KNIME

Open-source and free-to-use analytics software to automate data processing, reporting, machine learning and data science in one simple and intuitive environment, which lets developers and other stakeholders keep their focus on what they do best.

KNIME's visual programming environment makes data analysis accessible to people without a coding or scripting background. In a data-driven culture this is essential, because non-experts can use the available data independently, improve 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. It takes roughly 10 hours to get familiar with the workbench and 100-200 hours to become comfortable building workflows. The time investment pays off considerably, though: using KNIME saves time.

Workflows are reusable and shareable, so they rarely need to be recreated for each individual project. Repeated steps, such as everyday 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 all components when needed, which keeps business processes consistent. All KNIME workflows are self-documenting: knowledge is stored in the workflow itself and not in an employee's memory. Not everyone needs to become a KNIME specialist, but with basic knowledge even non-technical colleagues can make a meaningful contribution to data analyses.

As a free no-code/low-code platform, KNIME offers many possibilities, but a simple use case for more efficiency is automating Top 10 reports, which are otherwise often still made by hand every day, week or month, for example in Excel.

In this example, both the Category Manager and the Country Manager automatically receive a personalised email after a 30-second run, containing a Top 10 of products per product category or country. They don't have to download or look up anything; it lands straight in their inbox.

KNIME can connect directly to many on-premise and cloud databases, such as SQL Server, Amazon or Snowflake, but in this example the sales figures are read from Excel. The desired period and required columns are then selected:

• sales date, for monthly, weekly or daily reports
• article number and article name
• product category
• country or sales region
• revenue, margin or profit

KNIME workflows are easy to interpret and document well, which makes changes easy to implement. This workflow can easily be extended with, for example, the most important customers per country or the best-performing employees per branch. The challenge is therefore the integration of the right data sources rather than the reporting itself. Further refinements with embedded charts, attached PDF or Excel files, or links to real-time dashboards (in Tableau) are of course possible too.