Creating a Data-Driven Organization by Anderson, Carl -- Read -- Imperial Library of Trantor

Index

Preface

Summary Who Should Read This Book? Chapter Organization Conventions Used in This Book Safari® Books Online How to Contact Us Acknowledgments

1. What Do We Mean by Data-Driven?

Data Collection Data Access Reporting Alerting From Reporting and Alerting to Analysis Hallmarks of Data-Drivenness Analytics Maturity Overview

2. Data Quality

Facets of Data Quality Dirty Data

Data Generation Data Entry

Data entry error mitigation Exploratory data analysis

Missing Data Duplicates Truncated Data Units Default Values

Data Provenance Data Quality Is a Shared Responsibility

3. Data Collection

Collect All the Things Prioritizing Data Sources Connecting the Dots Data Collection Purchasing Data

How Much Is a Dataset Worth?

Data Retention

4. The Analyst Organization

Types of Analysts

Data Analyst Data Engineers and Analytics Engineers Business Analysts Data Scientists Statisticians Quants Accountants and Financial Analysts Data Visualization Specialists

Analytics Is a Team Sport Skills and Qualities Just One More Tool

Exploratory Data Analysis and Statistical Modeling Database Queries File Inspection and Manipulation Analytics-org Structure

Centralized Decentralized

5. Data Analysis

What Is Analysis? Types of Analysis

Descriptive Analysis Exploratory Analysis Inferential Analysis Predictive Analysis Causal Analysis

6. Metric Design

Metric Design

Simple Standardized Accurate Precise Relative Versus Absolute Robust Direct

Key Performance Indicators

KPI Examples How Many KPIs? KPI Definitions and Targets

7. Storytelling with Data

Storytelling First Steps

What Are You Trying to Achieve? Who Is Your Audience? What’s Your Medium?

Sell, Sell, Sell! Data Visualization

Choosing a Chart Designing Elements of the Chart

Focusing the message Organizing your data

Delivery

Infographics Dashboards

Monitoring use

Summary

8. A/B Testing

Why A/B Test? How To: Best Practices in A/B Testing

Before the Experiment

Success metrics A/A tests A/B test plan Sample size

Running the Experiment

Assignment Starting the test When do you stop?

Other Approaches

Multivariate Testing Bayesian Bandits

Cultural Implications

9. Decision Making

How Are Decisions Made?

Data-Driven, -Informed, or -Influenced?

What Makes Decision Making Hard?

Data

Data quality and lack of trust Volume Sifting signal from the noise

Culture

Intuition is valued Lack of data literacy Lack of accountability

The Cognitive Barriers Where Does Intuition Work?

Solutions

Motivation

Incentives and accountability Prove it! Transparency

Ability

Tie actions to outcomes Collaboration and consensus Training Consistency

Triggers

Conclusion

10. Data-Driven Culture

Open, Trusting Culture Broad Data Literacy Goals-First Culture Inquisitive, Questioning Culture Iterative, Learning Culture Anti-HiPPO Culture Data Leadership

11. The Data-Driven C-Suite

Chief Data Officer

CDO Role Secrets of Success

Where do CDOs report? A mandate to influence

Future of the CDO Role

Chief Analytics Officer Conclusion

12. Privacy, Ethics, and Risk

Respect Privacy

Inadvertent Leakage

Practice Empathy

Provide Choice

Data Quality Security Enforcement Conclusions

13. Conclusion Further Reading

Analytics Organizations Data Analysis & Data Science Decision Making Data Visualization A/B Testing

A. On the Unreasonable Effectiveness of Data: Why Is More Data Better?

Nearest Neighbor Type Problems Relative Frequency Problems Estimating Univariate Distribution Problems Multivariate Problems

B. Vision Statement

Value Activation

Index

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