In the last few months, executives and technical practitioners have told me more often than not that productivity at their company increased as a result of changes made to respond to the coronavirus pandemic.
This is consistent with CIOs rising to the challenge of rapidly enabling remote work. A majority of technologists say the pandemic has permanently increased the influence of the technology leader.
I’ve been bullish about the potential of accelerated digital transformation to power a faster bounceback to a bigger, better economy–but I don’t think I’ve grasped the half of it until now.
According to a recent working paper, the shift to working from home has lowered commuting time among Americans by more than 60 million hours per work day. Cumulative time savings exceed nine billion hours, of which about one-third was devoted to workers’ primary jobs.
I know not everyone writes code–and that’s a good thing, because there are many more types of work that need to be done in the world than just producing software. But to ballpark the potential for innovation this represents in terms I understand well, by a rough and ready calculation, three billion software engineering hours is enough to ship 100 Linux kernels.
We picked up that much time to devote to innovation in just seven months.
I am sure we will balance the extreme degree of working from home today with more time spent in traditional office settings (or new collaborative workspaces) as we emerge from the pandemic.
The verdict is in on how right we would be to refuse to settle for the pre-COVID status quo.
While CIOs can claim pride of place for delivering remote work capabilities 43 times faster than expected, there’s plenty of credit to go around in survey data from McKinsey & Company.
The firm asked respondents how long it took to execute a set of changes and how long that would have taken before the crisis. For most, their companies acted 20 to 25 times faster than expected–including adapting to changing customer expectations and increasing the use of advanced technologies for both decision-making and operations.
Here’s the provocative question this data raises.
This was done with little time to plan, under conditions of tremendous uncertainty, and no roadmaps.
Now we have time to plan for recovery.
Shouldn’t we do it in a data-driven way?
Wouldn’t a data-driven enterprise put telemetry in place to ensure that time freed up out of necessity is preserved–or increased–on purpose?
Shouldn’t we hold ourselves accountable for ensuring our organizations execute faster on the backside of the pandemic than we did lurching through its early days?
Can we stomach the possibility of (for example) the time to adapt to “changing customer needs and expectations” snapping back from the 21.3 days McKinsey’s data showed it actually took during the crisis to the pre-COVID expectation of 511?
I don’t think so–but a data-driven enterprise wouldn’t leave progress to chance. Over the next few months I am keen to see what I hope will be a systematic focus on building back better, intentionally.
Read about how Home Depot is doing data strategy right here.