Using Data to Shatter Assumptions About Your Business
A constant challenge for senior executives is understanding why some teams perform highly while others fail to live up to expectations. A lot of the time, the difference comes down to being able to make the right interpretation of data.
The Illusion of Data
In any well-run military operations center, screens display maps and continuous updates on actions occurring across the battlefield; the atmosphere is one of earnest, calm focus. But as a military commander in Iraq and Afghanistan, all too frequently I found that the operational picture was too clear and orderly.
It did not reflect the chaos of defending a village or patrolling rugged mountains that I found on the ground. Regardless of whether our depiction was cautious or triumphantly optimistic, I was confident it was wrong, because we lacked granularity and context. This left us in a tricky situation: not knowing what our reality is, but having technologies that provide an illusion that we did.
Today’s leaders are swimming in oceans of data, bombarded with insights that are oftentimes detached from business impact. Data alone does not provide a sufficient picture to drive business outcomes; it must be coupled with an understanding of the direction of the business. The connection between data and business context is paramount when making decisions in crises.
Using Data to Prevent Employee Burnout
When the COVID-19 crisis first hit in March 2020, our instinct at McChrystal Group was to dramatically increase the amount of time we spent together (virtually) in order to simulate the office environment. To evaluate our actions, we deployed a pulse survey and assessed the findings against data collected passively, using Microsoft’s Workplace Analytics Dashboard.
The results were a shock. This analysis and the Microsoft dashboards revealed that roles and accountability were not clear, as well as an underlying concern that we were heading toward employee burnout. Our collaboration hours had increased by almost 100%, and we had doubled the number of meetings.
Data on how your team shares information, conducts its meetings and collaborates is critical to understanding where your operational bottlenecks and efficiencies exist.
In these times of crisis, leaders can constantly collect this data via routine pulse surveys, qualitative interviews and tools to map passive communications. Once collected, it is essential to interpret the data through the lens of business performance.
In McChrystal’s case, we noticed that, while our pendulum had swung too far in our attempt to replicate the in-office environment virtually, we also did not want to return to our pre-crisis equilibrium. We needed to iterate to find a sustainable, effective collaboration model.
Finding the Balance Between Talking and Action
The dramatic increase in collaboration time risked generating repetitive information at the cost of bandwidth to actually do the work. In short, we needed to spend less time talking about the problem and more time solving it.
We reduced the number of meetings to free up space for smaller teams and individuals to execute on their piece of the plan.
To ensure we didn’t sacrifice useful crosstalk, we used network analysis to identify and pull key individuals from each business area into our communication rhythm.
A subset of formal and informal information brokers pumped intel to teams when and where they needed it, regardless of team or hierarchy. The result was more consistent and productive working time for independent groups.
Having a Single Source of Coordination
At the center of the network was our company’s director of operations, whose role became to understand our company’s network and then bring together key voices at opportune times.
This consistent voice and context enabled us, over time, to connect the teams responsible for revenue production, new product innovation and client success in ways they had not in a pre-crisis environment. The results from our network analysis started to morph, with more connection lines forming between teams, somehow becoming more collaborative despite the removal of human contact.
These sorts of network maps will look fundamentally different for every organization and need to be adjusted based on critical external factors, but capturing and acting on real-time employee feedback enabled us to develop a more effective and sustainable operating model.
Don’t Forget About 1:1 Connections
As we continued to monitor our response, passive data analysis revealed another significant risk: Three-quarters of our workforce did not have a recurring one-on-one meeting with their manager after COVID-19’s onset.
Deeper digging revealed that our agility as an organization — an institutional strength — was leaving individuals fluidly shifting between or supporting multiple efforts, without a clear one-to-one manager-employee relationship. That awareness spurred senior leaders to ensure they were checking in and helping to mentor teammates, regardless of whether they were working together.
Data Drives Performance
Data related to your people is both critical and dynamic, especially in periods of rapid change.
Individual coaching and development are vital, particularly for more junior teammates who may not have previously experienced a comparable situation either professionally or personally. For us, passive monitoring and in-depth discussion on the root cause have been critical to employee engagement and reducing undesired attrition during COVID-19.
Leaders must think critically about how their organization should operate to be most effective in the new normal. To track and effectively iterate on their operations, organizations should implement systems that automatically and regularly generate feedback on communications and execution. However, it cannot stop there.
Leaders are the necessary component to resolve the discrepancies between the idealized operational picture and real-world chaos. Without people to decide on and drive action, the insights are just numbers on a page.