End the data bottleneck: Why chemical production needs no-code data transformation

If you manage automation, engineering or manufacturing in a fine or specialty chemical environment, you’ve probably experienced this scenario. A batch doesn’t run quite right. Yield drops slightly on a specialized polymer line, or viscosity drifts out of specification during a critical phase. Although probably not catastrophic, you need to figure out the “why” before the next campaign.

What do you do? You check the dashboards, but the view is too high-level. You need to dig into the raw data: combining historical time-series process parameters from the DCS, batch logs from the MES and lab test results from the LIMS. And then, the waiting begins.

You submit a ticket to IT data analytics, but now you’re in line behind dozens of other requests. Maybe you download a massive CSV export yourself and brace for hours of wrestling with fragile, macro-heavy spreadsheets. By the time you finally clean the data and piece together what happened, the problem is now days old. The batch in question is history, and your team is on to the next problem.

In an industry where agility, strict quality tolerances and rapid formula adjustments are the difference between margin and loss, waiting weeks for data isn’t just an inconvenience. It’s an operational bottleneck and even a quality risk.

Why do you have to wait for chemical batch production data?

Today’s fine and specialty chemical facilities are data goldmines. Every valve stroke, temperature ramp, pressure shift and catalyst addition is captured somewhere across the plant floor. But often the people running the processes face barriers to accessing that data.  

The barrier usually comes down to how data pipelines are built. Historically, connecting disparate plant systems – for instance, pulling SCADA logs together with lab data and reconciling time stamps across different formats – has required specialized coding. It means writing queries, building custom scripts or relying on heavy enterprise data engineering pipelines.

Often, enterprise IT and data science teams are understaffed or overwhelmed. They work with enterprise architecture while the plant engineers focus most on batch cycles. When an engineer asks for a custom report, it gets translated into a project ticket with a multi-week turnaround time. As a last resort, plant teams resort to monstrous Excel workbooks, designed to piece plant data together. But spreadsheets have severe limits in a modern production environment:

• Version control: Everyone has a slightly different copy of the “master” workbook on their local drive, making version control a nightmare.
• Fragile formulas: When formulas are fragile, a single deleted column or shifted cell breaks the entire macro, forcing engineers to spend hours debugging formulas instead of analyzing the chemistry.
• Data prep time: Studies consistently show that data analysts and engineers spend more than 50% of their time just gathering, cleaning, and formatting data, leaving less time to actually interpret it and act.

Use case scenario: Tracking a yield variance in minutes versus days

Imagine this scenario on a specialty chemical plant floor. A production process lead is reviewing the previous week’s production batches. Campaign 4 yielded 3% less product than Campaign 3, using raw materials from a new supplier.

In the past, investigating this meant a familiar multi-step ordeal: export three days of continuous control system logs, extract lab assay results from the LIMS database, try to align the timestamps, and copy-paste thousands of rows into Excel. Suddenly, half the workweek is gone just trying to get the data into a readable format.

Now, picture that same scenario powered by no-code data transformation. Instead of waiting for a custom IT script or manually wrangling spreadsheet files, the production lead opens a visual data preparation environment. Without writing a single line of code, they:

1. Connect directly to the plant historian and the lab database through secure, pre-built connectors.
2. Transform the data visually using drag-and-drop workflows – filtering out idle times, standardizing units of measurement and joining process variables with lab results on a unified timeline.
3. Automate the cleaning steps so that every time a new batch runs, the transformation logic applies instantly in the background.

Within minutes the clean dataset is ready. The visualization reveals an unnoticed temperature spike during the second hour of synthesis, correlated directly with the slightly higher moisture content in the new raw material lot. The temperature profile gets adjusted for the next batch, preventing thousands of dollars in off-spec product.

Putting data in the hands of domain experts

No-code data transformation is not about replacing data scientists or bypassing IT governance, it’s about democratizing data prep so that domain experts can handle their own data within set guardrails. By removing technical barriers between plant data and insights, fundamental shifts start to emerge.

1. Speed and simplicity: Data preparation changes from a batch-processing chore into an instantaneous capability. Complex data joins, string cleanups and anomaly filtering are handled through visual logic blocks, allowing domain experts to build a data transformation pipeline.
2. True self-service: Heads of manufacturing and engineering no longer need to wait in an IT queue to answer ad-hoc operational questions. They can explore scenarios on the fly.
3. Role transformation: No-code tools absorb the tedious work of cleaning and formatting data. Instead of fixing broken spreadsheets, engineers and production leads shift more of their time to optimizing yields.

Increase agility in fine and specialty chemical environments

Agility is everything in fine and specialty chemicals, where there are frequent product changeovers, complex multi-step batch recipes, strict regulatory traceability and high-value, sensitive ingredients.

When customer demands shift or raw material variations force a mid-stream process adjustment, your data infrastructure needs to move at the speed of your chemistry. Waiting days or weeks for a data report means the opportunity to optimize has already vanished.

By embracing no-code data transformation, chemical production leaders can finally bridge the gap between plant-floor reality and rapid decision-making.

Explore Siemens’ tools to automate and scale no-code data preparation.