From the rise of marketing automation to the advanced capabilities of modern MarTech systems, AI has played an important role. Previously confined to customized email campaigns and chatbots, it now extends to customer data platforms (CDPs) that support individually tailored campaigns and greatly enhanced engagement, which ultimately drive conversions. Properly applied, it empowers consumers with greater control over their personal information.

However, with the rapid emergence of GenAI, we are entering a new world. It has become clear that personalized marketing, regulatory compliance and brand protection require rigorous governance and transparency about the data that fuels AI.

The disruptive nature of AI to business, and especially marketing, is just one reason why a working group of industry subject matter experts, whose companies are members of the Data & Trust Alliance, have created and released the first set of cross-industry data provenance standards. Developed to address transparency, trust and regulatory compliance for both traditional data applications and AI, these standards were to become a trust barometer for organizations acquiring data from third parties to fuel AI applications, including CDPs.

Although we are still in the early days of adoption, our belief is that these standards may change the industry’s approach to marketing technology for years to come.

Addressing common pitfalls via data provenance standards

GenAI has reenergized the marketing space, presenting both unprecedented opportunities and significant risks. The ability to generate content and insights from vast amounts of data can lead to groundbreaking marketing strategies. It also raises the stakes for customer data privacy management at global scale, a complex and rapidly evolving legal and regulatory landscape, and protection against damage to brand and enterprise reputation from data that is mishandled or the generation of inappropriate content. The responsibility for navigating this new landscape will increasingly fall on marketers. And your robust data governance starts with transparency into the source, lineage and allowed usage of data.

There are a number of common pitfalls to avoid – and clear ways that data provenance standards can help you avoid them.

Accountability

Companies will be expected to be accountable for their use of data – which won’t be possible unless you can trace data issues back to their source, along with a point of contact for inquiries. The Issuer and Source metadata of the D&TA Data Provenance Standards mandate clear documentation of the legal entity responsible for creating the data. This applies even if the data originates from a different organization than the one issuing the dataset. When every dataset can be traced back to its origin, with clear documentation and accountability, it becomes possible to resolve data issues efficiently, enhancing transparency and trust.

Data quality and fitness

Incomplete, outdated or incorrect data lead to inaccurate insights and ineffective marketing strategies. The D&TA standards require the identification of where, when and how the data was generated, as well as the best fit for purpose for the dataset. By documenting the source and best usage of data, marketers can build on reliable data, leading to more effective and trustworthy insights as well as wiser fiscal investments.

Regulatory and legal compliance

The regulatory landscape for data privacy is increasingly complex, with laws such as GDPR and CCPA imposing stringent requirements on data collection, storage, and usage. D&TA’s standards require data suppliers to specify the geographical location where the data was originally collected, which can be important for compliance with regional laws and understanding the data’s context. In addition, data storage and processing location requirements must be specified, along with the level of sensitivity assigned to the dataset, such as personally identifiable information, which dictates how the dataset must be secured and who can access it. Along with insights around personal data consent gathered at the time of data collection and how it can be legally used, these insights provide a framework for compliance. This ensures organizations can demonstrate due diligence and avoid hefty fines.

Integrated data

Data silos hinder a unified view of the customer, resulting in fragmented marketing efforts and inconsistent messaging. D&TA’s standards don’t just support data quality, they also promote data interoperability and integration. A set of quality standards — including identifying the method used to generate data, as well as its originating format – facilitates datasets being combined based on format type and generation method, thereby fostering data interchanges and driving towards a holistic customer view and delivering  cohesive and effective campaigns.

Enhanced security measures

In today’s business and societal climate, protecting sensitive customer data is essential. D&TA’s Data Provenance Standards include metadata identifying data sensitivity, which guides organizations in how datasets must be secured and who can access them. The standards also require data suppliers to indicate whether techniques were used to protect personal information by highlighting whether privacy enhancing technologies (PETs) or tools were applied to the dataset in order to remove, mask, or modify personal or sensitive personal information in the data. This information allows marketers to safeguard sensitive customer data and enterprises to minimize the risk associated with breaches and loss of data.

Ethical concerns

AI systems can perpetuate biases present in data, leading to unethical outcomes like discriminatory marketing practices. By requiring transparency into the origins and transformations of data, D&TA’s standards help marketers identify and mitigate biases and other potential ethical issues.

The future of AI in marketing with data provenance

The use of emerging AI capabilities in marketing is promising, but organizations face serious, even board-level risks. The importance of data management as a business discipline will only increase. Therefore, insights around data origin and where and how it can be used will be critical to capturing the full potential of AI – not only for efficiency, but for innovation, new value creation and competitive advantage.