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  • Data Governance and Data Quality

Data Governance and Data Quality: How They Work Together

Data Governance and Data Quality

In today's business world, data quality and data governance are essential.

Businesses rely on data for reporting and to make key strategic decisions. Data quality and data governance are essentially inseparable so it’s vital to consider both when looking into data management.

Is data governance the same as data quality?

Although they are related, data governance and data quality are not the same. Data quality relates to the level of accuracy, completeness, and consistency of datasets and values. Organisations need to have high-quality data they can rely on when making critical decisions. Poor data or not being able to trust data can result in businesses not making the right decisions, taking action in a timely manner, and missing out on opportunities. All of this can ultimately impact finances, making data quality management crucial.

Data governance on the other hand refers to the overall management and control of an organisation's data assets and is the framework for how data is handled across the business. It includes a combination of processes, policies, technologies and systems that help ensure the quality, integrity and security of an organisation’s data. Staff are given roles and responsibilities for managing and using data making it crystal clear who is responsible for what and the amount of access they have.

Main differences between data governance and data quality

Although they share similarities, there are some key differences between data governance and data quality:

Data Approach

Data Governance

Data Quality

Focus

Establishing policies, processes and technologies to manage data assets, ensuring the quality, integrity and security of data

Establishing data accuracy, completeness, consistency, validity, timeliness and uniqueness

Goal

Ensuring data is handled and managed correctly across the business

Ensuring data is held to pre-established quality standards and requirements

Extent

Entire organisation or business

Focus on datasets or projects

Responsibilities

Sets out data ownerships and responsibilities

Sets data permissions

Enforce data privacy and security policies

Data quaity standards and metrics

Data cleansing and validation processes

Activities

Policy development

Setting our data ownership and accountability

Data classification

Data access privileges

Data retention policies

Regulatory compliance

Data profiling

Data cleansing

Data validation

Data standardisation

Monitoring data

Establishing data quality dimensions

Benchmarking

 

What is the relationship between data governance and data quality?

Data governance and data quality depend on each other. It could be argued that the link between them is symbiotic as it’s based on mutual interrelationship. One would not be successful without the other with both required to successfully enhance the quality of your data. Many organisations are yet to understand just how important it is to ensure both data governance and data quality practices are in place. Some businesses may have every intention to improve data quality, but neglect to consider a wider data governance strategy. This can benefit data quality in the short-term, however without proper data governance, there are no guidelines in place to ensure data quality in the long-term.

Data governance helps organisations to:

  • be more transparent with how they use and manage data
  • have a standardised approach to data systems, policies and how the data is used
  • effectively deal with any data issues
  • ensure data compliance is followed correctly

Implementing data quality and data governance

Knowing where to begin when improving data quality and data governance can sometimes feel overwhelming, especially if your organisation has a wide variety of datasets from different sources. Knowing which ones to prioritise first or if any should be excluded can present a real challenge.

Data teams can work out which ones to focus on by considering the following:

  • Identify critical data: what data is the most important to the business such as data regularly used in reports and directly related to KPIs
  • Identify low quality data: data quality issues that could impact key decision making and performance, or pose a high risk if it is used in its current state

Once an organisation works out which area of data to address first, they can create a data governance framework for how the data is managed, used, accessed, and what privileges users have. Having business users and IT working together when implementing a data governance strategy gives it the best chance for success, especially when employees are fully informed, trained and knowledgeable of data processes.

Policies, requirements and rules for data usage are decided during this process. Data owners can specify key systems along with the processes required. Business users can specify what standards the data needs to follow as it moves through systems.

Understanding how data flows through a business or organisation and what standards are needed can help the data quality team create rules around data and its usage. Ineffective data governance can make it almost impossible to have a high level of data quality and vice versa.

Why is data governance important when improving data quality?

You cannot improve or maintain data quality effectively without data governance. It is able to help you with your data quality improvement efforts in the following ways:

  • Accuracy: Putting a plan in place that improves data accuracy, consistency, and ensures data is complete
  • Increased value: Maximising data value by ensuring it’s complete and trustworthy
  • Improved data management: Creates a framework for best practices and how the data is used and helps with determining policies and what to include when looking at data management
  • Compliance: Ensuring data compliance and that GDPR and other regulations are followed correctly
  • Security: Protecting sensitive data Determining policies knowing exactly what to include when looking at data management
  • Risk management: Reducing organisational risks where data is misused, impacted by data breaches, lost or not compliant

Data quality increasing urgency for data governance

There are five primary factors that are responsible for the increased urgency in data governance:

  • BI (business intelligence) and analytics
  • Data compliance, data privacy and financial regulations
  • MDM (master data management)
  • Integrating data with systems
  • Data quality assurance ensuring everyone buys into high data standards

It’s extremely difficult to have high quality data without a high level of data governance in place. If users don’t know how they should be using the data correctly, this can risk data breaches, non-compliance, inaccuracies, errors and even lost data. Poorly managed data undermines your business intelligence initiatives and makes it hard to trust any data that you have.

Establish data governance and quality today

It’s clear that data quality and data governance are crucial for any attempts to improve a business or organisation’s data quality. Both concepts work together so both need to be carefully considered during this process. If you’re struggling to make sense of all your data, we’re here to help. Our team of experts have years of experience managing data and can help create data management solutions that are tailored to your exact needs.

If you’re looking to improve your data quality and data governance, you can contact our experienced team who will find out what your current business challenges are to help you take charge of your data and unlock its full potential.

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