Mutually Reinforcing Strategic Priorities: Data and Knowledge Management Are Prerequisites for Achieving AI ROI

Whitepaper

Mutually Reinforcing Strategic Priorities: Data and Knowledge Management Are Prerequisites for Achieving AI ROI

A practical look at how better data and knowledge management help enterprises build a stronger foundation for AI ROI.

Download Whitepapaer

The Vision

Enterprises are recognizing the immense upside in treating knowledge as a strategic asset:

1.      By building proprietary databases and expert knowledge banks, enterprises create a durable competitive advantage.

2.      By enabling strong results from AI initiatives through strong data practices, enterprises maximize AI RoI and adoption.

The path to reaching this vision is not groundbreaking. However, it does require more structure and discipline than most organizations have today.

The organizations that win in the AI era are not the ones with the most sophisticated models.

They are the ones with the best-managed knowledge.

The Root Causes

For the past decade, organizations have invested heavily in cloud migrations, system modernization, data lakes that are arguably oceans, and now AI. However, AI RoI is lagging. Many AI-enabled results disappoint, insights are wrong, and outputs are unreliable. Before further investment, organizations should fix the root causes.

One root cause of poor AI results is the data layer.

  • The complexities of integrating structured and unstructured data from diverse sources and multiple clouds
  • The massive volumes of dark data that are unusable because of silos and human behavior
  • The increasing risks from governance gaps in data lifecycle management

Without data management, AI leverages incomplete and incorrect data to generate its outputs.

Another root cause of poor AI results is the knowledge layer.

  • The failure to clean and enhance legacy content and structures
  • The leakage of knowledge as professional hoard information or exit
  • The lack of continuous and sustainable initiatives that reduce manual intervention and rework

Without knowledge management, AI lacks context and tacit understanding to constrain its outputs.

Together, these gaps undermine the success and speed of AI implementation.

Objectives

This report seeks to briefly outline an end-to-end approach to data and knowledge management for enterprises seeking a solid foundation for AI deployment. It is not a comprehensive technical report, rather designed to help business leaders understand and identify where to prioritize their investment.

Contents

  • The Foundations:
    • Cloud Foundation: Single, hybrid and multi-cloud strategies
    • Information Foundation: Structured, unstructured and dark data; tacit knowledge
    • Semantic Foundation: Metadata, taxonomy, ontology and knowledge graphs
    • Operational-Enablement Layer: RAG
  • The Lifecycle:
    • Unified Governance Committee
    • Reducing the Human “Middleware”
    • End-of-Life Management
  • The Intelligence:
    • AI and BI modernization: Semantic Search, Agents, Front-End Platforms
  • 6 Musts for Change Management
  • 8 Questions to Assess Your Organization’s Maturity

Get the Whitepaper

Download Whitepaper

About the Authors

Whitt Ellis

Director, Operational Excellence

Shobhit Saxena

Director, KM Products and AI Enablement

Venkatesh Coimbatore Kannan

Associate Director, Data Engineering