Self-Service Data Analytics and Governance for Managers

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Nathan E. Myers. Self-Service Data Analytics and Governance for Managers

Table of Contents

List of Exhibits

Guide

Pages

Self-Service Data Analytics and Governance for Managers

Preface

Acknowledgments

About the Authors

Introduction

CHAPTER 1 Setting the Stage. Impact

Emergence of Data Analytics

Self-Service Data Analytics

Employee/Analyst/Operator Perspective

Managers' Perspectives

Executives' Strategic Perspectives

Arguments for Self-Service Data Analytics Tooling

Need for Self-Service Data Analytics Governance

CHAPTER 2 Emerging AI and Data Analytics Tooling and Disciplines

Introduction to Data Analytics Tooling

Internet of Things

Cloud Storage and Cloud Computing

Artificial Intelligence

Blockchain and Distributed Ledger Technology

Robotic Process Automation

Machine Learning

Optical Character Recognition/Intelligent Character Recognition

Natural Language Processing

Self-Service Data Analytics

Dashboarding and Visualization

Discussion with Paul Paris – CEO, Lash Affair

Conclusion

CHAPTER 3 Why Governance Is Essential and the Self-Service Data Analytics Governance Gap

Governance Is Essential

Inputs

Process Quality

Firm-Wide Build Inventory

Risk Assessments

Rationalizing Fragmented Governance

Mature Governance Frameworks

Data Governance

COBIT Framework for IT Enterprise Governance

Sarbanes–Oxley Act

COSO Internal Control Framework

Applicable COSO Monitoring principles:

COSO ERM (2017) – Integrating Strategy with Performance

SAS No. 1, Section 210

SAS 70, SSAE 16, SysTrust Engagements

Model Risk Governance

Self-Service Data Analytics Governance Gap

Structures Needed to Fill the Governance Gap

Governance and Oversight

Planning and Alignment

Policies and Procedures

Development Standards

Conclusion

Note

CHAPTER 4 Self-Service Data Analytics Project Governance

Securing Sponsorship and Establishing the Governance Committee

Extending Governance Precepts from Established Frameworks

Ensuring Input Integrity

Data Governance Committee

Data Asset Inventory

Data Lineage

Data Quality Review Procedures

Resolution Sequence

Opportunity Capture, Benefits Case, Sizing, and Prioritization

Self-Service Data Analytics Build Inventory Must Be Maintained

Self-Service Data Analytics Tooling Is Tactical by Nature

Project Development Audit Trail Must Be Captured and Retained

Reference End-User Analytics Tooling and Workflows in Process Documentation

Model Risk Governance

Organizational and Functional Goal Alignment

Adequate Training for Developers and End-Users

Establishment of Risk Assessment Criteria

Discussion with Jitesh Ghai, Chief Product Officer at Informatica

Conclusion

Notes

CHAPTER 5 Self-Service Data AnalyticsRisk Governance

Setting Risk Appetite in an Environment of Changing Performance Expectations

Data Analytics Risk Governance Enhances Value Creation

Data Analytics Tool Selection Drives the Level of Partnership with IT

Alignment of Finance Function Goals with Digital Transformation Capabilities

Data Analytics Risk Governance

Assessing Risks in the Analytics and Automation Environment

Seven Relevant Risks to Data Analytics

Impact Scale for the Analytics Risk Environment

Likelihood Scale for the Analytics Risk Environment

Vulnerability Scale for the Automation and Analytics Risk Environment

Speed of Onset Scale for the Automation and Analytics Risk Environment

Developing the Portfolio View of Risk

Developing Risk Responses and Controls in the Analytics and Automation Environment

Interview with Two Big Four Audit Executives

Conclusion

Notes

CHAPTER 6 Self-Service Data Analytics Capabilities in Action with Alteryx

Alteryx Functionality. Understanding the Data

Importing the Data

Cleansing the Data

Blending and Joining

Enriching Data

Data Transformation

Data Visualization

Alteryx in Action. Whole Market Trend Analysis

Industries: Comparative Performance

Information Technology: Trend Analysis Over Five Years

Top Performers in the Information Technology Industry

Apple Inc.: Sales and Earnings Trend Analysis

Interview with Dean Stoecker, Co-Founder and Executive Chairman, Alteryx, Inc

Conclusion

CHAPTER 7 Process Discovery: Identify Opportunities, Evaluate Feasibility, and Prioritize

Business Case for Systems versus Self-Service Data Analytics

Process Discovery Phases and Methodology

Top-Down: Organizational Context

SWOT Analysis

Porter's Five Forces

Porter's Value Chain

Top-Down: Functional View

Suppliers, Inputs, Processes, Outputs, and Customers: Functional-Level

Middle Management: Define the Processes

Suppliers, Inputs, Processes, Outputs, and Customers: Process-Level

Bottom-Up: Map and Measure

Process Map (Swim Lanes)

Measure (Elapsed Time)

Automated Process Discovery

Conclusion

Notes

CHAPTER 8 Opportunity Capture and Heatmaps

Opportunity Inventory Matrix

Capturing Problem Statements

Uncovering Use Cases

Solutioning

Benefits Cases

Sizing Build Efforts

Project Acceptance Criteria and Organizational Constraints

Automation Heatmap and Prioritization

Workflow Tooling

Use Case Library

Multiplier Effect of Replication Opportunities on Project Benefits Case

Conclusion

Glossary

Index

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NATHAN E. MYERS

GREGORY KOGAN

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Skip ahead two years and suddenly you feel exposed. Which builds are being relied upon by regulators? Which builds are relied upon by customers? Did the individual who put them in place have adequate knowledge of the underlying processes to build reliably and effectively? Were they well versed with the data analytics tools and technologies deployed? Were such builds adequately tested? Precisely how many builds exist across the organization? If key software vendors raise the price of basic licenses, is any of the work salvageable for migration to a new platform? You are being challenged by key internal clients on the quality of the financial deliverables that your team prepares, but you learn your team has simply been taking analytics build outputs at face value. They no longer understand the longhand processing steps that have been automated, as the team has experienced significant turnover over the last two years. This has resulted in the tools effectively becoming “black boxes,” where the transformation steps embedded in them are obscured and difficult to decipher. You fear that your organization has fallen into a common trap; by moving away from regimented technology release cycles toward a decentralized change model, you have lost control.

Governance, or lack thereof, is perhaps the strongest harbinger of control and stability, in an environment where self-service data analytics is prevalent. Effective governance is particularly critical due to the expected growth pattern of data analytics adoption, once the floodgates are opened. Without the benefit of governance to keep pace with the decentralization of development capabilities, organizations can find themselves struggling to demonstrate process effectiveness; they may not have clear visibility into the degree to which they are dependent on off-the-shelf software applications; they may lack adequate information upon which to base risk assessments; or they may get it abjectly wrong. Governance must provide guidelines aimed at ensuring the quality and integrity of processing inputs; that processing solutions implemented are appropriate, adequately tested, and operate effectively; that minimum standards of project documentation are met; and that risk assessment and mitigation activities can be demonstrated in the thoughtful deployment of analytics tooling.

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