As the year continues, AI maintains its position at center stage holding the attention of marketers and consumers alike. We’ve learned by now that effectively prompting AI and asking the right question is critical when leveraging it for business, and at the core of AI is data – the building block for making it work.
But not all data should be utilized for AI. Just like building a house, you want to make sure the two-by-fours are straight and none are missing within the wall. Likewise, when it comes to preparing your data for AI, organizations need to ensure accuracy and completeness. Failure to do so can lead to inaccurate responses due to a weak foundation.
Here are a few tips to get started:
To best prepare your organization’s data for AI, you need to build a clear vision of defined goals outlined in an AI roadmap. Without understanding your goals, you won’t know the correct data and documentation to prepare to support your AI. You could potentially run into costly issues without having a clear understanding of your infrastructure, what it can support, and how you can pivot with the speed in advancement of AI. Organizations need to make sure they have a solid building foundation for their data. To help, one of the first things to look at is a cloud-based data warehouse.
Data warehouses are often used for analysis and reporting, pulling multiple sources into one place. And the scalability of a cloud-based option further amplifies its advantage. They generally help support business intelligence (BI) activities like process analysis, performance benchmarking, and descriptive analytics. If an organization is not utilizing a data warehouse today, this is the first step to get started with AI. This database should act as the foundation to AI, where it can pull from a holistic view of your organization’s data and customer profiles, whether it’s from marketing, sales, service, etc. All this data housed in a central location will be beneficial not only for AI to work from, but for personalization efforts where multiple teams need the most accurate view of a person’s interactions with the organization.
When prompting AI, especially closed sourced, organizations need to make sure it has the correct flow of data, granting access to the users who need it. Providing your AI tool with a data dictionary will help it accurately understand your prompts and pull from the right data for each response. This way it will understand and cross-reference detailed definitions against attributes within your data set, resulting in accurate relationships and predictions for the best data-driven results for business and marketing decisions.
Depending on the age of your organization, data governance standards within your industry (specifically with heavily regulated verticals like banking and healthcare) and tech stack could influence the data you include or exclude in AI efforts. If your organization is leveraging a data warehouse, this is an ideal place to start since this should be your centralized repository for all your structured data. This will allow your AI solution to process your organization’s data most effectively.
Using structured data (like email, name, and phone number) versus unstructured data (such as visuals, audio, or feedback from social media) helps limit the possibility of unpopulated data rows and limited linage. If there are missing records in data sets, these should be removed as it could affect the outcome of your prompts with your AI tool. Having limited linage within your data will also hinder the ability of your tool to draw accurate conclusions to your prompts.
Preparing your organization's data for AI requires a clear vision and well-defined goals outlined in your AI roadmap. By establishing a solid foundation with the right infrastructure, such as a data warehouse, you can navigate the rapid advancements in AI and unlock its full potential for your business.
Learn more about how Merkle’s GenCX solution brings data together to create better customer experiences.