Big Data: Mass Data analysis for informed Corporate Management

When a major revenue-generating customer is lost, quick action is crucial—especially in controlling. Management, among others, is keen to understand the financial impact and the effects on operating results, as well as on sales, costs, and inventory. However, many companies struggle with this in practic

Most companies rely on their ERP systems, but these often have limited analytical capabilities, posing a significant challenge. Complex Excel analyses also carry risks: they can be time-consuming and prone to errors resulting from formulars that do not operate correctly. At the same time, business processes are becoming more complex, data volumes continue to grow, and IT landscapes expand with additional systems. The challenge remains to effectively analyze these large datasets. This is exactly where targeted mass data analysis comes into play, enabling companies to make tactical and strategic decisions.

In analyzing large volumes of data, numerous individual pieces of information are aggregated or structured according to specific criteria to provide a clearer overview of the current situation. Typically, the data exists as simple lists that are then aggregated to a more manageable, higher-level summary.

For example, daily order backlogs can be projected to monthly or yearly levels. Revenues can be displayed not only per order but also by customer. Production throughput times can be aggregated at the machine level, or working capital can be sorted by the age of inventory. In short, a confusing table with potentially hundreds of thousands of entries is transformed into an understandable, easy-to-read overview with just a few rows and columns, showing absolute values, relative figures, and even trends.

Reliable Data as a basis for informed decisions

Experienced executives usually know their company’s key performance indicators even without comprehensive mass data analysis. However, this deep knowledge can be verified, validated, and supplemented using modern analytical methods—such as identifying trends or gaining insights into business areas previously overlooked. This is the particular value of good mass data analysis.

A common misconception, however, is that merely collecting and analyzing large datasets will automatically reveal patterns or lead to concrete actions—following the idea: “Let’s just analyze and something will come out of it.”
Therefore, before analyzing mass data, the goal and purpose should be clearly defined: Is it for a strategic analysis? Who is the target audience? And how should the results be presented? The outcome could be a bar chart showing a timeline or a map visualizing regional sales distributions.

Excel as the standard, but with limits

Microsoft Excel remains the most widely used tool for short-term analyses. In theory, up to one million records can be stored in a table and summarized clearly with pivot tables. However, processing such large volumes often risks program crashes due to high computational demand.

For very large analyses—such as machine data or logistics movements that exceed one million records—specialized tools like Qlik, Power BI, Tableau, IBM Cognos, SAP BusinessObjects, Looker, or Domo are better suited. These platforms enable real-time analysis of big data and can be set up as control dashboards, allowing key metrics to be accessed at the push of a button.

Consulting as a key to success

In complex situations like these, an experienced consultant can provide valuable support. With expertise in areas such as operations and finance, and strong methodological skills, they can analyze large datasets and present concrete recommendations to management. They should also include financial impacts of individual measures—such as annual cost or revenue effects—within the implementation plans.