TOO MUCH MATH, TOO LITTLE MEANING
On January 23, 2019, I gave a keynote in New York City to an audience of seven hundred executives who specialized in leveraging data, technology, and algorithms to enhance the effectiveness of their marketing efforts.
I celebrated the great enablement and empowerment that the World Wide Web, search, e-commerce, mobile, and social networks had brought about, and I noted that on that day, the five most valuable companies in America (Amazon, Apple, Google, Facebook, and Microsoft) were pioneers and leaders in leveraging data, algorithms, technology, and networks.
Indeed, with continued advances in artificial intelligence (AI) as well as the increased speed and pervasiveness of connection made possible by 5G, the technological signs were clear: we were about to enter an age of even greater wealth and possibility.
However, something was amiss, and many storm clouds were gathering. The same data-driven technologies giving rise to such wealth and opportunity were increasingly being leveraged in harmful ways, leading to the breakdown of trust, increased polarization, and rising inequality.
As leaders of these companies we needed to acknowledge and address the downside caused in part by our maniacal focus on short-term, data-driven financial and engagement metrics. We saw this all around us.
Amazon had just seemed to overplay its hand by running a beauty contest and insisting on financial breaks to set up a second headquarters, apparently tone-deaf to how such behavior from a trillion-dollar company, led by one of the richest men in the world, would be perceived. A backlash had forced them to retreat from New York as a second headquarters.
Facebook and YouTube were increasingly being leveraged to great harm all over the world, and both firms appeared to be too slow in acknowledging and fixing these problems and were losing public trust.
The challenges went beyond technology firms.
Leaders from Wells Fargo had become so fixated on meeting account-opening goals that they misled their customers in highly troubling ways. Doctors at Sloan Kettering were questioning why some of their colleagues were getting financial stakes in the research the institution had been conducting. Leading consulting firms such as McKinsey were being accused of allegedly helping criminal leaders and pushing unsafe products.1
Our wealth and success and the sweet siren call of optimizing numbers had led us to overlook the collateral damage of forgetting, or neglecting to consider, the human side of the equation.
In my speech entitled “It’s Time to Optimize for the Citizen,” I suggested that we rethink our approach and consider that our customers were not only consumers but citizens. That as Elie Wiesel, Holocaust survivor, had noted, we should “Think Higher. Feel Deeper.”
The talk struck a chord. Everywhere, people were realizing that in an age where data and algorithms were upending everything, we need to keep our eyes open and minds alert because sometimes we can have too much math and too little meaning.
The Age of Data
Ninety percent of the data in the world today has been created in the last two years.2
Our current output of data is roughly 2.5 quintillion bytes a day.3
Companies in the US spent $18 billion on data centers in 2017—double the amount they spent in 2016.4
Organizations are expected to spend $203 billion on data analytics in 2020, up from $130 billion in 2016.5
And we’re just scratching the surface with these numbers. In the dialectic between the spreadsheet and the story, the former is dominating.
Organizations are drawn to data for many reasons, not the least of which is the emergence of incredibly sophisticated technologies that allow us to process and parse information with speed and insight. But here are more reasons why the spreadsheet is dominating the story:
• Data is plentiful.
• Data is not messy or nuanced like feelings.
• Data feels certain, while hunches are uncertain.
• Data is a common language that everyone in a global world can read.
• Data drives wealth creation, as illustrated by the data-driven companies of Google and Facebook, whose algorithms are powerful prediction engines.
• Data can be displayed using stunning graphics.
But just because all things data are dominant doesn’t make data a bad thing. In fact, it’s a mistake to characterize the spreadsheet as bad and the story as good. Both are crucial for organizations. More to the point, achieving a balance between the two is the hallmark of great companies.
The problem is that data’s seductiveness throws the dynamic off balance. Data helps us justify our decisions. It seems to mitigate our risks. It provides insights into consumer behaviors, which can shape our product and service development. It can save us and make us a lot of money.
But it can also seduce us into believing that data is all we need. When that happens, we lose the agility, innovation, and inspiration upon which organizations thrive. Data can and should be meaningful in every sense of that word. We shouldn’t use it just to quantify stuff and increase efficiency and productivity, but to gain “softer” insights. What are the ramifications of the employee survey in terms of morale and long-term tenure at the company? What patterns are our algorithms detecting that relate to our vendors, and what do these patterns mean for the ongoing tension and the conflict that flares up routinely?
When thinking about math and meaning, organizations also need to create programs and policies that aren’t dictated by the numbers—that might be contraindicated by the data. For instance, the numbers may tell an organization that they need to cut staff by 10 percent to maintain profit levels, but doing so may also demoralize employees. A more meaningful alternative may be to cut other costs proactively and preserve jobs and morale.
Yes, this may be a basic example, but it illustrates the need for an equal distribution of resources between math and meaning. To arrive at this distribution, let’s begin by defining and differentiating math and meaning and the need for both.
Different Types of Data: Math and Meaning
Simplistically, math is all the data flowing through organizations and meaning is all the intangible feelings and perceptions surrounding people, products, services, and the companies themselves. Less simplistically, math takes various forms—algorithms, AI, social media data, and so on. Meaning, too, can vary considerably, ranging from organizational purpose to employee beliefs about their companies to brand significance.
Organizations have always used data in various ways—surveys, budgets, focus groups—but because of technological advances, data has now become a ubiquitous presence in every nook and cranny. Consider why data is often talked about as “the new oil” and how it is such a valuable resource for every type of company:
• Fosters customer insight. When a Netflix subscriber selects a show to watch, 70 percent of the time it is from the recommendations Netflix serves up. A third of Amazon sales occur through the recommendations of what else other buyers purchased who bought the same product as you.6 Data reveals opportunities like never before, from maximizing marketing to developing new product ideas.
• Spurs continuous improvement. By comparing key metrics against historical performance as well as that of competitors, companies can create benchmarks to improve performance and provide feedback to each individual in objective versus subjective terms.
• Provides competitive advantage. In a world where product differentiation is narrowing and price comparison places downward pressure on margins, data allows for new forms of competitive advantage and monetization. The success of three of the five most valuable companies in America (Amazon, Facebook, and Alphabet/Google) is thanks to their vast swaths of data, which allow them to personalize and customize their services with speed and low cost.7 Data allows for low cost, speed, and high quality.
Now think about meaning. While it is less tangible than data, it is no less crucial to a company’s success. Consider the following forms of meaning and the questions that help elicit it:
• A brand’s reputation. Is it considered a quality product or a cheap one? Reliable or not? Does the brand connote trust and inspire loyalty or is it seen as utilitarian?
• Customer service beliefs. Do customers find servicepeople to be helpful and friendly or cold and bored? Do service representatives and salespeople forge relationships with their best customers or are relationships merely transactional?
• Company mission/values. Is the company known for consistent beliefs and principles or is it seen as amoral and fickle? Does the organization try to make its community, its industry, and the world a better place or is it motivated only by profit?
• Employee perceptions of the enterprise. Do they perceive the organization as a place where they can learn and grow or one that exploits their hard work and skills? Do they feel they are rewarded fairly or that the company is cheap? Do they feel included and affiliated with the organization or isolated and mercenary?
• The full significance of the data. What do all those facts and figures mean beyond the obvious? Yes, profits are up by 12 percent in June, but why? Is this an anomaly or is there an underlying trend to which we must pay attention? Did the new ad we ran have an effect or did our salespeople respond to a new incentive program?
This is just scratching the surface of what’s meaningful in organizations. It can take myriad forms—how the vast majority of bosses treat direct reports with respect and demonstrate empathy, for instance. Meaning can also show up in how the CEO speaks to industry analysts and the way bonuses are calculated and handed out. And meaning can emerge when Big Data is analyzed holistically—when diverse experts study it, debate it, and interpret it. Remember the story and the spreadsheet, and how meaning is one part of the story that the organization tells.
The Problem with Being Data-Myopic
Mark Twain said, “There are lies . . . damned lies, and statistics.”8 Perhaps that’s overstating the case in a memorably funny way, but organizations make a huge mistake when they become overly focused on the data. Some companies have learned this lesson the hard way.
In 2012, Adobe stopped doing annual performance reviews. These data-driven tools are essentially organizational report cards that measure performance by ranking achievement and improvement in a variety of categories. Donna Morris, Adobe’s senior vice president of people and places, likened the review to “annual dentist visits.” The company replaced these reviews with much less formal and much more relationship-intensive “check-ins”—one-on-one sessions with a boss where performance evaluation is conducted in a more relaxed and participatory manner.9
In an article in Fast Company titled “How Too Much Data Can Hurt Our Productivity and Decision-Making,” author Bob Nease made the point that “a deep dive into who buys your widget doesn’t generate value unless it helps you focus your sales efforts on better prospects and away from people who will never buy your stuff.”10 Nease added that people are unpredictable, meaning that just because you’ve accumulated lots of great data on a given group doesn’t mean that you can use it in a positive way. You may know that a given audience loves the color blue, but when you use it in your packaging, it turns off consumers. Why? Because people, unlike machines, are spontaneous, contradictory, and idiosyncratic.
A 2016 report from consulting firm McKinsey & Company interviewed top executives at leading companies on how they were using big data and analytics. One was Chief Risk Officer Ash Gupta of American Express, and he made an insightful point: “The first change we had to make was just to make our data of higher quality. We have a lot of data, and sometimes we just weren’t using that data and we weren’t paying as much attention to its quality as we now need to.”11
Now think back on these examples and the cautionary lessons they teach us:
• Data-centric interactions can be far less effective than human-centric ones.
• People don’t always do what data predicts they will do.
• Data is of wide-ranging quality, and if the quality isn’t good (or you don’t know that it’s good), you’ll be making decisions on weak foundations.
Despite Its Limitations, Data Will Become More Pervasive
It is critical that we constantly remind ourselves about the challenges of relying on data, since we will likely encounter data more than ever because of a number of factors:
• Accessibility. Data is available on a much more real-time, granular, and unified basis than ever before. The easier it is to obtain information—from the demographics of a website to social media friends and followers—the more likely organizations will capitalize on it.
• Storage and manipulability advances. It’s now possible to measure and store how a single individual interacts with every website component at every moment and link this information to other data about that individual. Lower storage costs combined with powerful computing capabilities make it possible to capitalize on this data and manipulate it in insightful ways. The thinking goes, “We have it, we can shift and shape it, we’ve got to use it.”
• Leadership tool. Just about every organization possesses a Bloomberg-like data terminal or dashboard for various levels of management. Data is the spine that holds the organization together and affects every significant decision and communication. Leadership’s embrace of data has a trickle-down effect, causing all levels of the organization to buy in.
• The AI age. Increasingly powerful computers input huge amounts of data and “learn” as they process information, getting smarter just as humans get smarter from multiple experiences. But computers, unlike humans, can capitalize on data-driven algorithmic decision-making, and organizations are increasingly relying on algorithms rather than people to make decisions.
How to Extract Meaning from Data by Tapping into People: The 6 I Approach
Over the years I have learned that the best way to gain insights and extract meaning from data is to follow what I call the 6 I Approach: Interpret, Involve, Interconnect, Imagine, Iterate, and Investigate.
INTERPRET THE DATA. Don’t just take all those facts and figures at face value. Sometimes, of course, they’re exactly what they seem. Other times, they can be misleading. For this reason, view ambiguous data (especially) from multiple perspectives. Develop hypotheses, search for patterns, look for outliers, create alternative scenarios to explain the information you’re receiving. Through interpretation you can enrich the data with meaning; you can identify the story it’s telling.
INVOLVE DIVERSE PEOPLE. As important as your analytics people are, expand the group that examines the data. When you involve people with various skills and perspectives, you’re likely to receive a richer interpretation. The analytics people may say, “The number of followers on our site increased 15 percent in the last month.” The marketing people may say, “That increase may be due to the incredibly successful brand licensing program that launched last month.” The human resources people might say, “Every time we have a significant increase in followers like this, we have a corresponding increase in job applicants.” The importance of diverse people is shown in debacles like the Gucci Instagram ad that resembled black face or the Pepsi ad with Kendall Jenner that misfired at every cultural level.12
INTERCONNECT TO LARGER TRENDS AND EVENTS. What does the data mean relative to an emerging trend that’s having a profound effect on your industry? How does the information you’ve gleaned relate to a competitor’s new product introduction? Making these types of connections helps you take the data one step further, determining if it’s going to have a short-term or long-term impact, if it’s suggesting the end of a trend or the beginning of a new one.
IMAGINE AND INSPIRE SOLUTIONS. Too often we look at the data and allow it to set boundaries: “We can’t go into Market Z as planned because the numbers indicate sales of our category is starting to fall off.” Rather than allowing the data to limit options and actions, explore the solutions it might inspire. If the numbers show that your product category isn’t doing as well as it once did in Market Z, is there an emerging opportunity because the market still has potential and competition will be reduced because of this data?
ITERATE. Data can spawn new and better data. Is there a test you might run based on the information you’ve gathered that can produce more insightful facts and figures? Can you think of fresh ways to generate feedback that might provide multiple perspectives and explain surprising, disturbing, and promising data?
INVESTIGATE PEOPLE’S EXPERIENCES. In a given organization, you have hundreds or thousands of people with data-relevant insights because in the past—whether while part of your organization or with a previous employer—they experienced something applicable to the current information. For instance, someone was part of a company that experienced a huge social media spike because they ran a Super Bowl commercial that went viral. As a result, this employee can relate their experiences to the current data on a similar topic. Tapping into this by seeking out relevant employees and asking about the data may provide ideas that would not otherwise be articulated.
Never forget that data tells a story beyond the facts and figures, but this story can only be told when you find ways to tease out the meaning.
The Need for a Human-Centric Data Policy
Beyond questioning and exploring the data, organizations need to create policies and protocols for it. The tilt toward data wouldn’t be so harmful if companies enacted basic rules to mitigate the damage caused by overdependence. Meaning naturally flows back into an environment when companies filter all the facts, figures, and other information through a human lens.
To that end, here are some suggested filters:
DETERMINE WHAT DATA IS WORTH RECEIVING AND ELIMINATE THE REST. This is a simple but very effective step that many companies don’t take for fear of missing something. Do you really need to receive five financial reports that essentially provide the same information? Is it necessary to parse the same data through three filters? Do service evaluation reports have to be issued weekly (versus monthly)? Even if something is missed, this type of limit ensures that there will be time for nondata discussion, questioning, and reflection. With apologies to Samuel Taylor Coleridge, here is the mindset that data limits help avoid:
Data data everywhere,
So much data I will sink.
Data data everywhere,
Pray, who will help me think?
FLAG BAD DATA. Invariably, the increase in data corresponds to the increase in data that is slanted, outdated, or just plain wrong. Without a system to identify suspicious or overly misleading facts and figures, companies will create strategies based on inaccuracies: garbage in means garbage out. Companies need an ombudsman who makes judgments about data, jettisons obviously wrong information, and warns people when data is weak or unreliable.
STOP USING DATA AS A CRUTCH. As I’m sure you’re aware, managers justify their decisions by referring to what a report told them. Even when they make a bad choice, they say, “The data made me do it.” Don’t let people lean on their data or that’s exactly what they’ll do—it’s human nature. Data should inform and enlighten, but it shouldn’t be the basis of every decision. Encourage people to justify their decisions based on data plus many other factors—discussion, brainstorming, past experience, creative alternatives, and so on.
ASK QUESTIONS DATA CAN ANSWER, NOT DATA-DRIVEN QUESTIONS. This distinction will help avoid focusing on the wrong things. For instance, companies may focus on how they can reduce time per customer service call to cut costs and increase productivity. This focus stems from the data—it’s been proven that reducing customer service calls by thirty seconds results in a 10 percent reduction in costs and a 5 percent increase in productivity (I’m just making up these numbers). So the question—can we reduce costs and increase productivity?—arises from the data. But this may be the wrong question. The real question may be: How can we increase customer satisfaction? It may be that shortening customer service call time alienates people and produces lower customer satisfaction. The key, therefore, is formulating questions independent of existing data.
MEASURE JUDICIOUSLY. Just because you can quantify every aspect of employee performance doesn’t mean that you should. Employees often feel as if every task they perform and every keystroke they make is being recorded and assessed. This Big Brother mentality is counterproductive in the long run, even if it may help improve efficiency in the short term. People need the freedom to take risks and sometimes fail. If they believe their every move is being watched and measured, their morale will plummet. Obviously, measurement of some things is necessary. But when it seems like everything is being analyzed and quantified, then people respond poorly.
When It Comes to Data, You Ain’t Seen Nothing Yet
It is almost redundant to describe an organization as “data-driven”; it is difficult for any organization to thrive without this as the core organizational driver. In fact, forget thrive. In the algorithmic age, no company can survive without a data strategy. But this is just the beginning, and the coming months and years are going to see data rise to even greater heights. The danger is that the data strategy becomes the only strategy, and everyone’s focus becomes building the best algorithm and forgetting the meaning part of the equation.
Each year I attend the Consumer Electronics Show, and in 2018, I was struck by three trends/developments that are going to intensify our data-centric mindset:
1. Sensing/Senses. Due to AI and embedded chips, a growing number of devices and technologies can anticipate what we need before we do: cars that not only drive themselves but anticipate accidents, medical systems that trigger warnings or call in for help, and so on. In terms of sensory-intensifying technologies, 4K OLED screens will take our video experiences to another level, while nontethered, mobile, and affordable ($200 to $400) VR systems from Google/Lenovo and Facebook/Xiaomi allow us to sense new worlds and maybe a new level of empathy and presence.
2. Expansion and Encroachment. Every technology and every industry is expanding and encroaching out of its category. Here are a few examples regarding AI and voice. New TVs from LG and Samsung embed Amazon or Google into their interface and credit their enhanced pictures less to hardware improvements and more to software driven by AI enhancements. Automobiles can now be driverless due to embedded AI and voice, including a customized new voice interface from Mercedes.13 And in case you missed it, the Chinese are here and dominating as never before. One out of three exhibitors were Chinese firms; some called it the Chinese Electronics Show. The Chinese are going to encroach and expand into AI, Internet of Things (IoT), and much more at scales that will bewilder.
3. Augmenting and Accelerating. We are on the cusp of a quantum jump in technology capability. New chips from Qualcomm and others will now allow for twenty or more hours of mobile phone battery use. A key constraint to phones has been their need to be recharged. Now they will last longer, and with the next generation of wireless charging, they can be recharged faster. While still a couple of years away at that time, we were already seeing demonstrations of 5G technology, which is one thousand times faster than 4G and LTE (Long-Term Evolution)!
Events like the Consumer Electronics Show will affect all organizations, directly or indirectly. Companies are going to be scrambling to find new ways to accelerate customers’ experiences through better technologies. They’re going to use their technology to encroach on others while defending intrusions onto their turf, as traditional boundaries between companies and categories disappear. And they will be awash in data from sensing systems that “read” people’s activities in science fiction–like ways.
What Meaning Looks Like in Action
As the previous section suggests, organizations can redress the balance between math and meaning by enacting data policies that allow people to use their creativity and ideas more effectively. Meaning can also be added in other ways to the numerical thinking that dominates companies. More specifically, organizations can focus a variety of policies and processes to elicit positive employee traits—creativity, empathy, loyalty, and relationship-building.
Costco, for instance, has long enjoyed a reputation for putting its people first. Not only do they pay above the industry norm but they take other, often extraordinary steps to accommodate the requirements of all types of employees. For instance, they offer health insurance and other benefits to part-time employees, a costly policy. They also encourage their employees to think long term—for employees who stay at least one year, the turnover rate is around 5 percent. They also give their managers unprecedented ability to make decisions for their groups independently; they’re not hamstrung by ironclad policies and procedures. As a result, managers are able to use their creativity and initiative to make changes that feel right to the people with boots on the ground.
Costco is a hugely successful company, but its operating margin is 3 percent, compared to Walmart’s 6 percent. Its labor expenses are 70 percent of its budget, a high percentage. Looking only at these and other numbers, it would seem that Costco couldn’t possibly succeed. Yet founder Jim Sinegal figured out early on that if he could free employees to use their considerable strengths as people and provide them with an environment and wages that suited them, these efforts would translate into high customer satisfaction.14
In a 2014 Harvard Business Review issue, former Netflix Chief Talent Officer Patty McCord wrote “How Netflix Reinvented HR.”15 One of the article’s themes is that Netflix chose to rely on common sense and trust their people’s desire to do the right thing to guide their HR efforts rather than traditional data-based measures. For instance, instead of the standard vacation policy—employees are limited in how much time they can take off yearly (X number of vacation days, personal days) and must file formal requests through various forms—Netflix created an honor system. Essentially, it leaves vacation time up to individual employees. It’s an informal system where common sense, rather than rigid requirements, determines when people take time off and how they report it. While there are general guidelines (e.g., financial people shouldn’t schedule vacations when the department is swamped) and more structure for people in call centers, the majority are given a great deal of freedom. Netflix has also freed many of their employees from inflexible policies regarding travel—as McCord writes, the Netflix policy toward travel expenses is summed up in five words: “Act in Netflix’s best interests.”16 As a result, they largely eliminated the cumbersome expense account reporting process.
These and other policies help Netflix employees feel respected and trusted—that they’re being treated as adults rather than misbehaving children. They want to reciprocate in kind, contributing to the company’s profitability and growth. Freed from the reporting requirements and strict parameters that most employees resent, they relish their cultures and find more meaning in them.
Starbucks has developed a system that facilitates their baristas’ interactions with customers, allowing them to relate to them as individuals. In retail situations, many salespeople relate to customers in a generic way; they may be polite and friendly, but they treat everyone the same way. Starbucks has a data feedback loop that helps baristas know customers’ tendencies, preferences, and idiosyncrasies. One way Starbucks collects customer data is through their rewards programs. Customers gain rewards (e.g., discounted favorite drinks after a certain number of purchases) for specific buying behaviors, and in turn data is collected about these behaviors when customers use the Starbucks app—data about what their favorite drinks are, what days they usually come in to purchase these drinks, and so on.17
Armed with this data, baristas can greet customers by name, anticipate their drinks and how they like them, and comment on any deviations from traditional ordering practices. If they usually come in on a Tuesday and arrive on a Friday, the barista can say, “Hey, Joe, I’ve never seen you here on Friday; something special going on?” In this way, baristas can form a connection to their customers, communicating that they’re seeing them as individuals rather than as generic customers. This makes the job more interesting and more of an organic experience for baristas, and it helps customers feel acknowledged as unique individuals. Starbucks is capitalizing on data in a highly human-centric manner.
In January 2018, Lawrence D. Fink, CEO of the huge investment firm BlackRock, took the extraordinary step of writing to the heads of leading organizations and informing them that they needed to do more than be profitable if they wanted BlackRock’s support; they had to make contributions to society in some way. In the past, investors like Fink usually cared only about the numbers. His letter signals a change—a change toward a more meaningful direction. Instructively, Fink noted that profitability was still crucial as an investment factor, but that sustainable organizations must also recognize their responsibilities as world citizens.18
But finding the balance between math and meaning isn’t limited to philanthropy. As you’ll recall, meaning comes in many forms, and organizations need to be aware of all of them. For instance, JPMorgan Chase has had a lot of success with their Sapphire Reserve card, which targets millennials, because card rewards are tailored to what is meaningful to their audience. Based on data, they determined that what was important to millennials was experiences—more specifically, travel and food experiences. To that end, they structured rewards for using the Sapphire Reserve card around these experiences—for every dollar users spend on food and travel, they receive three points (and only one point for nonexperiential expenditures). On top of that, when users spend $4,000 in the first three months of card ownership, they receive up to $750 in travel credit. JPMorgan Chase links users to their own travel agents, and if customers use these agents, their points double.
The card costs $450, which is a lot of money relative to other cards, and the company was criticized when they first introduced it; the critics were convinced the company was going to lose money. But meaning triumphed, and the card has been a significant success.19
JAYNE ZENATY SPITTLER
Pioneering with Data and Passion
To some extent, the tension between story and spreadsheet has always existed. Even years ago, numbers possessed power, and people sometimes failed to recognize that story could be equally powerful, especially when balanced with data.
In 1982, when I joined Leo Burnett, I shared a room with other newbies waiting for media-buying openings while we were trained on the basics of media. Jayne Zenaty ran media research and was responsible for what was referred to as the PIT (which we called People In Training but I sense was just a reference to the large room where we all sat like workers in a pit).
Aware of my math undergraduate degree, Jayne had me help her build the case for a new emerging medium called cable television. She had me gather the data demonstrating that cable was spreading geographically and beginning to erode broadcast television in the markets it had most deeply penetrated. Jayne wanted management to start paying attention and prepare our clients for this new media.
The head of our media department at the time grew up in a broadcast television world and took delight in its negotiations and deal making. He was skeptical of anything new and would often throw our numbers back at us as irrelevant, wrongly calculated, or out of context. Each time Jayne would retreat and rethink about how to tighten the math and improve the story. Based on her graduate school experience with a National Science Foundation two-way cable project, she passionately believed that cable was going to be the next big thing and knew the data supported her.
But Jayne also realized she needed to “sell” the data, to breathe life into it with dramatic projections of how people’s television viewing habits would change, and how this would transform the entire discipline of media buying. By telling a story that offered hope (unprecedented targeted advertising opportunities) as well as fear (losing clients if the agency didn’t transform its media-buying strategy), Jayne prevailed. Not only did she tell the story to our boss—she also lobbied early cable pioneers like Ted Turner to develop the audience data to support buying cable. Cable exploded, and we were early and ready.
Learning from Jayne, who was in many ways my first boss, was something I never forgot. When you believe something new is coming that others resist, combine cool, calculating math with deep passion and persistence to tell a compelling and numerically tight story.
KEY TAKEAWAYS
• We need to pour data through a series of filters that separate the fool’s gold of information from the nuggets of wisdom.
• The most successful leaders and organizations will leverage data in ways that extract and amplify meaning and not just math, asking the right questions and involving diverse perspectives when analyzing data.
• We must recognize that human judgment and intuition are often necessary to perceive data’s true significance.