Analytics critical to decisions about how to return to work
Data plays a critical role in the decisions organizations make as they bring employees back to work, for both increasing production and managing their physical space.
Analytics is playing an important role in determining just how businesses can come back as state economies slowly reopen. Not only are restaurants, hair salons and other businesses forced to close by the COVID-19 crisis returning to work, but office employees who had been working from home are now heading back to their physical workspaces.
Two of the main ways analytics is helping organizations return to work are by giving them an understanding of how aggressively to restore operations to pre-COVID-19 levels and grow from there, and how to physically bring back employees as safely as possible.
"In the last [couple of months], we've gone from crisis to how do we stabilize and operate in this context," said Francois Ajenstat, chief product officer at data visualization pioneer Tableau. "Now, every business is thinking, how do we reopen, and how do we reopen safely considering that different offices will have different parameters, different states have different laws, and then we're going to want to try to go and figure out how do we grow in this next normal."
Analytics gives organizations a view of their business. If they made the decision to furlough or perhaps even lay off employees, data is helping them decide if and when to restore their workforce. If they shut down certain operations or reduced output, data is helping them decide if and how to ramp back up.
And since organizations won't be allowed to bring all their employees back together because of physical distancing restrictions -- only a certain percentage will be able to be in one space on any given day -- analytics also helps organizations decide who should work with one another and how often each employee should come in versus work remotely.
As organizations bring employees back to an office environment amid the COVID-19 crisis, they will have to make decisions about how many can work together at one time and how to manage their proximity to one another.
Meanwhile, state governments continue to use data to determine when to lift the restrictions that are now beginning to allow employees who have been working from home since March to head back to their offices.
You and I don't know what that data will say. That data might say we should be accelerating our return to work. That data might say we should be slowing our return to work. But to make those decisions without data is foolish.
Dave MenningerResearch director of data and analytics research, Ventana Research
"Data is important, and all of the actions should be based on data," said Dave Menninger, research director of data and analytics research at Ventana Research. "You and I don't know what that data will say. That data might say we should be accelerating our return to work. That data might say we should be slowing our return to work. But to make those decisions without data is foolish."
The business
The first thing any organization must do as it considers returning to some facsimile of normal operation, both in terms of production and returning personnel to their physical environs, is look internally.
Organizations need to examine key performance indicators, looking at data from the past couple of months and making projections and then comparing those projections with data from the pre-COVID-19 past. That helps tell an organization that had to make cuts due to the economic crisis how quickly it can begin to restore personnel and production levels.
"There's a couple of things, and one is understanding your performance," Menninger said. "That's a key aspect of analytics -- understanding your current performance, extrapolating from that performance, planning and looking forward with that information -- and finding some patterns in the past that perhaps might be useful."
Doing an internal analysis can also help an organization find ways to cut costs it may not have taken advantage of in the past.
Trimming costs, meanwhile, is something many enterprises don't do when the economy is more stable and their profits more predictable, but economic uncertainty forces organizations to more closely examine their spending, said Mike Palmer, CEO of analytics startup Sigma Computing.
"One thing to look at is how to optimize the business -- where do I have efficiencies that I can gain, how many do I have?" Palmer said. "There are so many questions that the average company doesn't effectively answer in good times because they don't focus on optimization."
Palmer added that since it has been more than a decade since the last economic downturn, some in leadership positions will be making the hard choices for the first time, and data will help them to see how to proceed as they return to work.
"We have a generation of directors over the last 11 years and have made their way to that decision-making position that have never seen a budget cut," Palmer said. "That's amazing to think about. They're going to need help."
Stability, meanwhile, is important for enterprises that had to make sometimes drastic cuts as they begin hiring back employees and restoring operations. And stability, in unstable times, means cash.
Cash brings assurances that an unstable stock price or borrowed money doesn't. Even that, however, comes back to analytics.
"Small businesses, venture-backed businesses, even large businesses are securing more access to cash, so you have to have enough cash to get through the situation," Menninger said. "How do you know how much cash you need? You have to analyze what your performance has been in this new situation."
No matter what organizations decide as they deal with the economic downturn caused by the COVID-19 crisis, Menninger added that they need to be flexible.
If an enterprise weds itself too closely to one path and can't adjust if something changes unexpectedly, it's at greater risk of suffering losses than an organization that builds in options.
"You don't know how many ups and downs we're going to go through, and we don't know how fast or slow the recovery is going to be," Menninger said. "If you bake these things into such a rigid process that they can't change, then you're strapping yourself to a rocket or train or something that can't be redirected."
Office space
While data drives the decisions about how and when an organization can return its business to pre-COVID-19 levels, analytics also is critical in the corporate world as companies consider bringing workers back to a physical space after they've been working remotely the past few months.
Sitting in at a desk with a co-worker three feet to the right, another three feet to the left, and still another just on the other side of a computer monitor -- while air is being recycled and piped in through ducts -- is no longer feasible given the need to keep a safe distance. For many businesses, therefore, bringing the entire work force back at once will be impossible in the foreseeable future.
Organizations will have to bring in some employees on some days, and others on other days.
Analytics, meanwhile, can be used to optimize the decision about who to return to work with whom.
"We are not going to be in a place where everyone is in the office together anymore -- there will be physical constraints, six-foot constraints, health constraints," said Amir Orad, CEO of Sisense, a BI vendor whose platform aims to enable business users with an expertise in data science. "So who should come to the office with who -- Team A and B, Team B and C? Which people, and how do you know?"
The answer lies in the data. That information can reside in email and chat data -- who is communicating with who most often -- and card swipes to track who is traveling from one floor to another to communicate with people from other departments.
Data can also show that communication between one department and another may have dropped during the pandemic with Zoom and Teams calls unable to foster the same level of collaboration as being together in the same space, and that perhaps communication between them needs to be restored.
"You can use analytics to identify the interactions," Orad said. "You might believe that sales and marketing should be together, but actually the data shows that sales should be next to legal and finance because they need them more day-to-day.
"Analytics can really help you make, in large companies, data-driven decisions about who should work with who," he continued. "Analytics can provide a lot of information, connecting disparate data sources that no one thought to combine."
One thing executives might look at as they bring their workforce together is the incidence of COVID-19 among their employees compared to the general public. If, once they've reopened their doors, the rate of employee infection mirrors that of the public at large, it can be assumed that they're not getting sick because they started coming to work.
If instant-read thermometers become available, fever data is something that can be traced and used in decision making. Employees can be checked as they enter the workplace -- and sent home if they have a fever -- and the incidence of fevers in a given workplace can be compared to the incidence in the population as a whole.
Meanwhile, public data aids the decision-making process just as it's helping governments as they go through various phases of reopening their economies. If there's a surge in new COVID-19 cases in a given area, businesses there may want to return to remote work for a time. Or if new cases all but disappear, perhaps that will be a signal to accelerate the return to work. Contact tracing information, as it becomes available, will be important.
"Fundamentally, across each phase -- stabilization, reopening phase and growth -- you want to use the best data available so you can make sound decisions, prudent decisions that will help you grow," Ajenstat said. "There's data as a fundamental layer across everything, and you can use data to help every step of the way."
