SMEs and data governance: an approachable guide

Many SMEs struggle with understanding and implementing effective data governance practices which underpin all data-driven decisions. In this approachable guide, we'll take a look at what data governance is, why it's important for SMEs and some best practices for getting started.
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Small-medium enterprises are constantly looking for ways to improve their business operations and make data-driven decisions. However, many SMEs struggle with understanding and implementing effective data governance practices – the very practices which underpin all data-driven decisions. In this beginner’s guide, we’ll take a look at what data governance is, why it’s important for SMEs and some best practices for getting started.

What is data governance?

Data governance is the set of policies, procedures, and standards that govern how data is collected, stored and used within an organisation. It ensures that data is accurate, consistent, and compliant with regulations and industry standards. This includes the management of data quality, data security, data privacy, and data lineage.

At Vaxa Analytics, we group data governance under our Data Management offering which, together with Data Strategy and Data Analysis, form the foundations for extracting actionable insights from your data and helping you answer the questions you didn’t even know you should be asking.

Why is data governance important, even for SMEs?

Data governance is essential for SMEs to make sure that their data is accurate, consistent, and compliant with regulations and industry standards. Without effective data governance, SMEs risk making decisions based on inaccurate or unreliable data, which can lead to costly mistakes and negatively impact their bottom line. Additionally, with increasing regulation on privacy and data management, it’s more important than ever for SMEs to have a strong data governance program in place.

Data governance doesn’t get any easy the larger an organisation gets. Indeed, it’s quite the opposite.

Setting solid foundations helps to avoid growing pains in the future. Thankfully, SMEs benefit from their smaller scale, meaning small changes have a relatively large impact on the organisation’s data governance maturity.

Getting started with data governance

An excellent first step in implementing data governance in a small-medium enterprise is to establish clear roles and responsibilities for data governance within the organisation. Assign specific individuals or teams to manage data governance and ensure that everyone within the organisation understands their roles and responsibilities. This is vital as it sets the foundation for effective data governance by ensuring that there is someone accountable for managing data governance within the organisation.

From there, the next logical step is to develop policies and procedures for data collection, storage, and use. This includes auditing your current policies (if any) alongside the data collected from the systems. From there, begin by crafting a data retention policy, data classification policy, data access policy, and data breach response plan. It’s important to physically document these policies (not just assume staff know what to do) because it establishes guidelines and rules for how data should be collected, stored, and used within the organisation, which will ensure that data is accurate, consistent, and compliant with regulations and industry standards. This step also ensures that everyone in the organisation understands and follows the policies and procedures, which will help to minimise the risk of data breaches and other data-related issues.

Now, let’s not kid ourselves – data governance has a wide scope and it isn’t a two-step process! Here are more activities that SMEs should consider — in rough order of priority (and roughly aligned to organisational scale):

  1. Regularly review and update policies: Review and update data governance policies and procedures regularly, such as annually or when regulations change, to ensure that they remain effective and compliant with current regulations and systems in use.
  2. Monitor and measure data quality: Implement processes such as data quality checks, data profiling, and data validation to monitor and measure data quality. Use data quality dashboards and reports to track data quality over time and take action to correct any issues that are identified.
  3. Ensure data security: Implement appropriate security measures such as encryption, access controls, and network security to protect sensitive data from unauthorised access and breaches.
  4. Educate employees: Provide regular training and education to employees on data governance policies, procedures, and best practices, such as data privacy and security awareness training.
  5. Consider getting help: If you’re struggling to implement data governance in your SME, consider engaging a consultant to assist. This is a specialist field, after all. Consultants can help you assess your current data governance practices, identify areas for improvement, and provide guidance on how to implement effective data governance.
  6. Implement automation: Use automation tools to automate data governance processes, such as data masking, data archiving, and data lineage tracking. This will help you to reduce the time spent on manual data governance tasks and increase the accuracy of your data governance.
  7. Implement Data Governance Platform: Implement a Data Governance Platform that will provide a centralised location to manage data governance policies and procedures, monitor data quality, and track compliance.
  8. Establish a Data Governance Council: Establish a Data Governance Council, comprising representatives from different parts of the business, to provide a common forum to discuss and resolve data-related issues and to make decisions on data governance policies. Note that this is more applicable to the larger end of the SME segment.

Cheat sheet for data governance terms

There’s a lot of terminology n the data governance space, and they’ll likely come up in any organisation’s data governance journey. Here’s a quick guide to the most common:

  • Data steward: A person or group responsible for managing and maintaining the quality and accuracy of a specific set of data within an organisation.
  • Data lineage: The ability to trace the origins, history, and movement of data through an organisation’s systems.
  • Data dictionary: A document or system that defines the different data elements and their meanings within an organisation.
  • Data quality: The degree to which data meets the needs of its intended users, is accurate, and is free from errors and inconsistencies, typically assessed under “the 7 data quality dimensions”.
  • Data security: Measures taken to protect data from unauthorised access, use, disclosure, disruption, modification, or destruction.
  • Data privacy: The protection of personal information and sensitive data, including compliance with regulations such as the EU General Data Protection Regulation (GDPR) and Australia’s Privacy Act.
  • Metadata: Data that describes other data, such as data element definitions, data lineage, and data quality rules. Metadata can help classify and categorise data, and therefore help make decisions on the appropriate rules and policies to put in place.

Data governance is essential for small-medium enterprises to make sure that their data is accurate, consistent, and compliant with regulations and industry standards. By following these best practices, SMEs can establish a strong data governance program that will help them make data-driven decisions and stay competitive in today’s business environment.

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