As key business decisions are now data-driven, organisations are increasingly turning to data lakes to store raw business data. Statistics show the growing need for data lakes, with the market ranging from 7.05 billion US dollars in 2023 is expected to rise to 34.07 billion US dollars in 2030. However, the flexibility and scalability of data lakes can present unique challenges when it comes to managing data quality and compliance.

This is where data governance comes into play. Data governance includes policies and procedures to ensure data quality, security and compliance. Understanding the best practices for data governance in data lakes helps organisations stay in control and ensure the integrity of their data.

What is data governance?

Data governance is a set of rules and processes that organisations can use to efficiently manage their Data use. It defines roles and protocols for the creation, storage and management of data to ensure data integrity and consistency. The main objective of data governance is to ensure that data is accurate, consistent and accessible to those who need it, while ensuring compliance with regulations and internal policies.

Data governance enables functioning frameworks and standards for the management, access control and use of big data to optimally exploit the potential of data analytics.

Data Governance: Fundamentals, Challenges and Solutions in Data Management

What is a data lake?

A Data Lake is a central repository that stores large amounts of data in an unstructured form. In contrast to conventional Data warehousesWith data lakes, which store data in a structured format, organisations can collect and store data from different sources without the need for prior processing or schema definition. This makes them ideal for processing different types of data and supporting various analytical use cases.

Find out how data lakes serve as central collection points for huge and diversified data volumes and enable efficient big data analytics.

Data Lake: Simply explained

Advantages of data governance in data lakes

The integration of data governance in data lakes offers numerous advantages for a company:

• Improved data qualityData governance ensures that the data stored in a data lake is accurate and reliable. By implementing data entry and validation standards, organisations can reduce errors and inconsistencies. This leads to a better Decision making and more meaningful insights from trustworthy data.
• Increased data security: With data governance, companies can create clear Security protocols for their data lakes. They can also define who is authorised to access certain data and how it can be used. This reduces the risk of data breaches and ensures compliance with legal and regulatory requirements.
• Better data organisationWith the right governance, the data in a data pool is well organised and easily accessible. This saves time and resources, as users can find and use the data they need more easily.
• Better compliance with regulationsData governance helps companies to fulfil legal and regulatory requirements. With clear policies and procedures for data management, organisations can ensure that data is managed in accordance with relevant laws and regulations, such as GDPRrecorded, stored and processed. This reduces the risk of penalties and strengthens the trust of customers and stakeholders.
• Cost efficiencyData governance helps to prevent the accumulation of redundant or unnecessary data. This allows companies to optimise their storage and reduce waste. It helps to reduce the cost of data management while maintaining a high level of security. Data quality to be maintained.

Data security and data protection are central functions for securing operational data and value chains. Protect yourself in the best possible way and get comprehensive advice on this topic:

Data security: compactly explained

Challenges of data lake governance

Organisations can face several challenges when integrating data governance with data lakes. Here are some of them:

• Data silosMany organisations store their data in separate systems or departments, which can lead to data silos. These silos can make it difficult to implement a governance strategy for the entire data lake. As a result, organisations can struggle to ensure consistent data quality and accessibility.
• Security problemsLarge amounts of sensitive data stored in data lakes are an attractive target for cyber threats. Implementing access controls, encryption and other security measures in large data lakes can be complex and resource-intensive.
• Limited resources and expertiseMany organisations lack the right resources and expertise to implement data governance. The lack of qualified personnel can hinder the development of efficient governance policies. This gap can lead to poorly managed data lakes and missed opportunities for data insights.
• Maintaining data conformity: The data stored in data lakes can come from different regions with different data sources. legal and regulatory requirements originate. It can therefore be a challenge to ensure compliance, especially with cross-border data flows.
• Changing data landscapeIt can be difficult for organisations to keep up with the changing data landscape. As a result, data governance frameworks can quickly become outdated, making it difficult to maintain reliable data governance.

Companies must utilise the opportunities offered by artificial intelligence while ensuring that their applications comply with legal and ethical standards. Find out here how to set up compliant processes:

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Best practices for successful data governance

Consider the following best practices to implement successful data governance in a data lake:

1. define clear goals

Start by defining the goals of your data governance initiative. For example, do you want to improve the quality of your data or improve compliance with regulations? Setting clear goals at the beginning will help you throughout the governance process.

2. setting up a governance framework

Create a structured framework for data governance. This should define the right roles and responsibilities for data and include guidelines for data access, security and quality. An appropriate governance framework will help you align your data strategy with business objectives and maintain reliable data.

3. involve stakeholders

Remember to involve stakeholders from different departments early on in the process. They can provide valuable insights into data needs and challenges. This collaboration also fosters a shared understanding and commitment to governance efforts.

4. assignment of roles for data management

Appoint Data controller or owner for certain data sets within the data lake. These individuals can help maintain data quality and enforce data governance policies. They can also ensure that the data is properly monitored and maintained.

5. implementation of data standards

Develop and enforce data standards for formats, definitions and quality metrics. Consistent data standards reduce confusion and improve the usability of data. This also ensures that users are trained in the use of these standards to promote compliance.

6. monitor and check data

Regularly monitor data quality and compliance with governance guidelines. Conduct audits to identify problems or areas for improvement. One Continuous monitoring helps to maintain the integrity of the data lake and ensure compliance with governance practices.

7. offer training and support

Provide training programmes for employees to ensure they understand data governance policies and practices. Ongoing support will empower users to manage data responsibly. This investment in training promotes a culture of data stewardship within the organisation.

8. review and update of the governance guidelines

Review and update your governance framework regularly to adapt it to changes in the data landscape. As new technologies and data sources emerge, policies may need to be adapted. Staying up to date will help you maintain effective governance over time.

In our article, we show you why good data quality is the key to reliable processes and how you can ensure this for your company:

The 5 most important measures for optimal data quality

As the world becomes increasingly data-driven, the importance of data governance in data lakes is growing. By adopting best practices and overcoming challenges, organisations can transform their data lakes into powerful assets that drive innovation and growth.