SAP Agile Data Preparation - ADP - Some of the Ess...

stefan_geiselhart2 · ‎04-04-2017

SAP Agile Data Preparation – Video Blog

At the bottom part of this page you will find the link to the videos.

Blog History:

04/03/2017 - initially created + added video link Steps 1 - 12 (~20 minutes)

04/04/2017 - added video link: Step 13 - Cleansing and Data Quality (~4 minutes)

04/11/2017 - added video link: Step 14 - Filtering your Worksheet (~2 minutes)

04/18/2017 - added video link: Step 15 - Operationalize your ADP Worksheets (~4 minutes)

Introduction ADP:

SAP Agile Data Preparation transforms data into actionable, easily consumable information by providing fast, self-service access to high-value data. Empower business users to instantly improve the value of data by discovering, prepping, and sharing data. Optimize ITs ability to govern how business users are preparing data by monitoring and operationalizing data access and usage. Accelerate business efficiency with trusted data by helping data stewards define, assess, and improve data. Drive more successful analytics, data migration, and master data management initiatives with data preparation capabilities for everyone. Available on-Cloud and on-Premise.

The leveraged services of the SAP HANA Platform are (mainly HANA EIM Options):

Smart Data Integration (SDI)

Smart Data Quality (SDQ)

Enterprise Semantic Services (ESS)

HANA Rules Framework (HRF) – optional component

Architecture:

Purpose:

In this video you will get to know some of the essentials of SAP Agile Data Preparation – ADP.

It does not cover the entire functional scope of ADP, it outlines some of the most commonly used functionality.

I am trying to enrich this and other blog entries with some more features and use cases of SAP ADP.

Used media/script:

The video describes 15 steps. Before each step is conducted, a brief wrap-up slide will introduce you to the executed activities and used features.

Use Case:

Typical ADP use cases span analytics, data migration, data quality assessment and all flavors of integration. However, the use case described in this video blog example is pretty straightforward:

A business user wants to combine some sales data with customer master data. One of his aims is to share his results with others. Moreover, he wants to define business rules on worksheets that enable options such as data quality dashboards or the like. Data stewardship capabilities must also be feasible and he expects to get some better insight into the profile of his data.

Summary:

In a nutshell, these are some of the features that the video will show and explain:

Video 1:

Data acquisition via file upload, paste from clipboard

Worksheet creation, combination, pivot/unpivot

Deduplication of customer master data

Action history – do/undo steps

Ad-hoc data profiling with HANA ESS (Enterprise Semantic Services) – natively integrated into ADP

Creation of calculated columns

Creation of business rules and rule collections (ADP Business Rule Repository)

Creation of data domains, scorecards and scorecard sets to enable data steward like capabilities

Schedule scorecard set and drill-down into data quality

Table export, reuse of HANA tables for data domain

Video 2:

Data Quality using HANA SDQ - Cleanse Worksheet (define cleanse settings/output)

Video 3:

Filter worksheet on cleansing results, rule results, various columns of you base dataset

Video 4:

Operationalize your measures that you have undertaken within an ADP worksheet and hand it over to IT as an *.hdbflowgraph file to get it refined further

Video Links: