Monday, 18 October 2021 18:59

SAC - Easy understanding of SAP Analytics Cloud

Written by Vaibhavi Keni
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SAC – SAP Analytics Cloud

Statistics say that trillions of data are generated from ERP (Enterprise Resource Planning) systems globally on yearly basis. The primary focus of any organization irrespective of the size and hierarchical structure is to find ways to visualize data in the most effective manner.

An Intuitive web-based SAP platform that supports Planning, Business intelligence, and Predictive Analysis with exceptional AI(Artificial Intelligence) features is one of the most powerful tools in business transformation by which developers can curate dashboards that are user-friendly and conversational. For further insights on this topic, this article is for you.

What is the difference between Business Intelligence and Data Analysis?

The differences between Business Intelligence and Data Analytics are subtle and confusing. But ultimately it completely depends on the business requirements which methodology/process to implement. The conclusive point is both methodologies aim at improvising the decision-making of businesses.

  • Business Intelligence: Extract, Transform, and Load data into a structured format helping the right people take the right decisions. Business Intelligence means backtracking of data for better understanding and answers the ‘What has happened’ aspect.

  • Data Analytics: Collection and cleansing of the raw data. Data Analytics adopts looking forward perspective answering ‘What will happen. It is real-time in most scenarios.

The architecture of SAC:

The various factors that are taken into consideration while deciding the landscape/architecture of the SAC are:

  • Hosted Edition: Public, Private
  • Release Cycles: Quarterly, Fast track Update
  • Control Practices
  • Maturity of lifecycle management 

DataSources in SAC :

  • Get data from a data source: SAP BW, SAP Universe, SAP HANA, Google BigQuery, SAP BPC, and SQL data sources

  • Import a file from your computer: CSV, XLSX, Google Sheets

Attributes of Dataset: 

Measures- Quantitive data fields

Dimensions- Qualitative data fields

GeoEnrichment: A salient feature that SAC offers to the BI community for the creation of geographical representations for example heat maps on the basis of Coordinates (latitude and longitude) under the Actions section.

Data Modelling in SAC :

  • Model Requirements: This checks and verifies the data whether valid or not. If no issues are detected at this stage the data is ready to get sliced and diced as per business requirements.
  • Modeling Options: There are two options in this stage either to enable planning or fill the empty cells with the hash ‘#’ symbol

Data Quality and Distribution checks:

Additional features that SAC offers to developers is to analyze issues per dimension (rows and columns)

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