This article summarizes a workshop hosted by Secoda and Pickaxe Foundry tailored for data engineers tasked with the job of constructing and managing the infrastructure that powers AI. It focused on the intricacies of preparing, managing, and optimizing data systems to support and accelerate AI initiatives. In this session, Eric Callahan (Principle Data Solutions at Pickaxe Foundry), Lindsay Murphy (Head of Data at Secoda), and Etai Mizrahi (CEO at Secoda) discussed a comprehensive approach to AI readiness, emphasizing the importance of AI foundations, data quality, scaling, and output validation.

What is AI readiness?

Being AI-ready means your organization has the necessary data infrastructure, quality, and management practices in place to support AI-driven processes and decision-making. It's crucial to recognize that jumping prematurely into AI without a readiness roadmap can lead to inefficiencies and, ultimately, unsuccessful AI projects. This preparation is not solely technical; it encompasses organizational culture, data governance, and a well-defined data strategy as a foundation. We break AI readiness into 3 main buckets: Technical, Strategic, and Organizational readiness.

Technical: Technical readiness involves assessing your current data infrastructure to ensure it can support AI initiatives and ensure a high level of data quality. This includes evaluating your data storage solutions, computing resources, and the scalability of your systems and pipelines to handle the processing demands of AI models. A lack of focus on having the appropriate foundational infrastructure will result in wasted resourcing, tech debt, and poor data quality in the future.

Strategic: Understanding AI foundations involves aligning AI initiatives with business objectives. It's about identifying areas within your organization where AI can create the most value, whether by enhancing customer experiences, optimizing operations, or unlocking new opportunities for innovation. A key part of this strategic foundation is having a roadmap as part of the data strategy that outlines the steps from initial exploration to full-scale implementation, including pilot projects and scalability plans.

A vital component of strategic readiness is stakeholder engagement. It's essential to have buy-in from all levels of the organization, from executive leadership to the teams that will be working with AI technologies. This ensures that AI initiatives are well-supported and aligned with the overall direction of the business.

Organizational: Organizational readiness also means having the necessary data governance frameworks and permissions to ensure a successful rollout. This includes considerations around data privacy, security, and the ethical use of AI, ensuring that models are transparent and accountable. For example, by surfacing data quality information directly where data users already discover and explore data increases trust and transparency in the models that are powering analysis.

Why is data quality important?

Inaccuracies in data can significantly impair the performance of AI models, leading to misleading insights and decisions - think “garbage in, garbage out”. By establishing strong governance, investing in the right tools, prioritizing metadata management, implementing continuous monitoring, fostering a culture of data quality, and regularly validating and cleaning data, organizations can create a robust foundation for AI readiness. 

These steps not only enhance the reliability of AI models but also support better decision-making and operational efficiency across the organization:

1. Establish Strong Data Governance Policies 

Define clear data ownership and stewardship by assigning responsibility for data quality to specific roles within the organization to ensure accountability. Establish rules and procedures for data governance and stewardship, including how data is collected, stored, processed, and shared, to maintain consistency and integrity across the organization.

2. Invest in Data Quality Tools and Technologies

Data monitoring and observability tools like Secoda can automatically detect and correct errors, inconsistencies, and duplications in data sets. They provide visibility into the health of your entire data infrastructure including data freshness, distribution, and lineage (across tables, columns, pipelines, dashboards, and sources) to quickly identify and resolve issues. Secoda can also help in profiling data to understand its quality and lineage.

3. Prioritize Metadata Management

Catalog data assets: Use data catalogs to document available data, including metadata that describes its source, structure, and usage guidelines. This helps users find and understand data, facilitating better data quality and compliance.

Enrich metadata with context: The context provided through effective metadata management is crucial for AI models to interpret the data correctly. Beyond basic descriptions, include information about data quality metrics, ownership, and any known issues or limitations.

4. Implement Continuous Data Quality Monitoring

Establish quality metrics and thresholds: Define specific metrics for data quality, such as accuracy, completeness, and timeliness, and set acceptable thresholds for each.

Monitor data quality in real-time: Use automated monitoring tools to track data quality continuously against established metrics and thresholds. This enables early detection of issues, allowing for prompt resolution before they impact AI systems.

Regularly conduct data audits to assess data for quality issues. This includes validating the data against known standards and rules, and conducting manual checks when necessary. Based on the results of each audit, data teams will be able to implement the appropriate data cleaning processes to establish routines for cleansing data. This should be an ongoing process to ensure data remains high quality over time.

5. Foster a Culture of Data Quality

Ensure that everyone in the organization understands the importance of data quality and their role in maintaining it. Provide training and resources to promote data quality as a shared goal. Assigning target data quality scores and incorporating them into quarterly goal setting or OKR planning is an effective way to reinforce its value to the organization.

Scaling and Validating AI Systems

Scaling AI systems requires a thoughtful approach to infrastructure and data management, ensuring that the systems can handle growing amounts of data and complexity without compromising performance or hallucinating. Additionally, validating AI outputs is essential to maintain trust. This involves continuous monitoring and evaluation of AI predictions and decisions against real-world outcomes, adjusting models as necessary to improve accuracy and reliability.

Lindsay and Eric identified 6 steps organizations can take to benchmark and validate AI outputs:

1. Define Clear Objectives and Success Criteria

Understand what you aim to achieve with your AI initiatives and how they align with broader business objectives. This clarity helps to define success criteria in qualitative and quantitative terms. This could include accuracy rates, performance improvements, customer satisfaction levels, or other KPIs related to the AI application.

2. Utilize Historical Data and Expert Knowledge

Use historical data as a baseline to compare against AI-generated outputs. Paired with engaging experts familiar with the data and business processes to validate AI outputs, this can help in setting realistic benchmarks and understanding nuanced aspects of the data.

3. Implement Rigorous Testing Protocols

Develop a testing framework to apply a structured approach to testing AI outputs against predefined benchmarks. This should include a mix of automated and manual testing methods.

Use diverse datasets and ensure that the data used for testing is representative of real-world scenarios and covers a wide range of cases. 

4. Adopt Continuous Monitoring and Feedback Loops

Continuously monitor performance with monitoring tools to automatically track the performance of AI systems over time. This allows for the detection of drifts in data or model performance. Establish channels for collecting feedback from end-users and stakeholders on AI outputs to build a feedback loop.

5. Benchmark Against Industry Standards and Competitors

Look into existing benchmarks and best practices within your industry for similar AI applications. This can provide a reference point for what is achievable and expected. Where possible, assess how comparable AI systems perform on similar tasks.

6. Prioritize Transparency and Explainability

Opt for models that provide not just accurate predictions but also explainable ones. This transparency aids in validating AI outputs and building trust among stakeholders. Clearly document the assumptions made during the AI model state any limitations. This helps in setting realistic expectations and understanding the context of the benchmarks.

About Secoda:

Secoda is the fastest way to explore, understand, and use data. Powered by AI, Secoda creates a single source of truth for an organization’s data by connecting to all data sources, models, pipelines, databases, warehouses, and visualization tools. Secoda consolidates multiple tools into a single data management platform to simplify your data catalog, lineage, governance, monitoring, and observability processes. Companies like Chipotle, Cardinal Health, Kaufland, and Remitly use Secoda to get visibility into the health of their entire stack, reduce costs, and help their data teams run more efficiently. Get a custom demo and learn more about Secoda today.

About Pickaxe Foundry:

Pickaxe Foundry helps their clients solve their most difficult challenges, deliver best-in-class data warehouses, analytics and automation, launch amazing new products, and radically improve martech stacks to optimize every dollar spent. At Pickaxe Foundry, Eric helps clients such as Telemundo, Peacock, YES Network, and others to design holistic data systems which enable actionable insights.