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Why Cloud Data Governance is Complicated

|Chief Scientist

Data governance by itself is a complicated procedure that is further compounded when performed in the cloud. In essence, data governance has the same principles and main components, regardless of whether data is stored on-premises or in cloud data stores. However, the execution approach needs to be different to reach the same data governance goals.

In this post, we will examine why and how cloud data governance differs from traditional data governance and why it’s complicated to keep cloud data well-governed.

Data Governance Is Still The Same

Data governance has the same goals, regardless of the data location. Data governance aims to ensure that data is safe, secured, protected, reliable, documented, controlled, and evaluated.

Different organizations may have different immediate data governance concerns; for instance it could be to build healthy processes around data handling, to improve operational efficiency around data, or to increase data accessibility in the organization.

Regardless of these the cornerstone of data governance remains the same. View this guide for a complete discussion. 

  1. Accountability, the buck has to stop somewhere. For effective data governance, there must be established and credible accountability. 
  2. Uniformity of laws and regulations to ensure that the data is secured and handled according to all external laws. 
  3. Entrust data administration to a single anointed individual who is responsible for ensuring that all rules and regulations are reported and followed. 
  4. Ensure that the data is high quality and trustworthy to produce trusted analysis. 
  5. Transparency ensures a permanent record of all governance actions, procedures, and audits.
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The Bigger Meaning of Cloud Data

There is no major difference in data governance between cloud and on-premises data storage. The simplistic view is that of course, the storage is different. While this is the most obvious difference; by itself does not have much impact. It’s what this difference leads to that creates the major differences and complications in data governance within the cloud versus on-premises. The difference stems primarily from the fact that cloud data is easily shared and accessed by multiple teams within an organization and shared across partner organizations. 

Cloud Data Stores

Cloud data stores enable a more economical way to store massive amounts of data easily. This means that more data of different types is stored by more and more diverse teams within the organization. Such data is distributed, may be intertwined with sensitive data, and is, therefore, harder to govern.

Cloud Data Processing

The processing of data is in many cases different than that of on-premises data processing. For example, when moving massive amounts of raw data stored in data lakes using ETL/ELT processes to data warehouses. This data is typically both structured as well as semi-structured data. Such operations are usually performed by several teams in the organization, making it more difficult to control. Whereas when this is conducted on-premise, there is typically one team involved with data processing.

Another complication is the ease with which data processing can be shared through the cloud. Data processing has become more user-friendly. This enables more data use-cases, thus expanding the data use processes you need to govern.

Cloud Data Access

In addition, with data democratization processes, more users from more teams, including non-tech savvy users, are accessing data to make value of it. In many cases this has to be done with differential privacy, creating a significantly higher volume of data access and variety, increasing the complications of data governance. 

Common Challenges In Cloud Data Governance

Tracking Data That Changes Rapidly

Data is constantly changing. As this data is placed into data stores and lakes, especially data stored on the cloud it can easily become a data swamp teeming with disorganized, intertwined data.

Tracking Sensitive Data

In the cloud, it is easy for data lakes to become data swamps. As we mentioned above, sensitive data is often intertwined, interwoven, and often obscured. If we don’t know where sensitive data is located it is almost impossible to secure in an effective way.

Data Ownership Is Harder To Pinpoint

It is very difficult to keep track of data stored in cloud-based data stores. Further, conflicting authorizations may arise because there may not be one person in charge. In these cases, the productivity and usefulness of the data are greatly diminished as users struggle to gain access to the data quickly. 

More Data Users

One of the major advantages of using cloud based storage is that it allows teams spread throughout the organization to access the data more quickly and easily. However, this also results in major security concerns; as access must be granted based on various authorizations and is further compounded by the fact that there is no external control over who has access to the data.

Keeping Data Secure & Compliant

More data leads to more responsibility. Data security has become increasingly complex and sometimes even conflicting regulations have created a labyrinth of different security requirements. Lack of ownership futher obscures who should have access and and for how long.

Agile Data Governance With Satori

Satori solves these challenges for our customers by providing an agile data governance solution. Satori is designed to meet the requirements for organizations using a large amount of cloud data, including numerous data users who access data that is continuously changing. Capabilities like continuous sensitive data classification, applying security policies across all databases, data warehouses, and data lakes from a single location, and enabling easier data sharing with self-service data access help organizations meet their data governance goals in the cloud.

Read here about how Satori simplifies cloud data governance or book a meeting with one of our experts.

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About the author
|Chief Scientist

Ben is an experienced tech leader and book author with a background in endpoint security, analytics, and application & data security. Ben filled roles such as the CTO of Cynet, and Director of Threat Research at Imperva. Ben is the Chief Scientist for Satori, the DataSecOps platform.

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