In Q1 of this year, I worked with a process engineering team at a major semiconductor company. They had everything you would expect: strong technical talent, solid data, and access to capable analytics tools.

But when yield issues surfaced, the analysis stalled. Like many of these large semiconductor companies, the data lived in multiple places. Between Power BI dashboards, Excel reports, and SQL databases, only a handful of people could actually query the data. By the time everything was pulled together, the opportunity to act quickly had passed.

Sound familiar?

If you’ve worked in a fab, lab, or manufacturing environment, my bet is that you’ve encountered a similar situation.

The challenge isn’t usually figuring out how to analyze the data. It’s getting the data into a usable form – and inside the tools people actually use – fast enough to support decisions.

For teams using tools like JMP, the hard part is often not the analysis itself. It is connecting that analysis to the systems and workflows people already rely on every day.

Many software integrations run smoothly, but the ones that underdeliver typically run into the same set of challenges. Based on real patterns I’ve experienced across engineering and manufacturing teams in the semiconductor industry, here are five questions to ask before integrating analytics software into your existing systems.

1. Can the software actually talk to your data?

Analytics only works if engineers can actually get to clean, structured data. I realize this sounds obvious, but it’s also where many projects slow down.

Most data in typical semiconductor environments lives in:

• SQL databases
• Test and metrology systems
• Internal dashboards and reporting layers
• Ad hoc Excel workflows

I regularly encounter teams exporting static reports from dashboards into Excel just to make data usable for analysis. Sure, that method works at a small scale, but it deteriorates quickly as data volumes grow or workflows become more frequent.

In another recent case, a team shifted from exporting dashboard reports to connecting directly to their SQL database and running independent queries. The technical change was simple, but the impact was meaningful. Removing the intermediate steps reduced friction and gave engineers faster access to the data they needed to investigate process issues.

Even then, there were challenges that remained. Inconsistent naming across tables, missing values and different schemas, IT dependencies for drivers and access – all too familiar challenges. Sound about right?

The team ended up using JMP to connect with, explore, and analyze data from multiple sources, but integration success still depends on data being accessible and structured.

In most environments, the deciding factor is not the tool itself. It is whether that tool can sit alongside existing systems without requiring everything else to change.

2. Can the software adapt to your data security and governance requirements?

Analytics decisions don’t happen in isolation. That is especially true in semiconductor organizations. Before any new tool is used in production, questions come up around:

• Data privacy and ownership
• Access controls
• Auditability and documentation
• Internal or regulatory compliance

For example, in talking with a lead semiconductor team in late 2025, much of the discussion focused on the handling of their data: what is collected, what is stored, and where processing occurs.

Similarly, when exploring advanced capabilities like AI with a semi giant in March of this year, they emphasized keeping their data within their internal ecosystem and maintaining strict control over how it is accessed and shared.

These are not edge cases. They are part of the standard evaluation process.

Teams often evaluate how software such as JMP fits with governance expectations, including who can access analysis results, how work is documented and reviewed, or whether workflows align with existing IT policies.

Governance doesn’t necessarily slow analytics down; in fact, it ensures the results can be trusted and used at scale.

3. Does the software make your engineers' workflows easier?

Tools may be technically sound but still fail to gain traction if they don’t fit into how engineers truly work.

In most environments, workflows evolve out of necessity. Typical workflows include steps such as:

• Export data from a reporting system.
• Clean it manually.
• Run analysis.
• Repeat the same steps next time.

If your shiny new analytics tool simply replaces an old set of manual steps with a new set of manual steps, engineers are likely to revert to the same archaic, yet familiar, processes they are familiar with. So much for the adoption of the new tool.

New tools need to make workflows easier, not just different. The most effective workflows reduce as much friction as possible along the path from data to analysis. The equation is simple: easily connect to data + analyze it + automate repeatable steps = tool adoption!

Another common pattern is fragmentation. Scripts, reports, and insights live in separate files or environments, making it harder for teams to collaborate or reproduce results. In one discussion, this scenario showed up as “all the wires crossed,” resulting in pieces going missing when moving workflows into a shared system.

One reason teams adopt tools like JMP is that they can move from data preparation to analysis to visualization in one environment, reducing unnecessary handoffs. An important feature in JMP, Workflow Builder, emerged a few versions ago and has had a significant and positive impact with the engineers I work with in the semiconductor industry. This tool allows the users to create complex scripts through clicks – as opposed to having to learn how to code. The best part is that the scripts are then easy to manipulate, update, and share. Even when the file names or column headers are different, JMP prompts the user to identify the missing piece rather than just breaking.

Once the friction of mundane tasks is reduced, teams often find they have greater freedom to explore more complex questions with new analysis techniques. Just this past May, I was working with a process engineer that showed interest in moving beyond traditional DOE into more adaptive approaches, especially with limited wafer availability and tight tool constraints. In these cases, capabilities in JMP Pro, such as its Bayesian Optimization platform, can provide guidance on how to learn from each experiment and focus efforts on the most promising conditions rather than testing everything. It helped that process engineer maximize each experiment while avoiding wasted runs.

The lesson is simple: Integration succeeds when it simplifies the existing workflows, not when it replaces them with something more complicated.

4. Can the software perform and scale as your data and usage grow?

What happens when things inevitably get messy?

In a perfect world, analytics projects start with clean data sets and controlled examples. As we all know, real environments are totally different.

In April of this year, I worked with a team focused on process improvement who realized they would need to analyze millions of rows of historical data. Their plan included all the common approaches, including pulling entire data sets into memory. We identified (and they knew from experience) that this would just lead to slow performance, failed queries, and frustrated users.

The solution wasn’t using a different tool. It was changing how the system handled data:

• Aggregating data at the database level.
• Using views or stored procedures.
• Bringing only relevant subsets into analysis.

The same principle applies organizationally. A workflow that works well for one engineer doesn’t automatically scale across teams, fabs, or use cases.

If a team already has JMP in place, a practical question is how easily that usage can expand, while avoiding creating isolated workflows or new silos.

Scaling analytics requires repeatable processes, not just capable software. In some cases, teams solve this by combining direct data access with shared reporting layers. For example, after connecting directly to their database, a team I had worked with throughout 2025 moved forward with JMP Live to publish and share results. This saved days (if not weeks) of manual report generation and made analyses accessible to the entire enterprise, not just to JMP users.

5. How well does the vendor support you beyond the initial rollout?

The difference between successful and stalled integrations often shows up after the initial rollout.

Across semiconductor user groups I’ve been a part of, one of the most consistent themes is the need for accessible support and practical training. The new features are always cool, but to drive true organizational adoption, the next steps are upskilling and knowing how to apply what was learned.

That includes:

• Help with data access and queries.
• Guidance on advanced methods like DOE.
• Clear paths for new users to get started.

In recent sessions with teams at multiple semiconductor companies I work with, once users understood the scope of support resources that JMP makes available through documentation, on-demand training, and direct technical help, they became much more comfortable using analytics tools as part of their daily work.

So, what’s the impact? For me, it’s pretty straightforward.

Without support, your fancy new tool goes underused. With support, the tools become part of the workflow.

At its best, the relationship feels less transactional and more like a technical partnership where challenges arise, solutions are discussed, and innovation thrives.

Integrating analytics software is not just a technology decision. Semiconductor organizations are making decisions about how data is accessed and structured, how engineers and scientists work, and how they manage governance and scale.

The teams that see the strongest results rarely try to solve everything at once. They start with one meaningful workflow tied to real engineering work, prove its value, and expand from there.

For organizations using analytics platforms such as JMP, JMP Pro, or JMP Live, that approach creates momentum that lasts. Because analytics only becomes valuable when it connects to the work people are already doing.