New norms borne of the COVID-19 pandemic – especially work from home and increased consumer use of digital channels – will change the nature of upcoming IT investments. Over the last few decades, I have led or advised on many technology projects triggered by shifts in political, regulatory, commercial or social norms. My observation is that macro shifts don’t generally produce new technologies – but rather lead to an uptick in adoption of existing technologies. These new investments, in turn, reinforce the new norms.
If history is any guide, the primary driver for IT investments in the aftermath of the COVID-19 crisis will be cost savings. We saw this after the 2002 dot-com bubble and again after the 2007-2008 financial crisis. In both cases, companies fell back on traditional IT cost-reduction levers: postponing capital expenditures and upgrades, slowing or canceling discretionary projects, and reducing headcount. More savings came from offshoring and cloud-based services, both enabled by newer networking and computing technologies.
Traditional Cost-Savings Levers Have Been Squeezed Out
Those cost-cutting steps were the low-hanging fruit. The next wave of savings will require harder decisions around organizational design, processes and policies, system renovation and replacement, and talent management. Compounding the challenge, many companies that implemented new IT during cost-reduction cycles took shortcuts that increased technical debt. They now find themselves with operating models riddled with interdependencies (both known and unknown) that hinder transformation. For example, modern cloud-based analytics solutions may be heavily reliant on legacy systems, frequently accompanied by complex customizations, which only a few long-time employees know how to maintain.
To identify opportunities for cost savings and productivity gains in the post-pandemic work environment, businesses will need two new capabilities:
- Measuring more things, including human variables affecting productivity.
- Getting better at turning data into insights.
Actionable Insights On Employee Behavior
The solution to both of these challenges is multi-layer telemetry, in which sensors and software agents collect relevant data and use advanced analytics to turn that data into actionable insights. Think of multi-layer telemetry as the Internet of Things (IoT) reinvented for a new decade, expanded to collect data about people as well as things.
The concept is not new. Even as far back as 2008, Bank of America used telemetry to understand why productivity varied in call centers using identical tools. Standard call-centric metrics and analytics did not explain the difference, so researchers decided to capture a different type of data – measurements of how much time employees spent in face-to-face interactions with peers and managers, collected by sensors called sociometric badges.
The unexpected insight: the amount of interaction between agents and their co-workers and managers predicted nearly one-third of the variation in productivity among groups. Simply changing a policy to allow agents to take breaks as a group decreased average handling time by 23%, saving more than $15 million annually.
Putting Multi-Layer Telemetry to Work
Fast forward to the present. Organizations planning the post-pandemic workplace can use telemetry to understand the factors affecting productivity when employees work from home. Existing data like email and chat logs will yield some insights, such as the communications patterns of the least and most productive employees and managers. Adding sensors and agents to internal software will generate deeper insights.
For example, a few lines of code can record when employees deviate from the recommended process when using a workflow system. Correlating these alerts with productivity data can show which employees need more training – or might reveal that the deviation actually increases productivity.
Other ways to use telemetry to cut costs in a post-pandemic world include:
- Comparing productivity of teams with frequent manager engagement (online and in-person) vs. teams conducting formal quarterly or biannual reviews with incentives. Do teams with more highly engaged managers manage changes in scope or objectives more reliably?
- Measuring the impact of various application features and functions on customer experience and operational efficiency. Does Feature A speed up decision-making or just contribute to information overload?
- Comparing the behavior of the highest performers against all staff. Do high performers collaborate more? Less? Differently?
Sensors, Bots & The SDLC
In the wake of COVID-19, we expect companies to make investments that will ultimately save money. Multi-layer telemetry fits the bill by providing insights about increasing productivity when more employees work from home and more customers use digital channels.
Unlike data mining and log analytics approaches, multi-layer telemetry reduces the complexity and cost of analyzing the factors impacting people’s productivity and behavior by adding agents and sensors to collect the data associated with their actions as they are taking place and then delivering it in a readily usable format that can be assessed in real time. Organizations looking to assess the relevance and potential value of this capability should take the following steps
- Confirm the potential value of insights to be gained by identifying the one or two things that would be useful to know about the way people engage or transact. Examples might include time to respond to an event (email, chat, automated alert) linked to specific correspondents, subject, location and/or time of day. Perform an initial set of analytics by extracting this information either manually or through batch processing to determine that the insights obtained are, in fact, useful.
- Pilot standards, reference implementations and developer libraries for agents/sensors that demonstrate how this information can be consistently collected and published from selected technical stacks without significant changes to applications integrity or performance.
- Simplify the setup and use of analytics sandboxes to encourage and accelerate experimentation using data agent/sensor data (either through in-house or cloud-based approaches).
Multi-layer telemetry is a proven approach for gaining operational and behavioral insights through focused and simplified collection and analysis of select but key pieces of information. When implemented in a disciplined way, it can enable organizations to discover more meaningful insights faster, and at lower cost, than more traditional event analytics approaches, making it a particularly attractive alternative in environments where cost reduction is a priority.
Visit our COVID-19 resources page for additional insights and updates.
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