Product Information
Leveraging Machine Learning with SAP Analytics Cloud Webcast Recap
Source: SAP
This was an SAP webcast from last week. Tomorrow there is a related webcast called Smart Predict – register here
Source: SAP
“We have to do more with less”
Source: SAP
How embed machine learning in the application
How get the information to the right decision at the right time in the right context
Source: SAP
Data discovery has failed to meet expectations
Analytics is subject to human bias; selection bias as you can’t ingest all information
How get to insights; typically through visual recognition; takes too much time causing delayed insights
Next step to solve; plan, use machine learning – eliminates bias, uncovers insights; machine learning recommends next action to take
Adoption of machine learning in analytics is quite low; usually machine learning is geared towards data scientists
How make machine learning accessible to the BI user
Source: SAP
Planning is fragmented, ignored, reactive
Organizations do planning with spreadsheets today, end up with version control issues, and planning process breaks down, and takes 3-6 months and gets ignored
Bring planning with machine learning and analytics, you can monitor and plan
Source: SAP
Not every user will adopt machine learning models
Make it easier to get answers naturally; use natural language
Explain a measure with a single click; what drives revenue, use machine learning in background
Human does not need to code a model
If a business analyst knows it is a classification model (will customer buy x) they can do it and will know if model is quality.
Source: SAP
Going from data to insights as quickly; address data quality
Source: SAP
Type in questions, like “what is the revenue for 2019” and then see the results in a visualization
Users can see visualizations without selecting chart type, measure
Increase abilities of business analyst
Source: SAP
Help automatically interesting patterns in data, influencers, drivers of revenue, determine anomalies in dataset, where are the outliers
Simulate drivers, what are the impacts
Source: SAP
Detect where you might have challenges – an early warning system
Click of a button with time series analysis, you can see if risk to make target
Source: SAP
Classification is Y/N – will someone buy? Churn or not?
Regression – how explain something? What are variables contributing to measure
Time series – here what has happened historically and project to future, and if trend continues, where end up
Smart features with pre-built content (15 industries) and 27 line of business use cases (finance, HR, marketing)
Source: SAP
Plan head count expenses, where allocate
Work with business
Collaborate with stakeholders
Come up with plan
Trends on expenses
Enhance where employees may churn
Track performance against key KPI’s and embed insights into HR SuccessFactors
Question & Answer
Q: What does predictive power and confidence mean?
A: An indication of 2 qualities
Predictive power of a model – how much of the thing you are trying to explain, were we able to explain based on data provided
Confidence – how apply to new data and be valid
Prediction confidence is more critical
Q: Difference from machine learning?
Machine learning foundation can be used by developers
Related
More upcoming webcasts – register today:
November 14 SAP Analytics Cloud Integration with SAP BW: Best Practices
November 20 BI: SAP Analytics Hub and SAP Hybrid Analytics Strategy
December 4 INFL: Mobile BI Influence Council Re-launch
December 11 BI: Overview of Latest BI Features in SAP Cloud Analytics
December 12 BI: SAP Analytics Cloud – Connectivity Deep Dive
January 15 BI: What’s New with Application Design in SAP Analytics Cloud
January 22 BI: SAP Analytics Cloud Roadmap Update for Developer APIs and Integration
January 23 BI: SAP Digital Boardroom and SAC Mobile App – What’s New, Best Practices and Roadmap
January 24 BI: SAP Analytics Cloud for SAP BW and BW/4HANA
January 30 BI: SAP BW/4HANA 2.0
January 31 BI: SAP Analytics Cloud Roadmap