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DataOps.liveDec 7, 2021 3:00:00 AM4 min read

Data Governance: An Imperative of the Data Lifecycle

As enterprise data continues to explode in volume, velocity, and variety, executive leaders must implement mechanisms to ensure that this data is always governed and secure. Therefore, the question that begs is how do organizations make sure their enterprise data is governed and protected? 

In summary, the solution to the challenges framed by this question is formulated in two words: Data governance.  

Let’s expand on this solution by considering what data governance is and why it is necessary. We will then look at how we integrate Snowflake’s Data Governance Accelerated program with our DataOps data orchestration platform to provide organizations with a robust and stable solution to the challenges around protecting enterprise data. 

What and why data governance?

Techtarget.com describes data governance as the “process of managing the availability, usability, integrity, and security of [enterprise] data, based on internal (and external) data standards and policies that also control data usage.”  

The current (and future) data privacy regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) are mandating that organizations pay close attention to data privacy and data security to protect customer and institutional data and ensure that they do not have to pay exorbitant fines if caught in breach of these regulatory requirements.  

In summary, a successful data governance strategy assures that data is consistent, does not get misused, and can be trusted. 

The #TrueDataOps philosophy posits that, while data governance is imperative, it is also possible to implement both a data governance strategy and an Agile framework. The TrueDataOps.org website asks the following questions:  

Governance or agility? Do you have to compromise one to get the other? Or is the way we approach data evolving? 

The concise answers to these questions are as follows:  

You can have both governance and agility. You do not have to compromise one to get the other. And there is a fast-moving evolution in the way we approach data.  

Data Governance, DataOps and Snowflake  

Let’s expand on these answers by considering the relationships between data governance, our DataOps data orchestration platform for Snowflake, and Snowflake itself. 

Snowflake has just released their new Governance Accelerated program for Snowflake partners that integrates with their governance capabilities. The Snowflake website describes the value its Governance Accelerated program adds to its partners and their customers:  

Data Governance Accelerated: The next step to simplifying data protection.” 

With our native governance capabilities, Snowflake and our partners enable customers to reduce risk and achieve compliance by helping them easily understand and protect their data.”  

At this juncture it is vital to note that data governance of Snowflake and DataOps. Therefore, let’s look at how DataOps and Snowflake implement their data governance frameworks. 

In practice, the data governance lifecycle consists of the following elements:  

Develop a governance strategy and framework   

The first step in any data governance initiative is to define the organization’s strategy and framework in detail. This element must be framed in its own lifecycle within the data governance lifecycle as it is can only be an iterative process.  

Why? 

As highlighted in our book titled “DataOps for Dummies:” “It is no secret that data and IT requirements are increasing exponentially.”  

The added challenge to the rapidly increasing data volumes is that the percentage of unstructured data (or dark data) is outstripping the structured data generated over time. Statistics reported by the IDG show that unstructured data is growing at 62% per annum, and by 2022, 93% of all data will be unstructured. 

Unstructured data is essentially all data that is not stored in a traditional, structured database format. It could be anything, such as media, text data, audio, or imaging. And because this data does not fit into a traditional database format, ensuring that it is kept governed and secure will never be a simple task.  

Legal data privacy and compliance regulations are fluid. They are continually being updated and revised. Therefore, it is vital to ensure that your data governance policies, strategies, and frameworks are regularly reviewed and updated. 


Create and maintain governance objects

As described in our blog post titled “DataOps Builds Integrated Governance Solutions with Snowflake’s Data Governance Accelerated Program,” data governance is predominantly split into the following areas:  

  • Creating the data governance objects such as tags, masking policies, and row access policies 
  • Ensuring that the right groups of users have the necessary permissions to use the data linked to these governance objects 
  • Applying these objects to the Snowflake object that need governing like tables and views as well as grant management on each object 
  • Observe and report on all database object transactions and user interactions.

Conclusion

In summary, data governance must form a cornerstone (or pillar) of every organization’s data ecosystem. Without designing, implementing, monitoring, and maintaining a robust data governance framework, companies will almost certainly run into data quality and data security issues and even end up in trouble with the relevant regulatory authorities.  

The good news is that our DataOps.live platform is the “first and only platform to provide full lifecycle management control over these objects and their application.” It also includes observability and reporting capabilities, producing the information required by the business to ensure that its data governance policies and rules are correctly applied. Suppose these rules are not correctly implemented; the data governance team can implement a fix as soon as the reports show any errors or shortcomings in the organization’s data governance strategy. 

 

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