Your Transformation Data Is Telling You Something. Are You Listening?
Most executives do not have a shortage of transformation data. They have a hard time knowing which data matters, what it means and when it should change a decision.
The real question is not whether a transformation can be measured. It is whether leaders can use the data they already have to spot risk early, protect value and change course before a people or adoption issue begins to derail a transformation.
The data is already being created. Employees use new systems, work moves through new processes, service levels shift and teams change how they operate. Those signals can reveal where a transformation is working and where it is getting stuck, but only if leaders connect them and use them to steer decisions.
For years, much of that information sat outside the change strategy. Transformation teams relied heavily on stakeholder feedback, periodic surveys, training completion, communication metrics and conversations with leaders to understand whether a change was gaining traction.
Surveys, training completion, stakeholder feedback and leadership conversations still matter. But on their own, they cannot tell an executive whether people are working differently or whether the transformation is delivering value. They become much more useful when they are connected to operational and financial data.
Organizations increasingly have access to operational, workforce, adoption and performance data that can show where transformation is working, where friction remains and where the original plan may need to change before value is lost.
That creates an important shift in perspective for executives: transformation should be managed less like a fixed sequence of activities and more like a business system that can be monitored, diagnosed and adjusted as evidence emerges. Transformation itself can uncover friction that prompts new ways of working, however, if the data is showing that the plan is not working, the plan has to change.
The traditional measures tell only part of the story
Most transformation dashboards are good at showing whether planned activities happened: communications were distributed, leaders participated, employees completed training and the new technology was deployed. Those are useful indicators of execution, but they do not prove that the organization has changed how work gets done or that the expected business value is being realized.
All of those things matter. None of them necessarily means the organization has changed.
An employee can complete training and still struggle to perform a new process, just as a team can log into a new system while continuing to rely on an old workaround. This is the “uncovered friction” mentioned above. Leaders can complete every required communication without the behaviors underneath them changing in a meaningful way.
This distinction matters because significant transformation value can disappear during execution. McKinsey research (2023) found that organizations lose an average of 42% of the potential financial value of a transformation during the execution and sustainment stages, while only 12% of respondents reported sustaining transformation gains for more than three years. (McKinsey)
The implication is not that traditional change measures are irrelevant. It is that leaders need to connect them to stronger evidence of what is actually happening in the business while there is still time to respond. This is a call to action for leaders to shift transformation direction while there’s time, versus just highlight that more is now understood.
At Switch, we see this as the next step for change management: moving from engagement alone to using data to improve performance. Communications, training and stakeholder alignment still matter.
Move from activity to evidence of change
The more useful question is no longer simply, “Did we execute the change plan?”
It is, “Is the organization actually changing, and is that change producing the result we expected?” If the answer is no, what can I as a leader do about it now that I’m informed?
Answering that requires connecting three kinds of evidence: what the transformation team delivered, whether people are working differently and whether those new behaviors are producing the business result the transformation promised.
Consider a major technology implementation. Training completion and communications reach can tell leaders whether employees were prepared for launch, while system utilization, transaction patterns, error rates and support requests begin to show whether they can actually operate in the new environment. When those signals are viewed alongside cycle time, productivity, customer experience or other measures tied to the original business case, leadership gets a much clearer picture of whether implementation is translating into value.
The same logic applies beyond technology. An operating model change may ultimately show up in faster decisions, different handoffs or fewer escalations, while a process transformation may become visible through improvements in throughput, rework or customer outcomes. Employee sentiment and manager feedback can then help explain why those patterns are emerging.
The value is not in any one metric. It comes from connecting the signals. A spike in support requests may mean little on its own. When it appears alongside slower cycle times, more workarounds and weaker results in the same part of the business, leaders have a pattern worth investigating.
Activity tells you what you did. Behavior tells you what changed. Business performance tells you whether it mattered.
The best transformation data does more than describe what has already happened. It gives leaders an early warning. Workarounds, repeat support issues, slow handoffs and widening differences between similar teams can point to value leakage before the financial results fully show it. These signals are not proof of the cause, but they tell leaders where to investigate before the problem gets bigger.
The underlying data exists, you just have to know how to harness it and act
One of the misconceptions about becoming more data-driven in transformation is that organizations need to build an entirely new measurement infrastructure.
Often, they do not.
Much of the information needed to understand whether a transformation is working already exists across the organization. HR may hold workforce and employee data, while IT can see system utilization and support patterns. Operations understands process performance, Finance tracks benefits and business outcomes, and customer-facing teams may see changes in service or experience. The transformation team adds another layer through readiness, sentiment and stakeholder feedback.
Each function sees only part of the story. When the data stays separate, leaders get competing views of the transformation. When it is connected, they can see patterns that a status report or enterprise average can easily hide.
Imagine an enterprise platform that looks successful at first: training completion is high, most employees are logging in and stakeholder feedback is positive. But one region is taking longer to complete a key process, generating more support tickets and still relying on spreadsheets outside the system. The headline numbers say the launch worked. The connected data says leadership needs to look closer.
The important insight is not simply that adoption is “low” in that region. The combination of signals tells leadership that something about the transformation is not working as intended.
The cause could sit in capability, process design, system configuration, incentives or local leadership. It might even reflect a legitimate difference in how that part of the business operates. The data does not make the decision for leaders. It tells them where to look.
Better data should change the plan
This is where the opportunity becomes more consequential.
Organizations often create detailed transformation plans months before implementation. Stakeholders are segmented, communications scheduled, training designed, leader activities mapped and milestones established. That discipline is necessary, but the plan is still based on a set of assumptions about what the organization will need.
Once execution begins, leaders start getting evidence.
Strong adoption in one business unit and persistent friction in another may suggest that those populations no longer need the same support. If employees understand a change but struggle to execute it, another round of communications is unlikely to solve the problem; the intervention may need to shift toward capability, process or leadership. And if adoption appears strong while the expected business result remains elusive, leaders may need to question something more fundamental than the change strategy itself.
This is the difference between a transformation driven primarily by the calendar and one increasingly informed by signals from the business.
The plan and milestones still matter, but they should not prevent leaders from responding when reality unfolds differently than expected. Leaders need a repeatable sequence: discover meaningful signals, analyze the patterns behind them, understand what is driving the change and act by redirecting attention, support or resources. Research on transformation turning points reinforces the importance of detecting problems early and responding in ways that preserve momentum and improve outcomes. (EY). Data matters when it changes what leaders do next.
More data does not automatically mean better decisions
There is an obvious risk in all of this.
Organizations can respond to the push for better measurement by creating enormous transformation dashboards filled with dozens of metrics that few leaders know how to interpret.
A dashboard becomes a problem when it gives leaders more numbers but no clearer decision. Too many metrics create noise, increase reporting work and make it easier for teams to avoid the harder question: What is the data telling us to do differently?
BCG research underscores why keeping the focus on value matters. In one study, 57% of transformations failed to meet their targets for value, timeline or both. Separate BCG research found that the average share of anticipated transformation value realized had declined from 73% in 2020 to 45% in 2022. (BCG)
The answer is unlikely to be another dashboard. It is better visibility into the relatively small number of conditions that help leaders understand whether people are changing, whether the transformation is producing the expected result and where intervention could materially improve the outcome.
That requires discipline. Instead of asking what else can be measured, leadership teams should ask what they would want to know early enough to act differently.
Data changes the role of the transformation team
This shift also has implications for the people responsible for leading change.
Transformation and change teams have traditionally been expected to manage stakeholder engagement, communications, training and readiness. Those capabilities remain essential, but they now need to be paired with the ability to interpret what is happening across the organization, connect those signals to business outcomes and recommend where leaders should intervene.
Change leaders do not need to become data scientists. They do need to know how to connect employee behavior, operating data and business results; spot meaningful differences across teams or regions; and ask the right questions when the numbers do not line up.
The conversation with leadership then changes.
Instead of saying, “Here is what we delivered,” the transformation team should be able to say, “Here is what the data is showing, here is what we think is driving it and here is what we recommend changing.”
This is where an experienced transformation partner can add real value: helping leaders decide which data matters, connect signals held across the business, separate symptoms from root causes and turn what the data is showing into a focused response.
What executives can do now
Executives do not need a sophisticated analytics platform to start working this way. They do need to be more deliberate about the information they use to steer transformation.
1. Start with the outcome, not the dashboard.
Define what the transformation is intended to change in the business, whether that is cost, speed, growth, customer experience, productivity, risk or capability. Then work backward to identify the behaviors that should produce that result and the signals that would indicate those behaviors are taking hold.
2. Distinguish execution from adoption and value.
Leadership should be able to see the difference between whether the change plan was executed, whether people are actually working differently and whether those new behaviors are producing the intended result. All three matter, but they answer very different questions.
3. Look across the business before creating new measures.
The most useful transformation signals may already exist in HR, IT, Finance, Operations, customer functions or other parts of the organization. Connecting existing information can be more valuable than adding another survey or reporting process.
4. Pay attention to variation.
Enterprise averages can create false confidence. Differences across functions, regions, roles or employee populations often reveal where friction exists and where leadership attention will have the greatest impact.
5. Decide what would cause you to act differently.
Before adding a metric to a transformation dashboard, ask what decision it could change. Leaders should know which signals warrant investigation, additional support, a change in approach or escalation.
6. Give teams permission to adapt.
Better information has little value if the transformation team must continue executing a plan simply because it was approved months earlier. Teams need enough flexibility to redirect effort when the evidence warrants it. This same principle applies to AI transformation: organizations need a common enterprise backbone while allowing functions to move at different speeds based on opportunity, readiness and risk. See our related article, AI Transformation: Standardize the Backbone, Not the Journey [LINK].
The goal is not to turn transformation into a perfectly measurable science. Organizations are complex, human behavior is difficult to predict and data will never eliminate the need for leadership judgment. But executives no longer have to steer major transformations primarily through milestones, anecdotes and periodic status reports. They can see more of what is happening while it is happening.
The organizations that use that visibility well will not necessarily be the ones with the most data. They will be the ones that connect data to decisions quickly enough to matter, identifying friction earlier, concentrating resources where they are needed and changing course before small adoption problems become large value problems.
This is the work Switch helps leadership teams do: define the few outcomes and behaviors that matter, bring together data that sits across the organization and turn those signals into better decisions about where to intervene. The goal is not another dashboard. It is a clearer view of what is happening and a faster path to action.
The advantage is not knowing exactly how the transformation will unfold. It is knowing sooner when reality is telling you the plan needs to change.