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How To
April 15, 2021
How to automate big data governance

Companies deploying big data analytics to gain competitive advantage can quickly sour their successes by lacking a big data governance strategy. Which turns their data assets into data liabilities.


In this article, we dive into the field of information governance and information management and explore how to set up and automate a big data governance program for success.

What is big data governance? 

Big data governance is a set of processes and principles that ensure the high value of data throughout its lifecycle. 


Big data governance applies to practices, tools, and people, and leverages all the resources to achieve more valuable data.


Concretely, big data governance operates over multiple domains:

  • Transparency and data lineage. How did the data travel and transform from its raw data form when collected from data sources to its cleaned version when inserted into data storage (data lakes, databases, etc.)?
  • Data quality. Data quality assures the data is of high consistency, reliability, and integrity. It assures data can be trusted and was not corrupted throughout its lifecycle.
  • Data accessibility. Data access is managed on a granular level - every operative, who needs data has access to it, and siloed data is not an excuse that prevents data practitioners from accessing data. At the same time, accessibility is monitored and granted on a needs-only basis, to shield data from prying eyes.
  • Data privacy and security. Safety is not limited to just sensitive data or data breaches. Regulatory compliance requires that the data use is respectful towards the privacy of the users whose data is analyzed. Data governance policies set out rules regarding when and how data should be anonymized, deleted, or generally secured to prevent any unnecessary breaches into the privacy of users.
  • Ownership and collaboration. Big data policies define who is the owner of each dataset and job within the company (and is accountable for the data source), as well as other roles within informational governance, to assure the rules set out in the policy are applied and respected.


How is big data governance different from regular data governance?

Big data governance is almost identical to regular data governance in its principles and domains of operation.


The main difference is how big data governance is practically implemented. The practicalities differ due to the different nature of big data, the so-called 3 Vs of big data:

  • Data volume. The quantity of data in big data systems is much bigger than in regular databases.
  • Data variety. Big data is not predominantly relational but takes many unstructured and semi-structured forms.
  • Data velocity. Data is produced and ingested faster than regular data, often requiring special integrations with the Internet of Things (IoT) systems and streaming technology to handle continuous data production and ingestion.


The different nature of big data puts constraints on how to implement big data governance. For example:

  • Data quality is harder to assert. If relational data and smaller datasets allow visual inspection, the quantity of big data is so big that special analytical techniques and tooling are implemented to guarantee data quality.
  • The variety of data requires different data integrity checks. When the producer of regular data is known, it is easier to set up concrete practices, which check if data has been corrupted. When the incoming data is not structured, it is harder to determine if the data collected has deteriorated. This is why many big data systems implement the eventual consistency architectural design.
  • Etc. 


The multifaceted nature of big data governance raises the question: is it worth implementing it?

What are the advantages of big data governance?

Implementing strong big data governance offers multiple advantages: 

  1. Trustworthy data. Multiple aspects of big data governance result in a higher quality of data, which increases its trustworthiness. 
  2. Better decision-making. Decision-making based on trustworthy data leads to better insights and understandings. Without a governance framework set up to guarantee valid data, decisions are still made but are skewed towards errors and red herrings that stem from impoverished data.
  3. Regulatory compliance. There are multiple regulations legislating data practices. One of the most famous ones is the European General Data Protection Regulation (GDPR). Adopting proper big data governance results in regulatory compliance, which gives your organization peace of mind and legal support for your operations.
  4. Clarity of operations. Policies set out in big data governance give you clarity of data operations - they make it clear which operational area you need to improve.
  5. Greater involvement across silos. Data governance policies involve data practitioners, business users, and management, making data the responsibility of several actors and therefore foster greater involvement of stakeholders across silos.
  6. And many more.

How to implement big data governance?

Big data governance implementations require you to set up a big data governance framework with 3 areas:

  1. General policies.
  2. People and roles.
  3. Processes. 

The implementation guidelines below offer best practices for each area.

1. General big data governance policies

We outlined the main areas for big data governance before:

  • Transparency and data lineage. 
  • Data quality. 
  • Data accessibility. 
  • Data privacy, security, and regulatory compliance.
  • Ownership and collaboration.


For each area prepare a policy document that outlines what needs to be done to improve the area.

2. People and roles

Depending on your team size, you might want to assign one or more people to the following roles:

  1. Data owner. A data owner is accountable for the quality of data. They maintain the ETL pipelines and make sure data is consistent, of high integrity, and high quality. 
  2. Data steward. Data stewardship refers to the role of explaining governance policies to other stakeholders, fostering the understanding of big data governance, and checking the standards outlined in the governance policies are effectively applied.
  3. Solutions and data governance architect. A data architect within the team takes care of architecting the data security, access, and monitoring for all data systems. 
  4. Compliance specialist. An expert with a legal and/or financial standing serves the role of compliance specialist, to make sure governance policies are aligned with compliance needs.

3. Processes

Keep in mind that the governance program is a practice, not a project. With that continuous nature in mind, the best practices are:

  1. Start small. Identify a manageable business problem, whose solution will lead to a quick win.
  2. Set up goals. Make it clear what the outcome of implementation will be and measure its success.
  3. Use tooling for automation. Do not rely on manual operations for your governance. This can quickly lead to two people doing the same work, and implementation delays due to the manual effort involved. Rely on tools to help you automate the heavy lifting.
  4. Rinse and repeat. For a process to take place, repeat the cycle of identifying a problem, setting up a clear goal, and automating the governance process with the right tools.


How can Keboola help you automate big data governance?

Keboola is an end-to-end data operations platform that comes with in-built data governance tools, that help you automate a lot of the heavy lifting when implementing your big data governance policies:

  • Track data lineage and operational metadata, describing user activity, job activity, data flow, schema evolution, data pipeline performance, compliance with your security rules, etc. Keboola implements data governance by design, which offers you extensive people tracking and audit capabilities as well fingerprinting to comply with regulatory standards on one hand, and fully understanding the data lineage on the transaction - and event-level on the other hand.
  • Deploy the Data Catalog to centralize and unify data definitions, hence increasing data understanding and accessibility across business departments. Unified definitions allow you to increase data quality as well, by disambiguating different interpretations of the same incoming data. 
  • Guarantee best-in-class security standards out of the box. 
  • Support different governance roles, by using granular access practices, which safeguard data safety and privacy, while empowering every user to get the data they need to do their best work. 


But Keboola is not just a tool for automating big data governance. It is designed to automate and speed up all data operations, from ETL pipeline construction and maintenance to deploying machine learning models in production

We offer a no-questions-asked always-free tier. Try Keboola out and check for yourself what Keboola can do for you.

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