Data Strategy in an AI-First World: Here’s Where to Start

Your company is talking about AI at every turn, but are you building it into your data strategy? No data strategy is complete without AI because this technology is at the heart of creating personalized, secure brand experiences people trust. And with nine out of 10 people choosing to do repeat business based on positive experiences with a company, you want those experiences to be personal, memorable, and secure. 

Even though AI is transformative, meeting customer expectations becomes a real challenge – especially when all the data you need is spread everywhere across your organization.

Here’s how to deliver those amazing experiences by having AI at the forefront of your data strategy.

Data strategy is key to realizing the potential of AI

Data serves as the linchpin of every organization and forms the foundation upon which AI can elevate your use cases with personalized experiences. 

A robust data strategy defines the purpose for collecting the data, the type of data required, and the most effective methods for collecting, unifying, and activating it. Data strategy answers critical questions such as: 

• Are you working with quality data (i.e. de-duped, error-free, and labeled appropriately)?
• Where does the data originate? 
• Where is the data sent to? 
• Is it being used to its fullest potential? 
• Who has access to this data? 
• Does the data include sensitive elements?

Having answers to these questions help you complete the puzzle by understanding how and when your customer is interacting with your brand. Together, the answers not only form the foundation of your data strategy, but they also provide critical context AI needs to power personalization.

Data strategy with trust as a key tenet

You need to also consider and address the AI trust gap. To provide the personalized experiences your customers expect from you, it’s critical to have your AI model grounded: a process that involves securely providing your model with the customer context it needs to respond more accurately. This context is also key to preventing AI hallucinations, wherein AI generates information that sounds confident and correct, but is actually leading people astray with false information.   

Keep in mind the delicate balance between personalization and customer data privacy regulations you need to strike: Personalizing interactions means making customers feel understood versus watched. Your customers need to trust that AI models are not storing sensitive data or using their personal sensitive data to train your models.

Steps for creating an AI-focused data strategy

Following this process will set you on the road to realizing the potential of AI through data:

1. Start with business objectives and use cases: It’s like your home remodeling — you think about how you can make your home more functional and efficient, not necessarily what hammer or power drill you would use for the remodeling. Identify the specific use cases you are trying to solve and see how AI can help meet those requirements. What are your top business objectives that can benefit from AI, such as process automation, segment creation, creating product descriptions and case summaries? Look for processes that can be streamlined to be efficient. Think big and start small. 
2. Ask: Who would benefit from this? Is it your customers, partners, employees? Would it increase revenue? Would it improve customer experience? Also, how would you measure success? 
3. Identify, collect, and aggregate data: Understand data sources and how the data flows within your organization. Identify what data is needed to fulfill the above use cases. Take into consideration all the various data sources where you might have customer data residing, such as data warehouses, data lakes, operational data stores, and even spreadsheets. Also, data could be structured or unstructured, ranging from sales transactions to call recordings to social media comments. To achieve good AI outputs, ensure your data is complete, accurate, reliable, relevant, and timely.
4. Ensure regulatory compliance: Based on a recent survey, 79% of IT leaders believe generative AI will introduce new security risks to company and customer data. Prioritize data privacy and security as you craft your data strategy. Use best practices, such as data encryption, multi-factor authentication and identity and access management to ensure safeguard data security. Checks and balances from both humans and technology are a way to protect your customers, your company, and your ethical standards.
5. Establish data governance: Implement robust data governance practices to ensure data integrity, security, and accessibility. Strong data governance frameworks lay the foundation for AI initiatives. 

With deeper data insights and the power of AI, your business can deliver improved customer experiences: enabling product recommendations, services, and personalization that resonates — while helping you gain efficiencies in operations and revenue along the way.