for optimal data protection and compliance.

At this point, most of the heavy lifting is simplified, paving the way for access management. Data must be available and accessible to only authorized users and only for a period of time until they are not required to have access to that data, such as in the case of employment termination. An effective way to govern access to sensitive data is through establishing role-based access and preventing excessive privileged access.

Organizations must also develop privacy impact assessment and data protection impact assessment processes. These processes are designed to ascertain the privacy and security risks associated with personal data and technologies as early as possible.

Data lifecycle management is yet another significant part of cloud data management. Unfortunately, many organizations face difficulties that arise due to the complexities of the data lifecycle. Only by automating the processes around data from its creation to its archiving can an organization ensure data quality, data security, and privacy.

Last but not least, automation should be at the core of an organization’s cloud data management strategy. By automating processes, organizations can speed up the discovery, cataloging, and governance processes, preventing inefficiencies, inconsistencies, and human errors that often arise due to manual approaches.


To Conclude

As organizations are turning to cloud-first strategies, it has become ever more important for them to have an efficient cloud data management system in place. Though frameworks like the CDMC exist that offer best practices capabilities around cloud data management, organizations might still not be able to make the most of these frameworks without proper expertise.


Fuente: Dataversity