Intro -- Delivering Business Analytics: Practical Guidelines for Best Practice -- Copyright -- Contents -- Preface -- Why You Need to Read This Book -- How to Use This Book -- Note -- Acknowledgments -- Part One: Business Analytics Best Practices -- Chapter 1: Business Analytics: A Definition -- What Is Business Analytics? -- Core Concepts and Definitions -- Note -- Chapter 2: The Competitive Advantage of Business Analytics -- Advantages of Business Analytics -- Competitive Advantage -- Maximizing Economies of Scale -- Quality Improvement -- Challenges of Business Analytics -- Minimizing Transaction Costs -- Bounded Rationality -- Establishing Best Practices -- Notes -- Part Two: The Data Scientist's Code -- Chapter 3: Designing the Approach -- Think about Competencies, Not Functions -- Incremental Value Comes from Continuous Improvement -- Management Principles -- Drive Outcomes, Not Insight -- Management Principles -- Automate Everything Non-Value-Added -- Management Principles -- Start Flexible, Become Structured -- Management Principles -- Eliminate Bottlenecks -- Management Principles -- Notes -- Chapter 4: Creating Assets -- Design Your Platform for Use, Not Purity -- Management Principles -- Always Have a Plan B -- Management Principles -- Know What You Are Worth -- Value Changes over Time -- Management Principles -- Own Your Intellectual Property -- Self-Interest Isn't Always in Your Favor -- Management Principles -- Minimize Custom Development -- Inventions Don't Need to Remain Proprietary -- Management Principles -- Chapter 5: Managing Information and Making Decisions -- Understand Your Data -- Quality Data Is More Than Just Accurate Data -- Management Principles -- It's Better to Have Too Much Data Than Too Little -- Being Smarter Involves Being Predictive -- Being Predictive Requires History -- Management Principles.

Keep Things Simple -- Results Don't Require Complexity -- Management Principles -- Function Should Dictate Form -- Management Principles -- Watch the Dynamic, Not Just the Static -- Knowing What's Abnormal Helps with the Unknown -- Management Principles -- Note -- Part Three: Practical Solutions: People and Process -- Chapter 6: Driving Operational Outcomes -- Augmenting Operational Systems -- The Background: In the Quest for Standard Processes, Flexibility Always Suffers -- Why Should You Care? -- What Not to Do -- The How-To Guide: Run It in Parallel and Reuse It -- Benefits and Limitations -- Breaking Bottlenecks -- The Background: Every Gateway Becomes a Bottleneck Given Enough Traffic -- Why Should You Care? -- What Not to Do -- The How-To Guide: Become an Enabler, Not a Gatekeeper -- Optimizing Monitoring Processes -- The Background: Maintaining Value Takes Time Away from Creating Value -- Why Should You Care? -- What Not to Do -- The How-To Guide: Automate the Monitoring Process -- Encouraging Innovation -- The Background: Success Creates Inertia -- Why Should You Care? -- What Not to Do -- The How-To Guide: Establish an Innovation Lab and Separate Disruption from Continuous Improvement -- Notes -- Chapter 7: Analytical Process Management -- Coping with Information Overload -- The Background: Having Too Little Insight Is as Bad as Having Too Much -- Why Should You Care? -- What Not to Do -- The How-To Guide: Automate the Majority, Review the Important -- Keeping Everyone Aligned -- The Background: Too Many Answers and Not Enough Time -- Why Should You Care? -- What Not to Do -- The How-To Guide: Move from Meetings to Workflows -- Allocating Responsibilities -- The Background: Local Improvements Rarely Drive Global Efficiencies -- Why Should You Care? -- What Not to Do -- The How-To Guide: Allocate Responsibilities across Outcomes and Functions.

Opening the Platform -- The Background: Outsourcing Intellectual Property Rarely Leads to Competitive Advantage -- Why Should You Care? -- What Not to Do -- The How-To Guide: Treat Everyone as if They're Part of Your Team-Even Outsiders -- Part Four: Practical Solutions: Systems and Assets -- Chapter 8: Computational Architectures -- Moving Beyond the Spreadsheet -- The Background: What's Easy Isn't Necessarily Right -- Why Should You Care? -- What Not to Do -- The How-To Guide: Use the Right Tools for the Job, Not Just What's On-Hand -- Build Around Automation -- Scaling Past the PC -- The Background: Scale Takes More Than Just Making Sure Everyone Uses the Same Tools -- Why Should You Care? -- What Not to Do -- The How-To Guide: Drop the Desktop and Move to a Common Platform -- Staying Mobile and Connected -- The Background: When the Job Runs Up against Systems Architecture, Architecture Always Loses -- Why Should You Care? -- What Not to Do -- The How-To Guide: Allow Desktop Processing, but Grant It with Conditions -- Smoothing Growth with the Cloud -- The Background: Platform Upgrades Are Costly and Disruptive -- Why Should You Care? -- What Not to Do -- The How-To Guide: Put the Extra Work into the Platform and Make It an Enterprise Cloud -- Notes -- Chapter 9: Asset Management -- Moving to Operational Analytics -- The Background: The Best Answers Are Worthless if They're Not Timely and Actioned -- Why Should You Care? -- What Not to Do -- The How-To Guide: Establish a Dedicated Operational Analytics Framework -- Measuring Value -- The Background: Without Tangible Returns, It's Impossible to Transform an Organization -- Why Should You Care? -- What Not to Do -- The How-To Guide: Establish a Standard Value Measurement Framework -- Measuring Performance -- The Background: Without Knowing Where the Problems Are, It's Impossible to Fix Them.

Why Should You Care? -- What Not to Do -- The How-To Guide: Establish a Standard Quality-Measurement Framework -- Benefits and Limitations -- Measuring Effort -- The Background: Without Knowing Where the Bottlenecks Are, It's Impossible to Fix Them -- Why Should You Care? -- What Not to Do -- The How-To Guide: Establish a Standard Effort Measurement Framework -- Note -- Part Five: Practical Solutions: Data and Decision Making -- Chapter 10: Information Management -- Creating the Data Architecture -- The Background: Without Effective Data Management It Is Impossible for Teams to Scale -- Why Should You Care? -- What Not to Do -- The How-To Guide: Design the Analytical Datamart, Don't Create a Shanty -- Understanding the Data Value Chain -- The Background: It's Not Enough to Go from Insight to Action-You Need to Know How You Got There -- Why Should You Care? -- What Not to Do -- The How-To Guide: Track Data Lineage through the Data Value Chain -- Creating Data-Management Processes -- The Background: Monolithic Processes May Be Efficient but They Are Never Reusable -- Why Should You Care? -- What Not to Do -- The How-To Guide: Separate Data Management into Four Activities -- Capturing the Right Data -- The Background: When There Is More Data Than Time, Everyone Needs to Make Hard Choices -- Why Should You Care? -- What Not to Do -- The How-To Guide: Use What Works, Ignore the Rest -- Notes -- Chapter 11: Decision-Making Structures -- Linking Analytics to Value -- The Background: Good Insight Doesn't Necessarily Lead to Good Decisions -- Why Should You Care? -- What Not to Do -- The How-To Guide: Don't Just Provide Insight-Establish a Strongly Defined Scoring Process Built on Measurement and Recommendations -- Reducing Time to Recommendation -- The Background: Sunk Costs Can Constrain a Team's Ability to Scale -- Why Should You Care? -- What Not to Do.

The How-To Guide: Don't Compromise and Don't Convert-Get the Asset Where It Needs to Be in the Form It Needs to Be -- Enabling Real-Time Scoring -- The Background: Up-Front Analysis Isn't Always Possible -- Why Should You Care? -- What Not to Do -- The How-To Guide: Go Real-Time or Go Home -- Blending Rules with Models -- The Background: Focusing on What We Know Means Missing What We Don't Know -- Why Should You Care? -- What Not to Do -- The How-To Guide: Take Advantage of the Best, Throw Away the Rest -- Appendix: The Cheat Sheets -- Glossary -- Further Reading -- About the Author -- Index.

AVOID THE MISTAKES THAT OTHERS MAKE - LEARN WHAT LEADS TO BEST PRACTICE AND KICKSTART SUCCESS This groundbreaking resource provides comprehensive coverage across all aspects of business analytics, presenting proven management guidelines to drive sustainable differentiation. Through a rich set of case studies, author Evan Stubbs reviews solutions and examples to over twenty common problems spanning managing analytics assets and information, leveraging technology, nurturing skills, and defining processes. Delivering Business Analytics also outlines the Data Scientist's Code, fifteen principles that when followed ensure constant movement towards effective practice. Practical advice is offered for addressing various analytics issues; the advantages and disadvantages of each issue's solution; and how these solutions can optimally create organizational value. With an emphasis on real-world examples and pragmatic advice throughout, Delivering Business Analytics provides a reference guide on: The economic principles behind how business analytics leads to competitive differentiation The elements which define best practice The Data Scientist's Code, fifteen management principles that when followed help teams move towards best practice Practical solutions and frequent missteps to twenty-four common problems across people and process, systems and assets, and data and decision-making Drawing on the successes and failures of countless organizations, author Evan Stubbs provides a densely packed practical reference on how to increase the odds of success in designing business analytics systems and managing teams of data scientists. Uncover what constitutes best practice in business analytics and start achieving it with Delivering Business Analytics.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.