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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
A powerful new mindset for data leaders in any organization
In The Data Hero Playbook: Developing Your Data Leadership Superpowers, veteran data professional and thought leader Malcolm Hawker offers fresh and exciting new ways to collect, manage, and use data. Called “Heroic Data Leadership,” Hawker's new mindset for data professionals will unlock the true potential of your organization's data. It puts to bed the limiting, counterproductive mindsets that often plague data leaders and offers original and effective alternatives you can apply immediately to generate tangible business results.
The book shows you how to re-center customer satisfaction within your data strategy and convincingly demonstrates why sound data management must be paired with the delivery of value to the customer in order to have a significant impact on your company's bottom line.
Inside the book:
- Step-by-step recommendations for productive behaviors and best practices within your data teams
- Illuminating quotes and anecdotes from data professionals leading data functions at large companies today
- Narratives and stories explaining how to transition from stale, limiting data mindsets to more productive and effective approaches
An essential resource for data professionals at organizations of all types and sizes, The Data Hero Playbook is the hands-on roadmap to data leadership that managers, analysts, executives, entrepreneurs, and founders have been waiting for.
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Seitenzahl: 446
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Title Page
Introduction
Chapter 1: The Data Hero Origin Story
Notes
Chapter 2: The Data Hero Superpower: A Positive Mindset
What's a Mindset?
Mindset and Corporate Culture
Traits of a Positive Mindset and Acts of Data Heroism
Notes
Chapter 3: The Anti‐hero: Limiting Mindsets
All‐or‐Nothing Thinking
Lack of Accountability
Blaming Others
Avoid Challenges, Reluctance to Take Risks
Embrace the Status Quo, Resist Change
Failure to See Positive Intent
Notes
Chapter 4: The Wrath of the Anti‐Hero in Data and Analytics
The Unwillingness to Quantify the Value of Data
Data Literacy and Blaming Customers for Product Failures
Extreme Forms of “Data First” or “Data Driven”
Data Culture Is a Dependency to Deliver Value and Is Somebody Else's Problem
Garbage In, Garbage Out
Seeing Negative Intentions in Others
Deterministic, “All‐or‐Nothing” Thinking in a Probabilistic World
Notes
Chapter 5: Reinforcement Mechanisms in Data and Analytics
Market Realities
Information Technology Ecosystem Feedback Loop
Analyst Influences
Consultant Influences
Vendor Influences
Social Media Influences
Technology Influences
Notes
Chapter 6: Putting Your Customer at the Center of Everything You Do
Become Customer Driven, Not Data Driven
Focus on Customers and Their Business Processes, Not Technology
Assume Positive Intentions, Have Empathy
Better Aligned Incentives and Success Metrics
Proactive Engagement and Feedback Loops
Revisit Organizational Structures, Roles, and Responsibilities
Notes
Chapter 7: Integrating Product Management as a Discipline Within Data and Analytics Teams
The P&L North Star
Hire a Product Manager
Embrace User‐ and Customer‐Centric Design Methodologies
Hire a Value Engineer and Measure the Cost and Benefit of Everything
Implement a “Go to Market” Function; Repackage Governance and Literacy
Evolve Your Organization Toward Customer and Product Centricity
Notes
Chapter 8: Embrace Agility and a Relentless Focus on Value Delivery
The Data Strategy MVP
Success Metrics/Business Cases
Scope, Approach, and Roadmap
The Data Governance Model
The Data and Analytics Organizational Model
Notes
Chapter 9: Look Inward Before Looking Outward
Be Humble
Embrace Critical Thinking
Lead by Example
Make Room for Failure
Be Practical
Notes
Chapter 10: Looking Forward
Natively Digital
Data and AI Haves and Have‐Nots
DataOps and the Convergence of Data and Product Functions
Data Monetization and Widespread Data Sharing
Data Consortiums and Governance Networks
Data Sustainability
Data as an Asset
In Closing
Notes
Index
Copyright
About the Author
Acknowledgments
End User License Agreement
List of Illustrations
Chapter 4
Figure 4.1 Business contexts
Figure 4.2 Levels of data governance
Figure 4.3 Data governance cost/benefit paradox
Chapter 5
Figure 5.1 The IT Ecosystem Feedback Loop
Figure 5.2 Top roadblocks to data and analytics initiatives
Chapter 7
Figure 7.1 The Data and Analytics Metrics and Value Framework
Figure 7.2 Business value driver map.
https://www.superchargedfinance.com/bl
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Figure 7.3 Growth‐centered perspectives
Figure 7.4 Organizational chart
Chapter 8
Figure 8.1 Data Strategy MVP
Figure 8.2 Definition of a data product
Figure 8.3 Iterative MVP approach
Guide
Cover
Table of Contents
Title Page
Copyright
About the Author
Acknowledgments
Introduction
Begin Reading
Index
End User License Agreement
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The Data Hero Playbook
Developing Your Data Leadership Superpowers
Malcolm Hawker
Introduction
Despite billions of dollars invested in data teams and technologies, many organizations struggle to extract meaningful value from their data. Chief data officers (CDOs) and other data leaders face persistent challenges, including short tenures, dissatisfaction with data programs, and unrealized returns on investment. Why? Thanks to the explosion of AI, there is a growing and seemingly unsatiable desire for data and the insights it provides, yet the chasm between the unrealized potential value of data and it’s actual value is greater than it has ever been. And it’s getting bigger every day.
This is creating a “do or die” moment for CDOs and other leaders of data and analytics functions. The time for heroic action is now, lest data leaders be slowly replaced by others more able to overcome the many barriers that have beguiled the data industry for the last two decades. The key challenge facing CDOs isn't a lack of technology or best practices – it's a limiting mindset that prioritizes the status quo over innovation and customer impact.
During my tenure as an industry analyst, I engaged with thousands of senior data leaders across many industries. The patterns were clear: they struggled because of a failure to embrace a growth‐oriented mindset. These CDO struggles play out within an industry rife with forces that help to reinforce old and outdated approaches to managing data that have been proven, time and again, to simply not work. However, there is a better way. This book introduces the Data Hero Mindset, an approach that empowers data leaders to focus on business value, adaptability, and customer needs – which are necessary ingredients for data leaders devoted to breaking free from the crippling grip of the status quo.
This book explores how ingrained mindsets hinder data success, the cultural shifts needed to overcome them, and practical steps data leaders can take to become true data heroes. It's not just about governance or frameworks – it's a call to action for completely rethinking the role of data leadership in the AI era. If you're ready to challenge the status quo and become the data hero your organization needs, let's begin this journey together.
Chapter 1The Data Hero Origin Story
About two years ago on a sunny summer day in my home in Florida, I had a realization. For several years prior I had been ruminating on the issue of why so many data leaders fail to deliver meaningful value to their companies. Despite a recurring string of major technical innovations and no shortage of best practices, frameworks, and expensive consulting engagements – and massive ongoing investments in data functions dating back over a decade – the statistics on chief data officer (CDO) performance have been consistently bleak.
By 2021 the question of why so many data leaders are failing had become something of an obsession for me. Then out of the blue, I figured it out.
I had long known the core issue was a “people problem,” because if it was process or technology related, the problem would have been solved a long time ago. But simply saying it's a “people problem” isn't itself meaningful – such is the case with all platitudes.
What I came to realize is there's an epidemic within the world of data and analytics. It's affecting everyone in one way or another, and worse yet, it's so insidious, most are completely unaware of its reach or its impact. It influences every major data initiative, every technology deployment, and every analyst and consulting engagement. It hinders every attempt to fix the problem, and it's running completely amuck.
The epidemic is an ongoing and widespread embrace of a highly limiting mindset that favors the status quo over growth. This mindset is manifested in how we express our beliefs related to our customers, our roles, and our data. And because it informs how we view our world, it touches everything.
This book explores these limiting mindsets and what data leaders must do to overcome their negative impacts. So, if you're a data leader or CDO tasked with promoting a culture of data‐enabled decision‐making within your company, then you've come to the right place. This book is dedicated to sharing insights on the behaviors, and more importantly the mindset, that data leaders must adopt to succeed in an era of constant change and artificial intelligence (AI)‐fueled disruption.
What exactly do I mean by a mindset? Before we deep dive into the world of data and analytics, here's a story to help explain what I mean.
Let's imagine you're a baker with 10+ years of experience, backed by a degree from a well‐regarded culinary school. Your passion is baking bread, and you've just relocated to a small town where you're the only person in town who still makes their bread the old‐fashioned way, by hand and with great attention to quality and detail.
You open a new storefront, and every morning you line your shelves with your freshly baked bread. For a small store there is an amazing assortment of breads, and your bakery is located on a main thoroughfare with steady foot and vehicle traffic in the middle of the town. Better yet, you're the only bakery in town.
Unfortunately, several months after launch it becomes abundantly clear that you’re struggling to grow your business. You are paying your rent, but you’ve had to let go of the one employee you originally hired to help with your launch. You’re working 60–70 hours a week and just barely making ends meet. Your business is floundering, and you’re uncertain how much longer you’ll be able to operate. It quickly becomes clear that if drastic action is not taken, it’s likely you’ll be unable to continue.
What state of mind will best serve you in your attempts to rescue your business?
At a high level, you have two choices: embrace a mindset that will help you grow from the situation or embrace one that will reinforce the status quo but most certainly means the demise of your business and your physical and mental health.
Taking a more positive, growth‐centric mindset requires you to acknowledge that something isn't right with the product and that improvements need to be made to what's being offered to better meet (and understand) customer needs. An individual with a positive mindset would acknowledge the challenge, be grateful for candid feedback from their customers, and seek opportunities to improve. You may think that you have the key to making the best, most‐beloved bread in the country, but your customer feedback tells you differently.
In contrast, a baker who embraces a mindset that reinforces the status quo is one who chastises the customers for a failure to appreciate the hard work invested in the breads and for being ignorant to the nutritional superiority artisanal breads have over their heavily bleached and bland big‐box competitors. Rather than acknowledge a failure and learn from it, the baker with a more limiting mindset seeks to point fingers, avoid ownership, and embrace a victim mentality.
As counterproductive and toxic as these limiting mindsets are, in the world of data and analytics, we embrace them often. And we do it so often, most of the time we don't even know we're doing it.
My desire to identify the root cause of why so many CDOs are struggling came to a head during my time as a data and analytics analyst at the notable research firm of Gartner. In that Gartner role, which I held from 2019 to 2022, most of my time was spent talking with chief information officers (CIOs), CDOs, and others in senior leadership roles supporting data functions at companies around the globe. It was my job to impart the knowledge and data management best practices I had gained after more than 25 years in similar technology‐centric leadership roles and while also working as an IT and product leader, a consultant, and a software and data vendor.
What I learned while an analyst, among many things, is that the problems caused by these limiting mindsets are utterly pervasive. The challenges caused by these mindsets were starkly juxtaposed to the growing belief that data is critical to business success in the digital age.
Regardless of its status as the new oil, or the new gold, or the new whatever, by late 2021 the latent potential for data to fuel competitive differentiation had become front‐of‐mind for many chief executive officers (CEOs) and corporate boards. Study after study showed the unrealized value of data within organizations, and by the height of the global pandemic (and partially because of the pandemic), companies were anxious to make that potential a reality.
This data gold rush had its beginnings more than a decade previous but had lost significant traction due to the failure of “big data” to drive meaningful benefits for most companies implementing it. The pandemic reinvigorated a C‐level focus on data, as disruptions to global supply chains and drastically changing customer behaviors exacerbated the need to have immediate access to accurate and trustworthy insights as not only a competitive differentiator – but for many – their corporate survival. In the span of just a few weeks, fueled by the disruptive force of the pandemic, the need for good data went from a “nice to have” to a “must have” – an evolution I discussed often while a Gartner analyst.
Companies that had a solid foundation of data, supported by exceptional leaders with growth‐oriented approaches to data management and data governance, weathered the pandemic storm – and in many cases – prospered from it. Those companies that lacked the leadership needed to successfully leverage the data required to take decisive actions to mitigate the impacts of the pandemic floundered.
This need for data – and the invaluable insights they can provide – further buttressed by the meteoric growth of Generative AI (GenAI) continues to fuel what could rightfully be called a golden age of data and analytics. Characterized by drastic increases in the number of CDOs, increasing investments in data and analytics technologies, and widespread awareness of the transformative power of data, this golden age is only just beginning.
For CDOs and other executives in senior data leadership roles, the AI‐driven data gold rush puts those leaders in a highly enviable position of, metaphorically speaking, operating the only gas station in a city with a rapidly expanding population and an insatiable thirst for travel. There's never been a better time to be in the business of data than over the last five years, and – thanks to AI – this is likely to remain true for at least the next five years. Nobody can express with any certainty what an AI‐enabled future looks like, but when it comes to AI, three things are for certain:
(1) As a technology and productivity tool, AI is in its early infancy. However, there is a broad and increasing consensus that the potential value of AI is unlike anything we've ever experienced in modern times, including the birth of the Internet. As of late 2024 there are rightful concerns being expressed about the high initial costs (and comparatively low initial returns) associated with this explosion, but the fact hundreds of millions of people around the globe are using GenAI to enhance their individual productivity strongly suggests this technology is here to stay.
(2) It's being widely and aggressively deployed at companies across the globe, which could all be best described as extremely early adopters.
(3) Data is the fuel to the AI gas tank, and the success of any company building or using AI‐based systems will depend on the quality of data used as inputs to AI‐based processes.
This is why it's a great time to have a data‐centric career, and it's a great time to be a CDO. Gartner couldn't have said it any better than in its annual survey of CDOs in 2024, which stated, “The CDAO has the opportunity to be the hero of the information age, with D&A maturity accelerating financial performance by as much as 30%.”1
Given the impeccable nature of the timing, one could assume that any organizations investing in data and analytics functions, including CDOs and other data professionals, would currently be experiencing a bounty of data‐enabled riches. Being in the right place at the right time certainly has its advantages, so congratulations to anyone with “data” in their work title. It's a great time to be in data.
This confluence of factors would naturally lead many to conclude that the modern‐day data gold rush is providing a bounty of riches for those companies and those CDOs choosing to invest in data and analytics programs. While this is true for a small number of companies and executives, overall the data appears to tell a very different story.
What I experienced while an industry analyst, and continue to witness today, is a prolonged inability of CDOs to bridge the gap between the potential value of data and the actual value data teams are providing to their organizations. In my daily conversation with CDOs while an analyst, I would repeatedly hear the frustrations of those data leaders' inability to drive meaningful outcomes for their companies. The experiences and perspectives I gleaned through qualitative research conducted during my client inquiries were routinely validated through multiple quantitative research surveys, all of which came to similar conclusions: the potential to leverage data for competitive advantage is very real, but only a select few are capitalizing on that potential, putting many CDOs in a highly precarious position.
For example, a recent survey of CDOs from Gartner found that only 44% of data and analytics leaders report that their team is effective in providing value to the organization.2 This number is truly remarkable – especially when you consider that it is a CDO self‐assessment and that bias can heavily skew our perspectives of our own capabilities.
Similar studies have shown that only 24% of companies characterize themselves as being “data driven”3 and that only 30% of CDOs say they are meeting objectives on the return on investment (ROI) from data and analytics.4 An inability to extract the latent value of data within organizations is reflected in the short tenures of CDO, which are half those of their CIO counterparts – even as the CDO role is now present in more than 80% of all companies.5 In a 2023 survey of 3,000 CDOs, IBM found that only 8% of them deliver performance that is highly differentiated from their peers, an extremely small group IBM calls “value creators.”6
What the data for the last 5 years consistently shows is that companies see the potential transformative power and value of data and, as a result, are aggressively expanding their investments in the people, processes, and technologies needed to best support it. Unfortunately, these investments are not yielding the returns that most – including those in data leadership roles themselves expect.
“Every year you look at the [CDO] survey, and the scores … would start to decline. Do we have a data culture? Do we think the [CDO] role is successful? The [survey] scores were not improving, so clearly something is amiss.”
Allison Sagraves, former CDO of M&T Bank;CDO Matters, Episode #31
This divide between the potential value of data and the actual value data are providing was expressed in stark terms in late 2023 when Gartner noted:
Many data and analytics governance and MDM programs continue to fail despite decades of effort. Working harder at cataloging data issues and building large committees does not work. Successful best practices exist but are not being used. To succeed, D&A leaders should jettison outmoded practices.7
A year later, Gartner suggested that 2024 was a “make or break” year for CDOs, suggesting that a prolonged inability of data leaders to deliver business value could possibly lead to an assimilation of the data and analytics function into the IT department or individual business functions.8
Sadly, the career and business risks associated with a prolonged inability of data leaders to drive meaningful value for organizations is not new. Gartner – and plenty of other analyst firms and consultants – have been ringing this alarm for several years.
These poor performance numbers and short CDO tenures, at a time when the potential to extract value from data has never been greater, necessarily begs the question of why.
If companies have been significantly investing in the teams and people needed to better manage and govern data for more than a decade and the latent potential of data to transform a business is universally acknowledged, why are so few companies succeeding at it? Why is the tenure of CDOs so low, and why are so many data professionals frustrated?
Why, at a time when there are more opportunities (and desire) for companies to leverage data for competitive advantage than ever before, are so few data leaders and professionals able to deliver meaningful value?
By the start of 2022, answering this question became a top priority and a big reason why I decided to leave Gartner. I had reached the pinnacle of any career focused on advising C‐level technical executives, and I had a great position at a great company. I was disproportionately influential in a multibillion‐dollar software industry, and I could travel the globe speaking to thousands of executives at swanky industry conferences. I absolutely loved being a Gartner analyst, but two years into the job, I could feel something wasn’t quite right.
Yet, as great a job as being a Gartner analyst was, by early 2022 I had reluctantly concluded that Gartner was one of the many reasons why CDOs were failing to deliver meaningful value, and by association, so was I. Coming to this realization was a painful yet liberating experience that required several months of introspection. I will explain my reasoning in far more detail in Chapter 5, but the short version of the story is that many of the recommendations I was giving CDOs and CIOs every day were having little impact or were simply ignored. Worse yet, some of my recommendations were reinforcing highly antiquated approaches that have been proven ineffective time after time after time – yet were foundational aspects of my analyst playbook.
Once I realized that as a Gartner analyst I was potentially doing more harm than good for some of the professionals I was dedicated to helping it became time to leave. More important, I realized that I had fallen into the very same trap that most of my clients had also fallen into – which is a dogmatic embrace of a mindset purpose‐built to reinforce the status quo.
Not only was I doing the same thing over and over and expecting a different result, but I was telling my C‐level clients to keep doing the same thing over and over with the expectation of a different result. Coming to this realization that I was helping to reinforce a status quo that does not well serve data leaders was difficult, but necessary. It’s also one of the reasons why I wrote this book.
“The harsh reality is that data governance, as performed today, is not working. So, if we want to lead, we have to recognize that and we have to do something different. Maybe that involves dissecting things – figuring out what portions really do work, building on them, dropping the rest. Or maybe it means something altogether different. My own view is that we are trying too hard to make things work in the current organizational context, which is just not ‘fit for data.’ And we have shied away from the big, hard, important problems, such as ‘What does fit for data look like?,’ ‘What organizational structures work well with data?,’ ‘How do we get everyone involved?,’ and ‘What does it really mean to be data‐driven?’”
Tom Redman, author of People and Data;CDO Matters, Episode #29
Once unshackled from the many constraints imposed on Gartner analysts (many for good reasons) and having the freedom to explore alternative perspectives on the status quo, I set out to fill in some of the gaps in my industry knowledge that had formed while living in the Gartner thought bubble. To answer my gnawing question of why so many data leaders were failing to deliver value to their companies, I needed a wider perspective from a wider set of people, so with the backing of my new employer Profisee, I hit the road.
Mid‐2022 through 2023 I visited more than 20 cities across the United States, Canada, and Europe visiting data practitioners and leaders from across every industry and vertical. I went to multiple conferences – often in speaking roles – and I was able to hold meaningful and frank conversations in ways I couldn't have done at Gartner, and I slowly began to inch closer to answering the one question dominating my thoughts for the previous three years.
At these conferences I was able to meet with other notable academics and thought leaders in the data and analytics space – many of whom I had admired over the years – and some of whom have even become my friends. The community of people who regularly speak at data‐related conferences is a relatively small and tight‐knit group of experts, all of whom have a lifetime of experiences to share.
However, unlike me, many of them have only limited experience as data practitioners or leaders. The same is true with industry analysts. This doesn't necessarily mean they don't have valuable insights to offer, but it does mean they've never had to implement the things they're recommending.
What I saw at conferences is a tendency for the thought leaders and experts to have some of the exact same frustrations as data leaders, born from years of making recommendations that aren't significantly advancing our profession. As data leaders seem stuck in a respective pattern and unable to drive meaningful value, the same is true of the thought leaders who are supposed to be providing the insights needed to drive meaningful value. Rather than offer advice that works, many of the brightest minds in our industry are consistently reinforcing old patterns that have been proven to not work – many of whom seem blissfully unaware they're doing it.
On top of my travel to conferences, I started building a community of data leaders and practitioners on LinkedIn – which provides insights on the needs and thought processes of data leaders in ways I could never have experienced at Gartner. LinkedIn is not immune to bias and the vagaries of the “algorithm” in terms of what you see and who you interact with, but it's still a fantastic venue to connect with other data professionals who are willing to share invaluable knowledge about the state of the data nation. With 16,000+ followers (and growing!), my LinkedIn presence has been an important part of my daily routine where I am able to share what I know in exchange for learning from others. And learning, I most certainly am.
In June 2022 I also launched a podcast called CDO Matters, and more than 60 episodes later, I've had some amazing conversations with some truly brilliant CDOs about their biggest challenges. I’ve included many transcribed quotations, taken from published episodes of my podcast, from these experienced professionals through the text of this book. Previously in my career I would never have guessed that at an age that qualifies me for AARP, I would have a podcast and be an online content creator and an industry “thought leader.” I'm living proof that old dogs can indeed learn new tricks, the least of which now also includes how to write a book. Go figure.
The combination of my incredible appetite for research, my love of complex problem solving, my very fulfilling years of experience at Gartner, my 25+ year career in software and data, my LinkedIn community, my podcast, and the love and support of my wife is what finally led to what you're now reading.
Once I was able to determine that an overly limiting mindset focused on the status quo is the root cause of an inability of data teams to deliver transformative value, many of the associated issues I had been studying for years that previously seemed intractable suddenly made complete sense.
A widespread embrace of a highly limiting mindset provides fertile ground for the creation of a dysfunctional relationship between the producers and consumers of data, and it disempowers data leaders from innovating and influencing any meaningful changes to their organizational cultures. It reinforces an inward focus that puts more value on the management of data than customer satisfaction, and in doing so it inverts the entire data value proposition.
CDOs and those working for them may be implementing the right technologies and following data and analytics “best practices” as diligently as possible, but a mindset that focuses on limitations and problems is having a disproportionally negative impact on CDO results. Ideas or practices that may be good in theory, or even when proven through scientific research, are failing to deliver when implemented in an environment managed by people focused on problems and not on growth.
Albert Einstein once famously said, “You cannot solve a problem with the same mind that created it.” This is the sad reality for many data professionals today – where the status quo is so rigidly engrained in the mindsets they apply to their data, their customers, and their companies – that it's hindering their ability to see and appreciate different ways of solving these old problems.
Data leaders and practitioners are not the only people in the data industry who suffer from limiting mindsets. Industry analysts, consultants, conference speakers, and software vendors all play a role in reinforcing these perspectives, and all help to reinforce and repeat approaches that consistently fail to deliver meaningful results. Just as data leaders are stuck in the status quo, so are the various players you would hope are somewhat “immune” to the forces that hinder growth and innovation in the data world. But sadly, they aren't.
This is because a negative feedback loop powers an ecosystem of consultants, analysts, and software vendors that act in unison to reinforce many of these highly dysfunctional mindsets. The inability of consultants, analysts, and other thought leaders to promote meaningful change in the world of data and analytics, or deliver meaningful value to their clients, creates fertile ground for the same frustration, finger pointing, and disempowerment that similarly plagues data leaders. I cover this negative feedback loop in much greater detail in Chapter 5.
These limiting mindsets have taken deep root in our companies and within our people, and worse yet, examples of this limiting mindset are widely embraced as dogma and are almost never questioned by anyone in the industry. Case in point: garbage in, garbage out. A continued reliance on mindsets that disempower data leaders while blaming others for their problem is producing a lack of innovation, rigid thinking, and a recursive negative feedback loop. The more we continue to invest in these highly limiting mindsets (and fail to challenge them), the harder it becomes to embrace the behaviors that are proven to deliver meaningful business value.
“The thing that kept coming back for me was that data governance was the Achilles heel of most programs and our ability to deliver results. And the simplest thing I did was, I Googled ‘data governance,’ as I wanted to know how people were defining it. And it came back with a page‐long definition. I’m not kidding. It was two paragraphs, and it was a full page. And I remember distinctly, I shut my browser, and I stepped away from my laptop, and I determined that I’ve got to write a book. There’s got to be a better way. How do you succeed with anything that’s a page long in terms of how you describe the work? Why are we still doing the same things with data governance in the data space? It’s illogical to me.”
Laura Madsen, author of Disrupting Data Governance;CDO Matters, Episode #9
Most ironically, an unconscious devotion to these limiting mindsets is helping create situations where data leaders are modeling the exact opposite behaviors that they expect others to embrace. This phenomenon is covered in more detail in Chapter 4 on data culture.
Empowering data leaders to do away with these old ways of thinking in favor of new, more productive perspectives is a core theme of the book. I call this new way of thinking the Data Hero Mindset, an approach that jettisons the limiting perspectives that no longer serve data professionals and wildly embraces those that take a more positive view of their role, their customers, and their data. In doing so, the Data Hero Mindset enables hard‐working and well‐intentioned leaders and practitioners to become the harbingers of growth their organizations so desperately need, and it will finally allow organizations to realize the full potential of their data.
Our journey starts with a deep dive into an explanation of a mindset and examples of limiting mindsets in action in companies today. We'll than transition into a discussion around more positive mindsets in action, which will culminate in specific recommendations data leaders can take to promote the culture needed within their teams to shift toward more positive, growth‐centric mindsets. I conclude the book by sharing some of my perspectives on how our industry will change when more of its leaders more widely embrace acts of data heroism.
For many, realizing they may have subconsciously played a role in contributing to the ongoing failure of their companies to realize value from data may be uncomfortable. Breaking the behavioral patterns that have disempowered data professionals for decades will not be easy – and I suspect many will choose not to and vilify me in the process.
This book and the perspectives in it will surely be seen by as provocative, if not heretical. It provides a completely different perspective on the root causes of the issue of why companies are struggling to get value from data than anything being shared at industry conferences, on LinkedIn, or in any books on the topic. This book challenges many preconceived notions shared within the data community about why there is friction between producers and consumers of data. It will challenge the status quo and, in the process, I hope make data leaders think differently about their roles and the value they add to their companies. In keeping with one of the main traits of a growth mindset, I truly hope that anyone reading this book will receive my feedback in the spirit in which it's given – which is a sincere desire to see the data profession grow and data professionals to thrive. This is especially the case for my fellow thought leaders, partner consultants, and former analyst peers – all of whom I firmly believe have nothing but positive intentions – but who may also be reinforcing a status quo that’s crippling our industry.
Before diving in, I should note that what you're reading is an entirely qualitative analysis. I've spoken to thousands of leaders in the data space, but the insights are entirely mine and are not backed (yet) with significant quantitative or academic rigor. In the spirit of being more “data driven,” I invite any readers who may be more academically inclined to consider research to either prove or disprove all the assertions in this book. After all, if we committed to being data driven, we should be building data to explain why we struggle at providing value from it.
It's also important to note that the perspectives and recommendations I’m making in this book are most relevant to what could best be described as more “centralized” expressions of data teams in most organizations. These groups tend to have a mandate to support both corporate and functional levels of the organization, and they tend to operate from within an information technology function as a shared service – but not exclusively. This is not to say that data teams that exist within functionally aligned departments (like finance or marketing analytics teams) won’t also benefit from this book – they certainly will. However, the insights and recommendations are generally provided in the context of a data function that supports every aspect of a business, and not just a subset of it.
Finally, I will say that as excited as I am to have come up with an answer the question that's beguiled me – and an entire industry – for years, the excitement is tempered with the knowledge that I've helped to reinforce many of the limiting behaviors described in this book. However, having all of us take agency over the many previous decisions that have helped create the mess we're in is a key step in moving through it.
So, in many ways, writing this book is a form of therapy that I hope will put others on the same road to a recovery of more positive ways of thinking about how we approach data management – and it's a journey that I hope you'll join me on. Overcoming the mindsets that hold us back will enable all of us to transform into the exceptional leaders our organizations desperately need and will finally allow the full promise of data to be fulfilled in your company.
Let's become the data heroes our companies so desperately need us to be.
Are you with me?
Notes
1
Gartner (2024). CDAO Agenda 2024: reinvent yourself or risk failure. 12 March 2024.
https://www.gartner.com/en/documents/5272363
(Accessed: 7 January 2025).
2
Gartner (2023). Gartner survey reveals less than half of data and analytics teams effectively provide value to the organization [Press release]. 21 March 2023.
https://www.gartner.com/en/newsroom/press-releases/03-21-2023-gartner-survey-reveals-less-than-half-of-data-and-analytics-teams-effectively-provide-value-to-the-organization
(Accessed: 7 January 2025).
3
Wavestone (2023). Newvantage CDO study, 2023.
https://www.wavestone.us/wp-content/uploads/2022/12/Design-2023-Data-Analytics-Survey-Report.pdf
(Accessed: 7 January 2025).
4
Gartner (2021). CDO Agenda 2021: influence and impact of successful CDOs in the Sixth Annual CDO Survey. 31 March 2021.
https://www.gartner.com/doc/4000087
(Accessed: 7 January 2025).
5
HBR.org (2023). Why do Chief Data Officers have such short tenures?
https://hbr.org/2021/08/why-do-chief-data-officers-have-such-short-tenures
(Accessed: 7 January 2025).
6
IBM (2023). 2023 Chief Data Officer Study: turning data into value. 20 March 2023.
https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/cdo-2023
(Accessed: 7 January 2025).
7
Gartner (2023).
Predicts 2024: data and analytics governance requires a reset
. December 2023.
8
Gartner (2024). CDAO Survey: CDAO Agenda 2024: reinvent yourself or risk failure. March 2024.
Chapter 2The Data Hero Superpower: A Positive Mindset
In the opening chapter I stressed the importance of a more positive, growth‐oriented mindset. What exactly does that mean? What impact do positive mindsets have on individual and corporate productivity? What behaviors and perspectives typify people with more limiting mindsets, and how could they impact job performance? Could an unproductive, limiting mindset be sufficient to hold back an entire industry from providing prolonged and meaningful value?
In this chapter, we will explore these questions in more detail.
What's a Mindset?
A mindset is an established set of attitudes that influences a person's perceptions, beliefs, and behaviors. In the context of a corporation, a mindset can also describe a group's attitude, which in turn affects how individuals in the group approach their work, solve problems, and interact with each other. Mindsets are extremely influential in how we all view the world and, in turn, how we choose to engage with it.
A person with a more limiting mindset will tend to see problems and not opportunities, and they tend to be highly resistant to change. Mindsets that hinder growth are characterized by doubt, pessimism, and complaining – and those who embrace them are prone to blaming external forces and people for a failure to overcome obstacles. These people are also more likely to see another's intentions as negative and not supportive. People with more limiting mindsets tend toward black‐and‐white thinking and will often see choices in business as an “all or nothing” propositions.
Your mindset influences everything you think and everything you do. The worst (or best) part about a mindset is that they have highly reinforcing properties: if you think positively, you are likely to create more positive situations and outcomes, and if you think negatively, you're likely to create more negative situations or outcomes. And the more outcomes you drive (one way or the other), the more the mindset is reinforced.
I was first exposed to the concept of a mindset when I read Mindset: The New Psychology of Success, by Stanford Psychologist and Professor Carol Dweck, published in 2015. I first read this book in early 2022 after joining Profisee because our CEO, Len Finkle, is passionate about the importance of all employees having a growth mindset – so much so he made reading the book part of the company onboarding process.
What Dweck shares in her book is that there are essentially two mindsets that we (and by association, our organizations) can embrace: either a growth mindset or a fixed mindset. Dweck argues that those with a growth mindset, who believe abilities and intelligence can be developed through hard work and dedication, are more likely to achieve success. She asserts that a growth mindset fosters resilience and persistence, which are crucial traits for business leaders and organizations aiming for long‐term success.
Conversely, Dweck sees fixed mindsets as those that tend to be more intractable, as embodied within people who choose to avoid challenges, ignore feedback, and give up easily. People with a fixed mindset, according to Dweck, are more interested in being told they are right than in learning, and they are more likely to see their level of intelligence as static. When given the choice between validation and growth, people with a fixed mindset will typically choose the former.
The words Dweck uses to describe either ends of the mindset spectrum are a rooted in quantitative academic research and may often do not completely align to the mindsets that I’ve encountered over my lengthy professional career – particularly those that stand in opposition to growth. As such, in this book I will use the terms “positive” and “limiting” to describe the opposite mindsets that I see as being mission critical to either promote or change. The perspectives I describe on either end of this spectrum are firmly rooted in, but not limited to, the mindset definitions posited by Dweck and other researchers in this space. This is why I’ve used different words than Dweck to describe these polar opposites, since what I’ve observed goes beyond the boundaries of Dweck’s research.
This is particularly the case in my use of the word “limiting” to describe the mindsets that are the opposite of those needed to promote growth. Being unable or unwilling to grow is one thing, but forwarding perspectives and embracing behaviors that actively subvert the stated goals of your company and your department is another thing entirely. If you embrace behaviors that harm your customer relationships, then the impacts of your choices go well beyond simply embracing the status quo or being “fixed.” They hinder your company, they hinder your career, and they are limiting in every definition of the word.
There are many possible psychological explanations for many of the examples of what I'm calling either positive or limiting mindsets in this book. Often, they align to what Dweck would call a growth or fixed mindset. Other times, they may align to other phenomena, and when they do, I'll share those details and cite my sources or experiences.
Ultimately, as you'll read in more detail later, this book is about empowering data leaders. I'm not writing this to judge; I'm writing this to help improve the careers and results of data professionals. If you come to realize that the behaviors and perspectives I'm labeling in this book as more “limiting” describe you, then you have a choice to make. Either you can reject my thesis and double down on the status quo or you can take the opportunity to explore different ways of thinking about old problems. I hope you choose the latter.
That's because the former reinforces the status quo, where available data suggests the odds that you'll deliver meaningful value for your company are significantly worse than a coin flip. Rejecting the status quo will open the door to entirely different perspectives on how to leverage the power of a positive, growth‐centric mindset in your job as a data professional. In doing so, you'll learn that growth mindsets are quantifiably proven to increase job satisfaction, retention, and productivity. Embracing more of a growth mindset seems like a winning proposition to me (even if you think I'm completely wrong), but as you'll read in the following chapters, I didn't always have this perspective. I only recently broke free of the mindset that was limiting my personal development and career growth, and thankfully so.
If you are struck by how often your current perspectives may align to a highly limiting mindset, take comfort in knowing things don't have to stay that way. Mindsets can thankfully shift, and I'm living proof. For decades, I embodied the behaviors of a person with a problematic mindset toward the data I managed, my role as a data professional, and my customers. For more than a decade I managed my career in full alignment with more of a very limiting mindset. Yet somehow, I've lived to tell the story of what it takes for a data professional to break free from a mindset that hinders their growth, and you're reading it now.
So, if my use of the words “suboptimal” or “‘limiting’” to describe any of the perspectives or behaviors that you embrace causes you discomfort, or perhaps even makes you a bit irritated (or even slightly angry), then good. Let that discomfort be the motivation you need to take a step back and critically evaluate your desired results versus your actual results. Are you where you want to be in your career, or your company? Are you committed to helping your company leverage the transformative power of data? Do you want that big promotion? Do you want to change?
If you've read this far, then clearly you answered “yes” to one or more of these questions, and you're interested in finding ways to break from the status quo. That's a great thing, and it's a necessary step in your individual transformation. You're one step closer to becoming your company's data hero.
Mindset and Corporate Culture
The importance of organizational culture cannot be understated – and is the topic of hundreds of books and research papers. The old saying “culture eats strategy for breakfast” reflects the reality that a dysfunctional culture will ultimately thwart all efforts to execute on a business strategy.1 You can throw all the money in the world at processes and tools, but if the individual people of the organization cannot work together or trust each other, then all collective actions are likely doomed to fail.
An organizational culture is defined by the norms that characterize an organization. Norms are the collective values, beliefs, and practices of the organization. Norms significantly influence how individuals behave in an organization, and like a mindset, they can have both positive and negative impacts. Productive and economically successful organizations are often characterized by the norms of collaboration, innovation, and integrity.2 Conversely, norms such as a resistance to change, a lack of accountability, and a loyalty to the past can hinder corporate performance.
Certain values can drive behaviors that lead to positive results, and certain values can lead to negative results. This is true at an individual level, and it's especially true at a group or norm level – where the impacts are magnified and recursive. This is how an organization's overall “culture” is intrinsically interconnected with how people think about their jobs.
The difference between a mindset and a value is nuanced. Mindsets shape how individuals perceive and react to situations, while values are the core principles that guide behavior and decision‐making. Values influence ethical and moral judgments, while mindsets influence attitudes toward learning, growth, and challenges.
The work of Carol Dweck, and others, has shown that collective mindsets can shape and influence the values of its members. An organization that embraces a growth mindset focused on learning and development will promote values of innovation and collaboration. Just as values can be collectively expressed as norms, mindsets can also be collectively expressed – influencing both individual and group perspectives and, ultimately, corporate productivity.
There is a deep interconnection between mindsets, values, and behaviors within work settings. Mindsets shape values, and values drive behaviors. And in group environments, the behavior of one can have a significant impact on the behavior of others, resulting in situations where one person synchronizes to the behaviors and emotional state of another. The idea my perspectives can influence your perspectives is known as “emotional contagion,” where data suggests that emotions can indeed be contagious. Emotional contagion can be triggered by facial expressions, by indirect interactions (like sending an emotionally charged email), or simply by observing the behavior of others.3 It can even be triggered through indirect exchanges, like through social media.4
Emotional contagion can influence all range of possible behaviors, both positive and negative. Positive emotional contagion is linked to better interpersonal functioning and prosocial behaviors.5 An example of positive emotional contagion is seeing somebody smile and then feeling more positive as a result. Another example is where you observe somebody expressing empathy or sympathy, where you respond by expressing the same.
Negative emotional contagion works the same way, but in reverse. If you see somebody who is visibility frustrated or deflated, you are more likely to feel the same way. However, unlike positive emotional contagion, data suggests that negative emotional states tend to have a stronger affective impact than positive emotional states.6 That means if you're repeatedly verbalizing a frustration related to how difficult you believe you job is because you think the data you're working with sucks, then your actions will negatively influence everyone around you – since your negative emotions will have far more impact on others than any positive ones.
The combination of the behaviors we witness in others, and the mindsets we embrace, can significantly influence our work behaviors and, therefore, our corporate culture. Not only can culture be influenced by our own views of the world, but it can also be influenced by the behaviors of the company itself.
In their 1992 book titled Corporate Culture and Performance, John P. Kotter and James L. Heskett studied a wide array of companies to conclude that business success can have a positive impact on corporate culture.7 When managerial actions drive changes in group behaviors, shared values change to reflect the actions required to deliver the desired outcomes. Put another way, a corporate culture can be meaningfully influenced by business success, and this success tends to act as a feedback loop.
