Keys to AI Implementation Strategy for D&A Architects

Data and analytics technical professionals need a holistic roadmap to deliver well on analytics and AI initiatives.

AI implementation strategy is a must for data and analytics leaders

With the rapid advancement of generative AI (GenAI) and large language models (LLMs), enterprises are poised to transform their analytics and AI landscapes — and evolve GenAI from short-term AI pilots to holistic enterprise solutions.

However, implementing these technologies requires a robust AI implementation strategy. Here are some key considerations for the D&A tech professionals tasked with executing those plans.

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    AI implementation requires skills, roadmaps and governance

    D&A architects must integrate and scale GenAI and LLM solutions while ensuring data and AI literacy, governance and trust across diverse data and deployment methods. Here’s what to consider.

    What AI implementation issues do D&A tech professionals face?

    GenAI implementations are fast evolving from short-term pilots to holistic enterprise solutions, adding pressure to the AI implementation challenges that D&A domain architects already face:

    • GenAI tools, specifically those based on LLMs, are enabling enterprises to develop innovative analytics and AI solutions, but the ever-expanding hype, risk and technology ecosystem are challenging organizations to identify and deliver appropriate use cases, technology products and solutions.

    • Enterprises are getting better at programming self-service analytics and developing AI models, but many face a lack of data and AI literacy, difficulties in scaling technology and processes, and ongoing issues with governance, privacy and trust.

    • The growing diversity of data, models, platforms and deployment methods also creates traceability, reproducibility and consistency challenges with metrics, features, datasets and models.

    • Democratization of data gives both technical and subject matter experts the opportunity to discover unique insights. It requires upskilling and training investments to succeed.

    Gartner for Technical Professionals (GTP) is a specialized service that provides in-depth technical research and insights tailored to the needs of IT professionals and architects who are tasked with implementing technical domain strategies. Talk to Gartner to learn more.

    4 AI implementation actions for D&A architects

    D&A technical professionals can zero in on these key initiatives to smooth AI implementation:

    1. Experiment and adapt with LLM-based solutions:

      • Conduct pilots validated by technical and business stakeholders.

      • Design an agile, extensible architecture to integrate new data sources, LLMs and applications.

    2. Drive self-service and democratization initiatives:

      • Implement data and AI literacy programs to enable users to derive meaningful insights from data.

      • Offer a variety of augmented analytics and AI platform options for different user personas.

      • Provide a unified view of features, metrics and models through semantic layers, feature stores and model management.

    3. Combine responsible AI practices:

      • Leverage explainability techniques and frameworks to interpret model outputs.

      • Address fairness, bias mitigation, ethics, risk management, privacy and regulatory compliance.

    1. Implement a federated analytics architecture:

      • Analyze distributed data with a federated approach, balancing both centralized and decentralized analytics development and management.

    • Analyze distributed data with a federated approach, balancing both centralized and decentralized analytics development and management.

    • Enable agility and consistency in analytical data definitions by fostering a culture of analytics product management.

    • Develop a standardized scalable architecture that fosters a strong partnership between IT and the business.

    You may also like “What Is AI-Ready Data? And How to Get Yours There” for more on the steps D&A leaders must take to get data ready to capture the promise of AI initiatives. 

    Benefits of implementing a federated analytics architecture

    A federated analytics architecture is a pattern within the enterprise architecture that allows interoperability and information sharing between semi-autonomous, decentrally organized lines of business, information technology systems and applications.

    It builds on the decentralized management principles of a data mesh architecture, and commonly occurs in two patterns: One based on decentralized data; the other on centrally managed data.

    In both cases, though, organizations decentralize domain analytics ownership, management and governance to business units for domain-centric concerns while guiding on enterprise-level standards and policy. This mesh-like architecture is, therefore, less a technical construct and more a logistical (or organizational) construct requiring matured capabilities in data and analytics management and governance programs throughout the organization.

    What federated analytics architecture requires

    Successful implementations of this analytics mesh architectural pattern require the following:

    • Mature, federated data and analytics governance: Governance policy, procedure and tools are implemented at both global and local levels throughout the architecture. Collaboration between business unit and enterprise governance teams ensures that analytics products delivered meet both specific business needs and adhere to the global principals to meet organizational strategic goals.

    • High data literacy: Both developer and consuming users have a comprehensive understanding of the analytics they manage, deploy and consume. Increased data literacy directly improves the quality of analytics produced and builds trust for analytical consumers. 

    • Willingness to accept responsibility: Because this represents a cultural shift in management, it is imperative that organizations are willing to accept and be responsible for the management, maintenance and governance of analytics assets.

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