Accelerate System Implementation through
Data Orchestration

A large online pharmacy had selected a widely used inventory management system for the pharmaceutical industry. Before it could be implemented, however, the company’s existing data had to be adapted to the rigid structure required by the new software.

The pharmacy operated across different countries through online marketplaces, receiving source files in multiple languages. Although the datasets contained broadly equivalent information, column names, descriptions, formats, and structures varied from one market to another. Product codes also had to conform to specific formats, while data types and field lengths needed to be aligned, datasets merged, duplicates removed, and missing values standardized.

Within a few weeks, Rulex delivered a solution covering the data preparation, transformation, and quality requirements needed to move forward with the new inventory management system.

From fragmented source files to a system-ready dataset

The process began by bringing multilingual files from different markets into a common structure. Instead of importing and combining each dataset individually, entire folders could be processed together, with newly added files automatically incorporated into the same flow. Information about the source file could also be retained as an additional attribute, preserving context such as the country associated with each record.

Import problems became visible as files were ingested. If an inconsistency prevented a file from being parsed correctly, users could identify the issue immediately and investigate it directly. Compared with a script-based process, where locating the problematic file or record may require additional investigation, this made troubleshooting considerably more immediate.

Once consolidated, the data could be reshaped according to the inventory system’s specifications. Pharmacy employees adjusted field names and data types, standardized product codes and field lengths, and combined information using built-in configurations and simple formulas. Multiple transformations could be carried out within the same visual environment rather than implemented as a collection of separate scripts.

Data quality formed the final part of the preparation process. Missing values, inconsistencies, and duplicate records were identified and resolved before the information was passed to the new inventory system.

Once the required operations had been configured, new incoming files could follow the same preparation logic automatically. Employees could therefore manage the data flow themselves without ongoing maintenance or the need to develop new custom scripts as files arrived.

01. Data orchestration that removes repetitive work

Rulex Platform can collect and aggregate information from heterogeneous files, databases, cloud storage, and enterprise repositories within a single data flow, converting incoming data into a common format for further processing. Dedicated import tasks support formats such as Excel, CSV, JSON, XML, and Parquet, as well as local and remote sources.

Connections can be configured as Saved resources, stored at environment level and reusable across different tasks and flows, or as Custom connections, defined specifically for the task in which they are used. Single files, multiple files, or entire folders can be ingested and combined into datasets, supporting recurring data preparation across large volumes of information. By automating these activities, teams can spend more time on analysis, optimization, and forecasting rather than repeatedly collecting and preparing data.

02. WYSIWYG data transformation

Rulex Platform provides a visual drag-and-drop environment for building data workflows, while a WYSIWYG interface lets users work directly with the information they are transforming. Multiple preparation operations can be configured without writing custom scripts, from structural changes and derived attributes to filtering, joining, and data quality operations.

Behind the visual interface, Rulex Platform generates optimized code for every operation, automatically documenting the steps performed and supporting source-control versioning. Each operation is also recorded in an interactive history, where individual steps can be reviewed, modified, and repeated. This combines the accessibility of visual data preparation with the traceability needed to debug, maintain, and evolve workflows over time.

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