SAP ALM Analytics: How to build analysis covering all ALM components with the SAP Analytics Cloud

Build compelling and efficient dashboards combining all your Application Lifecycle Management (ALM) Data.

Motivations

• Exploit 100% of your ALM data to generate new insights.
• Orchestrate all your ALM platforms/tools to provide operational or decision-making reports to IT departments and SAP competence centers.
• Enhance the lifecycle of SAP applications to deliver high quality solutions faster and more efficiently. 

Audience

• SAP Project Managers, SAP Basis Team, SAP CC leads.

SAP ALM Analytics: Phases

SAP ALM Analytics relies on 3 phases:

1. Exploration: Visual exploration to understand the characteristics of large data sets: Nature, amount, completeness, correctness, dimensions, measurements.
2. Combination: Collection and integration of data from multiple sources put together, or consolidated providing users with a unified view.
3. Analysis: Discovering useful information, informing conclusion and supporting pro-active decision-making and abnormal situation prediction.

Exploration

• Tools: OCC Focused Insights and Focused Run.
• Characteristics: Mass processing of large set of data with fast access to raw data.

In this example, a simple OCC dashboard aggregates Load, Users, Usage and Performance indicators for multiple locations:

This data model is exposed to the SAP Analytics Cloud via an OData import connection as a single data source for the next phases.

Combination

Tools: SAP Analytics Cloud story

Characteristics: Custom views for unification/aggregation based on multiple data sources: Import OData Focused Insights/Focused RUN, BW Live/Import, HANA views, external OData.

In this example, a SAC story enriches an initial data model with geo-localisation information to exploit the map capabilities as shown below:

In addition, a heat-map for week day distribution and a trend charts completes the story visualisation.

In the example below, the same data model is extended with Focused RUN (SUM, OCMon), SAP Solution Manager (CPU, response time) and external metrics to propose a unified "control center" view

Analysis

Tools: SAP Analytics Cloud analytics applications

Characteristics: analytics application combining standard charts, web scripting and machine learning algorithms.

In the following example, the initial datasets imported from SAP Solution Manager are manipulated with data sciences/machine learning algorithm for metrics correlation, forecast, anomaly detection and seasonality analysis.

Thanks for reading

Related articles:

• https://www.dhirubhai.net/pulse/focused-run-sap-solution-manager-system-anomaly-xavier-dupeyrat/
• https://www.dhirubhai.net/pulse/advanced-analytics-sap-application-lifecycle-xavier-dupeyrat/
• https://www.dhirubhai.net/pulse/alm-analytics-applications-sap-cloud-xavier-dupeyrat/

SAP Analytics Cloud Connectivity:

• https://help.sap.com/viewer/00f68c2e08b941f081002fd3691d86a7/release/en-US/bdf055159cbb4f36b26c93ccb0c56066.html