A Blog by Jonathan Low


Jun 17, 2019

Why Organizational Analytics Predicts Success In the Digital Economy

In an era when computers and algorithms can do most of the number crunching, it is the social science behind the institutional behavior where the best hope for change may lie.

Most organizations are generating the data they need to understand their successes and their failures.

What they often lack is the ability - and the will - to interpret that information accurately and then act on it. JL

Neil Irwin reports in the New York Times:

What it means to do a job well is changing faster than people’s ability to navigate those changes. People who seek well-paying, professional-track success face the same challenges: the rise of a handful of dominant firms; a digital reinvention of business models; and a  changing understanding about loyalty in the employer-employee relationship. The ability to collect and analyze vast amounts of data about how people work, and what makes a manager effective holds the solution. The key is to listen to what data has to say and develop the openness and interpretive skills to understand what it is telling us.
On the day I met Brett Ostrum, in a conference room in Redmond, Wash., he was wearing a black leather jacket and a neat goatee, and his laptop was covered with stickers that made it appear you could glimpse its electronic innards. That was logical enough, because those circuits were his responsibility: He was the corporate vice president at Microsoft in charge of the company’s computing devices, most notably Xbox and the Surface line of laptops and tablets.
It was early 2018, and things were going pretty well for him. Despite Microsoft’s lineage as a software company, and as a brand not exactly synonymous with good design, it was making the most of its late start in the hardware business. Mr. Ostrum and his team were winning market share and high marks from critics.
But he saw a problem on the horizon. It came in the form of extensive surveys Microsoft used to monitor employees’ attitudes. Mr. Ostrum’s business unit scored average or above average on most measures — except one. Employees reported being much less satisfied with their work-life balance than their counterparts elsewhere at the company.

This was both personally upsetting and strategically dangerous. Mr. Ostrum’s group of 700 people included hard-to-replace engineers with specialized skills. If they started quitting, Microsoft’s fragile gains in hardware could be lost.
So Mr. Ostrum did what managers do: He called a meeting. But the best his staff could do was guess. Could the issue be that so many people had to work odd hours? It seemed logical enough — the devices had a global supply chain, necessitating lots of travel and phone calls to distant time zones — but upon inspection the data didn’t hold up. Could there be a few rotten middle managers asking too much of their subordinates? That also didn’t check out; aggressive bosses and laid-back bosses had unhappy teams in roughly equal proportions.
Mr. Ostrum needed to find another way to solve the mystery of the miserable employees. “The goal was to help my team learn what was actually going on, versus our knee-jerk reaction,” he told me in Redmond. “Sometimes that gut instinct can be very, very wrong.”
That day, I was at Microsoft’s headquarters researching a book that aims to answer one simple question: How can a person design a thriving career today?

In nearly every sector of the economy, people who seek well-paying, professional-track success face the same set of challenges: the rise of a handful of dominant “superstar” firms; a digital reinvention of business models; and a rapidly changing understanding about loyalty in the employer-employee relationship. It’s true in manufacturing and retail, in banking and law, in health care and education — and certainly in tech.
What it means to do a job well is changing faster than most people’s ability to navigate those changes. This has made the workplace seem scarier, particularly to midcareer people who suddenly find that their parents’ advice — show up early, work hard, learn your craft — is no longer enough. But just as important, these changes have conferred an advantage on those strategic enough to shift their approach.
If you’re looking to make a career out of creating great art, or changing the world through activism, or otherwise eschewing the conventional business track, I wish you the best. But this article isn’t for you: I’m here to address those seeking fortune in modern capitalism. And across industries, I’ve found, more and more of the most compelling opportunities are at companies that dominate their fields — global, profitable, well managed, technologically adept.
I’m not arguing that this is entirely a good thing. Clearly, consolidation gives large employers too much power to hold down wages, and political clout they can use to tilt the field against competitors and entrench advantages. Worse, as the recent tech backlash shows, the concentrated might of the Silicon Valley titans is disturbing in ways we are only starting to comprehend.
What I am arguing is that even if there is legislative action or antitrust enforcement to rein in these companies, their rise is driven by powerful technological forces that aren’t going anywhere. As a result, these superstar companies — and the smaller firms seeking to upend them — are where pragmatic capitalists can best develop their abilities and be well compensated for them over a long and durable career.
This applies for people who just graduated and are entering the work force, and for those weighing their next step after decades in the corporate trenches. Even for those who never show up for a job at a mega-corporation, many of the traits it takes to succeed within them are becoming essential in other settings, including smaller companies, government and the nonprofit world.
Microsoft, which as I type this is the world’s most valuable public corporation, with a market cap of just over $1 trillion, is a prime example. From its origins selling operating systems and basic software, it now sells products including game consoles, cloud storage and LinkedIn subscriptions. As it has grown, the obvious disadvantages of bureaucracy have been outweighed by some not-so-obvious advantages of scale.

One of them — the ability to collect and analyze vast amounts of data about how people work, and what makes a manager effective — would turn out to hold the solution to Brett Ostrum’s conundrum. The same approach is essential for even those who aren’t managers of huge organizations, but are just trying to make themselves more valuable players on their own corporate team.
Back in 2015, inside Microsoft’s human resources division, a former actuary named Dawn Klinghoffer was taking on a difficult task. She was trying to figure out if the company could use data about its employees — which ones thrived, which ones quit, and the differences between those groups — to operate better.
In one case, she and her team had found that when people moved to another unit within the company, they tended to become more engaged and ultimately to become more valuable employees. Yet surveys showed that workers felt it was easier to quit Microsoft than to transfer internally.
At the time, company rules inadvertently discouraged transfers. You had to log at least 18 months in your current position before you were eligible; and to interview for a new role, you had to get permission from your manager, which had a predictably chilling effect. When Ms. Klinghoffer’s team showed Microsoft the evidence, it relaxed the rules, and transfer rates soared.
Ms. Klinghoffer was frustrated that this and other insights came mostly from looking through survey results. She was convinced she could take the analytical approach further. After all, Microsoft was one of the biggest makers of email and calendar software — programs that produce a “digital exhaust” of metadata about how employees use their time. In September 2015, she advised Microsoft on the acquisition of a Seattle start-up that could help it identify and act on the patterns in that vapor.
Called VoloMetrix, it had been founded a few years earlier by Ryan Fuller, a former management consultant. The start-up had gotten good at analyzing vast amounts of metadata from office productivity software. One of its foundational data sets, for example, was private emails sent by top Enron executives before the company’s 2001 collapse — a rich look at how an organization’s elite behave when they don’t think anyone is watching.

Together at Microsoft, Mr. Fuller and Ms. Klinghoffer set a goal of figuring out what behaviors — especially those that individual employees had control over — tended to predict and contribute to success within the company. They called their work “organizational analytics.”
They wanted to know things like: Is there an optimally productive length of the workday? Should salespeople focus on deep contact with a few clients or shallow relationships with lots of them? Ms. Klinghoffer and Mr. Fuller came up with some answers that amount to a data-driven guide to being a successful employee — not just at Microsoft, but at nearly any ambitious corporation.
One of their findings was that people who worked extremely long work weeks were not necessarily more effective than those who put in a more normal 40 to 50 hours. In particular, when managers put in lots of evening and weekend hours, their employees started matching the behavior and became less engaged in their jobs, according to surveys. Another finding was that one of the strongest predictors of success for middle managers was that they held frequent one-on-one meetings with the people who reported directly to them. Third: People who made lots of contacts across departments tended to have longer, better careers within the company. There was even an element of contagion, in that managers with broad networks passed their habits on to their employees.
Some of these nuggets are unsurprising: Communicate well, take care of your people. But their work would turn out to have a surprising usefulness in solving Mr. Ostrum’s puzzle. To understand why, it helps to know a little bit about something completely different — the modern history of baseball.
When Michael Lewis published “Moneyball” in 2003, it gave a name to a phenomenon that was transforming baseball. Teams were relying on increasingly sophisticated analytics to make strategic decisions, like which players to sign and in what order they should bat.
The events described in the book, and popularized in a 2011 movie adaptation, captured that movement in its relative infancy. Today, a system of cameras and radars called Statcast records every move on a baseball field, tracking not just easy-to-tabulate outcomes like hits and outs, but the angle at which a ball comes off the bat, how fast the ball spins coming out of a pitcher’s hand, and how efficiently a fielder makes his way to the ball.

The baseball consultant Vince Gennaro calculated that the very first game in which Statcast was used generated, all by itself, around 20 times the cumulative data that had been collected on the 190,000 major league baseball games played to that point.
The goal of all that analytics work was to help teams make better decisions. But there is another side to the Moneyball revolution in baseball: The ability to use this data is the key to success for players themselves.
Joey Votto, the star Cincinnati Reds first baseman, is a case in point. One of the earliest lessons of the analytics revolution was that traditional baseball wisdom placed too little value on players who were good at getting on base through walks. That skill is about succeeding through inaction — not swinging the bat — rather than about exciting kinetic action.
Mr. Votto has been one of the kings of that approach, exercising remarkable discipline. “I think a very important part of a hitter’s process is in not swinging at balls and swinging at strikes,” he told me. “So if I’m not mistaken, I’ve done relatively well in that category.” (This may be his Canadian modesty speaking. He consistently has one of the highest on-base percentages in the sport, and it is what helped earn him a quarter-billion-dollar contract and a 2010 M.V.P. trophy.)
Beyond his ability to be a living exemplar of that early Moneyball-era insight, he is also an avid student of the lessons that newer baseball analytics provide. He monitors the launch angles and velocity of balls coming off his bat, and his rate of successfully making contact with the ball across all parts of the strike zone, so that he can know where to put in extra practice.
He’s had to learn how to distinguish random statistical fluctuations from a trend. “It can be difficult, because oftentimes even something that has been a trend over two weeks, that’s still too small a sample to react over,” he said. “But when you’re living it, it doesn’t feel that way.”
And he has faced public criticism, especially on sports talk radio, for an approach that cold hard data shows is the best way to win baseball games, but which old-school fans don’t always love. He was once asked in a radio interview what statistics he used to evaluate how he was doing, if he was so dismissive of traditional metrics like batting average and runs batted in.
You know, it’s going to drive a lot of people crazy when I respond, but I can’t help myself,” Mr. Votto began. “It would probably be ‘weighted runs created plus.’”
Abbreviated as wRC+, the statistic incorporates the value of each player’s hits and walks, including an adjustment for the parks he plays in and the overall balance of the game in the era in which he plays.
Mr. Votto may be an extreme example — more conversant in advanced statistics and more cerebral about using them than most players. But every successful baseball player in 2019 is making at least some use of these terabytes of data, trying to improve upon his strengths and address his weaknesses.
The “people analytics” work that Ms. Klinghoffer and Mr. Fuller have been conducting at Microsoft — and that their counterparts have been performing at other giant technologically advanced companies — has the same pattern. They are paid to make Microsoft more successful. But ultimately, all the data they generate provides lessons that individual workers need to use to become the Joey Votto of their own field. And Mr. Ostrum would be a guinea pig for just that.
To figure out why the workers in Microsoft’s device unit were so dissatisfied with their work-life balance, the organizational analytics team examined the metadata from their emails and calendar appointments. The team divided the business unit into smaller groups and looked for differences in the patterns between those where people were satisfied and those where they were unhappy.
It seemed as if the problem would involve something about after-hours work. But no matter how Ms. Klinghoffer and Mr. Fuller crunched the data, there weren’t any meaningful correlations to be found between groups that had a lot of tasks to do at odd times and those that were unhappy. Gut instincts about overwork just weren’t supported by the numbers.
The two kept iterating until something emerged in the data. People in Mr. Ostrum’s division were spending an awful lot of time in meetings: an average of 27 hours a week. That wasn’t so much more than the typical team at Microsoft. But what really distinguished those teams with low satisfaction scores from the rest was that their meetings tended to include a lot of people — 10 or 20 bodies arrayed around a conference table coordinating plans, as opposed to two or three people brainstorming ideas.

The issue wasn’t that people had to fly to China or make late-night calls. People who had taken jobs requiring that sort of commitment seemed to accept these things as part of the deal. The issue was that their managers were clogging their schedules with overcrowded meetings, reducing available hours for tasks that rewarded more focused concentration — thinking deeply about trying to solve a problem.
Data alone isn’t insight. But once the Microsoft executives had shaped the data into a form they could understand, they could better question employees about the source of their frustrations. Staffers’ complaints about spending evenings and weekends catching up with more solitary forms of work started to make more sense. Now it was clearer why the first cuts of the data didn’t reveal the problem. An engineer sitting down to do individual work for several hours on a Saturday afternoon probably wouldn’t bother putting it on her calendar, or create digital exhaust in the form of trading emails with colleagues during that time.
Anyone familiar with the office-drone lifestyle might scoff at what it took Microsoft to get here. Does it really take that much analytical firepower, and the acquisition of an entire start-up, to figure out that big meetings make people sad?
Sometimes, it does.
The nature of modern organizations is that they are so complex, with so many people doing so many unmonitored things, that it’s harder than it seems to know what’s really going on. As the economy is dominated by more of these large firms, that is going to be the case for more workers. Not many of us are laboring in a small factory, where the boss can look out her office window to the shop floor and directly observe inefficiencies.
For Mr. Ostrum, meeting bloat wasn’t just a mistake the middle managers beneath him were making. The people reporting to him constituted one of the groups conveying unhappiness over both work-life balance and 27 hours a week in large meetings. He was part of the problem. Fortunately for him, the diagnosis also implied the solution.
Mr. Ostrum and the analytics team showed managers the data and urged them to audit how many meetings they scheduled and to be honest about which were essential. On the employee end, they encouraged engineers and other nonmanagers to schedule time on their calendars for the kind of independent concentration that was being pushed into evenings and weekends by all those meetings. Simply accounting for that type of work on their schedules, as opposed to leaving it blank, made it less likely that a colleague would request a meeting during the appointed time.
The next step was to track progress. Many people use a fitness device like Fitbit to monitor their physical activity and prod them toward healthier behavior; similarly, a Microsoft Office feature called MyAnalytics allows users to receive nudges when their actions don’t line up with their stated goals.
“I get a ping every week that says, ‘Here’s what your week looked like,’” Mr. Ostrum said. “It shows how much focused time you had, how many off-hours you were on email, both sending and receiving. That’s what every individual gets, and sometimes it’s alarming to see how much email you’re doing over weekend or late nights, so it’s good for awareness.”
Ultimately, the work-life balance reports from his employees rebounded, the device unit suffered no outflow of talent, and Microsoft has continued making gains in the hardware market.
What Mr. Ostrum and the analytics team did wasn’t a one-time dive into the numbers. It was part of a continuing process, a way of thinking that enabled them to change and adapt along with the business environment. The key is to listen to what data has to say — and develop the openness and interpretive skills to understand what it is telling us.


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