What is the future of Artificial Intelligence (AI)? While nobody can completely know what will happen with AI, we can make educated guesses and predictions based on current trends. AI is rapidly expanding, with increased funding in the billions. Boards are mandating AI be implemented into products immediately, but organizations rarely have the right data to support these mandates. Let’s dive into AI basics, and gain a complete understanding of what data you need for successful AI.
The Relationship Between Data and Artificial Intelligence (AI): Why Does Data Matter for AI?
Data and AI are quickly becoming the foundation of modern businesses, supporting breakthroughs in decision-making, automation, and efficiency. Together, one supports the other!
High-quality data is essential for AI because it ensures accurate, reliable, and effective outputs, leading to better decision-making and more trustworthy results. Artificial Intelligence needs that data to tackle cognitive challenges (typically linked to human intelligence), such as pattern recognition.
For more information on how data can fuel AI (and all other areas of your business), we recommend checking out our ebook, Winning in the Era of Data Differentiation.
Is Data Essential for Artificial Intelligence (AI)?
Yes, data is essential for AI. But we’d like to add a nuance, only high-quality data will create good AI outcomes. Feeding your AI models with junk data (that is, data that is inaccurate, unconsented, or not understandable) will result in less-than-ideal results.
High-quality data is crucial because it ensures accurate, reliable, and effective AI models, leading to better decision-making and more trustworthy outcomes. AI relies on clean, consented, and enriched data to function effectively and tackle cognitive challenges, such as pattern recognition. Feeding AI models with poor-quality data can result in inaccurate predictions and flawed decision-making. Therefore, having AI-ready data, that is high-quality, governed, understandable, and available, is fundamental for successful AI initiatives.
How To Improve Data for AI
A high-quality data foundation is essential to support your AI outcomes. For more about how to create a strong data foundation, we recommend you check out our ebook, Winning in the Era of Data Differentiation.
5 Pillars To Get Your Data Ready for Artificial Intelligence (AI)
Consent-Based Data Collection
First thing is first, you’ll want to use consented data. With Tealium for AI, you can collect and capture real-time, comprehensive customer data generated across any device or platform. This process includes integrated consent management, tailored to your specific data definitions.
AI Compliance: Getting Ready For Regulations
Just like other highly regulated industries like Healthcare and Pharma, AI is likely to be subject to increasing and changing regulatory laws. Tealium includes built-in features such as consent integration, orchestration, obfuscation, encryption, and filtering. These tools ensure compliance with AI regulations and provide auditability for transparency.
Data That Is Organized and Prepped
Your data needs to be organized and prepped in order for AI models to use it. Tealium supports a schemaless data layer that uses plain English attributes and variable names to standardize and organize data according to your data strategy. It also provides tools to correct, modify, and maintain the integrity of incoming data.
Contextual Data Labeling and Enrichment
Contextual data labeling and enrichment involve using built-in tools to add relevance, metadata, and other contextual information. This includes elements like visitor profiles, audience badges, and calculated attributes. These enhancements help improve AI performance by providing more detailed and meaningful data for analysis.
Integration for AI Activation
You’ll want to use a platform that integrates with AI partners. With Tealium for AI, you can activate AI models in any tool or channel, from real-time AI outputs to targeted audiences or batch-based lists. Leverage our library of over 1,300 integrations, APIs, and functions to streamline the process.
