Creating a semantic layer with SAP Data Warehouse Cloud bridges the gap between data structure and business value.
Modern data management presents organizations with a frustrating paradox. Companies have more data than ever, yet the struggle to derive business value from that data has never been greater. Business leaders know that more data leads to better decisions, but are often faced with questions about how accurate the data they are given is or where it came from in the first place. Plus, having seen many peer organizations shaken to their core by security breaches, these leaders are concerned about the security of sensitive customer data.
Organizations need an affordable way to implement a secure data layer, accessible to business users, that can support comprehensive analytics on the full data set. What’s needed is a semantic layer to bridge the gap between the data structure and its value to the business. A semantic layer provides a natural language index to the underlying data and its organizational structure.
Ideally, the semantic layer is configured by someone who understands both the underlying data architecture and the reporting needs of the business. Implementing the semantic layer requires exposing appropriate fields in the data store to business users, while hiding any fields that are not relevant. Each exposed field is assigned a friendly, meaningful name. These fields are organized in a way that will make sense to business users, rather than the structure of the data store.
An Index to the Business
In addition to user-friendly field names, the semantic layer includes pre-defined filters corresponding to common restrictions that you may wish to include within queries. For instance, a pre-defined condition named “Filter: Current Year” would restrict the results of a query to data for the current year.
With the semantic layer in place, you don’t have to try to understand the data in terms of relationships between tables. As noted above, the semantic layer provides an easily accessible index to the underlying data. Basically it is a list of business terms organized according to business functions. You can query the data using terms and relationships you are already familiar with. Via a point-and-click interface, you can then customize the appearance of the report and the way in which the data is presented. For example, you might choose show the data in tables, crosstabs or graphs, and can add headings, text, and images as needed.
This approach provides self-service capability to create a wide variety of different queries and reports, without having to wait for support from IT or other technical help. You can create these reports without having to learn a query language or understand the structure of the data store. This is great both for business users, who can create their own reports as and when they need them, and for IT who will now be free to focus on other priorities.
To be fully effective, the semantic layer must provide:
- Usability / Understandability/ Acceptance
- Security & Governance
- Performance and Scale
SAP Data Warehouse Cloud supports implementing a semantic layer that meets each of these requirements. Let’s take a look at each of these considerations in greater depth.
Usability / Understandability/ Acceptance
A common complaint from the business is that IT takes too long to build or alter reports for them. When it comes to generating reports, business users increasingly want to control their own destiny. A well-designed semantic layer with agile tooling shows these users how modifying their query will result in different results, without any help from IT—while maintaining confidence that their results will be correct.
With SAP Data Warehouse Cloud, users benefit from intelligence embedded in the semantic layer. By analyzing metadata from the various data sources (both within and outside of the Intelligent Enterprise), SAP Data Warehouse Cloud can automatically propose the creation of global dimensions, facts and KPIs.
Security & Governance
Managing enterprise data in today’s environment requires adhering to a wide range of security and regulatory requirements. Because business users must know where the data came from, how it was transformed, what the refresh rate is, etc., the semantic layer needs make it possible for users to track data lineage to ensure both security and compliance.
With support from IT, a semantic layer can ensure that data has already been validated before exposing it to the end user. Additionally, the right governance processes will ensure that users will see data only that they are allowed to access. Without such a layer, your business will lack transparency into what data is being used, how, and by whom—all of which makes it difficult to identify and manage security and compliance risks.
Performance and Scale
The semantic layer inside SAP Data Warehouse Cloud doesn’t care how your underlying data model is defined, or whether the data resides in an on-premise data warehouse, an OLTP system, or in the Cloud. With SAP Data Warehouse Cloud, you can use the semantic layer to access to HANA’s powerful query execution engine, which will choose the optimal access and location of the data. The result is less effort in creating ETL jobs and more focus on getting the value out of your data.
By making it easier to create and execute queries, a semantic layer increases productivity, accuracy and consistency between reports. This performance boost helps out not just business users, but anyone in the organization who needs to create reports.
The mapping of business concepts to database structure is embedded in the semantic layer itself. This makes it much easier for developers to understand and maintain reports that have been created by others. Moreover, the semantic layer serves as a stable interface between reports and the underlying data stores. When you have to update the design or source of an underlying data store, you can do it once, centrally. You make the change one time rather than updating all of the reports that leverage the data store.
This reflects one of the key design principles of SAP Data Warehouse Cloud. Rather than implementing the semantic layer multiple times in various consumers on top of the data warehouse, SAP Data Warehouse Cloud embeds the semantic layer into the application itself, ensuring that the business rules are centrally defined.
This approach makes it much easier to maintain a layer of definitions. Defining the semantic layer once rather than multiple times provides cost savings and guarantees a common defined set of definitions across the enterprise.