Building a Data Foundation for AI Success
Artificial intelligence (AI) is transforming the world of business, enabling organizations to improve efficiency, productivity, innovation and customer satisfaction. However, AI is not a magic bullet that can be applied without proper preparation and planning. To leverage AI most effectively, organizations need a strong data foundation that can support their AI initiatives and goals.
A data foundation is the collection of data sources, data management processes, data governance policies and data infrastructure that enable an organization to access, analyze and make effective decisions. A solid data foundation is essential for AI because it relies on data to learn and generate insights and/or actions. Without good data, realizing value from AI is difficult, or even potentially damaging.
According to a report by McKinsey, there are at least 63 potential use cases where generative AI, a type of AI that can create new data or content, can address specific business challenges and produce measurable outcomes. However, to realize these benefits, organizations must have quality data that matches the needs for their AI models.
Here are some key steps to building a data foundation for AI success:
- Define your AI vision and strategy. Before you start collecting and using data for AI, it’s important to have a clear vision and strategy for what you want to achieve with AI. Define how to measure success and outline the ethical and legal implications of your AI applications. Align your AI vision and strategy with your business objectives and priorities, then communicate them to your stakeholders and employees. As discussed in a previous blog article, FTI is solidifying our strategy and exploring use cases for both internal team members and our external customers.
- Assess your data readiness and maturity. To understand your current data situation and identify the gaps and opportunities for improvement, conduct a data readiness and maturity assessment. Here are some questions to get you started; the results will help guide your organization in determining where to invest its time and resources, focusing on the most valuable opportunities first.
- Who are the key stakeholders?
- Where is your data located, and how do people gain access to it?
- How consistent and trustworthy is your data?
- How can you limit access to sensitive data such as PII, financial data and others? What does your data culture look like?
- Are people aware of the need for a data program and how it might impact their success leveraging AI?
- Implement a data quality management and data governance program. Data quality and governance are both crucial for ensuring that your data is accurate, complete, consistent and trustworthy for AI. Data quality and governance also help you manage compliance with regulatory requirements, meet and exceed customer expectations regarding data privacy and mitigate the risks of breaches, misuse or bias. Begin by establishing data quality and governance standards, processes, roles and tools, monitor and measure your data quality and governance performance regularly, then check and adjust from the feedback gathered.
- Enhance your data capabilities and skills. Leveraging AI most effectively requires the right data capabilities and skills in your organization. This includes having the technical skills to collect, store, move, scrub, analyze and use data for AI, as well as the business skills to understand, interpret and communicate the results and implications of AI. Create an environment that fosters data-driven mindsets and enables collaboration and innovation with data and AI. You can develop your data capabilities and skills through training, hiring, outsourcing or partnering with external firms with demonstrated expertise. Remember that data isn’t always structured and housed in streamlined, modern data architectures. Don’t forget to look for less-structured data sources such as spreadsheets, log files, images and pdfs, then create capabilities to ingest and expose those alternatives to your customers.
Building a data foundation for AI is not a one-time project, but a continuous journey that requires constant evaluation and improvement. By following these steps, you can create a strong data foundation that will enable you to leverage AI most effectively and achieve your desired business outcomes.