How SAP Data Warehouse Cloud strategy aligns with SAP Analytics Cloud
This blog post provides an overview about how SAP Data Warehouse Cloud’s strategy aligns with SAP Analytics Cloud’s strategy. In fact, the tight integration of SAP Data Warehouse Cloud and SAP Analytics Cloud unleashes synergies.
short excerpt of SAP Data Warehouse Cloud’s strategy
SAP Data Warehouse Cloud serves as a business data warehouse and can be used for data preparation and data modelling. SAP Data Warehouse Cloud is a complete end-to-end self-service data management solution in the cloud. It serves as a data warehouse enabling SAP Analytics Cloud to create visualizations and slice-and-dice data cubes. Furthermore, multiple user groups benefit from SAP Data Warehouse Cloud. Users from IT can pre-process & clean data sets using common modelling techniques such as SQL- or SQL-Script based views as well as graphical views, but also Python to transform raw data to high quality structured data. Business users can unleash full value through SAP Data Warehouse Cloud’s innovative business layer. It enables them to discover relationships between data elements and enrich data fields with business information. This allows users across business departments to instantly understand data in a self-service fashion. Consequently, SAP Data Warehouse Cloud customers benefit from data democratization – making information accessible and understandable to the average business user and not only to IT professionals.
short excerpt of SAP Analytics Cloud’s strategy
SAP Analytics Cloud’s Augmented BI strategy envisions intelligent algorithms automatically generating insights for end-users. This saves end-users from having to create visualizations and dashboards by hand and automates the creation of meaningful content. Even though SAP Analytics Cloud follows a self-service strategy for dashboarding, visualizations created by end-users usually take into consideration only a small subset of the available data. Users may choose their own filters, their own data dimensions and eventually their own chart types to visualize data.
This leads to users showing a certain extract of data by showing a few selected numbers of dimensions. The end-user is limited to the selected dimensions in his perception. Meanwhile, the explanation of certain values may be hidden due to the reduced scope of dimensions available. As a result, consumers of the visualization may develop biases or derive incorrect conclusions.
In turn, companies develop biased dashboards and hence biased decision making. Additionally, many organizations follow agenda driven BI journeys leading to the implementation of rigid organizational business processes.
SAP Analytics Cloud’s Augmented Analytics strategy challenges end-user bias by enabling exploratory BI journeys. SAP Analytics Cloud includes various smart features enabling end-users to automatically generate dashboards, charts and insights. SAP Analytics Cloud’s smart features allow end-users to break rigid analytics processes through assisted data exploration & visualization. This disrupts the status-quo of rigid organizational processes by providing agile BI journeys and ad-hoc analysis capabilities. Because of this differentiation in the market, SAP Analytics Cloud ranks as a visionary tool in the Gartner Magic Quadrants.
To sum up, SAP Analytics Cloud’s Augmented Analytics strategy resulted in smart features within SAP Analytics Cloud. This effectively creates more value of your data by avoiding end-user bias. Additionally, it enables exploratory BI journeys in an agile manner rather than static agenda-driven BI processes.
Alignment of SAP Data Warehouse Cloud’s and SAP Analytics Cloud’s strategy
Note, the product strategy defines the direction of future feature developments. This means, the following outlined angle envisions future product releases in perspective.
SAP Data Warehouse Cloud & SAP Analytics Cloud emphasize their fully-fledged self-service interaction with business end-users. Both tools embody ease of use and user-centricity. SAP Data Warehouse Cloud’s provisioning of a business layer and data democratization is key to Augmented Analytics. Data democratization leads to high quality structured data. Furthermore, data sets used for business users differ from data sets used by purely technical users. Business user focused data sets do not contain technical information and illegible characteristics. Every data cell should be understandable by business users. Consequently, data democratization guarantees high quality data sets with business user focus.
SAP Data Warehouse Cloud’s data democratization capabilities shall simplify analytics.
In fact, smart BI features benefit from higher quality data sets as results are more tangible and easier to understand. More importantly: insights themselves are of higher quality. The higher the quality of the data, the better the insights generated by smart features are. Augmented Analytics requires clean and especially business-user focused data sets. Otherwise, insights generated by intelligent algorithms happen on a technical level which results in hard to understand insights – this is avoidable by using business user focused data sets provided by SAP Data Warehouse Cloud.
State of the art BI practices often happen in silos. Different departments create their own reports based on their relevant data sources. However, data democratization leads to unified data structures across data sources within the company. This enables analytics to create more value from available data. For instance, it allows analytics to discover correlations or causality across business departments rather than only within one department’s data silo.
SAP Data Warehouse Cloud and SAP Analytics Cloud are becoming more and more user centered. End-users utilize self-service abilities daily. In fact, there is a lot of potential for service delivery to get the maximum value for customers. Consultants must proactively emphasize the tools’ work mode. It starts with conveying an agnostic attitude towards data. Beneficially, consultants set an example for customers to live exploratory BI journeys, as agile as reporting requirements change more and more frequently.