From mountains of paper to data silos

Data warehouse, data lakehouse or single point of truth - they are nothing more than digital storage systems. In other words, a huge silo. A depot. Or a warehouse. This is where all relevant company data is stored and brought together. This means: information that is important for the management level, for example, but also information that is useful for operational purposes. In other words, everything that can be used to achieve success and improve operations.

While robust data warehouses require structured data, i.e. a pre-designed schema, data lakes have no such requirements. They are therefore unstructured but cost-effective storage solutions that can also be combined with a data warehouse. This creates a data lakehouse. Here, the silo walls between the data lake and the data warehouse are removed so that data can simply be moved between the solid data warehouse and the flexible storage in a data lake. And there are suitable reporting options on top so that you don't lose the overview. An essential tool that no well-known software manufacturer can ignore these days - which is why SAP also has a solution at the ready: SAP Datasphere.

Not all reporting is the same

However, it should also be said at this point that a data warehouse is not necessarily required as the basis for every reporting requirement. So how does reporting from an ERP system differ from reporting from a data warehouse?

1
Reporting from
ERP system:

The ERP system tends to be used for operational analyses because it is based on live data. The focus is on short-term actions.

Example: Employees have to order production utensils and have many suppliers to choose from. To make the best choice, they analyze their payment practices. In this way, they switch from an operational activity, namely creating the order, to an analysis that tells them which suppliers have delivered on time in the past or where there have been problems.

2
Reporting from
Data Warehouse:

On the other hand, a data warehouse’s database is better suited to strategic issues – for example, to improve company performance – as these analyses are usually long-term in nature and also include data from the past. Artificial intelligence also plays a greater role in this area, as there is a more extensive database that AI can scrutinize in order to create forecasts, for example.

For example, it can be used to look at sales trends over the last few years, making outliers visible. Subsequently, it can be determined what the reasons for this were and how this can be prevented in the future.

One place for all data

A major strength of data warehouses is the fact that data from a wide variety of sources can be bundled here. And this is becoming increasingly important in the age of digitalization, as companies rely on many different systems and platforms. Most of these products now have good analysis capabilities for their own database - but only their own. And that is the problem.

The data warehouse, on the other hand, is a central point where everything comes together, is bundled and linked. And can subsequently be evaluated. So when it comes to mapping the big picture - and this is becoming increasingly important these days - companies can no longer avoid a modern data platform or data warehouse.

Layer by layer to data gold

And how does information from different data pools get into such a uniform silo? The magic word is: layers. Because every modern data warehouse - whether SAP Datasphere or Microsoft Fabric - relies on a layer concept:
1
Taping into source systems:
The first layer is about getting all the data into the system and thus tapping into all available source systems.
2
Refining data:
The next layer involves refinement in the form of adjustments. The data is filtered and assigned - depending on which of it is relevant for a specific area and a specific company objective. This results in data slices that are essential for the finance department, for example, or others that are useful for purchasing.

Important aspects:

Finally, visualization tools can be used to present the data clearly. The result is a modern dashboard that helps the respective department to make decisions. However, there are a few other important aspects that need to be taken into account for this to succeed:
1
Clear goal:
It is essential to know exactly what the respective department wants to achieve and what the objectives are. This is the only way to select the right data.
2
Clean foundation:
The cleaner the data is prepared, the easier it is to visualize later. AI can also provide support here - but only if the basis is right.
3
The time factor:
Such a changeover to a structured data warehouse is not completed in a few days, but takes time - but the effort pays off.

The future is called: Business Data Fabric

Modern data platforms also form the basis for the "business data fabric" concept. It enables companies to provide all data users with meaningful data, including the appropriate business context and logic. Put simply, relevant data is easily, quickly and simply accessible to employees. This is because the trend is increasingly moving towards self-service. This means that the respective department should be able to modify reports themselves to a large extent so that they do not have to call on the IT department for every adjustment. Although IT takes care of the basics, responsibility for reporting is increasingly being handed over to the specialist departments. And situational interventions are now also possible here, for example when data needs to be added retrospectively.

The SAP Datasphere

This is why major software manufacturers are investing heavily in user-friendliness. SAP is no exception. The well-known manufacturer's cloud data warehouse system is called SAP Datasphere. And scores with the following advantages:
1
Scalable data platform with layer principle
2
Data can be optimally prepared for different departments (Spaces)
3
user-friendly and accessible on the web
4
Perfect integration with SAP S/4HANA
SAP is also increasingly relying on cloud technology, as is Microsoft and its Microsoft Fabric data platform. So the days of classic SQL database tables are numbered and the era of modern data bundling can begin - in your company too.

Now you just need to decide on a system.

Our holistic advice

But it's not that easy, is it? We know: It all depends on many factors. Above all, it is important to analyze the existing system landscape and any connection options in order to decide what is more suitable. Our advantage? The scc team has expertise in both the SAP and Microsoft environments. This means that our focus is not on the manufacturer, but on you. And your individual situation.

Nowadays, a successful company can no longer manage without a data platform due to the number of different systems and data suppliers - including devices and machines as well as systems. Whether SAP or Microsoft - our advantage is that we are at home in both worlds and can therefore provide you with the best possible advice.
Clemens Stadler
LoB Manager Analytics & Planning