Skip to Content
Product Information

#ASUG SAP Analytics Cloud Influence Council Launch for Data Connectivity & Agile Data Preparation

This was a webcast the other week – you can watch the replay at INFL: ASUG SAP Analytics Cloud Influence Council Launch for Data Connectivity & Agile Data Preparation

ASUG Influence Council Participation Survey is at https://form.jotform.com/80144726627155

See more influence opportunities at next week’s ASUG Annual Conference/SAPPHIRENOW:

Grid

Session List

 

Source: SAP

Influence Councils are interactive forums where members can communicate ideas and concerns about product plans and SAP services directly to SAP development and product managers

The legal disclaimer applies, that things in the future are subject to change.

2adp.png

Source: SAP

Business Analysts, Report Developer, Business Users, IT Admins

Analysts, Designers, Decision Makers

Users of SAP BI, Explorer, Analytics Cloud, HANA

3adp.png

Source: SAP

4adp.png

Source: SAP

SAP’s overall strategic direction of HANA/Analytics group and how it all revolves around deriving ideas from facts or data through machine intelligence and machine support and human creativity to enable customers and their business users to make “confident decisions. ”

5adp.png

Source: SAP

To obtain from facts to ideas and decisions, start with the data. Above shows the 3 step process, where a user connects to data, prepares or cleanses it and finally explores, analyzes or uses machine learning to create predictions. This process needs to be as frictionless and smooth as possible. To be fast and efficient, “users need maximum agility to go back and forth freely between these stages, particularly in a self-service scenario”. (Source: SAP)

6adp.png

Source: SAP

SAP Analytics Cloud can connect to several data sources

7adp.png

Source: SAP

SAP has some limits in terms of “how flexible and how quick users can bring in data into” SAP Analytics Cloud and how quickly to go from data to analysis. SAP has a “strong structure and model first approach” based on the financial planning background of the solution, thus this makes some of the BI and data prep workflows more rigid than necessary. SAP knows that “that agility and flexibility are key ingredients for a self-service business user scenario” and that is why we work with high priority on those topics.¬† Source: SAP

8adp.png

Source: SAP

To prepare with agility, users want to be able to combine data from multiple sources, no matter if it Is an SAP source or 3rd party, no matter whether it sits in the cloud or on-premise and regardless of whether it is acquired or remote. A business user does not want to understand the underlying details, of where the data comes from and what type of source this is. They also want to be able to discover, prepare and share that data in a self-service fashion without any or minimal involvement from IT. So IT may have setup the connection especially to some of the central or governed systems, but then the business user wants to leverage in that system freely, based on their privileges. The preparation itself needs to be easy and be done in a visually understandable way for our business users and customers. And clearly, this needs to be a data first approach, so the shape of the data defines the semantic structure and it needs to flexibly adjust to it. Source: SAP

9adp.png

Source: SAP

To address these key points, SAP is working to introduce the concept of datasets into SAP Analytics Cloud.

10adp.png

Source: SAP

A dataset is a reusable entity that represents the source data and any transformation or combination of multiple sources afterwards. It can be enriched with some lightweight semantic information on top and allows immediate transition between data-prep workflows and visualizations, without a need to explicitly specify a model or transform the underlying data structure into a relational cube structure. And since it is a first class object inside SAC, it can be re-used, shared and you can define security on top of it. (Source: SAP)

11adp.png

Source: SAP

Decouple the data itself from the semantic on top allows the data driven flexibility and reuse on the data side, but also a gradual enrichment of those data sets with increasingly relevant semantical information. So by being able to combine, enrich and publish them to a larger audience we can achieve a gradual transition from the agile data preparation to governed semantic models. A dataset can be the starting point for a shared dimension, it can be used for currency or POI information or one or multiple can be the basis for a well-defined model, that will then be used to feed more data into it from other sources and other datasets. At the same time, those datasets can be the foundation of multiple models and are not bound 1:1 to a single model. (Source: SAP)

12adp.png

Source: SAP

The road map is shown above.

13adp.png

Source: SAP

The ASUG Influence Council Charter.

Source: SAP

The above shows how the council will work; dates are subject to change

Learn more at this ASUG session next week:

ASUG82308

Self-Service Data Access and Preparation with SAP Analytics Cloud
Thursday 11:00 AM Р11:40 AM 320D

Source: SAP

You must be an ASUG member to participate.

Question & Answer

How is this dataset different than the dataset concept available in Smart Predict?

They are related in terms of underlying concept, and this is being enhanced upon for agile BI purposes

Is this like IDT except in the cloud?

No, we are building a Modeller concept, but agile data preparation is nothing like IDT

Will this be like Trifacta, the data wrongling tool?

In concept, we are focused on providing the tools/capabilities to make it easy to wrangle data/data preparation for BI users.

Be the first to leave a comment
You must be Logged on to comment or reply to a post.