How Agilent Uses Workspace to Turn DEX Data into Confident IT Decisions 

Understanding every layer of the employee experience 

Device, operating system, browser, network and application performance can all affect an employee’s digital experience. When something changes, the team needs to understand which part of the environment is responsible, how widely employees are affected and where to investigate. 

For Steve and his team, having that level of detail is critical because the same symptom can have very different causes. A poor experience might originate with the device itself, an application, the network or something happening within the operating system. Being able to examine those layers separately helps the team narrow the problem before deciding what needs attention. 

Explaining a DEX score drop to leadership 

Agilent tracks its overall DEX score through a monthly dashboard. Because the score is typically stable, a sudden decline can signal a meaningful change in the employee experience and prompt the IT team to investigate. 

When the score fell from approximately 87.7 to 82, Workspace helped Agilent look beneath the overall score and understand what had changed. The team identified an increase in application and Windows operating system crashes as the primary contributors to the decline.  

These findings gave Agilent an evidence-based explanation to take back to leadership and showed the IT team where to investigate next. Instead of testing possible causes one by one, they could focus on the application and operating system crashes contributing to the decline. 

Making Nexthink data accessible across IT 

Using the data does not have to sit solely with specialists who know Nexthink’s query language and dashboards in depth. Steve can show colleagues how to ask a specific question in natural language, allowing them to investigate without first sending a request to the centralized Nexthink team. 

For example, someone investigating Microsoft Office performance can ask which version is experiencing the most crashes and receive a near-real-time answer based on Agilent’s endpoint data. A colleague who might otherwise have needed help constructing a query can get directly to the information they need.  

For teams dealing with questions throughout the day, that removes some of the dependency on a small group of Nexthink specialists and lets the central team spend less time fielding requests that colleagues can now investigate themselves. 

Narrowing network problems with evidence from the endpoint 

Agilent’s network team is newer to Nexthink, but has found that its data closely aligns with the information available through the team’s existing tools. Seeing the same problem reflected in both places has helped establish Nexthink as another source the team can use during an investigation. 

For Wi-Fi, Nexthink data can identify weak signal strength and show physical pockets of poor coverage that may otherwise be difficult to detect. Instead of starting with a general report that employees are having connectivity problems, the network and end-user technology teams can see where poor performance is being experienced and narrow the area they need to investigate.  

Having endpoint data alongside the network team’s existing view also helps when responsibility for a problem is not immediately clear. Both teams can look at the same employee experience and work from there, rather than spending the early stages of an investigation comparing separate assumptions about where the issue sits. 

Connecting investigation with remediation 

Finding the cause of a problem is only one part of Agilent’s support process. When the team identifies a condition that can be addressed repeatedly, Agilent’s Nexthink architect and support engineers can use Remote Actions and automation to build that response into the way the issue is handled.

The technical work still sits with the people responsible for the outcome. Scripting, validation and execution remain with Agilent’s technical teams, while the endpoint data helps them determine where a repeatable response makes sense.  An investigation can then inform how the same condition is handled in future, so the team does not need to start from the beginning each time it appears.

Making smarter PC lifecycle decisions 

Agilent is also using its endpoint data for decisions beyond troubleshooting. During hardware shortages, the IT team needed to determine which devices could remain in service and which genuinely needed replacing, rather than applying the same refresh policy across the entire environment. 

Using Workspace to explore device health and refresh data, the team could assess hardware based on the employee experience it was actually delivering. Devices that continued to perform well could remain in use longer, while those causing greater friction could be prioritized for replacement. At a time when hardware was limited, this gave Agilent a clearer basis for directing replacement investment towards the devices where it was most needed.

Supporting application investigations across regions 

Endpoint data can also help when an application problem does not affect every employee in the same way. In one case, employees in China reported that SAP CPQ was performing slowly, while users in the United States were not experiencing the same problem. 

Agilent used Workspace to examine the available endpoint data and confirm that the difference between the two regions was visible in the employee experience. Workspace was not responsible for resolving the application problem, but the end-user technology and application teams now had evidence of where the issue was occurring as they continued the investigation. That distinction is important for problems that cross team boundaries. The end-user technology team does not need to own the eventual fix for its data to help establish what employees are experiencing and give the team responsible for the application a firmer place to begin.

Building adoption through practical value 

Agilent is gradually expanding more proactive, data-driven ways of working across its teams and locations. Executive and site-specific dashboards have been created to help support teams identify problems earlier, but adoption still varies. Some teams continue to work more reactively and do not yet use Workspace or Nexthink data consistently.

Each time the data helps a team explain a DEX-score change, narrow an investigation or answer a question independently, people have another reason to use it in their day-to-day work. A support team does not need to change its entire way of working at once. It can start with a question or problem where the data helps it reach an answer more quickly, then build from there.

Key takeaway 

Agilent’s experience shows how DEX data can become part of the decisions IT teams already make, from understanding changes in employee experience to narrowing technical problems and making more informed PC lifecycle decisions.

For IT leaders trying to expand DEX adoption, making the data accessible to more teams can help bring it into their day-to-day work. Give teams the information behind the questions they already receive and opportunities to see how that information changes what they do next. Adoption has a stronger foundation when teams can point to decisions the data has already helped them make.

See how Nexthink Workspace can help more of your IT organization turn endpoint data into action.