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    Data Analysis and Harmonization: A Simple Guide
    Data Analysis and Harmonization: A Simple Guide
    Data Analysis and Harmonization: A Simple Guide
    Ebook166 pages1 hour

    Data Analysis and Harmonization: A Simple Guide

    By Jeff Voivoda

    ()

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    About this ebook

    For organizations, its imperative to have the ability to analyze data sources, harmonize disparate data elements, and communicate the results of the analysis in an effective manner to stakeholders.

    Created by certified enterprise data architect Jeff Voivoda, this simple guide to data analysis and harmonization begins by identifying the problems caused by inefficient data storage. It moves through the life cycle of identifying, gathering, recording, harmonizing and presenting data so that it is organized and comprehensible.

    Other key areas covered include the following:

    Seeking out the right experts
    Reviewing data standards and considerations
    Grouping and managing data
    Understanding the practical applications of data analysis
    Suggesting next steps in the development life cycle.

    Its essential to understand data requirements, management tools, and industry-wide standards if you want your organization to succeed or improve on its already strong position. Determine your next strategic step and manage your data as an asset with Data Analysis and Harmonization.
    LanguageEnglish
    PublisheriUniverse
    Release dateMar 25, 2011
    ISBN9781450298254
    Data Analysis and Harmonization: A Simple Guide
    Author

    Jeff Voivoda

    Jeff Voivoda is a certified enterprise data architect with more than twenty years of systems and data analysis and development experience. His technical experience spans mainframe, open architecture client-server, and web-based architectural solutions. He has spearheaded various information and data gathering efforts in and around the Washington DC, Maryland, and Northern Virginia technology corridors.

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      Book preview

      Data Analysis and Harmonization - Jeff Voivoda

      Data Analysis

      and

      Harmonization

      Jeff Voivoda

      iUniverse, Inc.

      Bloomington

       Data Analysis and Harmonization

      Copyright © 2011 by Jeff Voivoda

      All rights reserved. No part of this book may be used or reproduced by any means, graphic, electronic, or mechanical, including photocopying, recording, taping or by any information storage retrieval system without the written permission of the publisher except in the case of brief quotations embodied in critical articles and reviews.

      The views expressed in this work are solely those of the author and do not necessarily reflect the views of the publisher, and the publisher hereby disclaims any responsibility for them.

      iUniverse books may be ordered through booksellers or by contacting:

      iUniverse

      1663 Liberty Drive

      Bloomington, IN 47403

      www.iuniverse.com

      1-800-Authors (1-800-288-4677)

      Because of the dynamic nature of the Internet, any web addresses or links contained in this book may have changed since publication and may no longer be valid.

      Any people depicted in stock imagery provided by Thinkstock are models, and such images are being used for illustrative purposes only.

      Certain stock imagery © Thinkstock.

      ISBN: 978-1-4502-9824-7 (sc)

      ISBN: 978-1-4502-9826-1 (dj)

      ISBN: 978-1-4502-9825-4 (ebk)

      Printed in the United States of America

      iUniverse rev. date: 3/18/2011

      Contents

      Acknowledgements

      Foreword

      Introduction

      Chapter 1

      This Organization Has Problems

      Chapter 2

      Identify the Data Sources

      Chapter 3

      Reviewing the Data Standards

      Chapter 4

      Managing the Data

      Chapter 5

      Start Harmonizing the Data

      Chapter 6

      Grouping the Data

      Chapter 7

      Practical Applications of Data Analysis

      Chapter 8

      Presenting Your Analysis

      Chapter 9

      Analyze the Feedback

      Chapter 10

      Almost Done: Finalize the HDS

      Chapter 11

      Moving Forward After the HDS

      Chapter 12

      Wrap it Up

      Glossary

      References

      Acknowledgements

      First and foremost, I would like to thank my family for their continuous and unending support.

      I would also like to thank Kathy Sowell. Kathy is the President of Custom Enterprise Solutions/SowellEAC and widely considered an expert in the field of Enterprise Architecture. She graciously provided a foreword for this book. Her unremitting dedication to the practices of Enterprise Architecture provides a wonderful example of just how pervasive the data disciplines have become.

      In addition, I would like to thank Michael Drake. Mike is one of the top graphic artists in the industry and owner of the company Monkeebox, Inc. The illustrations he provided help drive home some of the critical points in this book by providing visual reminders of the ideas expressed in the text.

      Finally, I would like to thank all the people who will read this book and utilize some or all of the ideas and concepts expressed within.

      Foreword

      Enterprise Architecture and the Data Disciplines:

      A Symbiotic Relationship

      by Kathy Sowell

      When Jeff asked me to write a foreword to his book, I started thinking about the issues that are swirling in the enterprise architecture and data worlds today. Many of these issues involve the relationship between data and enterprise architecture. For example: Should an enterprise architecture contain a separate data view? Do we really need enterprise architecture as long as we have data modeling and data management? What is data without enterprise architecture? What is enterprise architecture without data? What is enterprise architecture, anyway? Is there really even any such thing as enterprise architecture?

      Enterprise architecture is a relatively young discipline (although for those of us who have been in the trenches from the beginning, thirty years or so seems like a long time!). So I will not try to definitively answer these questions yet. The point I want to make now is only that enterprise architecture has a symbiotic relationship with the group of data-related disciplines that require analysis of data—data management, data and information quality, data architecture—and that, like all symbiotic relationships, this one can benefit all parties involved.

      The Data Disciplines and Enterprise Architecture Can Benefit Each Other

      The data disciplines have sometimes been seen as highly specialized exercises in manipulating zeros and ones, number crunching, or other tasks that normal people don’t understand and don’t want to understand. Enterprise architecture, on the other hand, has been criticized on many fronts: some see it as a high-level, touchy-feely management exercise conducted at the arm-waving level, others as an excruciatingly detailed IT exercise (see above), and others as a plain old fraud. When the two disciplines work together in an intelligent way, however, the data disciplines can provide real-world practicality to enterprise architecture, and enterprise architecture can add accessibility and an appropriate level of detail for decision making to the data disciplines. Let’s look a little more closely at these benefits.

      Enterprise Architecture Puts Data into Context

      Enterprise architecture has a unique ability to exploit the potential of data analysis. How? By coaxing data out of the data stovepipe and enfolding it into a multifaceted, holistic study of a complete enterprise. Enterprise architecture has the ability to put data into context, making data analysis more meaningful and more fruitful. More specifically, enterprise architecture has the ability to:

      1.   represent different aspects of data in different, but logically consistent, artifacts that show how data operates in the enterprise; and

      2.   represent the different data requirements that arise in the different circumstances under which the enterprise must operate.

      For example, an activity model shows data at an abstract level, that is, data that is exchanged between abstract activities. A business node connection model then translates this abstraction into the actual data that is exchanged between the physical business nodes that perform the abstract activities.

      Under different scenarios, different data and information will be exchanged; a series of scenario sequence diagrams captures these different exchanges. And, in the different scenarios, the data requirements such as reliability, speed, accuracy, classification, and so on will be different; a corresponding series of information exchange matrices captures these differing requirements.

      It’s the Data, Stupid … or Is It?: Human Does Not Mean Stupid

      In addition to putting data into a meaningful context, enterprise architecture has the ability to represent data in human-friendly forms, making it more accessible to decision makers and clarifying its message. (The medium is still the message, even forty years after Marshall McLuhan made that point.)

      It seems obvious that this human-friendly quality of data is necessary and desirable in order for decision makers to make good decisions. In fact, one of the main reasons behind the creation of enterprise architecture, at least in my experience, was to make data itself, the representation of data, and the analysis of data more accessible to (human) decision makers.

      Lately, however, in some quarters of the enterprise architecture community, the meaning of the old saying It’s the data, stupid has been twisted. Instead of being a somewhat primitive way of saying that data is very important, it has come to mean instead that data, like winning, isn’t the most important thing, it is the only thing. More specifically, it has been used to imply that the representation of the data in ways that humans can understand, that is, through visual artifacts (pictures), is a dumbing down of the data and not worthy of grown-ups’ effort. I believe this attitude is a mistake and prevents us from taking advantage of one of the most advantageous features of the symbiosis between data and enterprise architecture.

      What about the Future?: Buzzwords Come and Go, But Data Lasts Forever

      Humans have been capturing and analyzing data for thousands of years, and we will continue to do so—we can’t help ourselves. On the other hand, enterprise architecture, as a discipline of sorts, has been around for only twenty-five or thirty years, depending on how you define its birth. Even at that, people have been predicting its imminent demise for at least ten of those years.

      Will enterprise architecture survive as a recognizable field of endeavor? Who knows? But in any case, the aspects of enterprise architecture that we have been addressing here—its ability to provide a context for the study of data and its ability to put a human face on data—will survive if we continue to provide for their care and feeding. We need to make sure that, whether the discipline of enterprise architecture survives and no matter how it evolves, we continue to take advantage of these aspects of enterprise architecture to enrich our conduct of the data disciplines.

      Introduction

      In my twenty-plus years of information technology consulting, I have been tossed into some pretty hectic situations. Whether it was to come in at project inception and get analysis activities off the ground, jump in midstream and get the data team over the proverbial hump, or worst of all hop directly into the frying pan after being directed to save our bacon, I always found myself answering the same question: Where do we start?

      This question is more than fair. In today’s sophisticated business technology environment, the sheer volume of data that

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