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#ASUG Webcast Recap Smart Predict Update Recap

You can watch the replay here BI: Smart Predict Update​ and slides are here

If you missed a previous webcast, go to ASUG BI Webcast Recording/Slides Reference List for 2019

Join us for these upcoming webcasts:

April 9 BI: Data Protection & Privacy with BW/4HANA

April 10 BI: What’s New with Application Design in SAP Analytics Cloud

April 11 BI: Hybrid Planning Scenarios Built by SAP Analytics Cloud for Planning and SAP Business Planning and Consolidation

April 16 BI: What’s New in Transitioning from SAP BW to SAP BW/4HANA

April 18: INFL: ASUG SAP Analytics Cloud Influence Council Launch for Data Connectivity & Agile Data Preparation

April 25 BI: Preview of Business Intelligence and Analytics Sessions at ASUG Annual Conference

Source: SAP

Richard Mooney, SAP, provided this update, with overall Smart Predict capabilities in SAP Analytics Cloud

How does it fit inside Augmented BI, and SAP Concept of Intelligent Enterprise and roadmap

Introduction: Augmented BI and the Intelligent Enterprise


Source: SAP

Key concept behind augmented analytics, how rethink analytics using intelligent augmentation

Take concepts today of dashboards, agile BI, reporting and connect intelligent capabilities to them

Data driven alerting, predictive and prescriptive, suggested points of interest, conversational AI

Bringing together human authored machine guided analytics to make it simpler for users to interact with analytics data


Source: SAP

Analytics Framework – what do you do with it?

Themes of assistive -machine learning to make it faster

Descriptive to describe it

Diagnostic, where you go into a deep understanding of something interested in

Predictive – build predictions, generate new data, make decisions

Prescriptive – use new data generated to make decisions

Combine with 2 product categories – planning and BI

Build capabilities to support business users – search to insight – conversational AI – find insights using natural language, and add it to your story

Interacting directly with data without understanding UI, makes it accessible

Smart Insights, Smart Discovery

Smart insights – instant analysis, use machine learning and statistics to tell you about your data, helps analyze data faster

Smart Discovery is a deeper analysis; articulate a question, such as order value, and what drives that; run machine learning algorithms against that data set to understand it better

Bring together all in one place; storybuilder to build story, key driver analysis, natural language generation, find relationships, outlier detection to find unexpected values, also has forms simulation

Smart Discovery does all the work for you and you can change, update to it and build story to tell story

BI features powered by machine learning – built within context of a chart or story, such as time series data, build forecast

Look at dataset, look at smart grouping

Apply machine learning augmentation in analytics process; extend AI, bring prescriptive and next best actions – bring predictive in prescriptive analytics

Intelligent learning to proactive actions/alerts

Augmented data preparation to draw more insight – planned for Predictive this year

Increase level of machine learning in planning


Source: SAP

Key areas looking at today

Will start as internal project, and publish material externally

Recognize that for people, predictive can be challenging to figure out best place to use it

Supported at data mining level, classification and regression level – open ended

Classification could be several use cases; want to do use cases but not know where start

Use case library – 5 use cases at moment

Provide high quality material that is useful for business users

Include demo data to try out sample

Presentation includes use case, ROI

KPI’s to improve, measure better, and a hands on tutorial to use Smart Predict

Use cases are above

Payment forecasting – already built out analytics content network capability (based on S/4HANA Finance OData Connection)

Are the use cases useful, and other use cases

Usually, use case comes down to having enough data to build predictions


Source: SAP

Launched Smart Predict June of last year, went GA in September 2018

German Football club is using Smart Discovery and Predict to improve performance, push to dashboards, used by executives to see where invest via wages and player acquisition

Shows benefit of predictive and BI together

See interest in segmented forecasting, automated forecasting of expenses by business unit and product area, what drives customer retention


Source: SAP

Not covered


Source: SAP

Context driven AI every where, as a menu option or button in area where doing the function

How connect Smart Predict process to story builder and planning

Not want to rebuild SAP Predictive Analytics on premise into SAP Analytics Cloud

Explainable AI – how made prediction and patterns found

Continue to invest in LIVE; need underlying platform to support the algorithms; easy to do with HANA as it has underlying algorithms; look to Q3/Q4

Already have live for Search to Insight

Near term support for Live will be about HANA


Source: SAP

Q2, publish predictive scenarios to SAP S/4HANA using the Pai framework; 12 use cases already delivered

Customers can use SAC and publish to S/4HANA and it will execute in S/4HANA

Payment forecasting is available in SAP Analytics Content Network

Choose explanatory variables you want to output to dataset for consumption in stories

Q3- HANA data connection, starting with tables and SQL views

Can already acquire data from OData

Refresh stories and datasets in Q3/Q4 – work with predictive data like agile BI

Longer term – acquire data in SQL databases, augmented data prep – to draw predictive insight


Source: SAP

Way Smart Discovery works today; flattens data to get insight to lowest level


Source: SAP

Where get to; get it at the customer level, takes transaction information by doing joins, merges and pushes it into auto derive features, like they did in Data Manager in Predictive on premise but make it easier


Source: SAP

Roadmap for live

Q3/Q4 timeline

Train and apply on remote HANA is planned and include augmented data preparation – “full self services for business users”

Question and Answer

Is there a way to upload or submit your own machine language algorithm, which would handle all the features SAP’s ML does today?

If you want to use algorithm outside of SAC, use HANA or Data Hub

When will it be possible to use time series forecasting on top of a model in a user friendly way? Moreover would be great to use a created model inside a story over the standard button for prediction

3 places where time series exist in SAC: 1) within a story – exists today 2) in planning 3) segmented in smart predict – look at how provide debrief story form in an easier way than today; not on near time road map

Would BW on HANA enough to use live scenarios?

if willing to move view to table, yes,

Does Beta program for Live Smart Predict is only relevant for customers? Can SAP Partners join as well?

if fulfill criteria described.

Is there a Special liecence needed to develop with Application designer in SAC?

comes with the BI license

Questions answered after the webcast:

Possible Use-Case for use case library: Pricing prediction for most optimized sales and margin return
We will definitely consider it, we need to add optimization libraries first which we eventually plan to do.
is there any kind of predictive asset management on top of use cases? e.g : adjust the predictive Algorithm %2B get it’s exact math ?
We provide configuration options to configure the business question and also debrief to explain the model.
will smart predict be useful for P&L ?
Yes, forecasting both the top and bottom line can show you risk factors and reasons why
What is the approach to tailor the predictive models for  scenarios where the model accuracy is not satisfactory ? Is there an option to switch between the models in SAC and models designed on HANA platform
Accuracy is not typically to do with algorithms, it is to do with data.  More data, greater accuracy.  In future we will provide data preparation to improve accuracy.
It is always mentioned that every user can use predictive. Isn’t it important to know how machine learning algorithms or other statistical algorithms act in the background?
I think it’s very dangerous to let all users work with this feature. Results can be misunderstood. What is your opinion or who should work in your opinion with this tool?
Users need to be able to interpret the results and we provide easy to understand explanations to help them, we also make it clear where a predictive model is not good and explain how make it better.  People are capable of driving cars without being mechanics.
Is there going to be a SAP course dedicated to Predictive? Currently there are two visible on SAC01 – Introduction To SAP Analytics Cloud | SAP Training (SAC01_EN_Col17) and SACP20 – SAP Analytics Cloud for Planning | SAP Training
We are adding Smart Predict to the new SAP Analytics Cloud OpenSAP course, soon to be released.
I’m interested in reviewing the demo in SAC, how can i get access?
We will make it available soon with the use cases.
is there a use case recommendation when to use predictive in SAP Data Hub and when in SAC ?
Data Hub is for data engineers, SAC for analytics
Smart Predict is not available in our labs. Is there any limitations ?
You need to have a Cloud Foundry enabled tenant, we are working to make sure all customers have this.
Could I have Predictive algorithm in my tenant demo?
You need to have a Cloud Foundry enabled tenant, we are working to make sure all customers have this.
I there predictive scenarios SAP data HUB isn’t covering ?
Same use cases different use types.  SAC is designed for business analysts.

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