6.5 Discussing Augmented Analytics

Question 1

SAC ML features (6)

Answer 1

1. Predictive forecast
2. Predictive scenarios
3. smart grouping
4. smart discovery
5. smart insight
6. search to insight

Question 2

What predictive features are supported on acquired data?

Answer 2

1. time series forecasting
2. search to insight
3. Smart grouping
4. smart insight
5. smart predict
6. smart discovery

Question 3

On acquired data, Smart predict is support only for (2):

Answer 3

-datasets
-planning models
NOT analytics models

Question 4

What predictive features are supported on live data?

Answer 4

1. time series forecasting
2. search to insight
3. Smart grouping
4. smart insight
5. smart predict

Question 5

What predictive features are supported on acquired data but not live data?

Answer 5

Smart Discover

Question 6

search to insight * doesn’t support which live connections

Answer 6

• SAP HANA Cloud

- SAP Data Warehouse Cloud

Question 7

smart insights ** for live data are only supported for

Answer 7

S4HANA on prem

Question 8

smart predict ** for live data are only supported for source

Answer 8

S4HANA on prem

Question 9

What is Predictive Forecast

Answer 9

uses historical data to predict values

Question 10

What algorithms does Predictive Forecast provide to choose from (4)

Answer 10

• automatic
• linear regression
• triple exponential smoothing
• add additional inputs

Question 11

Features of Time Series Forecasting (4)

Answer 11

• project expected values in future time periods
• validate quality w/ confidence internal, hindcast and quality indicators
• include additions factors (eg weather) to simulate values
• creates trend charts and line charts

Question 12

What is Smart Grouping

Answer 12

• creates segments of data (clusters) across several measures
• recommends # of groups based on your data

Question 13

What is Smart Discovery

Answer 13

• run on data sets to analyze data and generate a story

Question 14

What tabs are included in Smart Discovery Stories

Answer 14

• overview
• key influencers
• unexpected values
• simulations

Question 15

how does Smart Discovery - Core KPIs helps understand business drivers behind your KPIs (4)

Answer 15

• classification and regression techniques
• explore hidden structures and relationships
• intuitive charts
• natural language

Question 16

Smart Discovery - simulation features (3)

Answer 16

• see impact on KPI or value based on historical data
• experiment to see how particular dimensions/kpis will impact the outcomes
• add smart text explinations for visuals

Question 17

Because SAC runs on HANA, HANA’s core predictive features are available. What does this include? (2)

Answer 17

• Automated Predictive Library (APL)

- Predictive Analysis Library (PAL)

Question 18

What do you need to chose to form the context for Smart Discover (2)

Answer 18

• Target (measure/dim you want to know more about)
• Entity (dims you want to explore in relation to the target, and level of aggregation of data for analysis). You can an include specific members of dims

Question 19

What is on the Smart Discovery Overview page? (3)

Answer 19

• visuals to summarize target dim in relation to entity, has 2 parts:
• one half gives summary of data
• second half has visuals

Question 20

What is on the Smart Discovery Key Influencers page? (5)

Answer 20

• ranks up to 10 dims/measures that impact the target
• each has chart to show relationship w/ target
• summary of the takeaway from these visualiztions
• each chart has insight quality checkbox (eg confidence)
• measures and dims are aggregated at level of Entity

Question 21

What is on the Smart Discovery Unexpected Values page? (5)

Answer 21

pg 195