Monetizing Data Management

by Peter Aiken, Short Pockets

Overview

What's the Return on Investment (ROI) on data management? Sound like an impossible question to answer? Not if you read this book and learn the value-added approach to managing enterprise resources and assets. This book defines the five interrelated best practices that comprise data management, and shows you how by example to successfully communicate data management ROI to senior management. The 17 cases we share will help you to identify opportunities to introduce data management into the strategic conversations that occur in the C-suite. You will gain a new perspective regarding the stewardship of your data assets and insulate your operations from the chaos, losses and risks that result from traditional approaches to technological projects. And you will learn how to protect yourself from legal challenges resulting from outsourced information technology projects gone badly due to incorrect project sequencing and focus. With the emerging acceptance and adoption of revised performance standards, your organization will be better prepared to face the coming big data deluge!

Product Details

ISBN-13:	9781935504665
Publisher:	Technics Publications, LLC
Publication date:	11/01/2013
Pages:	96
Product dimensions:	0.25(w) x 7.00(h) x 0.30(d)

Table of Contents

Acknowledgements 1

About the Authors 3

Foreword 5

Executive Summary 9

Chapter 1 Data Management as a Prerequisite to Data Leveraging 13

What is data? An objective definition 13

Data leveraging requires architectural and engineering disciplines 17

What is data management? 19

Why is data management important? 22

An incorrect educational focus 24

Lack of agreement over who is responsible for data assets 25

Value-added data management is derived from data-centric development best practices 26

Data-centric approach leads directly to organizational productivity advantages 29

Data management consists of five integrated practice areas 30

Improving organizational data management maturity 31

Data management pay-offs 35

Chapter 2 Bottom Line Pay-offs: Eleven Financial Cases 39

How many times do we have to spend that money? ($10 million annually) 39

Who's doing what, and why? ($25 million annually) 41

Three ERP cases that also apply to software application package implementation 44

How much will the data conversion cost? (2 Years and $3,000,000) 44

How about measuring before deciding to customize? (If $1 million is substantial) 46

Is it really that complicated? ($5,500,000 and a person-century of labor savings) 49

Real solution cost ($30,000,000 versus a roomful of MB As) 53

Two tank cases 54

Why are we spending money on stuff we can't even use? ($5 billion) 54

The vocabulary of tanks ($4 million alternative to software package customization) 56

The additional 45% is worth $50 million 57

What happened to our funding? (at least $1 million in government funding) 60

But data stuff is complicated; how do I explain it? (£500 really increased project clarity) 61

Chapter 3 Real Live Pay-offs: Five Non-Monetary Cases 63

Determining objective selection criteria for legacy system consolidation (and avoiding a Congressional inquiry in the process) 63

Everyone has bills to pay (but some bills are more equal than others) 66

Identifying payment error correction and boosting troop morale? (priceless) 67

Saving warfighter lives (friendly fire death prevention) 69

Saving warfighter lives (US Army suicide prevention: a clear data governance success) 70

Chapter 4 And Then There Are the Lawyers: An Illustrative Legal Matter 73

Distinct incentives: Data management saves legal costs 73

Issue #1 Who owned the risks? 75

Issue #2 Who was the project manager? 76

Issue #3 Was the data of poor quality? 77

Issue #4 Did the contractor (Company Y) exercise due diligence? 80

Issue #5 Was Company Y's approach adequate? 81

Issue #6 Were required standards of care followed, and were work products of the required quality? 82

Conclusion: Your Charge 85

References 87