Our data tools are designed to assist leaders in managing data effectively. Our tools include a Data Literacy Self-Assessment, Data Quality and Machine Learning Readiness Test, and Root Cause Analysis. These tools help leaders identify areas for improvement and make data-driven decisions that impact their organization’s success.
You have been tasked with completing the AI Readiness Canvas to ensure our organization is fully prepared before any significant investment is made. This framework is designed to close the gap between technical aspiration and strategic reality by transforming AI from a speculative expense into a measurable engine for the business. Your objective is to work through the eight critical domains of execution.
Is your organization ready for machine learning, or are data quality gaps holding you back? Developed by Theresa Kushner and Thomas C. Redman for Data Leaders, this practical readiness test helps organizations assess the foundations needed for successful machine learning, including business alignment, trusted data, bias management, leadership, culture, skills and project delivery. Use the test to identify strengths, uncover gaps and prioritize the actions needed to build more reliable, responsible and valuable machine learning initiatives.
Why do organizations recognise data as a vital business asset, yet still struggle to manage it with the same discipline as finance, people or operations? This Root Cause Analysis tool uses a Fishbone Diagram to explore the underlying causes of poor information asset management. Drawing on research and expert insight, it helps leaders move beyond surface-level symptoms and examine the cultural, organizational and governance factors that prevent data, information and knowledge from being managed effectively. Use this tool to start sharper conversations, challenge assumptions and identify where meaningful change needs to begin.
Organisations increasingly realise that they must transform into true digital enterprises to create competitive advantage and ensure corporate survival. However, many organisations do not realise that successful digital transformation (DT) requires much more than technology; it can only succeed if they manage their data, information and knowledge as true business assets. This paper describes collaborative research conducted by academic and industry partners, a mutually beneficial journey spanning the past ten years. The aim was to develop a Holistic Information Asset Management (HIAM) model indicating the important areas of information asset management (IAM) that support the DT journey
In the knowledge-based economy the wealth-creating capacity of organisations is no longer based on tangible assets such as buildings, equipment, and vehicles alone. Intangible assets are key contributors to securing sustainable competitive advantage. It is therefore critically important that intangible Information Assets (IA) such as data, documents, content on web sites, and knowledge are understood and well managed.
