Editorial
The Gist
- Moral perspectives. Musk's views raise questions on "morality" and productivity.
- Remote productivity. WFH allows for deeper reflection on data models.
- Balancing act. Managers should explore innovative ways to ensure productivity.
I have been thinking a bit about Elon Musk’s comments during a CNBC interview regarding working from home (WFH). I thought about how the remote era has impacted analytics practitioners like marketers.
Musk believes productivity is best in person, even saying that it is wrong for people to want to stay home rather than go into an office. He sees it as much a "moral issue" as a productivity issue, considering that many workers do not have the option to stay at home.
The debate around working from home continues a few years after the pandemic forced people to adopt this mode. Since analytics work, like many other forms of knowledge-based activities, can be effectively carried out on a laptop, it is often included in these discussions.
But managers deciding if an analytics team should be remote or colocated must keep specific aspects of data analysis and time management in mind.
Is It About Morals or Productivity?
Musk’s use of the word “moral” caught my attention. Moral implies that a standard of right and wrong exists. But the details leading to a standard can change and are worth scrutiny. That is what is happening with the global workforce. The world has experienced more than three years of remote work as part of a global effort to reduce the spread of the COVID-19 pandemic, fundamentally shifting our understanding of work standards. These experiences demonstrated that, thanks to improved internet capabilities, enhanced laptop technology and streamlined processes, individuals have options for achieving and delivering results.
I don’t agree with Musk’s hot takes that people are on a “moral high horse” or that the “laptop class is living in la-la-land” in desiring a work-from-home environment. Much of what people like about WFH comes from better digital processes that have been refined to enhance productivity, not a blatant decision from knowledge workers to let the other workers “eat cake.”
Additionally, a compelling moral argument can be made against insisting on traditional commutes to work when more environmentally-friendly options are viable for delivering certain types of work. Many people face long commutes to their offices, which results in an increase in cars on the road, contributing to air pollution. By eliminating these commutes, people can use their time more productively, which could provide a significant boost to mental health for those stressed by driving. Furthermore, the traditional commute not only leads to a loss of productivity but also incurs significant costs over time.
While working at Ford, I remember an engineer who drove from home to work 100 miles a day, passing through the two most congested and traffic-jammed intersections in the Detroit region. In a contract role years ago, I had to drive 35 miles and work in the client’s Chicago office due to a policy that permitted employees to work from home only two days per week. Yet, my team and stakeholders were all located in Kentucky. Essentially, I was driving to sit in an office to do phone conferences and work I could have easily done from home.
If you were to survey people's thoughts on this debate, I am confident that most people aren't questioning the necessity of an office so much as they are reassessing routine behaviors. The pandemic provided everyone an opportunity to reflect on the habitual behaviors in both their work and personal lives.
Related Article: Collaboration Tools for Remote Workers Are No Longer Enough
What Data at Home Looks Like
In the context of analytics, a work-from-home environment can provide fewer scheduled interruptions, allowing for deeper reflection on data models and their applications. This does not mean an analyst is kicking up their heels on the sofa and staring at a paper containing a graph. Data analytics involves a series of steps: repeatedly examining data sets, cleaning data, reviewing outputs from data models, and then synthesizing all of this information to answer critical business questions.
A good environment helps analysts organize a deluge of information from those steps, which often become lengthy tasks if analysts must access multiple data sources and tech stacks. Deciding which tasks to do can be hindered if the business day is taken up by ongoing meetings, which may seem essential but also consume analysis time.
There are indeed some drawbacks to maintaining a remote analytic presence. When colleagues are physically distant, they might not witness the preparatory work required to derive analytic insights. This lack of visibility can obscure workflow issues that may arise and, more critically, the best ways to solve those problems. For instance, if an analyst seems slow at developing a dashboard, is it due to a lack of training on the dashboard solution? Or could it be a result of certain tasks that could be handled more efficiently with an updated querying tool? The answer depends on asking the right questions and understanding the value of each step taken to deliver a final result.
Verifying certain data contexts can be challenging when working solely behind a laptop screen. Ensuring data integrity is a task familiar to anyone working with engineered products. During my time as an engineer at Ford, there were numerous instances when I had to verify vehicle information, requiring coordination with the plant or even physically inspecting the vehicles myself.
Data, be it on a spreadsheet or in a data lake, is meant to reflect real world experiences. So, you have to know what world a given dataset is meant to make clear. If you have an outlier or a set of odd observations in a dataset, you must know what created those outliers. Is the outlier a result of human error, such as incorrect information entered in a form? Or are the outliers a set of observations that were misclassified by a machine learning model? These are important questions to consider in data analysis.
Marketers often overlook that data is not just a form of information but also a product of engineering. The way data is engineered significantly influences the time required to create a useful analysis.
Related Article: How CIOs Can Help Create a Work From Home Friendly Workplace
Is It Better to Have Analytic Teams Colocated?
This leads us to the core of the work-from-home versus shared office debate. Colocated analysts can potentially accelerate the rate of analytical tasks, especially if those tasks are recurring frequently and if verification requires direct observation of the data sources. In the context of engineered products, this means viewing data sources in a lab or on a factory floor. So, I can somewhat understand where Musk's perspective stems from. His key businesses revolve around engineered products (automobiles, trucks, space vessels), and the developmental resources involved in these industries can't be easily replicated at home.
Executives from the financial industry have also weighed in on the importance of co-location for knowledge-based work. Strategic decisions, especially in response to lightning fast changing market conditions, often rely on dedicated teams being within arm's reach of each other, if not easily reachable by phone.
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However, coordinating shared concerns regarding data cleaning and context can be effectively managed in a remote work environment just as well as in person. The crucial shift lies in ensuring the required level of quality. For example, team leaders must ensure follow-up calls and reviews take place to confirm the completion of tasks they cannot physically observe.
The answer to analytics at home versus in office will not be answered immediately, let alone easily. How people work matters, and the maturity of process has introduced new options in delivering that work. Thus, we can expect a variety of choices in the future, and it's crucial for managers to stay vigilant and adaptable in the coming months. Finding the right balance of agility and productivity for analytics offers the opportunity to explore innovative ways to deliver value.
We all have too much on our work plates, and we all are searching for the right data that indicates the best work arrangement for everyone.
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