A Blog by Jonathan Low


Apr 22, 2015

Is Data Becoming the New Middle Manager?

First it was machines. Any kind of machine, but definitely something mechanical or mechanistic that could do something a human used to do. 

Then it was computers and technology generally. They were the real threat. Along the way, the Japanese and then the Chinese came to embody the threat because they appeared to have figured out how to harness technology in ways that seemed inhumanly efficient - and much cheaper. We called them unfair even as we scurried to learn and adapt to their methods.

So now it's data. It's not just the machines anymore who are taking our jobs and livelihoods and raison d'etre. It's the algorithms and the big data that feeds them to whom we are ostensibly reporting and who are probably plotting how to replace us with something more profitable - and definitely less whiny, thanks very much - even as we read this.

How did data come to dominate management? Well, not by their little lonesomes, of course. It's just that - as the following article asserts - our ability to access, manage and interpret vast amounts of data in real time has reduced the need to have actual people do that. Or at least not as many people. Maybe.

What always drives these paroxysms of fear is that we can perceive the threat but we can not imagine the iterative benefit. Data is a tool. A very useful one. It will invariably make us better at what we do. And we will figure out ways to repurpose the resources whose functions it renders obsolete. How do we know that? Because we always have. JL

Christopher Mims comments in the Wall Street Journal:

It isn’t exactly news that businesses collect data and treat it as a competitive advantage. But a key enabler for startups founded in the past five years is that it’s now affordable to store and manipulate nearly limitless pools of data in near real-time.
Something potentially momentous is happening inside startups, and it’s a practice that many of their established competitors may be forced to copy if they wish to survive. Firms are keeping head counts low, and even eliminating management positions, by replacing them with something you wouldn’t immediately think of as a drop-in substitute for leaders and decision-makers: data.
“Every time people come to me and ask for new bodies it turns out so much of that can be answered by asking the right questions of our data and getting that in front of the decision-makers,” says James Reinhart, CEO of online secondhand clothing store thredUP. “I think frankly it’s eliminated four to five people who would [otherwise] pull data and crunch it,” he adds.
The story is the same at dozens of other startups, says Frank Bien, CEO of Looker, a company that offers a cloud-based service for turning a company’s data into dashboards that anyone in the firm can use. Across numerous interviews with other startups, as well as Looker competitor RJMetrics, I heard the same themes again and again: Startups are nimbler than they have ever been, thanks to a fundamentally different management structure, one that pushes decision-making out to the periphery of the organization, to the people actually tasked with carrying out the daily business of the company. And what makes this relatively flat hierarchy possible is that front-line workers have essentially unlimited access to data that used to be difficult to obtain, or required more senior managers to interpret.
In the past, says Mr. Bien, companies were beset by “data bread lines,” in which managers had all the data they needed, but their staffers had to get in line to get the information they needed to make decisions. In the world of just a few years ago, in which databases were massively expensive and “business intelligence” software cost millions of dollars and could take months to install, it made sense to limit access to these services to managers. But no more.Chubbies, a clothing startup that has achieved rapid growth by narrowly targeting college fraternities, is practically built on this devolution of power to its employees. Chubbies doesn’t even have a CEO; instead, it has four co-CEOs, charged with maintaining a “church of quadfecta of management excellence,” says Tom Montgomery, one of the four. Every co-CEO is in charge of his own business function, a structure that is repeated, fractal-like, all the way down the company’s hierarchy.

“All [our employees] have access to the same data we have access to,” says Mr. Montgomery. “When you don’t have a traditional CEO and final decision-maker, you have to trust all of these people to make the right decisions based on what they’re seeing. It takes a while to build up that trust, but once you do you can move much more quickly.”
Before, asking questions of the (often massive) pools of data a business like e-commerce would collect required first asking a staff data scientist, or else requisitioning the time of developers who should be coding. Querying a database could take time, and if you forgot an important column of data or had further questions, the process could stretch out over hours or days. But now, cloud-based services that connect to or ingest whole databases mean anyone in a company can, for example, instantly calculate the lifetime value of a customer according to where they came from and what they previously bought. Or a sales employee can throw as many different considerations as needed into a calculation of the return on investment on an advertisement.
Here’s a comprehensive example: In the old days, an associate specializing in events for clients might answer to a manager in the marketing department who would be tasked with thinking about why a company should be throwing events in the first place. But now, says Mr. Montgomery, the Chubbies co-CEO, his lone event planner can use an array of dashboards she has built to determine exactly how many Facebook likes, Instagram posts and sales arose from a particular event, since all these data are geo-coded and she can watch them change in the wake of an event. It’s entirely up to her to decide where, when or whether to hold future events. If anyone were to question her decision, she can simply back it up with data.
The spread of data to workers doesn’t mean that data scientists are any less valuable. Much of the data that goes into these dashboards must be cleaned up and verified, and even if it’s an algorithm doing that, whoever creates it must have a deep understanding of where this information is coming from, and sometimes, what it might mean.
It isn’t exactly news that businesses collect data and treat it as a competitive advantage. There are dozens of firms that offer business intelligence software, from stalwarts IBM IBM -1.14 % and SAP to relative newcomers Tableau, Qlik, GoodData and Birst. But a key enabler for startups founded in the past five years is that it’s now affordable to store and manipulate nearly limitless pools of data in near real-time. Amazon.com Inc.’s Redshift, a cloud-based “data warehousing” service, embodies this trend. Just over two years old, Amazon has said Redshift is the fastest-growing service it has ever launched.
The result isn’t really “big data,” just more data, more readily available, says Mr. Bien. The only “algorithm” processing the data and using it to make predictions is simply the humans scanning it for correlations. And now that every employee can have the tools to monitor progress toward any goal, the old role of middle managers as people who gather information and make decisions doesn’t fit into many startups. Nor do the leaders who remain need to poll middle managers to find out how employees are doing, since transparency and accountability are the essence of the data-driven company.
It isn’t the end of middle management, but it is an evolution. Every company I talked to had middle and even senior managers who operated as player-coaches, tasked with both doing things and directing others.
“I think at this point the stuff we’re talking about here is table stakes for running an online business,” says Robert Moore, CEO of RJMetrics. “It’s like Excel on steroids.”


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