Stronger data = stronger AI success

Before diving into AI-generated marketing, it's important to get your data foundation right. Brett Cella, our Regional Director for Ireland & VP of Analytics, believes every brand needs a strong data strategy. Here are his key tips for advertisers.

The race to harness AI in advertising is heating up, with marketers worldwide striving to leverage generative and predictive AI for a competitive edge. However, high-quality data is the crucial foundation behind these advanced technologies. Without a solid data strategy, even the most sophisticated AI models will fall short. 

In this article, I am going to share the essential principles for building a robust data foundation that every marketer needs to successfully integrate AI into their campaigns.

1. Data Quality and Curation

The saying ‘garbage in, garbage out’ is especially true for AI models. Advertisers need to focus on data quality by implementing rigorous curation processes. This means ensuring data accuracy, completeness, consistency, and relevance to the specific use case. Additionally, thorough data cleansing and preprocessing are essential to remove noise, handle missing values, and address any biases or inconsistencies.

2. Data Diversity and Representativeness

AI models perform best with diverse and representative data. Advertisers should aim to collect and curate data that captures a wide range of real-world scenarios relevant to their domain. This diversity helps AI models generalize effectively and avoid biased decision-making.

3. Data Governance and Ethics

With the rise of AI systems, data governance and ethical considerations are crucial. Advertisers must establish robust data governance frameworks to address data privacy, security, and compliance with regulations like GDPR and CCPA. Additionally, ethical principles such as fairness, accountability, and transparency should be integrated into the data lifecycle to mitigate potential biases and ensure responsible AI development.

4. Data Scalability and Infrastructure

AI models, especially those for generative and predictive purposes, often need large amounts of data for training and inference. Advertisers should invest in scalable data infrastructure that can efficiently handle large volumes of structured and unstructured data. This may involve using cloud computing, distributed storage systems, and advanced data processing pipelines.

5. Data Annotation and Labeling

Many AI models depend on labeled data for supervised learning. Advertisers should allocate resources for data annotation and labeling, either through in-house teams or crowdsourcing platforms. Consistent and high-quality labeling is crucial for training accurate and reliable AI models.

6. Continuous Data Monitoring and Feedback Loops

AI models need continuous monitoring, evaluation, and refinement. Advertisers should set up feedback loops to incorporate new data, user feedback, and model performance insights. This iterative approach ensures that AI models stay relevant, accurate, and aligned with evolving business needs.

The success of AI initiatives relies on the quality and robustness of the underlying data. By prioritizing data strategy and investing in the necessary infrastructure and processes, advertisers can create transformative AI solutions that deliver real business value.

Unlocking AI’s potential 

At Jellyfish, we know that the success of any AI initiative depends on solid data foundations. As leaders in the AI space, we specialize in creating and implementing crucial data strategies to power your competitive AI models.

Our expert team of data engineers, AI/ML specialists, and data scientists will help you:

• Set up comprehensive web, app, and customer tracking mechanisms
• Consolidate disparate data into unified customer profiles and data lakes
• Ensure data quality, governance, scalability, and ethical AI practices
• Leverage cloud platforms for custom AI development tailored to your needs
• Integrate AI solutions into marketing, operations, products, and core processes
• Implement continuous data monitoring and model refinement cycles
• Understand your 'AI Brand Awareness' across different AI platforms

With our proven methodologies and deep expertise, we'll help your organization lay the groundwork for transformative AI capabilities that drive innovation, process optimization, and a sustainable competitive advantage.

Don't let poor data foundations hold back your AI ambitions. Partner with us today to unlock the full potential of generative and predictive AI models that will propel your business into the future.