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Chapter 9 | Data Supply Chain Guidebook

Conclusion to the Data Supply Chain

Computational Thinking and the Data Supply Chain are challenging topics that can generate very complex discussions.

Wise leaders begin by updating the thinking inside their organization as soon as possible so that data-centric skills, tools and business models can be put to ethical, productive use. We've assembled some suggested next steps to get you and your teams started quickly.

100-Day Plan

  • Self-assess the Digital Fluency of your team or organization
  • Adopt definitions of each stage of the data supply chain
  • Determine key stakeholders for each stage—both for ideation and implementation
  • Educate yourself on the value models of data-centric offerings
  • Learn about the basics of data ethics and create data principles in consultation with user advocates, legal and compliance experts, business stakeholders and lay users
  • Run ideation and design sessions to craft ‘data stories’ which go through each stage of the data supply chain
  • Create a Digital Fluency plan for your part of the organization and a subset of that plan for data fluency
  • Identify digital ‘champions,’ 'explorers' and 'makers' throughout the organization who are skilled in data-centric value.

365-Day Plan

  • Publish a data catalog that details available datasets and APIs
  • Commit to data principles throughout the organization
  • Create an ‘options management’ system or similar tool that helps cross-reference and prioritize value propositions, datasets, and APIs as part of larger business models
  • Matchmake champions, explorers, and makers, and gently coordinate their efforts with an acting ‘Chief Entrepreneurship Officer’ or similar lead champion who has a high degree of Digital Fluency and strong connections with established leaders, innovators, and strategic partners
  • Implement basic ‘digital factory’ functions in your strategy and budget

While not exhaustive, the list should help you set a clear direction for your teams so you can stay on track to ethical, effective and valuable innovation with data.

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