How can decision makers use data and technology to ensure desired outcomes?
What is the best method for technology-based teams to communicate effectively with decision makers and maximize the return on their data and technology investments?
The Decision Intelligence Handbook answers these questions and more!
Decision Intelligence is a way to crystallize diverse technological assets into a renaissance of solutions, with action-to-outcome decisions as a unifying principle, to solve some of the most difficult and important issues of our age.
The DI methodology attests that structured decision making can be represented as a set of well-defined processes. These processes follow a lifecycle and encourage thinkers to look outside or within the circumstances, for a deeper understanding of the decision being made (and the context informing, restricting, and supplementing that decision).
The DI Handbook is a practical, roll-up-your-sleeves guide that teaches anyone how to engage with Decision Intelligence. The book is organized around a collection of nine Best Practices, including how to design a decision, and how you can start engaging in Decision Intelligence today.
For the everyday DI practitioner, The Handbook is a great tool for connecting Decision Intelligence to your organization, whether you’re supplying consulting services, infrastructure, or part of a managing team.
Decision Intelligence integrates the expertise from multiple perspectives so organizations can make the right decisions, leading to better outcomes.
DI serves as the “last mile” that connects Business Intelligence to AI by building from the framework of any business’ current decision-making model. DI starts with the decision, rather than the evidence; and inspires a deeper understanding of the actions you can take, the decisions that inform those actions, and the externals that directly impact an action, intermediate, or outcome. Data, models and human expertise come after the parsing of a true decision from goals or desires within a Causal-Decision-Diagram (CDD).
DI Handbook explains:
- What type of decision should a given organization make? Where and how does DI fit in that framework?
- How are CDDs created, maintained, read, and used? Can they be re-used from project to project?
- A “Starter-Kit” of DI documents and templates that you can tailor for your organization.
- How can I structure decision conversations around desired outcomes, and actions to achieve them?
- Where can I use collaborative tools to map AI alongside data and human expertise?
- How to find simplicity and order amid confusion of complex data, tools and decisions?
- How can I specify requirements for automated decision-reasoning simulations to my team?
DI Handbook rationalizes decision-making for executive, managerial and administrative decision makers alike. Amidst constantly evolving externals (which create a gap between decision intuition and reality) DI provides a methodology to understand, prepare and execute a plan to meet any organization’s goals.
DI Handbook presents a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers.
DI Handbook provides practical guidance for organizations to formalize decision making and integrate their decisions with data and human expertise.
DI Handbook chapters include:
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- Decision Requirements: Establish a communication process between diverse stakeholders and set expectations within your organization for collaboration between technologists and subject matter experts.
- Decision Modelling: The Decision Design Process: Align your team around the outcomes best suited for the organization by building a CDD which serves as a written model and represents the work of the entire team.
- Decision Modeling: The Decision Asset Investigation Process: Learn how to start collecting data, models and human expertise that will enable the computer simulation of any decision.
- Decision Reasoning: The Decision Simulation Process: For any given set of assumptions about externals, discover the actions that will enable your desired outcomes.
- Decision Action: Decision Monitoring (process D1): Select key KPIs and leading indicators for your outcomes to monitor as your decision plays out over time. When you see KPIs or Leading Indicators have moved outside a range that will allow you to reach desired outcomes, use simulation to enable root cause analysis so that you can change actions to achieve a set of desired outcomes. (This is especially useful when external factors beyond your control are changing rapidly over time).
- Decision Requirements: Establish a communication process between diverse stakeholders and set expectations within your organization for collaboration between technologists and subject matter experts.
Interested in learning more? Purchase the Decision Intelligence Handbook here.
Computer scientist Dr. Lorien Pratt is one of the pioneers of artificial intelligence and is credited with inventing transfer learning. Following a 36-year career delivering applied AI systems, along with leadership and program committee responsibilities at the industry-leading NeurIPS conference, Pratt is recognized alongside Marie Curie and others by the Women Innovators and Inventors Project. Today, Pratt is chief scientist at www.quantellia.com, where she continues to push the boundaries of technology as a creator of and evangelist for Decision Intelligence (DI), a cofounder of www.opendi.org, and through building Large Language Models (LLMs) integrated with DI and ML. Pratt’s The Decision Intelligence Handbook (O’Reilly, 2023) is receiving worldwide attention. With recent publications in Foresight, Futures, and Frontiers, Pratt's DI innovations connect AI to human decision making in a way that is transparent, auditable, and accessible, bringing this important technology to the masses, and reducing information inequality.
Nadine Malcolm
Nadine Malcolm has four decades of experience leading cross-functional, multi-national, and geographically distributed teams delivering end-to-end solutions to complex problems to customers in the US, Europe, and Asia. Before coming to Quantellia, Malcolm held management and executive positions in several Silicon Valley startups, including VP of Development at Ascent Logic Corporation which provided systems engineering tools to major aerospace companies and US government projects.
