Data Leadership for Everyone: How You Can Harness the True Power of Data at Work

INTRODUCTION

DATA, WE’RE NOT MAD, JUST DISAPPOINTED

Data is lazy. It sits in files or databases, minding its own business but not accomplishing very much. Data is like someone in their midtwenties, living with their parents, who won’t get off the couch and make something of their life.

Data is also the closest thing to truth we have in our organizations.

Data informs us about our businesses, and then we can either use that knowledge to improve what we do or decide that change is hard and go back to punching the clock, blindly hoping what we’ve always done will keep working. It is up to us—business professionals, executives, managers, frontline workers, entrepreneurs, all with the ability to be data leaders, regardless of whether the main functions of our jobs are related specifically to data—to motivate data to get up off the proverbial couch and make our organizations more successful!

In business, this means using data to understand organizational processes and opportunities and then doing things better to improve our business outcomes. Sounds straightforward, right?

The problem is we are generally awful at turning the potential of data into something of actual value.

At one extreme, we have the most innovative companies doing unbelievable things with data and analytics, like when SpaceX successfully launched a rocket into space and then used real-time analytics to control and safely land an unmanned booster rocket upright on a barge floating in the ocean. Seeing a video of the rocket landing backward for the first time, the most rational response a person can have is to say, These special effects look totally fake.

But they aren’t special effects at all. They’re real life!* And now, rockets are landing backward all over the place like it’s no big deal. Cars are logging millions of autonomous miles, we talk to our TVs, and everybody carries a device in their pocket that can instantly connect them to the answer to nearly any question they can imagine. And that last one has been the case for over a decade!

All these things are amazing, every single day. It is an incredible time to be alive, and we should all be walking around astonished by what surrounds us and in awe of what awaits us beyond the horizon.

Instead, our expectations have risen with these technological capabilities, and we tend to have diminished tolerance for failures to meet these higher standards. If Netflix buffers for five seconds on our sixty-five-inch TV, we call it a piece of junk. If we request a password reset email and it doesn’t immediately hit our inbox, we assume the company has been hacked and we start making plans to cancel our account and move to a competitor.

In our personal lives, we have such constantly profound data-driven experiences that it is especially frustrating in our work lives when we can’t even get the numbers on two reports to match! How is it that we can go from literally talking to our entertainment devices at home to feeling like we are back in the dark ages of the 1990s when we head to work?

It used to be that we went to work to access the good stuff. Just twenty years ago, the technology capabilities at the office were far superior to those that individuals could own, mostly because the costs of high technology were out of the reach of individual consumers. Only businesses with big checkbooks could afford the big servers and the software applications to run on them.

Sure, businesses today still have the big checkbooks and an ability to buy fancy technologies far beyond our consumer-grade phones. And clearly some, like the backward-rocket-landing ones, are putting that technology to productive use. But then why do so many of our companies have such difficulty with something as simple as basic reporting?

It’s because this is not a technology issue at all. It’s about how we’re using (or failing to use) that technology to leverage the information it gives us. Though data analytics almost always involves technology, it is not fundamentally about technology. Data is the result of our best attempts to record truths related to our businesses. Data analytics then evaluates and applies these truths to influence business activities so that future truths will, hopefully, improve by comparison.

Sometimes I wonder if organizations are even serious about being competitive within their industries. We see incredible innovation happening around us, but most of our organizations have failed to fully capitalize on the power contained in our data. We create pretty visualizations to obscure the unreliability of our data, and when we do luck into real data-driven insights, we often ignore them and revert to making decisions based on emotion or gut instinct.

Data is quickly becoming our most abundant resource while simultaneously propelling us to new heights in our ability to squander valuable things.

Our organizations currently find themselves largely incapable of harnessing the riches that data can provide. Instead, we find ourselves at greater risk, fearing data breaches and privacy violations that make the evening news and the trending topics on Twitter. It is not that we think data is worthless. Far from it!

Data being valuable is the premise on which I’ve spent decades building my career. The executives I encounter across every industry now see the potential value in data but struggle knowing what to do about it. Developing data-centric technology systems, growing consulting organizations, and leading massive change initiatives from the trenches have taught me how to navigate the innumerable complexities amid turning data into business success. Data leadership is a topic that connects everything I’ve learned, and the lessons in this book represent my life’s work and my best attempt to save you from many years of trial and error, plenty of questionable career decisions, and at least one overpriced piece of sports memorabilia.

Data is complex, just like the truths it attempts to describe. There are different perspectives, and it may not always be easy to identify which of those perspectives are correct in which circumstances. The systems and processes to record and relay data must serve many different purposes and have significant technology and business considerations and implications. Though the complexity is palpable (especially at large scale), if we can break it down into smaller, more actionable chunks, we will find data excellence is within our reach.

Beyond the pure joy that comes from turning bits of information into real impact, in the next few years, any organization that does not become fundamentally data driven will inevitably be destroyed by their competition, whether current adversaries or new disruptors.

I once had an employer tell me I couldn’t put a statement like that into a white paper because it was too provocative. I countered with, But it is true, and it is also incredibly important. Shouldn’t we share this with people?

Long story short, I don’t work there anymore.

Never be afraid to speak the truth just because someone might be uncomfortable with the consequences.

The idea that some organizations (and even entire industries) are still afraid to acknowledge that data competency is a requirement of competitiveness makes me chuckle and/or want to cry, depending on the day. Becoming proficient at turning data into improved business outcomes is now required for any organization to thrive. The competitive advantages gained by establishing a deep and detailed understanding of today’s truths will uncover countless opportunities otherwise hidden from our view. If we do not, our competitors will, and they are probably already ahead in the journey.

What is scarier than the fact that data excellence is now necessary (not just helpful) for success is that the bar of reasonable competence is moving so high, so fast, that I fear many of our existing organizations will simply disappear rather than adapt quickly and remain competitive.

If this all seems a little strange or esoteric, don’t worry. This whole introduction is like a data-flavored amuse-bouche (and if you needed to look that up, that makes two of us!). Throughout this book, we will cover so much about data and leadership and how together they drive organizational success, it will become second nature by the time you are done reading (or listening, if you’ve chosen the audiobook option with its lush baritone vocals).

And I guess now is as good a time as any to address my writing style. Some may call it informal whereas others, like my seventh-grade teacher (Mrs. Nelson), would probably say the style is linguistically a poor choice for the topic of discussion. I like to think of it as a little more approachable than your typical data or business book, but mostly I needed to keep us both awake while exploring subjects that can be a bit on the dry side. This does not mean I’m any less (or more) qualified, knowledgeable, or passionate about data leadership than if I wrote the way my public school education taught me. It’s just that I’m trying to make seventy-odd thousand words of data management and organizational theory a little more tolerable for all of us.

By studying the lessons in this book, you will learn how to maximize the value of data. You will see how it all fits together and will change the future of your organization. Whether you are an executive, sales rep, department head, copy editor, data scientist, or database administrator, this book is for you. It does not matter your role, just that you think data might help in some way.

If nothing else, remember that here we are among friends and that if we fail to deliver on the potential of our data, our companies are doomed. No pressure!

Let’s get started.

* Unless, of course, it turns out we really are living in a simulation like the Matrix or something.

PART 1

DATA LEADERSHIP FOUNDATIONS

1

THE VALUE OF DATA LEADERSHIP

WHAT MAKES DATA SO VALUABLE ANYWAY?

People generally acknowledge that data is valuable. If you walked into a random business somewhere and asked them if their data is valuable, you would get a bunch of nodding heads who were probably just being polite before they call security. But if, to avoid alarming everyone, you scheduled a proper meeting or focus group and asked the same question, they would almost certainly agree their business data is valuable. After all, hackers break the law to access organizations’ data all the time, so it must be worth something, right?

Before we go further, let’s just assume everybody is right: data is valuable. The more interesting question is Why is data valuable?

You can use data in a report, maybe run some metrics, inform an insight or two. But what makes those actions valuable? And not just theoretically valuable but actually valuable? Like real dollars, pounds, and euros valuable?

It’s not as immediately obvious as something data related should be, is it?

That’s because we get so lost in doing data stuff that we’ve lost sight of what even makes data valuable in the first place! We pull data, we nod our heads, we talk and talk and talk, and in the end, we have no earthly idea why we’re doing any of this. It’s time to wake up!

Everything we talk about in this book centers on using data to create something of value. Let’s refer to this as data value.

Think of data value as the ultimate measure of the true benefits our data sprockets and widgets give to our customers. This depends on not just what we provide to them but how well our consumers use our outputs (their inputs) to turn the potential value into real outcomes.

Data value is the difference in business outcomes with data versus what could be achieved without data.

Realize that data value is not really about data at all. It is about the positive change in business outcomes that happens by using data. And just as important is understanding the inverse relationship: if our actions related to gathering, interpreting, and using data create no positive differential in business outcomes, then we have done nothing of value.

In more practical terms, if we deliver a report that fails to change anybody’s behavior and thus creates no change in business outcome, then how could the real resources we used in creating that report be justified?

But data value can also be more nuanced. In my home suburb near Chicago, they tore down the long-shuttered Kmart to make way for a Mariano’s grocery store. The Dominick’s grocery a half mile down the street had shut down a couple of years earlier after succumbing to competitive pressures chain wide. It now sits empty. The former location of the Dominick’s was admittedly a bit awkward to access, but why go through the effort of a teardown/rebuild of an old Kmart when another, better-equipped facility was empty nearby?

Data value! Mariano’s recognized that while location is important, there was an opportunity to build an entirely new facility with plenty of parking and all the other amenities their customers value. They determined this would help their store stand out among crowded but aging grocery store competition in the local area. And in the years since they opened, Mariano’s has been the go-to premium grocery store option for miles around!

The implications of this outcome are significant: one fast-growing grocery store brand replaced a dying one a half mile away and was built on a property that used to house a failed precursor to Walmart and Amazon. A grocery store! This is a classic case study of old-school, low-margin businesses being disrupted by newcomers willing to take chances based on data.

WE CAN’T MANAGE WHAT WE DON’T MEASURE

If we think data-driven strategy and operational efficiencies are not being leveraged by the upstarts in our own industries, then we are missing everything. If we want our organizations to remain relevant, we must maximize the use of data to get better at what we do best. And this starts with really understanding how data value is quantified.

Data value is measured in three ways:

Revenue

Cost

Risk

These are the only ways data value is created.

Our efforts will have costs, which is a decline in value, and we hope the decline is more than offset by a combination of positive impacts. Increasing revenue and decreasing costs are straightforward: money in and money out. It is helpful to consider risk separately because risk management effectiveness cannot be measured by looking directly at results; the sample size of events that happen is too small to gauge the optimal strategy. It’s like insurance.

Also note that dollars and other hard currencies may not be the only unit of measure, since the objectives of an organization may be to provide another outcome, such as a public service or charitable function. If we think of increasing revenue as the organization’s ability to deliver more of that service or function, decreasing costs as improving the operating efficiency of delivery, and managing risk as managing risk, then we are all set! We may need to do a little semantic translation of some of the concepts, but the concept of data value very much applies in any type of organization.

The opportunity to deliver on the promise of data to our organizations is beckoning. Consider recent history and where it’s all heading from here. Looking back at workplace pictures from the 1990s, you might think, That’s so cute! Things were so much simpler then, with our enormous computers and phones with cords. We had predictions about what the future would hold but overemphasized physical innovations like flying cars over the impact of the internet and supercomputers in every pocket. We are now in the middle of a big societal shift, with a front-row seat to watch history repeat itself!

Today we are far beyond where we were, but nowhere near where we are going. That is, we are part of the way through a long journey!

Tremendous changes are happening with data and technology. We are transforming from batch processing to continuous availability and reliance on constant uptime. We once had to go sit at a desk to amplify our human capabilities with computers, then one day, laptops let us work from the couch, and eventually mobile devices allowed instant access wherever we want it. Evolving rapidly now, the Internet of Things (internet-connected devices with sensors, cameras, and interactive features) has created a world where we are living with an array of connected devices that are always on, always monitoring, and hopefully always improving our lives. Shh! Amazon Alexa probably heard that, and we don’t want to make her mad.

Everything is trending smaller, faster, and more connected, to the point that physical hardware endpoints are often reduced to their purely physical role. From RFID chips to beacons to fitness watches—by offloading the complex data processing, networks of cheap, independent sensors can become ubiquitous.

The stakes couldn’t be higher than they are with data. Why have we not fixed this by now? It’s not like data wasn’t around twenty or thirty years ago. It almost feels like we are honoring some silly tradition mandating that we stink at data management, but the pain is getting far worse because the pace of data growth, along with the technology capabilities that supercharge it, are increasing exponentially in their power. The gap between the exceptional data-driven organizations and the marginal ones is also ever increasing, and we are seeing the consequences everywhere we look, even with our hometown grocery stores.

We pay so much attention to data and technology that we may be inclined to think that data and technology are where we will find the solution to our challenges with creating data value. I believe this is wrong. The answers to realizing the value of data are found within the people and organizational dynamics of our companies. This book explains why this is the case and helps us understand what we need to do to overcome these challenges and create the data-driven organizations that will succeed in the coming years.

Data value is the common thread, and it is up to those of us who see its potential to take action to make it real. After all, the folks who do not yet see the value in data will not be the ones to pave the way.

DATA LEADERSHIP MAXIMIZES DATA VALUE

As was mentioned in the introduction, data doesn’t do anything by itself. When we say data in the context of data value and data leadership, we also mean all the technology capabilities, interfaces, artifacts, insights, actions, etc., that are related to the data. But make no mistake, data is the star of this show! Data is the influencer of value-creating actions, and the role of data leadership is to give data’s direction maximum reach.

There is an old-school discipline related to optimizing data efforts called data management. This is a collection of data-related functions that people conduct to ostensibly make data better. These are things like metadata management, data quality, and data governance, all of which you will find covered in detail later as part of the data leadership framework. We didn’t invent these concepts with data leadership, but we’re evolving the why and how we choose to invest our efforts at any point in time.

Data leadership gives us the ability to turn what feels like an impossible challenge into an engine of data-driven business improvement. The magic of data leadership is in balancing the various aspects of data, focusing our limited energy to create the most data value overall.

Maximizing data value is the most important thing.

We need to stop pretending that there is intrinsic value in investing energies in data capabilities and instead focus on creating value within our businesses as our top priority, with data as our guide. Traditional data management approaches do not focus enough on the contribution to real business outcomes, and this leads to wasted efforts and less impactful data in the end.

Businesses typically choose their investments based on an expected return. This comes from increases to revenues, decreases to costs, or improvements in risk management. These measurements sound familiar, right? The sad truth is most data management efforts do not even know their intended return on investment (ROI), let alone the returns they achieve. This is no way to run a business, and it is no way to lead data-driven change efforts. Data leadership keeps us later focused on the most important thing, maximizing data value.

Data leadership is how we choose to apply our limited resources to maximizing data value.

There are so many options for how our time, money, and other resources may be applied, and breaking them down and understanding when and how to apply them is the focus of much of this book. We will explore some of the foundational disciplines of data management and some of the technologies that can help us amplify them. These are the collection of tools in our tool belt, so it is imperative that we know what they are and how they impact data value. But just as important, we must learn when and why we should