Tata Steel is a global conglomerate reporting a total annual revenue of USD 113 billion. In Europe, Tata Steel is one of the largest steel producers serving multiple markets worldwide including construction and infrastructure, automotive, packaging, and engineering. The Internal Audit & Assurance department at Tata Steel Europe has 22 auditors. Data analysis for audit plays a central role in testing and validating controls, identifying anomalies and trends, as well as quantifying potential risks and savings.
In order to improve processes, Tata Steel Europe took different areas within the company where data analytics were being applied. The motivation: do more with the available data and move reporting away from the labor-intensive and error-prone Microsoft Excel.
Below are three examples of data analytics in action at Tata Steel Europe. In all cases a subject matter expert, who is often the lead auditor, together with an in-house KNIME expert worked together to do the data analysis.
Immediately after getting started with KNIME, and whilst still being in the experimental phase with hobby projects, time savings of up to 95% could be achieved in some use cases. Furthermore, by comparing KNIME results with Excel reports, several data issues and calculation errors were identified in the Excel reports. This was an even stronger motivation to get started with KNIME.
KNIME’s visual programming environment makes data analytics accessible to people who don’t have coding or scripting backgrounds. In a data-driven culture, this is essential because non-experts can independently use the available data, enhance control procedures, and turn insights into business value. It does take time to learn KNIME and basic data analysis skills are required to build workflows.
In the case of Tata Steel Europe, it took approximately 10 hours to get familiar with the workbench and 100-200 hours to become confident in building workflows. However, the time investment has significant rewards and is worth it. Using KNIME saves time.
Workflows are reusable and shareable, which reduces the need to recreate them from scratch for every single project. Repeated steps such as mundane pre-processing tasks can be captured as a component and used in other workflows or by other colleagues. Configuration changes can also be programmed to update across all components if needed – guaranteeing consistency in business processes. All KNIME workflows are self-documenting, meaning knowledge is stored in the workflow itself and not in the mind of an employee. Not everyone has to become a KNIME specialist, but with basic knowledge, even non-technical auditors can contribute meaningfully to data analytics in audit.