Turning Workplace Data into a Competitive Advantage

Organizations have more workplace data than ever before.

Occupancy platforms, room-booking systems, sensors, service requests, visitor systems, employee feedback, and financial systems are generating a constant stream of information. Yet many workplace leaders face the same challenge: they have more data, but not necessarily better insight.

My advice is simple: Don’t start with the data; start with the outcome.

Begin With the Question, Not the Dashboard

A single data source can tell you what happened. It often takes another source to explain why. Consider a conference room that consistently sits empty on Wednesday afternoons, despite Wednesday being the busiest day in the office. Booking and occupancy data can identify the anomaly, but they cannot necessarily explain it—but employee feedback might.

At our recent SPS Connected Workplace Experience event, we discussed an example where the explanation was remarkably human: afternoon sun made the room uncomfortable, and a group regularly ate lunch there beforehand, leaving a lingering smell of food. Sensor and utilization data showed what was happening, but employee input provided the context needed to understand why. That distinction matters.

The purpose of collecting and combining workplace data is to answer questions about the past and present that can inform decisions about the future.

Connect Data to Uncover Hidden Risks and Insights

Sometimes the insight is more consequential. One financial services organization saw production volumes at its internal print center decline sharply. On the surface, that might look like reduced demand and an opportunity to lower costs.

A deeper investigation incorporating logistics and expense report data revealed something very different. A newly hired analyst was sending sensitive pitch-book work to an outside print provider that had not been vetted for security or brand standards. The declining internal volume signaled potential information-security issues as well as compliance and brand risk.

That’s where connected workplace data begins to create a competitive advantage. It gives leaders context sooner, helps reveal risks that individual metrics can conceal, and enables more informed decisions.

Nicole Mangarella explains how SPS used a minimum viable product approach to focus on high-value data, build a strong foundation, and create a practical path to scale.

Build Trust Into Your Data Foundation

We tried to use the smallest amount of data and just focus on doing that really well before we looked at other areas to expand into. Nicole Mangarella Head of Global Technology & Innovation, SPS

AI doesn't fix poor data. It increases the importance of trustworthy, well-governed data. AWS Senior Solutions Architect Michal Kolodziej emphasized that AI needs more than large volumes of data. It needs data with sufficient precision and context to produce reliable answers that people can trust. Trust, in turn, influences whether employees and leaders will ultimately adopt AI-enabled tools.

That requires organizations to address some fundamental questions:

1. Where does the data reside and how is it updated?
2. What does it actually represent? 
3. Who owns it? 
4. Who can access it? 
5. How current does it need to be?

Even definitions matter. The same terminology might mean different things across systems, business units, or geographies. Without common definitions and governance, combining data can create confusion instead of clarity.

Work backward from the business outcome

One of the most useful principles Michal shared was AWS's practice of “working backwards.”

Michal Kolodziej explains AWS's "working backwards" approach.

Instead of beginning with the technology and looking for a reason to deploy it, start with the business outcome. Define the decision you want to make, determine what information is necessary to make it, and then design the technology, data architecture, and processes required to support it.

Start with the business problem and desired outcome, then design the technology and data solution to support it. Michal Kolodziej Senior Account Solution Architect, AWS

SPS followed a similar philosophy in developing our own workplace data capabilities. We began with a minimum viable product, using a focused set of data to solve specific problems well before attempting to connect with other sources. We involved information-security experts early, standardized the information entering our systems, and spoke directly with clients and frontline operational teams about the questions they needed to answer.

That foundation is allowing us to bring workplace data together in ways that help clients identify patterns, surface risks, and make more informed decisions.

Key Questions Every Workplace Leader Should Ask About Data

1. What business question are we trying to answer?

Starting with the decision keeps teams focused on useful outcomes rather than collecting data simply because it is available.

2. What data do we need to answer that question?

One data source rarely tells the full story. Occupancy, service, employee feedback, financial, and operational data may need to be connected to provide meaningful context.

3. Where does the data live, and can we connect it?

Workplace information often sits across disconnected systems. Understanding where it resides—and how those systems can work together—is essential to uncovering deeper insights.

4. How do we build trust in the data?

Determine consistent definitions, input processes, and ownership, and set governance and refresh standards. A clear understanding of what the data represents allows leaders to act with confidence.

5. What are employees and frontline teams telling us that the data cannot?

Quantitative data can identify a pattern, but human feedback often explains the reason behind it. Combining both creates a much fuller view of workplace performance.