Data Lake Governance: Pillars and Strategies for Effective Management

What is Data Lake Governance

Data lake governance involves managing and monitoring the data lake infrastructure as well as the data stored within the data lake. As organizations move to leverage data lakes, it is essential to have effective governance strategies in place to ensure that the data is protected, stored in a compliant manner and is properly & easily utilized.

IDC predicts that the world’s data will grow from 33 zettabytes in 2018 to 175 zettabytes by 2025, an increase of more than 5 times in just 7 years. Business are storing and relying on vast amounts of data and data lakes, because of their many advantages, are fast becoming the preferred solution to store, manage and access this data.

As businesses extract valuable insights and make data-driven decisions, data lakes are fast becoming increasingly important organizational assets.

Data lake governance is typically implemented as a set of rules, standard operating procedures, protocols, and patterns that the rest of the organization abides by.

Why do organizations need Data Lake Governance?

Depending on the need and organizational profile, different organizations will have more focus on certain aspects of managing and leveraging data lakes than other aspects. For example, financial institutions will typically prioritize compliance and security of their data lakes, while consumer startups are more likely to take advantage of the analytics capabilities that data lakes open up to understand their market and users better.

No matter if the organization wants to focus on particular aspect or get better at managing and leveraging data lakes in general, data lake governance is the first step towards ensuring a safe and efficient adoption of the technology by the organization.

Data lake governance provides several benefits, including:

  • Ensuring security and compliance with industry regulations.
  • Establishing a data-driven culture in which data is respected as an asset.
  • Creating efficient processes around onboarding new users access control.
  • Reducing costs associated with storage and maintenance of the data lake.
  • Improving operational efficiency by streamlining processes and access.

The 5 Pillars of Data Lake Governance 

A good data lake governance strategy is built around the following five pillars – Data security & accessibility, Data Quality, Data Resiliency, Organizational Agility, Cost Management & Optimization.

1. Data Security & Accessibility

Data security is probably one of the most critical aspects of any data lake governance plan. Especially considering the varied amount of data that is stored in a data lake, it is essential to ensure that there are well-documented SOPs in place that covers the security of the data in the data lake as well as frictionless access to that data to users who need it.

Security guard rails in form of SOPs, documentation and patterns should cover data classification guidance, PI & PII identification procedures, access control mechanisms and data encryption requirements and procedures.

As data security is strengthened, it is important to ensure that data accessibility to the required data by necessary resources is not hindered.

Overtly cumbersome or inefficient access control policies prevents innovation and quick decision making, thereby reducing the value that your organization can extract from the data lake investment. Inefficient access control may also result in users finding creative ways of bypassing the security controls, putting data security at risk.

Good data lake accessibility policies should include:

  • Clear procedures to onboard new users quickly.
  • Limit access of data to necessary resources.
  • Well defined SOPs for change management.
  • Monitoring & audit of access logs.

2. Data Quality 

The yearly cost of poor data quality runs in trillions of dollars.
According to Forbes, data scientists spend over 60% of their time organizing and cleaning the data.

Poor data quality can result in poor quality business decisions. In some cases, poor data quality can cause irreparable damanage to a brand’s reputation. 62% of consumers of this survey say that a brand will lose their loyalty if they deliver an un-personalized experience

It is clear that in order to get the most out of their big data investments, businesses need to be able to trust the data they are using.

Organizations, therefore, need processes in place for maintaining data accuracy and proactively detecting data quality issues. Such processes should also cover monitoring of existing datasets to ensure that they remain accurate over time.

Here are some ways in which a data lake governance framework can help maintain quality of the data:

  1. Setting up a catalog of approved external data sources and datasets to ensure that only accurate up-to-date data is used.
  2. Procedures to determine the quality of data before onboarding new source systems.
  3. Laying a framework for continuous monitoring and auditing of existing data sets in the data lake.
  4. Proactive alerting requirements and mechanisms for data quality issues.
  5. Defining enterprise data model and procedures to keeping it up to date as new types of data is added to the data lake.

3. Data Resiliency

Data resiliency is crucial in today’s data driven world as it helps organizations protect and maintain access to their critical data in the face of unexpected events such as natural disasters, cyber attacks, or system failures. Ensuring data resiliency helps minimize downtime, reduce data loss and ensure continuity of operations, helping organizations maintain customer trust and avoid potential financial losses.

Here are seven areas that a governance framework should focus on to help maintain and improve data resiliency in organizations

Data Backup and Recovery

A governance framework can lay out procedures, patterns and requirements for the rest of the organization to ensure that data is backed up regularly and recovery protocols are in place, allowing users and systems to regain access to data in case of an adverse event

Storage Redundancy

Data lake governance framework can lay out guidelines regarding underlying storage redundancy. Storage redundancy reduces the risk of data loss in case of a hardware failure.

Disaster Recovery Planning

Data lake governance framework can help put procedures and patterns in place to plan and prepare for disasters, ensuring that your organization is able to get back to on its feet within a pre determined amount of time with loss of minimal data.

Data Replication Guidelines

Data replication strategies can ensure that your data is always within reach, no matter where you are, improving its resiliency.

Network Resiliency

Network disruptions can be frustrating, but a governance framework can implement measures such as load balancing and failover solutions to ensure that your data remains available even under such conditions.

Monitoring and Alerting

Monitoring and alerting systems are foundational in ensuring data resiliency. With good observability in place, you can detect and respond to issues before their impact cascades into bigger problems.

Testing and Validation

To guarantee that your data remains resilient, it’s important to test and validate your resiliency measures regularly. A governance framework can enforce such procedures, giving you confidence in the robustness of your data protection plan.

4. Organizational Agility

Data Lake adoption, in itself, helps organizational agility by enabling centralized data management, flexible data structures, real-time data analytics, improved collaboration opportunities and improved scalability.

A good data lake governance strategy can further help organizational agility in the following ways:

Enabling Data Discovery and Accessibility: Data lake governance should lay the framework and procedures for defining organization-wide data model and keeping it up to date. A common data model, coupled with the data lake as a central repository for data, helps data consumers to discover and access the data they need to make informed decisions.

Streamlining Data Management

As the data lake governance framework lays out common SOPs and protocols for ingesting, maintaining and accessing data in the lake, it streamlines the data management process across the organization reducing the burden on individual teams to set their processes and procedures.

Improved Data Collaboration

Data lake governance strategy leverages the common data model and repository and layers on the definitions of different roles and their responsibilities in the lake, thereby making it easier for teams to collaborate and share data with each other.

Enhancing Data Security

As the Governance frameworks ensures security of the data as long as its SOPs and patterns are adhered to, teams can focus on driving business outcomes and do not have to worry about separately maintaining security of their data in the data lake.

Facilitating Compliance

Without a governance framework, teams are burdened with managing compliance on their own. A good governance framework helps organizations comply with data-related regulations, such as GDPR and HIPAA, by ensuring that data is properly managed and protected.

5. Cost Management & Optimization

One of the dangers of adopting on-demand, infinitely scalable cloud managed services is “runaway costs” where organizations end up with a much higher cloud bill than their budget.

Good data lake governance framework helps organizations manage their costs by managing the infrastructure and services related to the data lake and the organization’s usage of those services.

Here are five strategies to incorporate into your data governance framework to manage and optimize your data lake costs –

Continuous Cost Optimization through Data Classification

Data governance should lay out the framework to classify data based on its usage, criticality and value. Additionally, the framework should contain standard operating procedures (SOPs) and rules for tiered data management where data with certain classification is moved to infrequent access tiers that cost less compared to standard storage tiers.

Cost Optimization through Data Quality Management

If the governance framework ensures that the data quality is maintained, that automatically translates to reduced need for costly manual data cleansing and preparation processes.

Cost Optimization through Monitoring and Auditing

A good data governance framework must implement processes that continuously monitor the data lake for usage and costs and generate alerts if certain usage and cost thresholds are breached or anomalies are detected.


For most organizations that are investing in building out their data lakes, it is prudent to also think through and invest in a good data governance strategy from early on to get the most out of their data lake investment.

A good data lake governance strategy that covers the five pillars we discussed above will help organizations to become more agile, compliant and manage their costs well.

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