The Core Reality

Technology and data leaders are investing heavily in AI, analytics, and large scale data platforms. Budgets are approved. Teams are in place. Pilots are running.

Yet many initiatives fail to translate into durable, enterprise level value.

This decision workbook is designed to avoid that. It provides a structured lifecycle for evaluating AI and data initiatives assuring that value, data, and governance are aligned before significant capital, time, and reputational risk are committed.

What's in the workbook

Each designed to help leaders like you avoid common pitfalls, build successful frameworks, and deliver strategic value.

1

Data Project Kickoff Guide:
5 Steps to Evaluate AI & Data Initiatives Before You Invest

A list of questions to consider with your data experts to help validate assumptions, uncover risks, and ensure goal alignment.

2

Adopting a Recognized Value Framework
for AI & Data Initiatives

A framework to help you build a stronger enterprise business case by going beyond financial impact by adopting RVU for your data/AI projects.

3

Making the Right AI Investment Decision:
Build vs. Buy vs. Both

A guide to determine whether your data/AI initiative should be bought, built, or be a combination of the two, recognizing the strengths of each choice and often missed long-term impacts of each decision.

4

Data Evaluation Checklist:
Assessing AI-Ready & Defensible Data

A worksheet to compare data providers comparing their strength and weaknesses to determine which partner is right for you

PREVIEW

 

Data Project Kickoff Guide: 5 Key Points

As executive technology and data leaders accelerate investments in AI, analytics, and large-scale data platforms, the risk of misalignment and wasted spend increases. This guide provides a structured way to surface constraints early, reduce uncertainty, and enable more confident decision‑making across the full spectrum of data initiatives.

Problem Validation

The goal is to confirm that the problem exists, is strategically important, and is worth solving now.

  • Is this problem strategically material or an operational annoyance?
  •  
  • What is the measurable business impact of solving it versus doing nothing?

Solution Evaluation

AI should never be the default and should be evaluated against simpler alternatives and assessed for risk, cost, and long-term viability. Leaders need clarity on whether advanced methods are justified.

  • Does the challenge truly require AI?
  • Could it be solved more efficiently through process optimization or automation?
  •  
  • What are the known constraints or blockers for this problem?
  • Data availability, regulatory compliance, technical limitations?

Data Requirements

Availability isn't enough; execs must confirm the right data exists, it's usable, and that it can be governed sustainably at scale.

  • What data sources are essential for this initiative? What are optional?
  • Consider internal systems vs. external data, and whether external sources are licensed and approved for AI and generative AI use.

  • Examples: internal systems (CRM, ERP, transaction systems), external data (licensed news, company intelligence, regulatory data), and third-party sources approved for AI and generative AI use.
  •  
  • What specific data elements are required to achieve the intended business outcome?
  • Examples: entity resolution (customer or company identities, corporate hierarchies), enriched attributes (industry classifications, risk indicators, sentiment), event-level data (transactions, interactions, filings), and structured taxonomies that enable consistent classification and retrieval.

Complete the form to get the full workbook →