AI Ready Data Foundation powered by Modern Data Platform

The rapid adoption of artificial intelligence (AI) and generative AI (GenAI) has brought several challenges to the forefront, many of which are not new but have become more pronounced as enterprises strive to maximize value from their data. Key issues such as data quality, accessibility, visibility, platform scalability, diverse data formats, and compliance with evolving regulations are significant obstacles. Organizations across various sectors encounter difficulties when their data platforms are not equipped to support AI and GenAI applications. For example, a financial institution building AI-based forecasting models may face delays due to a lack of data transparency, low-quality data, and challenges in integrating disparate sources. These issues can lead to inaccurate forecasts, increased operational costs, and missed strategic opportunities. Fragmented, inconsistent, or outdated data ultimately hinders an organization’s ability to leverage AI's full potential, stifling innovation and slowing response to market demands. Furthermore, the effectiveness of AI and GenAI solutions relies heavily on the quality of the data fed into these systems, underscoring the critical need for AI-ready data.

But what does it mean for the data to be AI ready? The following 6 pillars can be termed as the key characteristics of AI Ready Data:?

The most impactful GenAI use-cases will only be unlocked with the characteristics of AI ready data are met. This will provide a solid foundation to enable teams to rapidly build, test and deploy their AI & GenAI based solutions which the business experts can use with confidence. To get the data to an AI ready state there are 6 key factors that need to be considered –

1. AI ready data strategy: To build and execute data strategies focused on enabling the rapid development and deployment of AI & GenAI solutions
2. Data Governance: Enable data scientists & AI/ML engineers to quickly and accurately discover, explore and use novel data sources to efficiently build new AI & GenAI solutions whilst adhering to the data compliance norms
3. Master data management: To have a single source of truth for core datasets and reduce potential for inaccurate AI results, thereby increasing trust in AI solutions?
4. Data quality: AI is only as good as the?data you feed it. Ensure the quality of the AI & GenAI solutions with curated and validated data
5. Scalable data architecture: Data is thoughtfully curated in a way that enables both exploration for rapid prototyping as well as supporting large AI / GenAI solutions as they scale
6. Data security & compliance: Reduce risk of AI / GenAI solutions introduced through inaccurate data, poorly maintained data and improper access controls ???????

Tools and technologies can help to put parts of the AI ready data in practice. Databricks is a good example for a modern data platform. The image below provides and overview of the core building blocks of Databricks.?

• Databricks promotes an open lakehouse architecture that allows you to integrate data from multiple sources and types at scale; helping break down data silos.
• Unity catalog allows for efficient data governance and management of data. With role based access controls, automated data retention policies and data lineage it enables governance and security of data and any solutions built on top. In 2024, Unity catalog was open sourced which allows for more seamless integration across other enterprise wide data governance tools.
• Databricks notebooks and Mosaic AI allows for efficient development and deployment of AI and GenAI solutions, which can be governed through unity catalog.

When such a platform is paired with the right approach for master data management, processes and operating model, the AI ready data state can be achieved.

In conclusion, the journey toward AI-ready data is not merely a technical challenge; it is a strategic imperative for organizations aiming to harness the power of AI and GenAI. By prioritizing the key characteristics of AI-ready data and leveraging modern data platforms like Databricks, alongside strong governance and quality practices, companies can unlock the full potential of their data assets. This transformation not only accelerates innovation but also leads to improved operational efficiency and better decision-making, positioning organizations for long-term success in an increasingly competitive landscape.

Authors:

Ashish Choraria ([email protected]), Senior Consultant, AI & Data, EY Consulting GmbH, Germany

Rudraksh Bhawalkar ([email protected]), Partner, AI & Data, EY Consulting GmbH, Germany