How to Navigate Transformation Despite Data Noise

Issue 214, May 29, 2025

Here’s the world we live in professionally: “In today’s world, business leaders must navigate rising global competition coupled with unprecedented interconnectedness, disruptive technological forces, persistent economic uncertainty and proliferating geopolitical crises,” says JPMorgan’s Chairman and CEO Jamie Dimon. (Axios)

And here’s the world many of us live in personally: “Body hacking is a favorite pastime, best exemplified by the Oura Ring, a quiet status symbol in the C-suite and among policy wonks who are obsessed with optimizing every facet of their lives. For years, the appeal has been clear: a continuous stream of biometric data — from sleep quality and heart rate variability to body temperature — promising insights that could unlock peak performance.” (NY Times)

In both cases, we are increasingly dependent on data and analytics to help us understand trendlines, market information, ourselves — and even the headlines. Yet as organizations navigate change and transformation initiatives, this data dependency creates a critical tension: The very information meant to guide change can paralyze decision-making when there’s simply too much of it.

The Paradox of Information Abundance

As we have cautioned more than once, the data you rely on is only as good as its input (including large learning models, AI, search, organizational systems and available content) and only as meaningful as its context, relevance, and interpretation. In case after case, AI continues to demonstrate bias where it is better at analyzing and reporting findings and facts than making predictions and drawing complex conclusions. If the prompt is to analyze large datasets and evaluate historical information to determine a trend, AI is up to the task. But to predict is risky because our data stores contain errors and omissions to inform a coherent, comprehensive decision. Since customer and performance data storage is also captured in siloed systems, connecting the dots (a requirement for informed decision-making) is a liability. So, now consider the flood of data at your fingertips that may be flawed. How do you trust it?

For organizations undergoing change and transformation, the distinction between the right data and the overabundance of data becomes a make-or-break deal. When there is simply too much information (data) delivered too quickly, we can easily become overwhelmed, confused and defeated.

Last week we wrote about managing today’s series of whiplashes (Trump has made over 50 flipflops over tariffs since his inauguration) in our marketplace as organizations are attempting to make the right decisions in a time of intense day-to-day ambiguity. Data point after data point provides some level of conflicting influence to feed our decision-making during these challenging times. We suggested the powers of resilience, patience and thoughtfulness allow clear headed thinking leading to sound (not knee-jerk) decision-making. Today, we are suggesting a variety of actions based on practical lessons learned that can inform how to contend with and manage data noise productively.

When Information Becomes Toxic

Here is an emerging trend, as reported by The New York Times: “Some Oura wearers are noticing an unintended side effect: High anxiety. Rather than helping them feel more in control of their wellness, the data only made them fixate on potential — and often nonexistent — problems.”

This anxiety applies equally to organizational data, particularly during periods of change and transformation. A growing problem of the data economy is that sometimes too much information breeds doubt, analysis paralysis, and transformation stagnation. Organizations get caught in what we call “dashboard hypnosis” — endlessly analyzing metrics without moving toward meaningful change.

“Since technology has become so integrated into our professional and personal lives, our concern is how we leverage technology, so the technology is serving us not the other way around,” says Shyamal Patel, Oura’s senior vice president of science. The same principle applies to organizational change and transformation: Data should accelerate change and transformation, not obstruct it. However, we desire more and more data to make us feel comfortable in the decisions we are making. We expect our systems to become more and more data-rich, almost to a fault, with the aspiration that we might need it at some point in the future. The truth is that much of that data never gets used.

The Coming Data Deluge

We are at a hinge point in the AI/technology era. Consider what Sam Altman and Jony Ive are up to with their new venture io with a device that “will be unobtrusive, fully aware of a user’s life and surroundings, and will serve as a ‘third core device’ a person would put on a desk after a MacBook Pro and an iPhone.” OpenAI is already predicting that the device will be popular, with Altman saying that “it will ship faster than any company has ever shipped 100 million of something new before.” (The Verge)

What are the implications of yet another onslaught of data streaming into our organizational consciousness and how we can synthesize this information to drive meaningful transformation? We view this scenario with some element of caution similar to the promise and hype of the Metaverse and other tech novelties. In fact, we’re in agreement with 77% of Americans who want companies to create AI slowly and get it right the first time, even if that delays breakthroughs, according to a 2025 Axios Harris 100 poll.

Yet, the visionaries see the world differently and they are developing new systems and products that most of us cannot even imagine. At 2040, our job is to reach for the stars but keep our feet on the ground (thanks Teddy Roosevelt) so, we raise the flag questioning hype and over-promise over practicality.

The Transformation Decision Trap: A Real-World Example

The constant stream of information on markets and the economy illustrates how data overload can derail organizational decision-making. Markets swooned when Trump first announced tariffs, then bounced back, and are now swooning again. The underlying anxiety? Growing US debt, the bond market, and the devaluation of the American dollar with no visible plan to reverse these actions.

For organizations navigating transformation initiatives, this creates a familiar dilemma: Which data points should inform strategic pivots? Which metrics actually predict change and successful transformation versus failure? With a nearly infinite number of dashboards available, leaders face paralysis by analysis just when decisive action matters most.

Consider this typical transformation scenario: A company is implementing digital transformation while simultaneously needing to track customer satisfaction scores, employee engagement metrics, technology adoption rates, revenue impacts, cost savings, competitor analysis, market trends, and regulatory changes. Each data stream tells a different story. As we said, it is likely overwhelming and confusing and filled with anxiety in terms of what an organization should base its thinking on. What is indicative? What is simply noise?

We are dealing with a rock and a hard place conundrum. Uninformed decision-making, made intuitively without fact-based information, can derail transformation efforts. But over-informed decisions can be just as risky, creating endless delays when speed and agility are essential for successful change.

From Data Collection to Transformation Intelligence

To effectively lead organizational change transformation in the era of overwhelming information and whiplash, make a shift from data collection to strategic data curation and contextualization. Here’s how:

1. Transformation-Grade Data Standards

Develop internal guidelines that define what kinds of data are considered “transformation-grade” based on change objectives.

Timeliness: Is the data recent enough to inform current transformation decisions?

Leading vs. Lagging: Does this data predict transformation outcomes or just report what already happened?
Source Credibility: Who generated the data? Can it be trusted during periods of organizational change?
Transformation Relevance: Does this data directly relate to your transformation goals, not just general business health?

2. Identify Change-Critical Signals

Not all data is created equal during transformation. Focus on metrics that specifically indicate transformation momentum. Invest in tools and teams that can distinguish between transformation-critical signals and informational noise.

Adoption rates of new processes, technologies, or behaviors
Resistance indicators that predict where change efforts may stall
Cultural shift metrics that show mindset and behavior evolution
Capability development progress in new skills and competencies
Stakeholder alignment scores across different organizational levels

3. Appoint Transformation Data Stewards

Much like financial controllers, organizations now need transformation data stewards — senior personnel who have these specific accountabilities.

Data integrity across transformation workstreams
Cross-functional data governance during periods of organizational change
Contextualization of metrics within the broader transformation narrative
Translation of data insights into actionable transformation decisions

4. Deploy AI as a Transformation Amplifier, not an Oracle

AI is a powerful amplifier for transformation insights, but its output is only as good as its input and context.

Treat AI-generated transformation insights like any other data source: test, validate, question
Combine AI analysis with human judgment about organizational culture and change dynamics
Use AI to identify patterns in transformation data, but rely on human wisdom for strategic interpretation

5. Practice Transformation Data Mindfulness

Just like Oura Ring users may become anxious when overanalyzing sleep scores, transformation teams can get paralyzed by data overload. Create a culture where data serves transformation decisions, not derails them.

Limit dashboard proliferation to essential transformation metrics
Schedule data-free decision windows where leaders rely on strategic intuition, remember with thought and a bit of luck, our intuition may be more accurate than what the troves of data are telling us
Celebrate decisions made with sufficient (not perfect) information

6. Conduct Quarterly Transformation Data Audits

Every quarter, review what data drove transformation decisions.

Did it accurately predict transformation outcomes?
Is it missing critical change indicators?
Is data still being collected that’s no longer relevant to transformation goals?
Which metrics are actually correlated with transformation success?

7. Weave Data into the Transformation Story

Great transformation leaders are also great storytellers. Frame data within a compelling transformation narrative that links analytics.

Transformation vision and desired future state
Change progress and momentum indicators
Cultural evolution and mindset shifts
Capability building and skill development
Clear next actions based on current data insights

Signals in Transformation Noise

Organizations don’t suffer from a lack of transformation data — they suffer from an inability to identify which data actually drive effective change. The organizations that thrive won’t be those drowning in metrics, but those moving confidently toward their transformation goals with the right navigational data. At 2040, we help clients understand that prospering through organizational transformation in the AI age is not about who has more dashboards, but who has the clarity to choose the right data, at the right time, with the right context — and act decisively to drive meaningful change.

The question isn’t whether you have enough data to guide your transformation. The question is whether you have enough wisdom to know which data matter for the change you’re trying to create.

Next week, we will cover the metrics that matter in the second half of 2025 to help guide you through the noise to achieve your change and transformation objectives.

Get “The Truth about Transformation”

The 2040 construct to change and transformation. What’s the biggest reason organizations fail? They don’t honor, respect, and acknowledge the human factor. We have compiled a playbook for organizations of all sizes to consider all the elements that comprise change and we have included some provocative case studies that illustrate how transformation can quickly derail.