Data Governance Before AI Ambition: A CDO’s Checklist for GCC Enterprises

Businesses in the whole GCC are rushing to implement artificial intelligence. Be it the automation of financial statements through the city of Riyadh or AI-enabled smart customer journeys of Dubai, all the way to chief executives launching AI projects.

Chief Data Officers (CDOs) are becoming increasingly aware that AI systems are only as good as the data they use. Lack of solid data governance and AI models leads to inaccurate outputs, data leakages, and non-compliance.

Therefore, before pouring funds into sophisticated AI models, executives of the GCC companies should first make themselves familiar with a checklist of data governance that enables them.

The GCC Data Situation: Huge Demand, Severe Restrictions

GCC nations are setting digital innovation examples in accordance with national projects (e.g., Saudi Vision 2030, UAE Centennial 2071). Still, this transformation goes hand in hand with stringent regulatory standards.

Countries’ local laws are quite similar: SDAIA rules in Saudi Arabia, CBUAE standards, data residency laws of the entire GCC, etc., all mandate local data holding, heavy encryption, and close scrutiny of personal data protection. AI deployment on the basis of data that is unclean, unstructured, or non-compliant can lead to regulatory and legal penalties. Data compliance is no longer optional in AI; it is an essential business requirement. 

Chief Data Officer’s Guide to Data Governance for AI Models

1. The Purity of the Data and Understanding of Its History

The success or the failure of AI initiatives largely hinges on whether the data used to train the model is of a quality standard. CDOs have to make certain that every bit of data they collect is traceable in that all the changes and their consequences at the source are fully captured.

• Check the legitimacy of your data; you have to remove the records that are duplicates, obsolete, or incomplete.
• Create data lineage. Document and track how a dataset is created (e.g., through integration with a legacy database), how it changes (e.g., through cleansing or transformation), and what datasets the resultant output of the AI will consist of.

2. Localization and Sovereignty Compliance

Regional compliance is mandatory across GCC member nations. Data should reside only on servers located in the country where it is collected. This requirement is one of the key regional data protection regulations. As an example, customer and operational data should not be stored on foreign or offshore servers if their collection happened on a regional level. 

Also, there’s strict control over how sensitive information is accessed and processed. Only certain individuals shall be allowed to have read/write/modify rights over the data, which would reduce the risk of a data breach or unauthorized processing.

3. Clear Ownership and Stewardship

Data ownership and stewardship help keep data clean, accessible, and well organized. Designate the data steward for the data within the company; that is, people should be given the authority and responsibility to manage data for the different departments. 

Define the rules for usage of data: Explain with clear guidelines how internal teams can feed their data into an external or an AI system. It also implies that teams should know about certain restrictions, for example, not feeding raw datasets but only the aggregated ones.

4. Continuous Governance Automation

It is not practical to monitor data manually at an enterprise scale. This is why automated methods for data governance that can monitor data on a continuous basis and detect deviations from established rules and notify the data team are required.

• Data metadata should be automatically labeled to help with data classification and search.
• Ongoing security scans and real-time monitoring should guarantee the enforcement of all the policies without any human interaction.
• In this way, any unauthorized data access or data misuse can be immediately detected and stopped.

Partner with AIQU for Enterprise AI and Data Solutions:

Data and AI transformation in the enterprise needs strong practical execution and compliance capabilities and is backed with proven technical expertise. AIQU’s solutions cover the entire spectrum of technology capabilities needed to bring intelligence into different business functions across the GCC region, whether it is AI and data management, cloud solutions, cybersecurity, or enterprise software applications.

AIQU’s team of over 800 consultants and experts provides comprehensive services in a wide range of digital capabilities to its clients throughout the Kingdom of Saudi Arabia, the United Arab Emirates and the wider MENA area. Our experts combine an in-depth understanding of local markets with global best practices to provide innovative, value-generating solutions to the enterprises.

Still looking for support to make the switch to AI while relying on a reliable data foundation? Call AIQU today to get hold of our experts.

Frequently Asked Questions

1. What is Data Governance?

Data governance is a framework that enables an organization to manage, secure, and use its data consistently and in compliance with regulations. 

2. What are the main differences between AI data governance and data governance in general?

Data governance in general has a major focus on the fundamental aspects like the security, storage, and quality of data. AI data governance, on the other hand, is not only about those features but also includes a series of controls specific to machine learning models, such as tracking and documenting the input data for models used for training purposes, detecting the occurrence of bias by the model, and guarding users’ data privacy during the processing phase. Finally, it also involves providing the transparency of the model’s final outcome (prediction).

3. What makes data governance a priority for GCC countries today?

The companies and businesses of the Middle Eastern countries are bound to the different regulatory systems, for instance, SDAIA in Saudi Arabia or the rules set out by the central banks. Implementing good data governance practices avoids the financial burden of non-compliance and, at the same time, facilitates better AI decision-making, as such models are more likely to be based upon the correct local information.

4. Is it possible for a company’s AI pilot project to proceed without a complete data governance strategy?

Of course, a trial can be done on a small scale without data governance, and in this case, it is quite a common thing. However, the moment AI is adopted at the large scale of the business, it is without exception that a lack of governance will eventually result in problems like a breach of data confidentiality, being nonconformant to laws (therefore risking penalties), or delivering the business results that are not trustworthy due to the lack of model reliability.

5. What is the right way for the CDO to bring a business board on board with a proposal to implement a governance initiative?

It would be best to present data governance to a business board under the angle of a tool that reduces the company’s risk level and gives the organization the chance to realize its targets. You should mention how data governance leads to more efficient AI implementations, how it is likely not to trigger non-compliance situations, and how, as a result, the company will benefit from an accurate and reliable business output.