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HAR VA R D B U S I N E SS S C H O O L P R E SS

The Architecture of Business Intelligence
Aligning a Robust Technical Environment with Business Strategies

E xc e r p t e d fro m

Competing on Analytics: The New Science of Winning
By

Thomas H. Davenport, Jeanne G. Harris

Harvard Business School Press Boston, Massachusetts

ISBN-13: 978-1-4221-2202-0

2202BC

Copyright 2007Harvard Business School Publishing Corporation All rights reserved Printed in the United States of America This chapter was originally published as chapter 8 of Competing on Analytics: The New Science of Winning, copyright 2007 Harvard Business School Publishing Corporation. No part of this publication may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form,or by any means (electronic, mechanical, photocopying, recording, or otherwise), without the prior permission of the publisher. Requests for permission should be directed to permissions@hbsp.harvard.edu, or mailed to Permissions, Harvard Business School Publishing, 60 Harvard Way, Boston, Massachusetts 02163. You can purchase Harvard Business School Press books at booksellers worldwide. You canorder Harvard Business School Press books and book chapters online at www.HBSPress.org, or by calling 888-500-1016 or, outside the U.S. and Canada, 617-783-7410.

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The Architecture of Business Intelligence
Aligning a Robust Technical Environment with Business Strategies

Moviegoers might understandably have the impression that the technical challenges of capturing and analyzing largeamounts of data have already been solved. In many Hollywood films, unlimited data is accessible to anyone with a laptop and knowledge of a few passwords (often obtained in seconds through adroit hacking). Fortunately for our privacy, however, it’s not quite that easy. Certainly, it has become technically feasible to capture and store huge quantities of data. The numbers are hard to absorb for all butthe geekiest, as data volumes have grown from megabytes to gigabytes to terabytes (a trillion bytes); some corporate databases are rapidly approaching one petabyte (a quadrillion bytes). While low-end computers and servers lack the power and capacity to handle the volumes of data required for analytical applications, high-end 64-bit processors and specialty “data appliances” can quickly churnthrough virtually unfathomable amounts of data. However, while organizations have more data than ever at their disposal, they rarely know what to do with it. The data in their systems is often like the box of photos you keep in your attic, waiting for the “someday” when you impose meaning on the chaos. Further, the unpalatable
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2 • building an analytical capability

truth is that most ITdepartments strain to meet minimal service demands and invest inordinate resources in the ongoing support and maintenance of basic transactional capabilities. Unlike the analytical vanguard, even companies with sound transaction systems struggle with relatively prosaic issues such as data cleansing when they try to integrate data into analytical applications. In short, while improvements in technology’sability to store data can be astonishing, most organizations’ ability to manage, analyze, and apply data has not kept pace. Companies that compete on analytics haven’t solved all these problems entirely, but they are a lot better off than their competition. In this chapter, we identify the technology, data, and governance processes needed for analytical competition. We also lay out the componentsthat make up the core of any organization’s business intelligence architecture and forecast how these elements are likely to evolve over time.

Analytics and the Architecture of Business Intelligence
Every company with serious analytical aspirations has an involved, committed IT organization behind it. For example, by capturing proprietary data or embedding proprietary analytics into business...