HBRs 10 Must Reads on Data Strategy (featuring Democratizing Transformation by Marco Iansiti and Satya Nadella) (Harvard Business Review) (Z-Library)

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HBR’s 10 Must Reads series is the definitive collection of ideas and best practices for aspiring and experienced leaders alike. These books offer essential reading selected from the pages of Harvard Business Review on topics critical to the success of every manager. Titles include: HBR’s 10 Must Reads 2015 HBR’s 10 Must Reads 2016 HBR’s 10 Must Reads 2017 HBR’s 10 Must Reads 2018 HBR’s 10 Must Reads 2019 HBR’s 10 Must Reads 2020 HBR’s 10 Must Reads 2021 HBR’s 10 Must Reads 2022 HBR’s 10 Must Reads 2023 HBR’s 10 Must Reads 2024 HBR’s 10 Must Reads 2025 HBR’s 10 Must Reads for Business Students HBR’s 10 Must Reads for CEOs HBR’s 10 Must Reads for Executive Teams HBR’s 10 Must Reads for Mid-Level Managers HBR’s 10 Must Reads for New Managers HBR’s 10 Must Reads on AI HBR’s 10 Must Reads on AI, Analytics, and the New Machine Age HBR’s 10 Must Reads on Boards

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HBR’s 10 Must Reads on Building a Great Culture HBR’s 10 Must Reads on Business Model Innovation HBR’s 10 Must Reads on Career Resilience HBR’s 10 Must Reads on Change Management (Volumes 1 and 2) HBR’s 10 Must Reads on Collaboration HBR’s 10 Must Reads on Communication (Volumes 1 and 2) HBR’s 10 Must Reads on Creativity HBR’s 10 Must Reads on Design Thinking HBR’s 10 Must Reads on Diversity HBR’s 10 Must Reads on Emotional Intelligence HBR’s 10 Must Reads on Entrepreneurship and Startups HBR’s 10 Must Reads on High Performance HBR’s 10 Must Reads on Innovation HBR’s 10 Must Reads on Leadership (Volumes 1 and 2) HBR’s 10 Must Reads on Leadership for Healthcare HBR’s 10 Must Reads on Leadership Lessons from Sports HBR’s 10 Must Reads on Leading Digital Transformation HBR’s 10 Must Reads on Leading with Purpose HBR’s 10 Must Reads on Lifelong Learning HBR’s 10 Must Reads on Making Smart Decisions HBR’s 10 Must Reads on Managing Across Cultures HBR’s 10 Must Reads on Managing in a Downturn, Expanded Edition HBR’s 10 Must Reads on Managing People (Volumes 1 and 2) HBR’s 10 Must Reads on Managing Projects and Initiatives HBR’s 10 Must Reads on Managing Risk

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HBR’s 10 Must Reads on Managing Yourself (Volumes 1 and 2) HBR’s 10 Must Reads on Mental Toughness HBR’s 10 Must Reads on Negotiation HBR’s 10 Must Reads on Nonprofits and the Social Sectors HBR’s 10 Must Reads on Organizational Resilience HBR’s 10 Must Reads on Performance Management HBR’s 10 Must Reads on Platforms and Ecosystems HBR’s 10 Must Reads on Public Speaking and Presenting HBR’s 10 Must Reads on Reinventing HR HBR’s 10 Must Reads on Sales HBR’s 10 Must Reads on Strategic Marketing HBR’s 10 Must Reads on Strategy (Volumes 1 and 2) HBR’s 10 Must Reads on Strategy for Healthcare HBR’s 10 Must Reads on Strengthening Your Soft Skills HBR’s 10 Must Reads on Talent HBR’s 10 Must Reads on Teams HBR’s 10 Must Reads on Trust HBR’s 10 Must Reads on Women and Leadership HBR’s 10 Must Reads: The Essentials

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On Data Strategy HARVARD BUSINESS REVIEW PRESS Boston, Massachusetts

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Copyright HBR Press Quantity Sales Discounts Harvard Business Review Press titles are available at significant quantity discounts when purchased in bulk for client gifts, sales promotions, and premiums. Special editions, including books with corporate logos, customized covers, and letters from the company or CEO printed in the front matter, as well as excerpts of existing books, can also be created in large quantities for special needs. For details and discount information for both print and ebook formats, contact booksales@harvardbusiness.org, tel. 800-988-0886, or www.hbr.org/bulksales. Copyright 2025 Harvard Business School Publishing Corporation All rights reserved 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@harvardbusiness.org, or mailed to Permissions, Harvard Business School Publishing, 60 Harvard Way, Boston, Massachusetts 02163.

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First eBook Edition: Jun 2025 ISBN: 979-8-89279-091-8 eISBN: 979-8-89279-092-5

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Contents Cover Title Page Copyright What’s Your Data Strategy? by Leandro DalleMule and Thomas H. Davenport Democratizing Transformation by Marco Iansiti and Satya Nadella Why Companies Should Consolidate Tech Roles in the CSuite by Thomas H. Davenport, John Spens, and Saurabh Gupta Developing a Digital Mindset by Tsedal Neeley and Paul Leonardi What Does It Actually Take to Build a Data-Driven Culture? by Mai B. AlOwaish and Thomas C. Redman When Data Creates Competitive Advantage by Andrei Hagiu and Julian Wright Building an Insights Engine by Frank van den Driest, Stan Sthanunathan, and Keith Weed Personalization Done Right by Mark Abraham and David C. Edelman

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Ensure High-Quality Data Powers Your AI by Thomas C. Redman The Ethics of Managing People’s Data by Michael Segalla and Dominique Rouziès Where Data-Driven Decision-Making Can Go Wrong by Michael Luca and Amy C. Edmondson Sizing Up Your Cyberrisks by Thomas J. Parenty and Jack J. Domet A Better Way to Put Your Data to Work Veeral Desai, Tim Fountaine, and Kayvaun Rowshankish Heavy Machinery Meets AI Vijay Govindarajan and Venkat Venkatraman About the Contributors Index

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What’s Your Data Strategy? by Leandro DalleMule and Thomas H. Davenport MORE THAN EVER, the ability to manage torrents of data is critical to a company’s success. But even with the emergence of data- management functions and chief data officers (CDOs), most companies remain badly behind the curve. Cross-industry studies show that on average, less than half of an organization’s structured data is actively used in making decisions—and less than 1% of its unstructured data is analyzed or used at all. More than 70% of employees have access to data they should not, and 80% of analysts’ time is spent simply discovering and preparing data. Data breaches are common, rogue data sets propagate in silos, and companies’ data technology often isn’t up to the demands put on it. Having a CDO and a data-management function is a start, but neither can be fully effective in the absence of a coherent strategy for organizing, governing, analyzing, and deploying an organization’s information assets. Indeed, without such strategic management many companies struggle to protect and leverage their data—and CDOs’ tenures are often difficult and short (just

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2.4 years on average, according to Gartner). In this article we describe a new framework for building a robust data strategy that can be applied across industries and levels of data maturity. The framework draws on our implementation experience at the global insurer AIG (where DalleMule is the CDO) and our study of half a dozen other large companies where its elements have been applied. The strategy enables superior data management and analytics—essential capabilities that support managerial decision-making and ultimately enhance financial performance. The “plumbing” aspects of data management may not be as sexy as the predictive models and colorful dashboards they produce, but they’re vital to high performance. As such, they’re not just the concern of the CIO and the CDO; ensuring smart data management is the responsibility of all C-suite executives, starting with the CEO. Defense Versus Oense Our framework addresses two key issues: It helps companies clarify the primary purpose of their data, and it guides them in strategic data management. Unlike other approaches we’ve seen, ours requires companies to make considered trade-offs between “defensive” and “offensive” uses of data and between control and flexibility in its use, as we describe below. Although information on enterprise data management is abundant, much of it is technical and focused on governance, best practices, tools, and the like. Few if any data-management frameworks are as

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business-focused as ours: It not only promotes the efficient use of data and allocation of resources but also helps companies design their data-management activities to support their overall strategy. Data defense and offense are differentiated by distinct business objectives and the activities designed to address them. Data defense is about minimizing downside risk. Activities include ensuring compliance with regulations (such as rules governing data privacy and the integrity of financial reports), using analytics to detect and limit fraud, and building systems to prevent theft. Defensive efforts also ensure the integrity of data flowing through a company’s internal systems by identifying, standardizing, and governing authoritative data sources, such as fundamental customer and supplier information or sales data, in a “single source of truth.” Data offense focuses on supporting business objectives such as increasing revenue, profitability, and customer satisfaction. It typically includes activities that generate customer insights (data analysis and modeling, for example) or integrate disparate customer and market data to support managerial decision-making through, for instance, interactive dashboards. Idea in Brief The Challenge To remain competitive, companies must wisely manage quantities of data. But data theft is common, awed or duplicate data sets exist within organizations, and IT is often behind the curve. The Solution

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Companies need a coherent strategy that strikes the proper balance between two types of data management: defensive, such as security and governance, and oensive, such as predictive analytics. The Execution Regardless of its industry, a company’s data strategy is rarely static; typically, a chief data ocer is in charge of ensuring that it dynamically adjusts as competitive pressures and overall corporate strategy shift. Offensive activities tend to be most relevant for customer- focused business functions such as sales and marketing and are often more real-time than is defensive work, with its concentration on legal, financial, compliance, and IT concerns. (An exception would be data fraud protection, in which seconds count and real-time analytics smarts are critical.) Every company needs both offense and defense to succeed, but getting the balance right is tricky. In every organization we’ve talked with, the two compete fiercely for finite resources, funding, and people. As we shall see, putting equal emphasis on the two is optimal for some companies. But for many others it’s wiser to favor one or the other. Some company or environmental factors may influence the direction of data strategy: Strong regulation in an industry (financial services or health care, for example) would move the organization toward defense; strong competition for customers would shift it toward offense. The challenge for CDOs and the rest of the C-suite is to establish the appropriate trade-offs between

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defense and offense and to ensure the best balance in support of the company’s overall strategy. Decisions about these trade-offs are rooted in the fundamental dichotomy between standardizing data and keeping it more flexible. The more uniform data is, the easier it becomes to execute defensive processes, such as complying with regulatory requirements and implementing data-access controls. The more flexible data is—that is, the more readily it can be transformed or interpreted to meet specific business needs—the more useful it is in offense. Balancing offense and defense, then, requires balancing data control and flexibility, as we will describe. Single Source, Multiple Versions Before we explore the framework, it’s important to distinguish between information and data and to differentiate information architecture from data architecture. According to Peter Drucker, information is “data endowed with relevance and purpose.” Raw data, such as customer retention rates, sales figures, and supply costs, is of limited value until it has been integrated with other data and transformed into information that can guide decision- making. Sales figures put into a historical or a market context suddenly have meaning—they may be climbing or falling relative to benchmarks or in response to a specific strategy. A company’s data architecture describes how data is collected, stored, transformed, distributed, and consumed. It includes the rules governing structured formats, such as databases and file

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systems, and the systems for connecting data with the business processes that consume it. Information architecture governs the processes and rules that convert data into useful information. For example, data architecture might feed raw daily advertising and sales data into information architecture systems, such as marketing dashboards, where it is integrated and analyzed to reveal relationships between ad spend and sales by channel and region. Many organizations have attempted to create highly centralized, control-oriented approaches to data and information architectures. Previously known as information engineering and now as master data management, these top-down approaches are often not well suited to supporting a broad data strategy. Although they are effective for standardizing enterprise data, they can inhibit flexibility, making it harder to customize data or transform it into information that can be applied strategically. In our experience, a more flexible and realistic approach to data and information architectures involves both a single source of truth (SSOT) and multiple versions of the truth (MVOTs). The SSOT works at the data level; MVOTs support the management of information. The elements of data strategy   Defense Oense Key objectives Ensure data security, privacy, integrity, quality, regulatory compliance, and governance Improve competitive position and protability

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Core activities Optimize data extraction, standardization, storage, and access Optimize data analytics, modeling, visualization, transformation, and enrichment Data- management orientation Control Flexibility Enabling architecture SSOT (Single source of truth) MVOTs (Multiple versions of the truth) In the organizations we’ve studied, the concept of a single version of truth—for example, one inviolable primary source of revenue data—is fully grasped and accepted by IT and across the business. However, the idea that a single source can feed multiple versions of the truth (such as revenue figures that differ according to users’ needs) is not well understood, commonly articulated, or, in general, properly executed. The key innovation of our framework is this: It requires flexible data and information architectures that permit both single and multiple versions of the truth to support a defensive-offensive approach to data strategy. A New Data Architecture Can Pay for Itself WHEN COMPANIES LACK a robust SSOT-MVOTs data architecture, teams across the organization may create and store the data they need in siloed repositories that vary in depth, breadth, and formatting. Their data management is often done in isolation with inconsistent requirements. The process is inecient and expensive and can result in the proliferation of multiple uncontrolled versions of the truth that aren’t eectively reused. Because SSOTs and MVOTs concentrate,

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standardize, and streamline data-sourcing activities, they can dramatically cut operational costs. One large nancial services company doing business in more than 200 countries consolidated nearly 130 authoritative data sources, with trillions of records, into an SSOT. This allowed the company to rationalize its key data systems; eliminate much supporting IT infrastructure, such as databases and servers; and cut operating expenses by automating previously manual data consolidation. The automation alone yielded a 190% return on investment with a two-year payback time. Many companies will nd that they can fund their entire data management programs, including sta salaries and technology costs, from the savings realized by consolidating data sources and decommissioning legacy systems. The CDO and the data-management function should be fully responsible for building and operating the SSOT structure and using the savings it generates to fund the company’s data program. Most important is to ensure at the outset that the SSOT addresses broad, high-priority business needs, such as applications that benet customers or generate revenue, so that the project quickly yields results and savings—which encourages organization-wide buy-in. OK. Let’s parse that. The SSOT is a logical, often virtual and cloud-based repository that contains one authoritative copy of all crucial data, such as customer, supplier, and product details. It must have robust data provenance and governance controls to ensure that the data can be relied on in defensive and offensive activities, and it must use a common language—not one that is specific to a particular business unit or function. Thus, for example, revenue is reported, customers are defined, and products are classified in a single, unchanging, agreed-upon way within the SSOT.

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Not having an SSOT can lead to chaos. One large industrial company we studied had more than a dozen data sources containing similar supplier information, such as name and address. But the content was slightly different in each source. For example, one source identified a supplier as Acme; another called it Acme, Inc.; and a third labeled it ACME Corp. Meanwhile, various functions within the company were relying on differing data sources; often the functions weren’t even aware that alternative sources existed. Human beings might be able to untangle such problems (though it would be labor-intensive), but traditional IT systems can’t, so the company couldn’t truly understand its relationship with the supplier. Fortunately, artificial intelligence tools that can sift through such data chaos to assemble an SSOT are becoming available. The industrial company ultimately tapped one and saved substantial IT costs by shutting down redundant systems. The SSOT allowed managers to identify suppliers that were selling to multiple business units within the company and to negotiate discounts. In the first year, having an SSOT yielded $75 million in benefits. An SSOT is the source from which multiple versions of the truth are developed. MVOTs result from the business-specific transformation of data into information—data imbued with “relevance and purpose.” Thus, as various groups within units or functions transform, label, and report data, they create distinct, controlled versions of the truth that, when queried, yield

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consistent, customized responses according to the groups’ predetermined requirements. Consider how a supplier might classify its clients Bayer and Apple according to industry. At the SSOT level these companies belong, respectively, to chemicals/pharmaceuticals and consumer electronics, and all data about the supplier’s relationship with them, such as commercial transactions and market information, would be mapped accordingly. In the absence of MVOTs, the same would be true for all organizational purposes. But such broad industry classifications may be of little use to sales, for example, where a more practical version of the truth would classify Apple as a mobile phone or a laptop company, depending on which division sales was interacting with. Similarly, Bayer might be more usefully classified as a drug or a pesticide company for the purposes of competitive analysis. In short, multiple versions of the truth, derived from a common SSOT, support superior decision-making. At a global asset management company we studied, the marketing and finance departments both produced monthly reports on television ad spending—MVOTs derived from a common SSOT. Marketing, interested in analyzing advertising effectiveness, reported on spending after ads had aired. Finance, focusing on cash flow, captured spending when invoices were paid. The reports therefore contained different numbers, but each represented an accurate version of the truth.

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Procter & Gamble has adopted a similar approach to data management. The company long had a centralized SSOT for all product and customer data, and other versions of data weren’t allowed. But CDO Guy Peri and his team realized that the various business units had valid needs for customized interpretations of the data. The units are now permitted to create controlled data transformations for reporting that can be reliably mapped back to the SSOT. Thus the MVOTs diverge from the SSOT in consistent ways, and their provenance is clear. In its application of the SSOTMVOTs model, the Canadian Imperial Bank of Commerce (CIBC) automated processes to ensure that enterprise source data and data transformations remained aligned. CIBC’s CDO, Jose Ribau, explains that the company’s SSOT contains all basic client profile and preference data; MVOTs for loan origination and customer relationship management transform the source data into information that supports regulatory reporting and improved customer experience. Automated synchronization programs connect SSOT and MVOTs data, with nightly “exception handling” to identify and address data-integrity issues such as inconsistent customer profiles. Although the SSOTMVOTs model is conceptually straightforward, it requires robust data controls, standards, governance, and technology. Ideally, senior executives will actively participate on data governance boards and committees. But data governance isn’t particularly fun. Typically, enterprise