By: Laura Bal, Senior Principal, Global Consulting, Digital Marketing, LTIMindtree

Digital marketing is rapidly evolving, fueled by the exponential growth of artificial intelligence (AI), particularly generative AI (Gen AI). Gen AI’s ability to produce creative content, analyze data, and predict customer behavior has opened up a world of possibilities for marketers. However, amidst the excitement and innovation, a critical aspect often gets overlooked: data governance.

A new forecast from the International Data Corporation (IDC) Worldwide Artificial Intelligence Spending Guide shows that global spending on artificial intelligence (AI), including software, hardware, and services for AI-centric systems*, reached USD 154 billion in 2023, an increase of 26.9% over the amount spent in 2022.

An effective data governance framework ensures that data is collected, stored, used, and disposed of in a consistent, secure, and compliant manner. In the context of Gen AI, data governance plays an even more crucial role, as the quality and integrity of data directly impact the effectiveness and reliability of AI-powered marketing campaigns.

Why data governance matters for Gen AI in digital marketing

Data is the lifeblood of Gen AI. The algorithms that drive these powerful tools are trained on vast amounts of data, and the quality of that data determines the quality of the AI’s output. Poor-quality data can lead to biased, inaccurate, or even harmful results.

Data governance practices address these concerns by ensuring that:

• Data is accurate and consistent: Data governance processes help identify and correct errors or inconsistencies in the data, ensuring that AI models are trained on reliable information.
• Data is secure and protected: Data governance establishes protocols to safeguard sensitive data from unauthorized access, breaches, and cyber threats.
• Data is used ethically and responsibly: Data governance principles guide the ethical use of data, protecting consumer privacy and ensuring that AI models are not used for discriminatory or harmful purposes.

“Artificial intelligence (AI) and machine learning (ML) continue to push the boundaries of what is possible in marketing and sales,” notes a report by McKinsey. It also states that businesses already investing in AI are seeing “a revenue uplift of 3% to 15%, and a sales ROI uplift of 10% to 20%.”

Essential data governance strategies for Gen AI adoption

As digital marketers embrace Gen AI, implementing a robust data governance strategy is essential. Here are some key steps to consider:

Data quality management

Define data quality standards: Establish clear and measurable data quality standards to ensure the accuracy, completeness, and consistency of AI training and deployment data.

Implement data quality checks: Implement automated and manual data quality checks to identify and correct errors or inconsistencies in the data.

Monitor data quality metrics: Regularly monitor data quality metrics to track progress and identify areas for improvement.

“Experian: 75 % of businesses believe inaccurate data prevents them from delivering a good customer experience.”

Data bias mitigation

Assess data for biases: Use data analytics tools and techniques to identify potential biases in training data.

Implement bias mitigation techniques: Employ data augmentation, reweighting, and adversarial training to mitigate biases in AI models.

Continuously monitor for biases: Regularly review and monitor AI models for potential biases and take corrective actions as needed.

Every year, poor data quality costs organizations an average USD 12.9 million. Apart from the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision making.

Data access control

Establish role-based access controls: Implement role-based access controls to restrict data access to authorized personnel only.

Implement data usage monitoring: Monitor data access and usage patterns to identify potential risks or non-compliance with data governance policies.

Review access controls regularly: Review and update access controls regularly to ensure they remain aligned with business needs and security requirements.

“The Verizon 2023 Data Breach Investigation Report found that a massive 74% of breaches were directly related to passwords that were either stolen, weak, or simply default passwords that organizations failed to change to more secure ones. This statistic highlights the importance of data access control to prevent unauthorized access to sensitive data used by AI models.”

Data security

Implement data encryption: Encrypt sensitive data at rest and in transit to protect it from unauthorized access.

Implement access controls: Employ robust access controls, such as multi-factor authentication and role-based access controls, to restrict data access.

Regularly conduct security audits: Conduct regular security audits to identify and address potential vulnerabilities in data systems and processes.

“According to Forbes, there were an estimated 2,365 cyberattacks in 2023 with over 343 million victims. This translates to roughly 6.5 attacks per day.”

Data privacy compliance

Adhere to relevant data privacy regulations: Comply with all applicable data privacy regulations, such as GDPR and CCPA.

Implement data privacy controls: Implement data privacy controls, such as data subject access requests and data erasure procedures.

Provide transparency and control: Provide individuals with clear information about how their data is collected, used, and shared.

“A Forbes Advisor survey shows that 76% of consumers are concerned with misinformation from artificial intelligence (AI) tools such as Google Bard, ChatGPT and Bing Chat.”

Data transparency and auditability

Document data practices: Document data practices, including data collection, storage, and usage procedures.

Implement data lineage tracking: Implement data lineage tracking tools to trace the origins and transformations of data.

Conduct regular data audits: These audits ensure compliance with data governance policies and procedures.

A recent study by Accenture found that only 36% of organizations clearly understand how their AI models are making decisions.

Ethical considerations

Ethical considerations in Gen AI marketing are vital to prevent biases, ensure transparency, protect consumer privacy, and maintain public trust. Addressing these ethical concerns helps foster responsible AI development, preventing misuse and promoting fairness in marketing practices.

Establish ethical guidelines: Develop clear guidelines for developing and using Gen AI in marketing.

Consider societal implications: Consider the potential societal impact of Gen AI applications and address potential biases or discriminatory outcomes.

Implement continuous ethical review: Establish processes for ongoing constant ethical review of Gen AI applications and data practices.

A Pew Research Center survey found that 72% of Americans are concerned about the way companies collect and use their data. This highlights the need for transparent data collection practices and strong privacy safeguards.

Implementing a data governance strategy for generative AI

A comprehensive data governance strategy for Gen AI requires a structured approach:

Assessment: Conduct a thorough evaluation of current data practices, identifying data sources, flows, and potential risks.

Objective definition: Clearly define the objectives of the data governance strategy, aligning them with business goals, ethical principles, and regulatory requirements.

Policy development: Craft comprehensive data governance policies encompassing data quality, bias mitigation, security, privacy, ethics, and access control.

Tools implementation: Utilize governance tools and platforms to automate data quality checks, enforce access controls, and track data lineage.

Employee training: Educate employees on data governance policies, procedures, and ethical considerations, ensuring they understand their responsibilities and the importance of data stewardship.

Continuous improvement: Establish a continuous improvement cycle to monitor the effectiveness of the data governance strategy, address emerging challenges, and adapt to regulatory changes.

Conclusion

Generative AI holds immense promise for transforming digital marketing, but its full potential can only be realized when coupled with sound data governance practices. By prioritizing data quality, security, ethical use, and transparency, digital marketers can harness the power of Gen AI while mitigating risks and ensuring responsible innovation. As the Gen AI revolution reshapes the digital marketing landscape, data governance will be a cornerstone of success, enabling marketers to navigate this transformative technology confidently and responsibly.

References

AI-powered marketing and sales reach new heights with generative AI, Richelle Deveau, Sonia Joseph Griffin, and Steve Reis, McKinsey & Company, May 11, 2023:

https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/ai-powered-marketing-and-sales-reach-new-heights-with-generative-ai#:~:text=Our%20research%20indicates%20that%20players,of%2010%20to%2020%20percent

How to improve your data quality, Manasi Sakpal, Gartner, July 14, 2021:

https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality

Over 75% Of Consumers Are Concerned About Misinformation From Artificial Intelligence, Katherine Haan, Rob Watts,  Forbes Advisor, July 20, 2023:

https://www.forbes.com/advisor/business/artificial-intelligence-consumer-sentiment/

The impact of bad contact data quality, Experian:

https://www.edq.com/globalassets/white-papers/the-impact-of-bad-contact-data-quality.pdf/

The One Practice That Is Separating The AI Successes From The Failures, Ron Schmelzer, Forbes, August 14, 2022:

https://www.forbes.com/sites/cognitiveworld/2022/08/14/the-one-practice-that-is-separating-the-ai-successes-from-the-failures/

 74% of Data Breaches Start With Privileged Credential Abuse, Securis, April 13, 2019:

https://securis.com/news/privileged-access-management/

Cybersecurity in 2022 – A Fresh Look at Some Very Alarming Stats, Chuck Brooks, Forbes, January 21, 2022:

https://www.forbes.com/sites/chuckbrooks/2022/01/21/cybersecurity-in-2022–a-fresh-look-at-some-very-alarming-stats/

Social media use in 2021, Brooke Auxier and Monica Anderson, Pew Research Center, April 7, 2021: https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/#:~:text=When%20asked%20about%20their%20social,are%20some%20stark%20age%20differences.

Worldwide Spending on AI-Centric Systems Forecast to Reach $154 Billion in 2023, According to IDC, IDC Research, March 7, 2023: https://www.idc.com/getdoc.jsp?containerId=prUS50454123