Mind+Machine: A Decision Model for Optimizing and Implementing Analytics

Cut through information overload to make better decisions faster

Success relies on making the correct decisions at the appropriate time, which is only possible if the decision maker has the necessary insights in a suitable format. Mind+Machine is the guide to getting the right insights in the right format at the right time to the right person. Designed to show decision makers how to get the most out of every level of data analytics, this book explores the extraordinary potential to be found in a model where human ingenuity and skill are supported with cutting-edge tools, including automations.

The marriage of the perceptive power of the human brain with the benefits of automation is essential because mind or machine alone cannot handle the complexities of modern analytics. Only when the two come together with structure and purpose to solve a problem are goals achieved.

With various stakeholders in data analytics having their own take on what is important, it can be challenging for a business leader to create such a structure. This book provides a blueprint for decision makers, helping them ask the right questions, understand the answers, and ensure an approach to analytics that properly supports organizational growth.

Discover how to:

• Harness the power of insightful minds and the speed of analytics technology
• Understand the demands and claims of various analytics stakeholders
• Focus on the right data and automate the right processes
• Navigate decisions with confidence in a fast-paced world

The Mind+Machine model streamlines analytics workflows and refines the never-ending flood of incoming data into useful insights. Thus, Mind+Machine equips you to take on the big decisions and win.

Table of Contents

Part I: The Top 12 Fallacies about Mind+Machine
Fallacy #1: Big Data Solves Everything
Fallacy #2: More Data Means More Insight
Fallacy #3: First, We Need a Data Lake and Tools
Fallacy #4: Analytics Is Just an Analytics Challenge: Part I: The Last Mile
Fallacy #5: Analytics Is Just an Analytics Challenge: Part II: The Organization
Fallacy #6: Reorganizations Won’t Hurt Analytics
Fallacy #7: Knowledge Management Is Easy—we Just Need Some Wikis
Fallacy #8: Intelligent Machines Can Solve Any Analytic Problem
Fallacy #9: Everything Must Be Done In-House!
Fallacy #10: We Need More, Larger, and Fancier Reports
Fallacy #11: Analytics Investment Means Great ROI
Fallacy #12: Analytics Is a Rational Process

Part II: 13 Trends Creating Massive Opportunities for Mind+Machine
Trend #1: The Asteroid Impact of Cloud and Mobile
Trend #2: The Yin and Yang of the Internet of Things
Trend #3: One-to-One Marketing
Trend #4: Regulatory Flooding of the Ring of Knowledge
Trend #5: The Seismic Shift to Payas-You-Go or Outputbased Commercial Models
Trend #6: The Hidden Treasures of Multiple-Client Utilities
Trend #7: The Race for Data Assets, Alternative Data, and Smart Data
Trend #8: Marketplaces and the Sharing Economy Finally Arriving in Data and Analytics
Trend #9: Knowledge Management 2.0—Still an Elusive Vision?
Trend #10: Workflow Platforms and Process Automation for Analytics Use Cases
Trend #11: 2015–2025: The Rise of the Mind–Machine Interface
Trend #12: Agile, Agile, Agile
Trend #13: (Mind+Machine)2 = Global Partnering Equals More Than 1+1

Part III: How to Implement the Mind+Machine Approach
Perspective #1: Focus on the Business Issue and the Client Benefits
Perspective #2: Map Out the Ring of Knowledge
Perspective #3: Choose Data Wisely Based on the Issue Tree
Perspective #4: The Efficient Frontier Where Machines Support Minds
Perspective #5: The Right Mix of Minds Means a World of Good Options
Perspective #6: The Right Workflow: Flexible Platforms Embedded in the Process
Perspective #7: Serving the End Users Well: Figuring Out the Last Mile
Perspective #8: The Right User Interaction: The Art of User Experience
Perspective #9: Integrated Knowledge Management Means Speed and Savings
Perspective #10: The Commercial Model: Pay-As-You-Go or Per-Unit Pricing
Perspective #11: Intellectual Property: Knowledge Objects for Mind+Machine
Perspective #12: Create an Audit Trail and Manage Risk
Perspective #13: The Right Psychology: Getting the Minds to Work Together
Perspective #14: The Governance of Use Case Portfolios: Control and ROI
Perspective #15: Trading and Sharing Use Cases, Even Across Company Boundaries