We won’t just sell you AI — we’ll help you build it

The journey to AI requires strong data fundamentals. Learn how a modern approach to data can empower you to build it for your organization.

Photo by Jopwell from Pexels

By Brock Heller, Sandra Ferrer-Nett, and Gilberto (Gil) Villavicencio

AI is a buzzword, but it can be a game changer if it’s built right. It’s driving investment, fueling innovation, and transforming industries. To truly harness the power of AI, you need more than just data — you need a robust, comprehensive modern data architecture. In this article, we’ll explore how building a modern approach to data can elevate your AI capabilities to drive transformative results.

Everyone wants to sell you artificial intelligence (AI), but we want to help you build it. Here’s how.

Build a modern data architecture for AI

A flexible, scalable, and secure data platform, across the entire data lifecycle, creates the engine that helps power your AI products. A modern architecture is:

• AI-native: Has the advanced analytics and machine learning (ML) capabilities built in, or available, and ready to integrate.
• Efficient: Eliminates data duplication, silos, latency, and reduces technical debt.
• Performant: Utilizes cutting-edge cloud technologies for lightning-fast data processing.
• Scalable: Handles explosive data and compute growth with ease.
• Low-maintenance: Simplifies management with automation and consistent processes.

Moving from legacy to modern data platforms gives you the performance, scalability, efficiency, and AI-enabled tools you need to be successful.

Choose the right data storage approach

Where your data sits directly influences the efficiency, usability, and scalability of AI applications within your data architecture. Know the pros and cons of the common types of modern data storage:

• Data lake: Handles structured and unstructured data, providing a rich and diverse foundation for ML, data science, and AI applications. However, it can be challenging to manage and secure due to its vast and varied nature.
• Data warehouse: Perfect for structured data and analytics, offering integrated solutions with robust SQL support, which is crucial for handling complex AI workloads. But it may not be as flexible or scalable when dealing with unstructured data.
• Data lakehouse: Combines the best of both worlds, serving as a unified platform for all data types and processing needs, making it highly adaptable for various AI workloads. However, it requires more sophisticated data governance and management strategies.

Organize your data for success

A well-organized data architecture enhances transparency across your data lifecycle and provides a clear roadmap for your users on where to get data from for their AI use cases. It is typically structured using distinct zones and layers:

• Raw zone: The ingestion layer that captures and stores unprocessed data. Ideal for exploratory AI use cases, but data can be messy and unstructured.
• Conformed zone: The transformation layer that processes and enriches data, resulting in cleaned and integrated data. Ideal for refined AI workloads, but the transformation process can be complex.
• Curated zone: The consumption layer with ready-to-use data products. Delivers data for specific AI applications, but maintaining data quality requires continuous monitoring.

Automate and streamline your operations

The efficiency of your data, ML, and AI operations (Ops) plays a fundamental role in appropriately managing and leveraging data for AI use cases. Done right, these embed the right people, processes, and technology needed to get the full benefit of your modern data architecture. Understand the terms:

• DataOps is primarily concerned with improving data management. It accelerates and enhances data handling and analysis by emphasizing automation, continuous process improvement, and fostering collaboration between business and technology teams. DataOps lays a solid foundation for AI applications by ensuring data is reliable, accessible, and ready for use.
• MLOps improves the lifecycle management of ML models. It streamlines the process of preparing, selecting, validating, and deploying the right models for data science projects. MLOps ensures the most suitable, efficient, and effective models are used for AI tasks.
• AIOps: This focuses on guiding AI models to perform optimally. It uses prompts and instructions to steer AI models effectively, ensuring they deliver accurate and reliable results. AIOps helps in creating AI systems that are more responsive, adaptable, and capable of handling complex tasks.

Change how you think about managing your data for AI

The evolution of data governance within an organization is typically framed within the context of the driving forces of its business environment. Know what it looks like to evolve from a more siloed, department-driven approach to a more collaborative, product, and domain approach for AI:

• Department-driven: Individual units manage data for AI based on the needs of individual business units and teams controlling practices.
• IT-driven: Centralized enterprise data management for AI with a focus on infrastructure and systems with command-and-control practices.
• Policy-driven: A strong focus on compliance with regulatory standards with policy (like SOX, HIPAA, GDPR, etc.) controlling practices.
• Product-driven: Product-based units manage data for product development and innovation, and customers drive practices.
• Domain-driven: A cross-functional and collaborative approach to managing data with collective processes that drive practices across products.
• AI-driven: Data managed specifically for AI with AI outcomes related to responsibility and ethical considerations driving standards and practices.

Embrace data and AI products

A product-based approach transforms traditional data architectures by decentralizing responsibilities to scale and shifting decisions for data used by AI closer to the teams that know their products and customers the best. Here’s the key principles to implement for a robust data and AI mesh:

• Decentralized domain ownership: Domain teams own their data and help determine what best feeds AI products.
• Data as a product: Data itself is managed as a product and developed to meet the needs of its AI customers.
• Data infrastructure as a platform: A self-serve data platform allows AI teams to quickly find and use data they need for their AI products.
• Federated governance: A collaborative governance structure takes inputs from the teams closest to their products and customers and produces shared knowledge.

A product-centric approach to data and AI moves ownership and responsibility closer to the teams that know their products and customers best.

Meet your AI goals with the AI Office

The AI Office is the vibrant hub to help orchestrate your AI transformation. By fostering collaboration among experts, the AI Office oversees, plans, and manages the integration of AI activities, and crucially, supports the development of a modern data architecture for AI. The AI Office helps ensure your organizational and data environments are primed to accelerate your AI progress.

Unite your teams toward data modernization

The AI Office works with others in your organization to guarantee your data architecture is reliable, enabling smooth operation of AI tools and applications. A robust setup ensures all teams have consistent access to essential resources, reducing errors and boosting efficiency.

Make your AI vision a reality

Embrace the future with the AI Office and propel your business forward. The AI Office captures the opportunity to lead in the AI-driven world, transforming innovative ideas into meaningful programs and projects. Here’s the essential components to its success:

• Executive sponsorship: Provides strategic direction and aligns AI with core business goals.
• AI steering committee: Creates a collaborative and cross-functional body of leaders and AI advocates to push forward AI progress.
• AI governance framework: Conducts outreach and gap analyses to ensure proper use of data with AI, promote responsible AI practices, and create transparency around the AI lifecycle.
• AI portfolio management: Efficiently manages AI programs and projects as well as associated resources across your organization.
• AI readiness assessment: Evaluates the readiness of your technical architectures to support AI initiatives as well as ensuring your teams have the necessary skills to develop and use AI.

Building your AI from the ground up

The journey to AI requires strong data fundamentals. At Slalom, we understand that AI is more than just a buzzword — it’s a transformative force that requires a solid foundation in modern data architecture, an enhanced approach to management and governance, and an organizational structure that fosters change.

Together, let’s build an AI-driven future where data is not just a resource but a catalyst for growth and innovation.

Slalom is a next-generation professional services company creating value at the intersection of business, technology, and humanity. Find out how to turn your AI aspirations into tangible business value today.