Understanding the Predictive Analytics Lifecycle E-Book

Alberto Cordoba

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch

Seitenzahl: 322

Veröffentlichungsjahr: 2014

Beschreibung

A high-level, informal look at the different stages of the predictive analytics cycle Understanding the Predictive Analytics Lifecycle covers each phase of the development of a predictive analytics initiative. Through the use of illuminating case studies across a range of industries that include banking, megaresorts, mobile operators, healthcare, manufacturing, and retail, the book successfully illustrates each phase of the predictive analytics cycle to create a playbook for future projects. Predictive business analytics involves a wide variety of inputs that include individuals' skills, technologies, tools, and processes. To create a successful analytics program or project to gain forward-looking insight into making business decisions and actions, all of these factors must properly align. The book focuses on developing new insights and understanding business performance based on extensive use of data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management as input for human decisions. The book includes: * An overview of all relevant phases: design, prepare, explore, model, communicate, and measure * Coverage of the stages of the predictive analytics cycle across different industries and countries * A chapter dedicated to each of the phases of the development of a predictive initiative * A comprehensive overview of the entire analytic process lifecycle If you're an executive looking to understand the predictive analytics lifecycle, this is a must-read resource and reference guide.

Leseprobe

Contents

Foreword

Preface

Acknowledgments

Chapter 1: Problem Identification and Definition

Importance of Clear Business Objectives

Office Politics

Note

Chapter 2: Design and Build

Managing Phase

Planning Phase

Delivery Phase

Notes

Chapter 3: Data Acquisition

Data: The Fuel for Analytics

A Data Scientist’s Job

Notes

Chapter 4: Exploration and Reporting

Visualization

Cloud Reporting

Chapter 5: Modeling

Churn Model

Risk Scoring Model

Notes

Chapter 6: Actionable Analytics

Digital Asset Management

Social Media

Chapter 7: Feedback

What the Different Software Components Should Do

Note

Conclusion

Appendix: Useful Questions

Bibliography

About the Author

Index

End User License Agreement

List of Illustrations

Figure 3.1 SAS/ACCESS® Software

Figure 4.1 SAS® Visual Analytics Dashboard

Figure 4.2 Model of Actionable Data Discovery

List of Tables

Table 6.1 SAS Project Activity Allocation

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Guide

Cover

Table of Contents

Begin Reading

Wiley & SAS Business Series

The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley & SAS Business Series include:

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Big Data Analytics: Turning Big Data into Big Money

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Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics

by Evan Stubbs

Business Analytics for Customer Intelligence

by Gert Laursen

Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure

by Michael Gendron

Business Intelligence and the Cloud: Strategic Implementation Guide

by Michael S. Gendron

Business Transformation: A Roadmap for Maximizing Organizational Insights

by Aiman Zeid

Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media

by Frank Leistner

Delivering Business Analytics: Practical Guidelines for Best Practice

by Evan Stubbs

Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition

by Charles Chase

Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain

by Robert A. Davis

Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments

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The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business

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Economic and Business Forecasting: Analyzing and Interpreting Econometric Results

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Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications

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Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data Driven Models

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Health Analytics: Gaining the Insights to Transform Health Care

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Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World

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Human Capital Analytics: How to Harness the Potential of Your Organization’s Greatest Asset

by Gene Pease, Boyce Byerly, and Jac Fitz-enz

Implement, Improve, and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education

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Killer Analytics: Top 20 Metrics Missing from Your Balance Sheet

by Mark Brown

Predictive Analytics for Human Resources

by Jac Fitz-enz and John Mattox II

Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance

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Retail Analytics: The Secret Weapon

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Social Network Analysis in Telecommunications

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Statistical Thinking: Improving Business Performance, Second Edition

by Roger W. Hoerl and Ronald D. Snee

Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics

by Bill Franks

Too Big to Ignore: The Business Case for Big Data

by Phil Simon

Using Big Data Analytics: Turning Big Data into Big Money

by Jared Dean

The Value of Business Analytics: Identifying the Path to Profitability

by Evan Stubbs

The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions

by Phil Simon

Win with Advanced Business Analytics: Creating Business Value from Your Data

by Jean Paul Isson and Jesse Harriott

For more information on any of the above titles, please visit www.wiley.com.

Understanding the Predictive Analytics Life Cycle

Alberto Cordoba

Cover image: © iStock.com/oliopi

Cover design: Wiley

Copyright © 2014 by Alberto Cordoba. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone], or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at http://www.wiley.com/go/permissions.

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Library of Congress Cataloging-in-Publication Data:

ISBN 978-1-118-86710-5 (Hardcover)

ISBN 978-1-118-93893-5 (ePDF)

ISBN 978-1-118-93892-8 (ePub)

Dedicated to my loving family

Foreword

With this book, Al has made an astonishing contribution to the growing body of knowledge about business analytics. The book offers an unprecedented look into the gory details of life as a programmer/analyst, BI specialist, researcher, project manager, data scientist, and consultant. It offers examples of problem solving that could only have been applied by using the progressive power of information technologies that was mastered in the 1990s and 2000s. Have there been other books about this topic? Of course, but none has portrayed the human side of this global endeavor with so much enthusiasm and humor before. For the first time, Al personifies the characters that play important roles in the lifecycle of the generation and use of predictive analytics, showing their creative abilities in industries such as banking, megaresorts, mobile operators, healthcare, manufacturing, and retail. These personifications helps us all better understand and manage this big and complex process of deriving information from data in today’s increasingly sophisticated race to drive productivity and innovation. This understanding is essential to excel at providing an outstanding customer experience, manage customer churn, and perform data-intensive marketing campaigns.

This book has many “novelistic” aspects and is very conversational. This way the stories do make each section more personal and relatable.

The “Note Files” in particular will be quite helpful for readers as they present examples of real-life analytics projects. These files can be used as good starting seeds for projects.

Leigh Watts

Preface

People get excited about big data and business analytics. That might sound ridiculous, but I’m not kidding. Over the years, I have traveled everywhere from Brazil to Japan working on analytics, and in every country, I have found a particularly peculiar brand of analytic fanaticism. These analysts are hilarious and downright exciting!

I wrote this book to help business and IT professionals understand the predictive analytics lifecycle. A reader can get a sense for the entire predictive cycle and therefore avoid potential risks. Often folks who work in a particular area of the analytics cycle—for example, analysis—have little understanding of another area, such as integration. This situation sometimes creates confusions, poor communication, and delays. This book has seven chapters, which illustrate a complete cycle from idea definition to feedback.

In each chapter, I added notes at the end with examples. The first chapter concentrates on the initial stages of defining the problem at hand and how to get the business customer to quantify the overall business value of the project. The note file contains a sample nondisclosure agreement. Chapter 2, the design chapter, focuses on the planning process. It shows how to define various components of the scope, such as what organizations are affected, what to expect, and the type of information required. The note file has a sample project for a data warehouse performance management project. Chapter 3, the integration chapter, discusses the process of bringing data together to build a file ready for analysis. This chapter includes two notes: One is a data-quality sample project, and the other is a file with a description of Hadoop and how it works with SAS. Chapter 4, the reporting and visualization chapter, illustrates how reporting and visualization techniques are used to review and make sense of data. The chapter includes a note file with an example of an analytic project focused on guest loyalty for a cruise ship. Chapter 5, the analysis chapter, presents how to build a couple of analytical models: a churn model and a scorecard. The note file presents concepts on fraud, waste, and abuse analytics. Chapter 6, about actionable analytics, describes how to use results from the predictive modeling in campaigns. There are two note files. The first has a simple assessment to identify gaps in a CRM analytics platform. The second is a sample project of the construction of a predictive analytics framework for a mobile operator. Chapter 7, the feedback chapter, discusses the iterative nature of the predictive analytics cycle and the importance of including feedback in the development of new models. The conclusion chapter provides a high-level view of the entire analytics lifecycle. The appendix contains more than 1,000 questions that can be used to qualify predictive analytics projects or simply to break the ice with both IT and business professionals interested in applied analytics projects.

This book is a mix of technical knowledge and business analytics humor. The names and the actions of the companies and employees have been changed in the interest of making the stories funnier and the content more readable. This book is for anyone who wants to gain a better understanding of the development cycle of business analytics in an entertaining way. Follow professionals of the Information Age as they tackle big data in this fascinating collection of case studies in different industries from around the world based on my real-life experience.

Acknowledgments

Since I began working with business analytics in 1985, I have had the good fortune to work and learn from some of the best minds in the world of data. When I joined SAS in 1993, I began to see the excitement that companies experience when they realize that they have finally found a way to use internal data to better understand both their organization and their customers and better manage their own performance by developing key performance indicators. I am grateful for the many conversations with my SAS colleagues and customers over these many years.

These conversations gave me the chance to see predictive analytics in action in different industries and different geographies, and helped me appreciate what predictive analytics contributes to the enhancement of customer experiences worldwide and how it generates value for organizations.

I feel fortunate to have this opportunity to say thank you to all of the amazing people who have coached me, including Jim Goodnight, Clive Pearson, Jeff Babcock, Eric Yao, Herbert Kirk, Lee Richardson, David Fender, Alan Spielman, Steve Gammarino, Rajani Nelamangala, Phil Hyatt, Helen-Jean Talbott, Chuck Zebrowski, Barrett Joyner, Leigh Haddon, Andy Bagwell, Jose Carvalho, Mariana Clampett, Barrett Joyner, Andre Boisvert, Carmelina Collado, Monica Grandeze, Marcos Arancibia, Kimio Momose, Carol Forhan, Bill Marder, Tony Pepitone, and Jon Conklin. Many more colleagues have contributed to my professional development, and I am grateful to them.

I can’t forget to mention my eldest daughter, Sienna, for her willingness to work with me on the project and her flexibility and insight into the manuscript. I would also like to thank my three younger children for their undying love and support: Ines, Sofia, and Diego. Finally, I want to extend a very special, love-filled thank you to my beautiful wife, Clara Maria.

Al Cordoba

Chapter 1Problem Identification and Definition

How executives focus resources and assess an organization’s readiness for meeting the challenges posed by new business realities

Recently I met with a pair of business executives at the Gaylord Convention Center near Washington, DC.

Two analysts glided their way toward me. I smiled and went in for handshakes, exclaiming “Hello there!” Their names were Zizi and Javier. Both worked for a big corporation right outside of the Beltway in Maryland. I quickly launched into a flurry of business jargon, briskly walking toward the coffee kiosk, mouth running at a hundred million miles per minute. The executives shuffled after me, saying “We are very interested in finding out more about developing a modern analytical system.”

I bought a soy latte with an extra espresso shot. As the caffeine kicked in, I started by asking, “What is your firm’s level of analytical maturity?”

Javier looked at me and said, “Before we get started, do we have an NDA in place?” A nondisclosure agreement is a document signed to protect both parties. (A sample agreement is presented at the end of this chapter.) “We sure do,” I answered. “Great! So let’s continue.”

Javier stammered, “I-I don’t know. I believe that analysis is a portion of the transformation cycle from data to knowledge to wisdom. So, probably the analytical maturity of an enterprise would tell how well it can leverage analysis and close the information gap. I am not sure where I would say our company is exactly.”

My eyes met his as I popped a huge sparkly smile. “Everybody knows the four key levels of an analytical framework are. . . .”

I waited for a response. Zizi replied, “Infrastructure, functionality, organization, and business, and these levels can be translated into an information evolution model for analytical applications.”1

Javier piped up, “What is the importance of this?”

I answered, “Those organizations that try simply to define and implement an advanced analytical solution in one step may end up taking far too long to finish building it and reap its benefits.”

Zizi lowered her glasses and continued my thought seamlessly. “And then, most likely, the analytical solution delivered will not meet needs because requirements usually change after an initiative is initiated or because the technology has already changed. We’ve been through that before.”

“Exactly!” I added, “There is an overarching need to build flexibility into contemporary analytical systems. Particularly now that data are growing exponentially and we are faced with big data everywhere. I believe enterprises need to assess the overall maturity of their analytical initiative and aim to add value incrementally rather than use an all-at-once approach. This is very important with the big data challenges. Results and challenges differ depending on the level of analytical maturity. I think the assessment of needs for an analytic platform or workbench should include choosing an appropriate software architecture for analysis and reporting, a hardware environment, a big data integration approach, and, of course, a data model for their structured data, among other things.”

They wondered, “Is that enough to ascertain success?”

I told it to them straight. “Hey, it’s anybody’s guess, but it increases the probability of success significantly!”

Results usually are measured in terms of effective usage of information technology (IT) investments and improved operational efficiency. Challenges primarily occur with IT infrastructure, culture, software technology, and functionality.

They looked at each other warily. I tried to reassure them a little bit. “Improved results usually are associated initially with having one version of analysis-derived information, the so-called truth, which improves the management of multiple departments. Some of the organizational challenges begin to take more focus and skills from the project team. Good results are associated with improved and faster decision-making processes than the competition.”

I decided not to mention the challenges that often occur at the business level, such as shifting business processes and methodologies to leverage new analytical capabilities for corporate performance management. Or changing business goals or objectives, based on insight gained. They were too apprehensive. Therefore, I wanted to stick with the most basic and positive aspects of reworking their business objectives.

I continued, “As your consultant, I have to ask you: Where is your firm going? Is the gut feeling still driving decision making? A successful analytical initiative needs good strategic business objectives.”

They winced at that statement. They knew I was right. Javier shook his head back and forth and sighed.

IMPORTANCE OF CLEAR BUSINESS OBJECTIVES

I patted them on the backs. “Business objectives must drive analytical initiatives and investments. The success of an analytical initiative should be measured by how it affects strategic and operational business objectives—not how many rows of big structured and unstructured data can be loaded into a data framework in six hours or the complexity of a model developed. This is particularly true when we consider the vast amounts of data that most organizations have accumulated and that continue growing.”

Obviously, the lack of clearly defined business objectives would make assessing the success or value impact of an analytical initiative impossible.

“Do you think that you can use an analytical framework to align IT system initiatives with business objectives and make strategic choices?” I asked the executives. “It could be the best thing that ever happened to you.”

I recommended that they conduct a business value evaluation prior to investing in an analytical initiative. “This evaluation will provide a quick and low-cost validation of an analytical project’s proposed direction and deliverables. This evaluation will also bring focus and attention to an analytical initiative within your IT organization and the potential business stakeholders. It can also pinpoint weaknesses and threats to the future project.”

Zizi asked, “Do you think the evaluation will start a dialogue between the internal groups like the IT organization and business users that will identify the business objectives?”

I smiled and said, “Yes. It will identify how analysis can contribute to the success of business objectives. It will set the scope and size of the project and determine the appropriate investment levels. This is just the beginning. Even if your analytical initiative is already under way, I think that if you take a step back to assess the initiative, you may discover new areas of additional leverage or new risks.”

I continued to urge them to face the bitter truths of today’s analytic realities. “It is important to ask questions to better understand which other business opportunities and objectives should be addressed and funded within the analytical initiative. It may be just as important to identify areas to keep outside of the project or that should come along in a second wave.”

I took out my phone, glancing at the time. “Why is your organization embarking in analytical applications and big data insight anyway?”

Zizi said, “Al, we need to stay competitive, and this is really exciting. We also have a new executive team with the right approach to data and what we can do with it. Just think where this could lead with your help.”

I appreciated that comment. “Thank you. I think we are on the right path. Business value in the real world can be achieved only when you leverage data that are relevant, accurate, timely, consistent, and, most of all, accessible. Most organizations that I have worked with start an advanced analytical project in an effort to drive revenue, increase profitability, optimize certain processes, decrease cost, make better decisions, manage the objectives, minimize risk, and/or improve infrastructure functionality. Does any one of those goals sound like your objectives?” They eagerly nodded.

I continued, “It sounds like you are planning to use analytical applications to gain a competitive edge in a highly competitive market. If so, are your specific business objectives well articulated? Do you already have your performance measures defined? How well and often are your key performance indicators (KPIs) measured and analyzed? Do the appropriate internal and external users have access to relevant data and analysis? Can you look at the KPIs and easily drill down for additional data? What would be the impact of new insights derived from increased or improved data access or analysis? What would be the impact of more real-time data and/or advanced predictive analysis? Is executive sponsorship and funding available?”

Their heads were spinning so I recommended an easy first step. “Analyze the strategic and tactical business objectives that will drive this analytical initiative and its funding. These objectives ultimately will define your project success.”

We looked at each other across the table in the atrium of Gaylord’s. An hour had gone by, and they confessed they were nervous. I could not blame them. The assignment to develop an analytical application initiative first requires a readiness test.

As obvious as it seems, an assessment of the IT organization and business user skills, levels of analytical activity, and culture will help the enterprise determine the probability of an analytical initiative’s success—before it makes any significant investments.

I flashed them a million-dollar grin and encouraged them to feel excited about the upcoming changes. “Before embarking on a big data acquisition adventure and its complementary analytical initiative, an enterprise like yours should complete a self-assessment to determine readiness. You must honestly evaluate your available skills, existing processes, and levels of analytical capabilities and culture so that, before spending considerable sums of money, you understand the challenges ahead and have a way to determine how to proceed and the likelihood of success.”

Zizi raised her hand. “To assess potential for analytical success, should we rate the level of engagement on analytics demonstrated by both our IT department and our business user community?”

I quickly replied, “Yes! First, you should rate the degree to which the following statements apply to your technical organization: Does IT understand the need for and potential of analytical applications? Does IT have the required skills and resources to support an analytical environment? Is IT taking responsibility for setting up an analytical infrastructure? Does IT act as a catalyst for technical improvements in the enterprise? Is IT respected within the enterprise? Does IT have a history of success?”

Javier lifted an eyebrow. “What about the business side? In your experience, typically, do business users understand the need for and potential of analytical applications? Do they have a history of funding and championing analytics initiatives? Are the business users the ones to drive IT to deploy new technology? Do they seek an active partnership from the IT organization? Should the business user community participate in the technology selection and adoption process?”

I responded, “Well, you are going to have to do the detail work of answering all of those great questions. I have seen a bunch of different combinations. However, the importance of a readiness assessment is undeniably clear. Any successful enterprise needs a portfolio of analytical applications to address the needs of a broad range of user requirements. But before it can develop that portfolio, the enterprise must determine what appropriate technical infrastructure and development methodologies are already in place, including: a platform to source data (e.g., a data mart, data warehouse, operational data store, multidimensional cubes, massive parallel processing (MPP) databases, big data framework), available data models and business definitions, rules for metadata use and integration, support for real-time use, access to cloud computing resources, and, when appropriate, methodologies for development, deployment, and change management. In addition, software for data management, data exploration, advanced analytics, and campaign management are also typically required.”

They looked a little flustered with my tech talk, but I wanted to cover a few more points before lunch so I quickly continued, “Initially, you should make sure that functionality is sufficient to ensure that an analytical initiative could deliver value. Later on, among many other tasks, you or your consultant team will define user requirements, decide whether to build or buy analytic applications, determine enterprise security and user access levels, assess scalability, and ask your IT counterparts to establish standards that match user types to appropriate tools.”

Zizi asked, “Shouldn’t the data be quality data to ensure that the analytical initiative delivers the expected value?”

I said, “You know the concept ‘garbage in, garbage out’? Definitely, data governance and data consistency are a high priority. For example, you should inventory data sources and means of access, identify data stewards, identify data quality solutions, and define methods to extract and transform data efficiently and correctly.”

I paused to think. “Be aware of timing. Many new infrastructure and functionality requirements are identified approximately six months after the initial analytical deployment. This makes an effective implementation methodology critical to ensure all the respective resources and skills are available throughout the system development life cycle to address those new requirements.”

Javier asked, “Will this technology assessment help validate technical and cost assumptions?”

I clasped my hands together and nodded slowly. “It will identify whether any critical factors were overlooked. It will spot potential weaknesses in the implementation of a plan.”

Zizi gave me a hard, discerning look. “What advanced analytical functionality does our company need, and what is the difference between that functionality and the kind of functionality we are using today?”

I beamed at her. “The analytical function can be seen in four main areas: integrate, report, model, and enable. First, “integrate” refers to the ability to collect and organize diverse data and make it ready and accessible for advanced analytical applications. It includes structured data like that generated in operational systems. Typically, it comes from database management systems. It also increasingly includes what is called nonstructured data coming from Web records and social networks and typically is very big. Today this data integration area is called information management. Second, we see an area for data exploration, visualization, and reporting. Third, “model” refers to the actual advanced quantitative modeling that takes advantage of statistical or mathematical techniques to gather information out of the data. The fourth area is execution. The predictive analytics function is an enabler of other applications like customer service, financial intelligence, or marketing services by improving the communications efficiency. I like your question. It is looking toward the future of analytics at your company. I see we are making progress, and I am becoming more confident about your company’s potential for success.”

As you can gather from the previous ideas, it is important to keep in mind that most organizations can derive great benefits when they provide these four functionalities using software as part of an integrated system within the context of an analytical framework. Most traditional analytic software platforms provide extraction, transformation languages, SQL generation, standard reporting, visualization, what-if analysis, alerts, corporate dashboards, statistics, data mining, advanced analysis and forecasting, campaign management, and optimization.

OFFICE POLITICS

Javier scribbled a few notes on his iPad. “Considering the types of insight required and the interaction with different types of users, how will we determine what functionality we need from the software we choose? There are so many choices.”

I agreed. “It is very confusing. You need to use a methodology to sort through all the vendors and tools. It is critical to have a clear objective in mind. For an analytical initiative to succeed, different types of users—personas—will need different software tools. Providing casual users like business analysts with analytical tools primarily intended for power users—that is, statistical programmers—will overwhelm the analysts, who most likely do not have the skills or the time to learn about these advanced tools. Likewise, asking power users—that is, programmers—to use simple reporting tools for their analysis work would not work. An inventory of existing tools and user types and their competency levels with a particular software tool will help the organization when the time arrives to select vendors. Most analytical vendors are beginning to deliver enhanced or next-generation products that merge data management, visualization, reporting, analysis, and communications functionality. As a result, a wider range of user types will have access to a broader range of functionality from a single and integrated analytical environment. Don’t forget the access control requirements for users.”

Javier asked warily, “Well, that all sounds good, but there must be a catch. What are some of the hidden costs associated with these analytical initiatives? Is that what people call the total cost of ownership?”

I told him the truth: “That’s correct! Over time organizations have adopted a large number of disparate and unrelated analytical technologies, adding to tool fragmentation in their organizations. This situation creates problems of support; when something fails, vendors blame each other. It also creates training problems when diverse applications work in different ways for no reason. This situation has also been complicated by the mergers and acquisitions within the analytical vendor community.”

Javier perked up. “Yeah! Our organization has been frustrated in our ability to deploy analytical solutions effectively because of the overabundance of unrelated end user technologies from various vendors. Our end users and the IT organization have deployed various analytical tools without much (if any) thought about integration, future needs, or issues. Unfortunately, they were reacting to the day-to-day pressures.”

I totally agreed with him. “A random portfolio of software tools in any organization typically contains products that are current and relevant, older but still-used products, and discontinued and unsupported fads. An organization may also have what is called shelfware, software that has been bought and nobody uses it.”

Zizi looked at her watch. “Look, we know that our organization needs to find the right number and mix of tools. To do this, we must stop the proliferation of analytical tools. That will ensure that we can centralize and provide a consistent and manageable analytical environment for our internal users.”

“To stop proliferation, you must enforce some standardization around analytical tools and governance for your data. This can be difficult because end users are usually partial to certain tools and resist changing the analytical tools they use and how they operate,” I added.

“Let’s don’t forget politics. Our analytical initiatives could span multiple business and functional groups in our organization. I could see that the politics associated with getting participation, data, and resources from the internal groups could introduce challenges and delays to an analytical initiative,” Zizi said.

Javier said thoughtfully, “Let me add something here. Our political and organizational challenges are unique. The politics of who has control related to visibility, information, resources, funding, and technology choices often leads to delays in IT initiatives. I think a cross-functional analytical initiative will fail quickly if it does not have a credible team leadership that anticipates and addresses these challenges.”

He obviously had extensive experience with office politics. I tried to sum things up as the minute hand clicked away on Zizi’s watch. “The readiness evaluation should include technology, people, process, and politics. It is a package deal.”

We shook hands and agreed to meet via Skype later on that week. I left them sitting at the table reevaluating their company’s subjective and objective levels of analytical well-being, excited about taking the plunge into a new and fascinating analytical mind-set for their company.

NOTE FILE: SAMPLE MUTUAL CONFIDENTIALITY AGREEMENT

THIS CONFIDENTIALITY AGREEMENT (this “Agreement”), effective this DATE, is by and between PARTY1 NAME (“PARTY1”) and PARTY2 NAME (“PARTY2”).

Representatives of PARTY1 plan to meet with representatives of Potential Partner to consider establishing a business relationship or providing products or services (collectively, the “Transaction”). In connection with the discussions, each party might disclose certain confidential information to the other. As a condition of disclosing confidential information, the parties have agreed to treat such information as stated in this Agreement.

IN CONSIDERATION of the mutual obligations of the parties, the parties hereby agree as follows:

“Confidential Information” Defined. “Confidential Information” means all information disclosed by or on behalf of the disclosing party to or obtained by the receiving party concerning the disclosing party’s business or any product or service developed (or proposed to be developed) by the disclosing party, and whether disclosed in writing, orally, or by inspection. Confidential Information may include, but is not limited to, developer information, pricing, customized products and services, designs, specifications, technical information, protocols, process information, code, software, financial data, business plans, marketing plans, trade secrets, processes, and techniques. Notwithstanding the foregoing, Confidential Information shall not include:

Information that at the time of disclosure is in the public domain or is otherwise available to the receiving party other than on a confidential basis;

Information that, after disclosure, becomes a part of the public domain by publication or otherwise through no fault of the receiving party or any third party under a confidential agreement with the disclosing party;

Information disclosed to the receiving party by a third party not under an obligation of confidentiality to the disclosing party; or

Information that is or has been developed by the receiving party (as evidenced by the receiving party’s records) independent of the disclosures by the disclosing party.

Covenant of Confidentiality. The receiving party agrees to retain in confidence all Confidential Information. The receiving party further agrees that it will not use or disclose to any third party, nor permit the use or disclosure to any third party of, any Confidential Information, except that the receiving party may make the Confidential Information available to its directors, officers, employees, and attorneys (collectively, its “Representatives”) who agree to be bound to the terms of this agreement and who reasonably need the information for the receiving party to evaluate the Transaction and, if the parties agree to undertake a Transaction, for the performance the receiving party’s duties in connection with such Transaction.

Covenant to Return Confidential Information. In the event the parties’ discussions terminate, or upon the earlier request of the disclosing party, the receiving party agrees to immediately return to the disclosing party all tangible and intangible documents and files obtained from the disclosing party containing Confidential Information and any materials created or derived from Confidential Information, by whomever or whenever made, without retaining any copies thereof. Once returned, the receiving party agrees to delete all electronic copies of the documents and files from the receiving party’s systems. The receiving party agrees to verify compliance in writing if requested by the disclosing party.

Non-Solicitation and Hiring. Neither party shall solicit, hire, or retain directly or indirectly any employee of the other party for a period of 12 months following the later of either the termination date of this agreement or the Transaction termination date.

Survival of Confidentiality Obligation. The confidentiality obligation contained herein shall survive the termination of such discussions and negotiations.