Discovering the Value of Data with SAP Business Data Cloud

Polpharma Biologics is the largest biotechnology company in Poland, developing and launching new biological medicines. The pace of growth is illustrated by the fact that when the implementation of SAP S/4HANA began in 2020, the company employed 400 people. By the time the system went live (in 2022), Polpharma already had 1,200 specialists on board.

The new integrated system improved data availability and integrity, enhancing the efficiency of critical operations and decision-making. However, in the area of data processing and analysis, two major challenges remained unresolved:

• The need to integrate data from various source systems in daily operations;
• The lack of efficiency in using collected data, caused by manual processing of information from different areas.

Therefore, the company decided to extend its system landscape with SAP Business Data Cloud analytics tools, specifically: SAP Analytics Cloud and SAP Datasphere. The SAP Analytics Cloud component enables data visualization using tables, charts, and maps, the creation of interactive dashboards, as well as the definition and management of custom reporting objects. It is also the core tool within the SAP product family for flexible planning, simulations, forecasting, and verification of costs and revenues across all aspects of an organization’s operations.

As for SAP Datasphere, it enables the integration, merging, and processing of data from various source systems (both on-premise and cloud), also remotely through an intuitive graphical interface.

At Polpharma Biologics, the primary expectation of the business was the ability to process and present data from the SAP system in the form of easily accessible and clear reports. The source systems used in the project so far included: SAP S/4HANA and SAP SuccessFactors.

Here we highlight some of the key business challenges and expectations, as well as the solutions delivered by All for One during the implementation.

Every production batch at Polpharma Biologics needs to undergo cost verification. It is also crucial to compare costs between production batches. The biggest challenge in presenting both cost and material variances was the large volume of data involved. BOMs containing 200–300 items made it virtually impossible to generate consolidated reports, for example covering batches across an entire year, directly from SAP.

To address this issue, we started with data modeling in SAP Datasphere. In this case, we used a single source system, SAP S/4HANA, but effective reporting required the integration of multiple views. This resulted in the creation of a fairly complex analytical model consisting of more than a dozen interconnected views and covering both transactional data and master data describing specific dimensions. It is worth noting that SAP Datasphere can use data from S/4 in remote mode, which ensures always up-to-date data in reports and compatibility of authorizations with the source system. Data for SAP Datasphere can also be loaded and stored in the data warehouse.

For the report in SAP Analytics Cloud, we assumed that a traditional dashboard — with extensive graphics visualizing the data — was not necessary. Instead, the focus was on enabling flexible data analysis. We prepared a pivot table that allows for flexible data filtering: expanding, aggregating, displaying additional attributes, rearranging, and more. Of course, in the data analyzer we can use predefined indicators existing in the model, but we can also create our own formulas, including simple ad hoc ones, using all available functions and defined measures. The analysis prepared in this way can be saved and reused at any time.

At Polpharma Biologics, production processes are divided into stages, each of which should be completed and confirmed within the planned timeframe. An additional complication is the overlap of certain stages in time. In this case, it was important to provide a clear and user-friendly visualization of the confirmation of a specific production process stage based on data from SAP S/4.

To carry out this task, we once again began by creating a data model in SAP Datasphere. This model was much simpler compared to the production variance model and was also based on SAP S/4 data.

Data modeling in Datasphere is highly intuitive. The easiest way to do it is through the graphical view, where functions, aggregations, filters, and data projections can be freely inserted. For example, a date stored as text can be converted into a proper date format and then linked to a calendar, which automatically generates the appropriate year, quarter, or month.

In SAP Datasphere, we can also use text, date, and numeric functions. Data can be combined using join and union options. In the join option, we can define left, right, or inner (with inner being the default). Data can be previewed at every stage of modeling.

In the production status report, data can be presented both in tabular form and as charts (heatmap, Gantt chart). In the table and heatmap, we can see the status of individual stages for production orders and for stages. The Gantt chart, for example, can be used to display the status of specific processes for a single production order.

The purchasing department at Polpharma analyzes KPIs that reflect data related to suppliers, purchase orders, a requesting department, order lead times, variances, delivery dates, employee workload, and more.

In this case, the data volume is significantly larger and comes from multiple areas of the system, making it impossible to generate reports containing all required information directly from SAP. Manual preparation of reports in Excel is very labor-intensive, and, in addition, the data is not updated in real time. Even the Vendor Evaluation module available in the SAP system was not sufficient to meet all business requirements.

As in the previous business cases, we started with data modeling in SAP Datasphere. We used data not only from SAP S/4HANA but also from SAP SuccessFactors in order to assign sales staff to the appropriate organizational units. In the SAP Datasphere analytical model, it is easy to define a measure, specify how it should be aggregated, and determine what is treated as an attribute. It is possible to set semantic types, define parameters that enforce report constraints, and establish relationships, i.e., associations to master data.

The prepared report presents the value of purchase orders along with the value of variances. More generally, these variances can be easily visualized for individual items in an in-cell variance chart.

It is worth noting that in SAP Analytics Cloud we can leverage artificial intelligence through the Just Ask feature, which allows us to list, for example, the value of purchases per supplier in 2025. We can also refine our query further, such as by asking for the top five suppliers.

Another example of using SAP Business Data Cloud solutions is the planning and monitoring of IT budget utilization, broken down by projects (WBS), teams, and purchase orders.

Since investment planning was done in an external tool and cost settlement in SAP, it was not possible to combine planning with budget utilization monitoring within a single tool. Therefore, the decision was made to implement a solution based on SAP Analytics Cloud.

With regard to project budget planning, an important requirement was the ability to review plans in various currencies (PLN, EUR, USD) and, of course, to introduce new plans in these currencies, taking into account the budget exchange rate.

The prepared solution makes it possible, for example, to increase a project plan by a specified value and distribute funds according to defined parameters (e.g., proportionally across quarters or based on predefined weights).

If a new project needs to be created, the easiest way is to use the pop-out option, where the project and its properties are defined. The next step is to enter the data: the account on which the value should be planned, the supplier, and the annual amount, which by default is proportionally distributed across the quarters.

After the data is published, it can be reviewed directly in the report, together with actual data. To present variances, we used an in-cell chart. Importantly, by actual data we mean invoiced values, whereas by forecast we mean values for which a purchase requisition or purchase order has been created, but this data has not yet been invoiced.

Polpharma Biologics plans to make broader use of SAP Business Data Cloud tools. There are upcoming projects related to budget and personnel cost planning. An ambitious goal is to integrate laboratory systems and applications with SAP Datasphere, as well as to extend the use of data analytics tools across the entire organization.

Polpharma Biologics is the largest biotechnology company in Poland, specializing in the development and production of biosimilar medicines based on living cells. As one of only a few companies worldwide, Polpharma Biologics carries out the entire drug development process – from cell line selection to producing medicines for patients worldwide. Two of its products have already received positive opinions from the most rigorous agencies granting marketing authorization for medicines: the U.S. FDA and the European Medicines Agency (EMA). The company operates state-of-the-art research and development centers equipped with next-generation laboratories and purpose-designed production lines in Poland and the Netherlands. It employs over 1,300 people.