SAP Analytics Cloud & SAP Data Warehouse Cloud: Key Feature Overview
SAP Analytics Cloud and SAP Data Warehouse Cloud are SAP’s strategic cloud solutions for Data Warehousing, Planning and Analytics.
In a nutshell:
- SAP Analytics Cloud (SAC) is unique in the market by combining Planning, Business Intelligence and Predictive Analytics in one cloud solution. It offers live access to SAP source systems (such as SAP HANA, SAP BW and SAP Data Warehouse Cloud) and is natively embedded into SAP business applications (such as SAP S/4HANA and SAP SuccessFactors).
- SAP Data Warehouse Cloud (DWC) is built on the in-memory power of SAP HANA Cloud and offers modern data warehousing tailored for business and IT users. It provides instant access to data via pre-built business content and offers a rich set of adapters to integrate data from various sources (incl. SAP and non-SAP).
While there are many good reasons to go with each solution individually, SAP Analytics Cloud and SAP Data Warehouse Cloud are even stronger together. In this blog post, I want to highlight some of the key benefits of an integrated scenario.
This blog leverages the Data Layer capabilities of SAP Data Warehouse Cloud, but most capabilities mentioned here are supported via the Business Layer as well. In the context of the Data Layer, SAP Analytics Cloud can consume all views of semantic type “Analytical Dataset” and switch “Expose for Consumption” enabled.
The data models and most of the examples below originate from my colleague San Tran’s blog “Modeling with SAP Data Warehouse Cloud”, which I can highly recommend for gaining hands-on experience.
One of the advantages of SAP Analytics Cloud is its capability to establish a live connection to various cloud and on-premise data sources. This means that data is not being replicated, but simply stays where it is.
This is also the case for the (live-) connection to SAP Data Warehouse Cloud, which comes with the following benefits:
- The user is always accessing the latest data, and changes made to the data in SAP Data Warehouse Cloud (or any of its federated source systems) are reflected instantaneously in the SAP Analytics Cloud story.
- SAP Analytics Cloud re-uses the data access authorizations from SAP Data Warehouse Cloud, and business users will see only the rows for which they are authorized.
- SAP Analytics Cloud can re-use and interpret the model semantics defined in SAP Data Warehouse Cloud, which implies that many features are available out of the box.
Altogether, this allows to establish SAP Data Warehouse Cloud as central governance layer and single source of truth for all enterprise data, without data duplication to SAP Analytics Cloud as analytical frontend.
From an implementation perspective, it is worth mentioning that the Analytical Dataset can be directly accessed from within a story, and it is not required to create a so-called live data model in SAP Analytics Cloud (as it is the case for SAP HANA and SAP BW live connections).
One User Experience & Seamless Navigation
SAP Analytics Cloud and SAP Data Warehouse Cloud provide the same user experience and product navigation. This is especially great for all users who create their own data model and then want to build a dashboard on top of it: Same look and feel wherever you are, and less time for user onboarding :).
Moreover, there is a seamless navigation between both solutions via our “Product Switch” on the top right side.
The configuration for this switch very simple, and it only needs the specification of the respective SAP Analytics Cloud URL in the System Configuration of SAP Data Warehouse Cloud (check here).
Master Data provides additional context for transactional data and enables business users to retrieve more value out of their analytical dashboards. Due to their tight integration, SAP Analytics Cloud can easily leverage Master Data that is maintained in SAP Data Warehouse Cloud.
- Displaying ID and Description
E.g. showing the product description instead of the product id.
- Displaying Dimension attributes
E.g. showing address information along with the customer’s name.
To achieve this, the Dimension containing the Master Data needs to be associated to the Analytical Dataset in SAP Data Warehouse Cloud. The annotations “Semantic Type” and “Label Column” can be used for ID and Description handling.
Displaying the unit of currency along with the key figure is particularly important when data is recorded in multiple currencies. Therefore, SAP Analytics Cloud generally highlights the currency (when available) and this is also the case for data coming from SAP Data Warehouse Cloud.
The respective configuration in SAP Data Warehouse Cloud is very simple and requires the following two steps:
- The currency attribute needs the semantic type “Currency Code” assigned.
- The measure needs semantic type “Amount with Currency” and the respective unit attribute needs to be selected as label column.
Moreover, SAP Analytics Cloud can leverage the currency conversion of SAP Data Warehouse Cloud to let business users convert the source currency into the currency of their preference. The latter can be easily achieved with an input parameter which propagates the user input (i.e. target currency) to the respective conversion formula in SAP Data Warehouse Cloud.
It is worth mentioning that we plan to further simplify the currency conversion. Details can be found here and here.
Multi-Language support refers to the capability to adapt the analytical dashboard to the language preference of the business user. It is specifically important for multinational companies where business users around the world want to consume the same content. It is thus great to see that both SAP Analytics Cloud and SAP Data Warehouse Cloud provide multilingual capabilities with more than 40 supported languages.
From a technical perspective, the dashboard translation consists of several aspects that need to be taken care of:
- Translation of SAP Analytics Cloud content.
- User Interface (incl. menus, error messages and help pages)
- Texts created in stories (incl. story names, story page names and chart titles). Details can be found here.
- Translation of model data.
- Model metadata (incl. measure names, attribute names and input parameters) [not yet supported]
- Dimension master data
The business user controls these two aspects via the “Language” and “Data Access Language” properties in the SAP Analytics Cloud user preferences. Note that the translation of master data (2.b) is carried out in SAP Data Warehouse Cloud and can be achieved via a Text Association. Here is a great blog who walks through the details of it.
Everyone loves the time features of SAP Analytics Cloud including range filters, hierarchy selection, variance charts and time-series forecasting.
We do support them as well for the SAP Data Warehouse Cloud live connection and here’s in short what you need to do to enable them:
- Automatically generate the time dimensions for your SAP Data Warehouse Cloud space. More details can be found here.
- Respect the naming conventions for your date attribute of the Analytical Dataset (e.g. the technical name of the date attribute needs to end with _DATE for day-level granularity). More details can be found here.
- Associate the respective time dimension from step 1 (e.g. SAP.TIME.VIEW_DIMENSION_DAY for day-level granularity) to your date attribute.
Hierarchies are a great way to structure data and let business users explore it efficiently (e.g. by doing drill-downs). SAP Data Warehouse Cloud supports both level-based hierarchies and parent-child hierarchies, and so does SAP Analytics Cloud on top of it.
All you need to do is associating the Analytical Dataset with the respective Dimension that contains the hierarchy definition. Details can be found here.
Input Parameters & Story Filters
SAP Data Warehouse Cloud currently supports two different types of user inputs, and both are surfaced via a prompt upon opening an SAP Analytics Cloud story.
- Input Parameter
Input Parameters allow the business user to actively influence the query result according to the need, e.g. by specifying the desired sales region or target currency. Technically, the user input is passed to the SAP Data Warehouse Cloud model and is applied in filter or calculation operations. They are thus a very generic and flexible tool.
- Story Filter
The so-called Story Filter is a SAP Data Warehouse Cloud feature that is specifically targeted for SAP Analytics Cloud as analytical frontend tool. It is used for filtering on the result query and directly configured on attribute level. Conveniently, the user is provided with a value help to select the desired dimension members.
Here’s a brief overview and comparison of the two concepts:
|Input Parameter||Story Filter|
|Use-Case||Used in Filter Node and Calculations||Used as Filter (on the highest level)|
|User Input||Free text||Selection of dimension members|
|Option Default Value||Planned for 2022.Q1||Yes|
|Option Mandatory||No (always mandatory)||Yes|
The interactive geo-maps of SAP Analytics Cloud are a great tool to analyze your data and detect patterns. This is also possible for the live connection to SAP Data Warehouse Cloud, which currently supports the Bubble Chart, the Heat Map and the Feature Layer.
Assuming you have longitude and latitude information of your location, all you need to do in SAP Data Warehouse Cloud is the following:
- Create a Dimension with a geo-coordinates column (details here). You can stick with the default selection for the Spatial Reference Identifier.
- Associate the geo-enriched Dimension to the Analytical Dataset which is going to be consumed in SAP Analytics Cloud.
- You are ready to do some spatial analysis in SAP Analytics Cloud.
MS 365 Add-In
The SAP Analytics Cloud Add-In for MS 365 lets you connect and explore your SAP Analytics Cloud data in the familiar Excel experience at any place and on any device. Further details can be found in the Microsoft App Store (check here).
Since recently, the MS 365 Add-In also supports SAP Data Warehouse Cloud as data source, which is great news for all Excel-fans out there.
First and foremost, I hope you enjoyed this feature overview and the various examples demonstrating that SAP Analytics Cloud and Data Warehouse Cloud are indeed better together. While we strongly focused on the analytical capabilities, it is important to highlight that we are working on a tight integration between SAP Data Warehouse Cloud and SAP Analytics Cloud Planning as well (and first planned deliveries are already published on our Roadmap Explorer).
Finally, as pre-integrated data and analytics platforms are getting increasingly important for business success, it is great to see that SAP Analytics Cloud and SAP Data Warehouse Cloud are already delivering on their part of SAP’s Unified Data and Analytics Strategy.
Very helpful! Thanks for your valuable contribution Mirko!
Outstanding and extremely useful update!
I appreciate the information and advice you have shared Mirko!