AI’s First Step is Data-Readiness. Here’s how Empowered Users and Data Platforms are Shaping the Next Stage of Generative AI

AI is the engine of today’s leading-edge innovations. And data is its fuel. If your fuel is not in an ideal state of AI readiness, your initiatives could flounder or even fail. Before you begin your next proof of concept AI use case, discover why data readiness needs to be your first step.  

Some organizations are already way ahead with AI use. In many cases, there are two standout variables in their approach: empowered AI users and sophisticated data platforms. These two variables seem to correlate strongly with AI success. In particular, they enhance AI development efficiency and help build trust in AI.

Expanding number of empowered AI users

Empowered AI users are characterized by three variables: data literacy, using real-time and streaming architectures, and leveraging domain-specific data products. Here’s how these variables impact AI.

• Data literacy: One of the biggest trends is the rise of data literacy, according to DataCamp. It found that 86% of leaders rank data literacy as essential for day-to-day work. Empowered AI users have expanded their data literacy, driving a demand for more sophisticated data products and faster development cycles. For example, the adoption of open standards, like Apache Spark for processing and Apache Iceberg and Delta for storage, is becoming essential.
• Real-time data and streaming architectures: The advent of these capabilities is enabling organizations to analyze large volumes of data in real time, powering actionable intelligence and instant decision-making. This is crucial for modern applications. It means that streaming data from social media, sensors, financial transactions and more can be processed rapidly. Plus, actions can be taken when they will have the greatest impact.
• Domain-specific data products: Another new trend is a shift to domain-specific data products, which are more effective than generic data products. They help give organizations granular control and support for complex use cases. Another customization capability is thanks to low-code/no-code tools, which are giving power users the ability to build customized dashboards and reports in ways that meet their specific needs.

Revolutionizing AI data product development

Generative AI is elevating the demand for data cataloging and test data generation because it can enhance efficiency and accuracy. Also, improved observability and DevOps practices, along with the separation of build-and-run teams, are accelerating delivery and standardizing management.

To bolster confidence, AI models must be fueled with unbiased, accurate and well-documented data. A wide range of innovations are revolutionizing the ability to create high-quality data, including data catalogs, data governance and modular data architectures. Here’s what that means for today’s organizations.

Generative AI in data cataloging and test data generation

One way to support the creation of high-quality, AI-ready data is with data cataloging. Data catalogs are organized inventories of organizations’ data assets. They continually crawl data sources to collect metadata, including structure, lineage and usage. Then they track changes over time. By supplying a holistic view of data, the catalogs help ensure accurate and well-documented data to feed the AI models.

Test data generation involves creating data that mimics, but is not related to, actual data. It’s used for running performance test cases. The process helps validate software functionality, spot potential issues and support performance under a variety of conditions. 

Data quality, lineage and governance

Data quality, lineage and governance are crucial for creating trustworthy AI systems. Data quality relates to the reliability and accuracy of data used to train AI models. Data lineage traces the data lifecycle, including its origins, movements, transformations and use. AI governance provides the frameworks, practices and policies required to track AI systems’ development, deployment and use. All three hold vital roles in the creation of AI systems that are responsible, ethical, and in alignment with regulatory requirements and your business objectives.

Modular data architectures

Taking a modular approach to data architecture offers organizations a key advantage: the ability to scale and update components without impacting the whole system. It employs independent, self-contained components that work together. Containerized technologies like Docker and orchestration tools like Kubernetes often come into play, allowing organizations to more easily update and manage AI systems. Done right, the results include increased flexibility, improved scalability and easier maintenance. 

Building trust in AI with high-quality data

High-quality, unbiased data is essential for responsible AI and building trust. Strong data governance and continuous monitoring support ethical AI practices, fostering long-term user confidence. According to Precisely, 76% of business leaders say that data-driven decision-making is the leading goal for their data programs. Yet, 67% don’t trust their data. 

The need for high-quality data

Unbiased, reliable and high-quality data is essential for AI model accuracy and trustworthiness. To ensure you’re feeding AI systems only the highest-quality data, you need to ensure your bases are covered in several categories, including relevance, completeness, representation, bias and timeliness. 

Data observability and ITIL integration

Data observability and ITIL (Information Technology Infrastructure Library) integration is the application of ITIL principles and processes to manage data systems, deliver data quality and establish reliability. Systematic implementation of data observability and integration into ITIL systems allows data teams to detect anomalies in real-time, perform root cause analysis rapidly, and drive trust and transparency in data products.

Two real-world, AI-ready data case studies

Foundry for AI by Rackspace (FAIR™) is helping organizations overcome their AI data challenges with proven processes, services and solutions.

• FAIR helped a U.S. packaging innovation and sustainability leader in the U.S. overcome its challenge of manually extracting data and processing key financial reports. Among the solutions delivered was creating a data strategy that combined disparate large data into a single data lake. Outcomes included reducing the time spent on customer data management and quality by 33%.

• FAIR helped a large healthcare services provider in Asia overcome the challenge of slow performance and existing reports negatively impacting innovation. Among the solutions delivered was integrating data observability to provide a single pane of glass for errors and warnings. Outcomes included access to unified data and the generate real-time insights.

To succeed in AI-driven initiatives today, it’s crucial for organizations to adapt, innovate and strategically invest in their data programs. FAIR offers services, expertise and solutions to help you accelerate the creation, optimization and management of AI-ready data. Ready to get started?