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Browse the BookThis sample chapter teaches you about predictive analytics in SAP Ana-lytics Cloud. It covers both smart assist and smart predict, and then offers examples of the functionality of each. A sample time series analysis will be created throughout this chapter.
Abassin Sidiq
SAP Analytics Cloud446 Pages, 2020, $79.95 ISBN 978-1-4932-1934-6
www.sap-press.com/5026
First-hand knowledge.
“Predictive Analytics”
Contents
Index
The Author
335
7
Chapter 7
Predictive Analytics
While most analytics use cases focus on analyzing historical data,
predictive analytics aims to forecast potential future developments.
Therefore, various practices like machine learning are embedded in
SAP Analytics Cloud.
When analyzing historical data, you can often observe patterns that oc-
curred in the past or learn from decisions that were made. However, this
data can also be used to gain insights about future developments or rela-
tionships between data points that may not be visible at first.
Smart assist and
smart predict
SAP Analytics Cloud offers a dedicated predictive analytics component that
provides various functionalities to support users in performing these kinds
of analyses. Those functionalities are either automated (smart assist) or
require users to define explicit predictive scenarios (smart predict).
In this chapter, we’ll first learn more about both smart assist and smart pre-
dict, then offer examples of the functionality of each. Smart predict allows
users to create very complex scenarios that can’t be covered in detail in this
book. However, a sample time series analysis will be created throughout
this chapter. Section 7.3 contains information on how to access more infor-
mation about smart predict.
Requirements for This Chapter
All examples in this chapter are based on previously created stories and
datasets. If you want to follow the examples in this chapter, you first have
to create the dataset, model, and stories in these chapters:
� Chapter 4, Section 4.2.1
� Chapter 4, Section 4.3
� Chapter 5
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7.1 What Is Predictive Analytics?
This section will focus on predictive analytics and how it’s differentiated
from the classical analytics field. Because the area of data science is very big
and can be separated into a lot of different fields, it won’t be the focus here.
SAP Analytics Cloud supports users by providing easy access to machine
learning algorithms and tools. Machine learning algorithms are mathemati-
cal methods that can, for example, recognize patterns in data or relationships
among data points. Those algorithms are usually applied automatically
within SAP Analytics Cloud and can’t be influenced by the user. However, for
some cases, a special environment is available wherein extended analyses
can be created.
We’ll explore all functionalities that belong to predictive analytics and pro-
vide practical examples ahead.
Smart assist The term smart assist groups all functionalities that support users by auto-
matically applying algorithms and functions to enable the analysis of data
for patterns and highlights. Smart assist includes the following functional-
ities:
Smart discovery � Smart discovery
With smart discovery, you can create an automated analysis of a model
(see Figure 7.1), which can be used to determine key influencers for a spe-
cific dimension or measure.
The function will automatically generate a story that contains various
charts and tables showing highlights and relationships. On top of that,
all values will be shown that don’t fit the automatically recognized rela-
tionships (outliers). Finally, smart discovery also provides a simulation
model that allows you to change single dimensions and measure the
effect of the change.
Smart insights � Smart insights
This functionality can be activated for each chart in a story and provides
explanations for specific data points (see Figure 7.2). Once you click on a
data point in a chart (e.g., a bar in a bar/column chart), smart insights can
be launched to find out which influencers contribute to this data point.
Smart insights has to be activated for each chart or table manually.
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7.1 What Is Predictive Analytics?
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Figure 7.1 Smart Discovery
Figure 7.2 Smart Insights
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7 Predictive Analytics
Search to insight Search to insight is another functionality to quickly access data and explore
relationships between data points. It can be launched from the home
screen or within the story. Afterwards, you can type in questions in natural
language (see Figure 7.3).
Figure 7.3 Search to Insight
The feature uses various machine learning algorithms to determine which
data model you want to search and which information you request. The
generated chart can be copied into a story.
R visualizations Although R visualizations are part of the story and behave like charts, they’ll
be covered in this chapter because they also allow you to apply algorithms
to forecast data. An example R visualization is shown in Figure 7.4.
What Is R?
R is a programming language that’s commonly used in the area of statis-
tics. It’s provided as an open-source language and is maintained by a very
big community. The language allows extensive data operations and can be
extended by packages.
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7.1 What Is Predictive Analytics?
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Figure 7.4 Sample R Visualization
Data transfor-
mations in R
R can only be used in SAP Analytics Cloud to create visualizations that aren’t
included in the standard portfolio of the story. However, this requires knowl-
edge of R. Data operations or transformations that are performed within the
R script can be executed, but the resulting data can’t be stored in a data
model. However, the result can be shown in a chart or table by using R.
Automatic forecastWhen using a time series chart, you can activate the automatic forecast. It
extends the time series chart by adding a forecast of how the values may
develop in the future (see Figure 7.5). The forecast parameters can only be
slightly adjusted.
Figure 7.5 Time Series Chart with Forecast
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Smart predict Next to the smart assist functionalities, SAP Analytics Cloud also offers an
extended working environment for power users called smart predict. In
general, users create predictive scenarios based on datasets and trained by
using their contents (see Figure 7.6).
Smart predict supports the following predictive scenarios:
� Classification
� Regression
� Time series
Based on the use case, those scenarios can be used to answer various ques-
tions. These include, for example, the customer churn analysis, time series
forecasts, or future developments. A detailed description of these scenarios
can be found in Section 7.3.
Figure 7.6 Training Predictive Scenarios
7.2 Smart Assist
This section covers all functionalities of the smart assist area in detail.
Some of the examples may use models or stories that you created in previ-
ous chapters. Of course, you can also use the presented features with your
own data. Be aware that the demonstrations are based on fictional data
from the demo data package, which may not always lead to useful results.
7.2.1 Smart Discovery
We want to use smart discovery to determine what factors influence the
Revenue measure in our Sales Data model the most.
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Create a new story and choose Run a Smart Discovery. Instructions on how
to create a story can be found in Chapter 5, Section 5.2. Choose the Sales
Data model created in Chapter 4, Section 4.3 and Section 4.5.
Configuring
smart discovery
You’ll now see the smart discovery sidebar, where you can further config-
ure some parameters (see Figure 7.7). Here, you can determine which target
variable (measure or dimension) you want to know more about. Click on
Select a Measure/Dimension and select the Revenue measure.
Figure 7.7 Setting Up Smart Discovery
Advanced optionsLeave the Version set to Actual and leave the Singular and Plural fields
empty. Those fields can be used to assign individual labels used later in the
automatically generated texts. You can also exclude measures and dimen-
sions from the analysis. This is very helpful to remove key influencers that
are inherently obvious.
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Remove all measures so that only Revenue remains. Then, remove the ID,
Stores, and Street dimensions because they provide no value for our analy-
sis and are directly related to the revenue. Both ID and Street are dimen-
sions with a very close relation to their respective data points. There is only
one value per ID and only a few values per street, which would result in a
very high mathematical influence on the revenue. However, this insight
has no real-world value. Initiate the process by clicking on Run.
Automatically
generated story
Wait a few seconds until the automatic story generation is completed.
Smart discovery will generate four pages in total:
� Overview
� Key Influencers
� Unexpected Values
� Simulation
If smart discovery is executed for a dimension instead of a measure, it will
only generate the first two pages.
Overview The Overview of Revenue page shows general information about the ana-
lyzed measure and generates overview charts and texts that outline strong
relationships within the data (see Figure 7.8).
Figure 7.8 Excerpt of Overview Page
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7.2 Smart Assist
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In general, the overview page offers a high-level overview of the data and
how several factors contribute to the measure. Some of the charts are inter-
active and allow you to generate further analyses.
Key influencersOn the Key Influencers page, you’ll find information about all prominent
relationships that were found in your data, accompanied by automatically
generated texts that provide explanations of the results of the analysis and
their quality (see Figure 7.9).
Figure 7.9 Key Influencers
Based on the results of the analysis, this page may show additional charts,
which focus on one or more key influencers. Every single chart allows you
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to add more key influencers to manually adjust them. Click on the bulb
icon to see a list of all key influencers.
Unexpected Values Smart discovery generates a model in the background, which it uses to
measure the relations in the data and the influence of each dimension. That
model is just an approximation of reality, which means that it can’t explain
all values that occur in the dataset. The Unexpected Values page shows a list
of all values that don’t fit the model (see Figure 7.10). All values are shown in
the table and in more detail in charts. If you click on a value, the charts will
automatically adjust.
Figure 7.10 Unexpected Values
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7.2 Smart Assist
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SimulationThe last page, Simulation, provides a very powerful tool. Here, you can
adjust single influencers and directly measure their influence on the mea-
sure. For each influencer, you can change the dimension member and the
revenue will change based on your decision (see Figure 7.11). On top of that,
the simulation page directly shows how big the impact of a dimension is.
Simulating a changeChange the parameters by using the input controls of one of the dimen-
sions to start the simulation (see Figure 7.12). Choose another product, for
example, and click on Simulate to see the effects of the change.
Figure 7.11 Simulation
Figure 7.12 Adjusting Simulations
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7.2.2 Smart Insights
While smart discovery analyzes a measure or dimension in general, smart
insights can be used to find out more about a specific data point. In general,
smart insights can be activated for every chart built on a supported data
source. If the amount of data is insufficient or the context is too detailed, it
may happen that smart insights can’t produce any results.
Open the Sales Analysis 2019 story created in Chapter 5, Section 5.9 and
switch to edit mode. Click on the Revenue (Variance) chart and open the
action bar by clicking on the icon with three dots. Click on Add Smart
Insights (see Figure 7.13).
Figure 7.13 Adding Smart Insights
Accessing smart
insights
Smart insights are automatically added as text below the chart showing the
most prominent finding (see Figure 7.14). You can either access the smart
insights by right-clicking on the chart or by clicking on View more… at the
end of the text.
Figure 7.14 Chart with Smart Insights
After you open smart insights, a sidebar will be shown on the right side of
your screen (see Figure 7.15). This sidebar contains details about all findings
that lead to the data point. You can click on each finding to see more details
and related charts.
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7.2 Smart Assist
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Figure 7.15 Smart Insights Sidebar
7.2.3 Search to Insight
Explore dataSmart assist functionalities are designed to provide easy and intuitive
access to data. While the data exploration mode already eases this process
(see Chapter 5, Section 5.2.2), search to insight allows you to use natural lan-
guage to analyze data.
Opening the searchSearch to insight is directly called from the home screen (see Chapter 3, Sec-
tion 3.1) Navigate to your home screen, click on Ask a Question, and select
the Go to Search to Insight entry (see Figure 7.16). You can also access the
search within a story by clicking the Search button at the top.
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Figure 7.16 Opening Search to Insight
After you open search to insight, the search screen appears (see Figure 7.17).
You can directly enter your question in the bottom, but the interface also
offers some proposals for searches and actions to perform.
Figure 7.17 Search to Insight
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Searching for dataSAP Analytics Cloud automatically indexes all models so that search to
insight can search through them. Enter the question “Show Revenue by
Supermarket” and press the (Enter) key. You will see automatic propos-
als while entering the question. Especially when you have many models
in the system, these proposals are very helpful to find the right model.
Once you’ve submitted the search query, a chart will be generated (see
Figure 7.18).
Figure 7.18 Generated Chart
Filter criteriaYou can extend the search by adding filter criteria (e.g., “for last year”) or
using the buttons below a chart to submit a proposed question. If you want
to use the chart within a story, you can directly copy it from here by clicking
on the Copy button and selecting Copy.
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7.2.4 R Visualizations
If you’re missing a chart in the standard portfolio or you want to perform
individual statistical transformations before visualizing a specific context,
R visualizations can be used to overcome this challenge. By using the open-
source programming language R, you can create individual charts. R servers
provide packages that include predefined charts and functions to manipu-
late and visualize data.
Scope In general, R can be used to transform data and implement data science sce-
narios. Because R can be used to generate charts and graphical elements, it
also can be used within a story. The R component in the story also can be
used to manipulate and transform data, but the results can only be visual-
ized and not stored in a data model.
Differences from
standard charts
R visualizations are also not interactive. Although R allows the creation of
interactive charts, this has to be performed completely in the R script and
isn’t compatible with other charts in the story. It’s also not possible to use
the builder or formatting options in R visualizations (see Chapter 5, Section
5.3). R visualizations are created in their own builder, which is only available
for this scenario.
R Server
R scripts have to be processed by an R server. SAP Analytics Cloud pro-
vides an R landscape by default that allows you to create R visualizations
without hosting your own R server. If you want to use your own server,
though (e.g., when the SAP landscape is missing the required packages),
you have to set it up beforehand. More information can be found in Chap-
ter 3, Section 3.3.4.
Requirements Because R is a statistical programming language, it requires some knowl-
edge to be used properly. SAP Analytics Cloud only provides a very limited
number of examples, which can’t be applied easily to your own data.
More Information about R
The following links provide more information about R and resources to
learn the language:
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� R Project
https://www.r-project.org/
You will find general information about R here. You can also download
R for your own desktop computer here. This is not required to use R in
SAP Analytics Cloud.
� R for Beginners
https://cran.r-project.org/doc/contrib/Paradis-rdebuts_en.pdf
This tutorial supports you in learning R and performing your first steps.
R visualizationsThe following example will demonstrate how to create R visualizations.
We’ll use a very simple script to get familiar with R in SAP Analytics Cloud
and the working environment. To start, create a new story and add a canvas
page to it. Click on the + button in the top bar and select R Visualization (see
Figure 7.19).
Figure 7.19 Adding R Visualizations
BuilderThere is a builder for R visualizations that will be shown in the right sidebar
of the story (see Figure 7.20).
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Figure 7.20 Builder for R Visualizations
Adding input data Because R can only work with data in flat tables, we have to first select a set
of data that then will be made available for the R script. Click on Add Input
Data in the builder to start the data selection process. Select the Sales Data
model and all dimensions for the rows. Confirm the selection by clicking on
OK.
Then click on Add Script to start the script editor. Go into full screen mode
by clicking on the Expand button at the top right of the builder. The
screen should now match Figure 7.21.
Figure 7.21 Script Environment
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Script environmentThe script environment is separated into four main parts:
� Editor
All R scripts are entered into this field. You can also access code snippets
here or search through the code.
� Environment
This area lists all available datasets. By clicking on the three dots next to
each entry, you can see a preview of the included data.
� Console
Because R can also return console entries (e.g., error messages), those are
shown here.
� Preview
This section previews the visualization that will later be added to the
story.
List all packagesWe first want to find out which packages are installed on the R server. Enter
the following script into the editor and click on Execute:
installed.packages(lib.loc = NULL, priority = NULL,noCache = FALSE, fields = NULL,subarch = .Platform$r_arch)
This code shows a list of all installed packages on the R server and will be
returned in the console. By going through that list, you can find out if the
necessary R packages are available to solve your challenge. If you are miss-
ing a package, you have to install it first. However, this is only possible on
your R servers. To use a dataset in an R script, you have to first attach it.
Enter the following code and execute it:
attach(Sales_Data)
From now on, you can directly reference dimensions and measures by sim-
ply writing their names.
Creating a
word cloud
Remove all code from the script editor and paste in the code shown in List-
ing 7.1.
# This code loads the required libraries.library(wordcloud)library(RColorBrewer)library(tm)library(NLP)
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# Attaches the dataset.attach(Verkaufsdaten)# Creates the word cloud.wordcloud(Supermarkt, rot.per=0.6, use.r.layout=FALSE)
Listing 7.1 Example R Script
This script will create a word cloud for the Supermarket dimension. A word
cloud visualizes the words in a dimension in the shape of a cloud and can
use a measure to determine which words occur most of the time. You can
ignore all lines in the code that start with a hash (#); those are just com-
ments, which won’t be processed by the R server.
After clicking on Execute, the chart will be previewed (see Figure 7.22). To
use the chart in a story, click on the Apply button.
Figure 7.22 Word Cloud
Script execution R scripts will always be re-executed when opening a story. If the script con-
tains random functions (like in our example), those may produce different
outcomes each time the story is opened. In our case, the word cloud func-
tion randomly defines the final layout.
7.2.5 Automatic Forecasts for Time Series
Creating a time
series forecast
When using a time series chart, you can extend it by activating the auto-
mated forecast. Create a new story with a new canvas page and add a new
chart of the type Time Series. Use the Sales Data model. Add the Date
dimension and the Revenue measure. You can also use the time series chart
you created in Chapter 5, Section 5.3.1.
Open the action bar of the chart and click on Add � Forecast � Automatic
Forecast (see Figure 7.23). This will immediately activate the time series
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forecast for the chart and show projected values (see Figure 7.5). On top of
that, you can change the forecast method (Advanced Options).
Figure 7.23 Adding Automatic Forecasts
The projected forecast will be added to the end of the time series automati-
cally (see Figure 7.24). The forecast will be shown in a blue area, which indi-
cates the upper and lower bounds of possible developments. The projected
values are shown in the middle of that area on a dotted line.
Figure 7.24 Time Series with Forecast
7.2.6 Smart Grouping
Supported
chart types
When using the bubble diagram or scatterplot chart type, you can activate
an additional function to group values. An algorithm is executed in the
background to check which data points are similar to each other and groups
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them automatically by assigning them different colors. You can compare
this procedure to the K-means algorithm. This algorithm works in a similar
way as it searches through a dataset for values that are similar to each other
and can be put in groups.
Enabling smart
grouping
You can activate smart grouping in the builder of a chart and configure it
there as well. You can determine the number of groups and custom labels
and include tooltip measures (see Figure 7.25).
Figure 7.25 Smart Grouping
The algorithm then automatically calculates groups of data points and col-
ors the data points in the chart accordingly. On top of that, a legend will be
shown as in Figure 7.26.
Figure 7.26 Scatterplot with Smart Grouping
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7.3 Smart Predict: Predictive Scenarios
Smart predict can be used to create extended predictive scenarios, which
can become very complex. We’ll create an example time series forecast in
this section. For that, we’ll use the Sales Data dataset uploaded in Chapter 4,
Section 4.2.1.
Then we’ll quickly elaborate on the regression and classification scenarios.
However, the focus will be more on use cases and requirements. In general,
it’s recommended to consult the product help when creating predictive
scenarios. It contains extensive information about using smart predict and
creating scenarios.
7.3.1 Time Series
While the automatic time series forecast (Section 7.2.5) can’t be modified,
the predictive scenario can be used to create extended forecasts. These allow
you to set your own variables and return statistical evaluation criteria.
Creating a
predictive scenario
Now, create a new predictive scenario. Open the main menu and click on
Create � Predictive Scenario. This will open the page to Select a Predictive
Scenario. Choose the Time Series option (see Figure 7.27). Enter the name
“Revenue Forecast” and click on Save.
Figure 7.27 Selecting Predictive Scenarios
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Selecting a dataset You’ll see text that asks you to configure the predictive model before train-
ing it. This can be done on the sidebar in the right. Click on the Name field
to open the dataset selection dialog. Now select the Sales Data dataset. This
will enhance the sidebar to show additional settings (see Figure 7.28).
The Variable Roles section is used to determine the roles of each column in
the dataset. The Signal Variable field should contain the measure for which
you want to have projected values for the future. Select the Revenue mea-
sure here.
Select Date in the Date Variable field. The Segmented By field determines
by which column the measure should be aggregated later. Select City in this
field.
Figure 7.28 Dataset Selection and Configuration
Training the model In the Training & Forecast section, you can exclude variables and change
further parameters (see Figure 7.29). Leave the standard settings in place
and start the model training by clicking on Train.
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Figure 7.29 Training and Forecast
The model training can take some time and may need several minutes to
finish. However, it will be processed in the background. Specifically, a new
predictive model will be generated for the time series forecast.
During the training process, you can view the list of Predictive Models at
the bottom of the page. This list shows all predictive models and errors if
they occur. You’ll also see all other predictive models here that are part of
the predictive scenario (see Figure 7.30).
Figure 7.30 List of Predictive Models
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MAPE value After the model training is completed, you’ll see the results which can be
used to evaluate the prediction (see Figure 7.31). The overview focuses on
the Mean MAPE value. The mean absolute percentage error (or MAPE)
value indicates how high the probability of an erroneous forecast is. The
lower it is, the lower the probability of an error is if the model is used to
forecast values.
Figure 7.31 Model Evaluation
MAPE Value
The MAPE value provides a good indication of the forecast quality. Al-
though a low MAPE value usually means that the model is good, you
should still check the results and see if they are realistic. This should be
done by analyzing the segments in detail.
If you run the training on your own system, the results may not exactly
match the example in this book.
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By looking into the Top Segments and Bottom Segments, you can see for
which cities the model created a good or bad forecast. In general, the MAPE
value of 2.3% indicates a high model quality.
Detailed analysisTo evaluate the model in detail, you can analyze each segment (in this case,
each city) separately. You can either click on a city in the Top Segments and
Bottom Segments list or scroll down and see a table of all segments and
their MAPE values.
Forecast versus
actual
Select the city Salinas in this example. As you can see in Figure 7.32, the
interface will now provide a chart to compare the forecasted values with the
actual data. The chart will also show the calculated value for the future (in
this case, January 2021).
Figure 7.32 Detailed Analysis of Salinas
ForecastsThe Forecasts area on the same page shows the exact values that were cal-
culated (see Figure 7.33). Next to the Forecast column, it also shows an
upper and lower bound of potential developments (Error Max and Error
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Min). Based on historic developments, SAP Analytics Cloud estimates the
revenue in Salinas to range somewhere between these values.
Figure 7.33 Forecasts for Salinas
Signal analysis The Signal Analysis tab provides more statistical information about the
analysis of each segment. The Signal Decomposition graph shows how the
values develop over time and is especially interesting when using multiple
forecasts. The Signal Statistics show statistical key figures that were calcu-
lated during the model training (see Figure 7.34).
Figure 7.34 Signal Statistics
Publishing
the model
After you’ve finished evaluating the model, you can publish the results into
a new dataset, which can be also visualized in a story. Click on the Apply Pre-
dictive Model button at the top. A new dialog will open wherein you
enter the name “Sales Data (Forecast)” and click on OK to confirm the data-
set creation. The model will now be applied and published as a dataset.
Because the process can take some time, you won’t receive direct feedback.
However, you can again track the status of the model. Once it’s completed,
it will show the Applied status (see Figure 7.35).
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7.3 Smart Predict: Predictive Scenarios
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Figure 7.35 Model Status
DatasetOpen the dataset you just created. During the model application process,
three new columns were added to the original dataset (see Figure 7.36). The
Forecast column shows the forecasted value for each data point as gener-
ated by the model. Each segment (here, each city) was extended by one
additional line for the date January 1, 2021. This line contains the forecasted
value and a lower and upper bound.
Figure 7.36 Extended Dataset
The dataset can be used in a story as a data source and visualized. More
information about creating stories can be found in Chapter 5, Section 5.2.
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7.3.2 Regressions and Classifications
Which predictive
scenario?
You can also use smart predict to classify datasets or create regressions. For
that, the following two scenarios are available:
� The classification scenario can be used to ask business-related questions
that have a binary response set (e.g., yes and no). You can, for example,
create forecasts that indicate if your customer will order with you within
the next three months or not.
� If you want to analyze how a measure is influenced by single factors and
find out more about their impact, you can use the regression scenario.
You can, for example, analyze which factors mainly influence your reve-
nue and if their impact is positive or negative.
This book will not cover these scenarios in detail. In general, they follow the
same procedure as that shown in the previous section. Most of the steps are
very similar or identical. After you select the dataset, you choose the target
variable to be analyzed and exclude obvious influencers up front. After the
model training, smart predict will automatically generate an overview to
evaluate the model. If you want to use the results from the model, you can
export them into a dataset, which can be visualized in a story.
7.4 Summary
SAP Analytics Cloud offers various functionalities to provide business users
with easy access to machine learning methods. While smart assist tools do
most of their work automatically, the smart predict interface can be used to
create advanced predictive scenarios.
Smart assist functionality is tightly integrated into the product and can be
launched either in a story or from the home screen. For smart predict, users
will work in a dedicated environment focused primarily on advanced users
or experts.
You’ve seen the third pillar of SAP Analytics Cloud in this chapter. The next
chapter will cover another pillar: the analytics designer. Similar to smart
predict, it targets more advanced users and allows them to create extended
reports and dashboards.
7
Contents
Preface ............................................................................................................................... 13
1 Introduction 19
1.1 What Is Analytics? ......................................................................................... 19
1.2 SAP’s Analytics Strategy ............................................................................. 21
1.2.1 Core Pillars of SAP’s Analytics Strategy ................................... 21
1.2.2 Comparing Cloud-Based and On-Premise Solutions .......... 22
1.3 Overview of SAP Analytics Cloud ........................................................... 24
1.3.1 Functional Areas ............................................................................. 25
1.3.2 User Interface and Core Functionality ..................................... 27
1.4 Architecture ...................................................................................................... 49
1.5 Summary ........................................................................................................... 50
2 Data Integration 51
2.1 Data Sources Supported by SAP Analytics Cloud ............................ 51
2.1.1 Data Sources for Live Connections ........................................... 52
2.1.2 Data Sources for Import Connections ..................................... 58
2.2 Connection Types .......................................................................................... 62
2.2.1 Live Connection ............................................................................... 63
2.2.2 Import Connection ......................................................................... 68
2.2.3 Choosing a Connection Type ...................................................... 72
2.3 Integration Scenarios for Live Connections ....................................... 72
2.3.1 Direct Connection via CORS ........................................................ 73
2.3.2 Connection via Reverse Proxy ..................................................... 78
8
Contents
2.4 Integration Scenarios for Import Connections ................................. 80
2.4.1 Connections to On-Premise Data Sources ............................. 81
2.4.2 Import Connections to Cloud Data Sources .......................... 83
2.5 Summary ........................................................................................................... 85
3 Navigation and Administration 87
3.1 Navigating the Home Screen and Main Menu ................................. 88
3.2 First Steps for Administrators .................................................................. 92
3.2.1 Users and Single Sign-On ............................................................. 92
3.2.2 Data Sources and Structures ...................................................... 94
3.2.3 Operational Concept ..................................................................... 95
3.2.4 System Landscape .......................................................................... 96
3.3 Administration Tools ................................................................................... 100
3.3.1 Security .............................................................................................. 101
3.3.2 Deployment ...................................................................................... 110
3.3.3 System ................................................................................................ 114
3.3.4 Administration ................................................................................ 116
3.3.5 Files and Folder Structure ............................................................ 120
3.3.6 Content Network ............................................................................ 123
3.4 Create Connections ....................................................................................... 124
3.5 Summary ........................................................................................................... 128
4 Data Modeling 129
4.1 Why Use Data Models? ............................................................................... 130
4.2 Types of Data Models .................................................................................. 133
4.2.1 Datasets ............................................................................................. 134
4.2.2 Analytical Models ........................................................................... 137
4.2.3 Planning Models ............................................................................. 140
4.2.4 Embedded Models ......................................................................... 141
9
Contents
4.3 Creating Models by Importing Data ...................................................... 141
4.3.1 Create a Model ................................................................................ 143
4.3.2 Edit Columns .................................................................................... 148
4.3.3 Executing Transformations ......................................................... 157
4.3.4 Generating and Saving the Model ............................................ 160
4.4 Creating Models from Live Data Sources ............................................ 161
4.5 Editing Models in the Modeler ................................................................ 170
4.5.1 Areas of the Modeler ..................................................................... 172
4.5.2 Editing Models ................................................................................. 175
4.6 Summary ........................................................................................................... 181
5 Business Intelligence: Visualizations and Dashboards 183
5.1 What Are Stories? .......................................................................................... 184
5.2 Creating Stories .............................................................................................. 186
5.2.1 Pages ................................................................................................... 188
5.2.2 Data Exploration and First Charts ............................................. 191
5.2.3 Story Interface ................................................................................. 197
5.3 Creating, Editing, and Formatting Charts ........................................... 201
5.3.1 Creating a New Chart .................................................................... 201
5.3.2 Adding More Charts ....................................................................... 207
5.3.3 Conditional Formatting ................................................................ 208
5.3.4 Showing Variances ......................................................................... 210
5.3.5 Other Chart Functionality ............................................................ 214
5.3.6 Defining Colors ................................................................................ 219
5.3.7 Formatting Charts .......................................................................... 220
5.3.8 Hierarchies ........................................................................................ 222
5.4 Creating, Editing, and Formatting Tables ........................................... 222
5.5 Geo Maps ........................................................................................................... 229
5.6 Texts, RSS Readers, and Other Elements ............................................ 232
10
Contents
5.7 How Viewers Interact with Stories ........................................................ 235
5.7.1 Filters .................................................................................................. 236
5.7.2 Dimension and Measure Input Controls ................................ 246
5.7.3 Chart Interactions .......................................................................... 248
5.8 Calculations ...................................................................................................... 253
5.8.1 Calculated Measures ..................................................................... 254
5.8.2 Calculated Dimensions ................................................................. 259
5.9 Story Design ..................................................................................................... 260
5.10 Sharing and Publishing Stories ................................................................ 263
5.10.1 Sharing, Exporting, and Publishing Stories ............................ 263
5.10.2 Publishing to Mobile Devices ..................................................... 267
5.11 Additional Story Functionalities ............................................................. 273
5.11.1 Creating an Embedded Model within a Story ....................... 273
5.11.2 Story Templates .............................................................................. 274
5.11.3 Blending ............................................................................................. 275
5.11.4 Comments ........................................................................................ 278
5.11.5 Bookmarks ........................................................................................ 279
5.12 Summary ........................................................................................................... 280
6 Planning 281
6.1 Planning in SAP Analytics Cloud ............................................................. 282
6.1.1 Data Entry and Version Management ..................................... 282
6.1.2 Planning within Stories ................................................................ 285
6.1.3 Planning Tools ................................................................................. 287
6.2 Creating and Setting Up a Planning Model ....................................... 290
6.2.1 Creating a Currency Conversion Table .................................... 292
6.2.2 Creating a Master Data Model ................................................... 294
6.2.3 Uploading Transactional Data to the Model ......................... 300
6.2.4 Setting Up a Planning Model ...................................................... 306
6.3 Planning-Specific Functionality .............................................................. 308
6.3.1 Versions and Data Entry ............................................................... 309
6.3.2 Allocating Values ............................................................................ 313
11
Contents
6.3.3 Allocations ........................................................................................ 316
6.3.4 Grid Pages ......................................................................................... 322
6.3.5 Value Driver Tree ............................................................................ 324
6.3.6 Data Actions ..................................................................................... 328
6.3.7 Calendar ............................................................................................. 330
6.4 Summary ........................................................................................................... 334
7 Predictive Analytics 335
7.1 What Is Predictive Analytics? ................................................................... 336
7.2 Smart Assist ...................................................................................................... 340
7.2.1 Smart Discovery .............................................................................. 340
7.2.2 Smart Insights .................................................................................. 346
7.2.3 Search to Insight ............................................................................. 347
7.2.4 R Visualizations ............................................................................... 350
7.2.5 Automatic Forecasts for Time Series ....................................... 354
7.2.6 Smart Grouping ............................................................................... 355
7.3 Smart Predict: Predictive Scenarios ....................................................... 357
7.3.1 Time Series ........................................................................................ 357
7.3.2 Regressions and Classifications ................................................. 364
7.4 Summary ........................................................................................................... 364
8 Analytics Designer 365
8.1 Differences between Stories and Applications ................................ 366
8.2 Creating Applications .................................................................................. 368
8.2.1 Development Environment ......................................................... 369
8.2.2 Creating New Application Elements ........................................ 373
8.3 Summary ........................................................................................................... 388
12
Contents
9 SAP Digital Boardroom 391
9.1 What Is SAP Digital Boardroom? ............................................................ 392
9.2 Creating Boardrooms ................................................................................... 397
9.2.1 Boardroom Types ............................................................................ 397
9.2.2 Using Charts in a Boardroom ..................................................... 400
9.2.3 Creating an Agenda ....................................................................... 401
9.2.4 Creating a Dashboard ................................................................... 407
9.3 Hardware Recommendations .................................................................. 413
9.4 Summary ........................................................................................................... 414
10 SAP Analytics Hub and SAP Analytics Catalog 415
10.1 What Is SAP Analytics Hub? ...................................................................... 416
10.2 Setup and Content Creation ..................................................................... 419
10.2.1 SAP Analytics Hub Cockpit .......................................................... 419
10.2.2 Edit Mode and Content Management .................................... 427
10.3 SAP Analytics Catalog .................................................................................. 429
10.3.1 Adding Content to SAP Analytics Catalog .............................. 429
10.3.2 Browsing SAP Analytics Catalog ................................................ 432
10.4 Summary ........................................................................................................... 432
The Author ....................................................................................................................... 435
Index .................................................................................................................................. 437
437
Index
A
Access ............................................................ 253
Account-based models .............................. 40
Action bar ............................................ 121, 354
Active Directory ................................. 93, 103
Actual data ................................................... 301
Administration ............................................. 87
datasource configuration ................. 117
deployment ............................................ 110
files ............................................................. 120
folder concepts ...................................... 121
interface ................................................... 116
object sharing ........................................ 122
SAP Analytics Hub ............................... 419
system ....................................................... 114
tools ........................................................... 100
Agenda builder .......................................... 402
Agendas ........................................................ 398
create ........................................................ 401
elements .......................................... 403–404
library ....................................................... 402
structure .................................................. 402
topic filters .............................................. 405
topics ......................................................... 403
Aggregation calculation ......................... 257
Aggregation dimensions ....................... 257
Aggregation types .................................... 167
Aggregations .................................................. 66
Allocating values ....................................... 313
Allocation action ....................................... 329
Allocations ............................................ 41, 316
confirm scope ........................................ 320
confirm step ........................................... 318
create ........................................................ 316
execute ..................................................... 318
rules ........................................................... 318
steps ........................................................... 317
Ambiguous relations ............................... 398
Analytical models ............................ 137, 147
components ............................................ 138
live connection ...................................... 139
model-wide settings ............................ 139
structure .................................................. 138
Analytics .......................................................... 19
cloud vs. on-premise .............................. 22
core pillars .................................................. 21
on-premise solutions ............................. 23
SAP's strategy ............................................ 21
software-as-a-service ............................. 23
Analytics designer ...................... 26, 48, 365
development environment ............... 366
further resources ................................... 388
limitations ............................................... 367
Apache Tomcat ...................................... 54, 79
API Reference .............................................. 369
APOS Live Data Gateway ........................... 55
Application programming interfaces
(APIs) .................................................. 84, 119
Application switch ....................................... 89
Applications ....................................... 185, 366
create ......................................................... 368
create elements ..................................... 373
device selection ..................................... 370
execution ................................................. 367
launch ....................................................... 371
outline ....................................................... 370
reference list ........................................... 370
scope .......................................................... 366
Asset management ................................... 428
Assigning interface .......................... 286, 315
Audit data ..................................................... 113
Audit log ....................................................... 427
Authorizations ................... 66, 95, 104, 180
roles ............................................................ 108
Automated forecast ................................. 354
Automatic forecast ................................... 339
B
Blending ............................................... 186, 275
settings ..................................................... 277
Boardrooms ................................................. 392
agenda ...................................................... 398
charts ......................................................... 400
context menu ......................................... 395
create ......................................................... 397
design ........................................................ 396
438
Index
Boardrooms (Cont.)
edit mode ................................................. 396
featured topics ....................................... 396
filters .......................................................... 401
multiple screens .................................... 393
navigation ............................................... 394
overview pages ...................................... 392
predictive analytics ............................. 401
save and launch .................................... 406
types ........................................................... 397
Bookmarks ................................................... 279
Branding ....................................................... 426
Bubble layer ................................................. 230
Builder ................................................. 202, 223
create filters ............................................ 237
create tooltip .......................................... 217
geo maps .................................................. 229
properties ................................................. 206
Business analytics ....................................... 20
Business intelligence .......... 20, 26, 30, 183
workflow .................................................... 38
Buttons .......................................................... 382
create ......................................................... 382
label ............................................................ 382
script .......................................................... 383
C
Calculated dimensions ................. 253, 259
Calculated measures ...................... 254, 277
Calculation rules ........................................ 288
Calculation types ....................................... 254
Calculations ................................................. 253
Calendar ....................................... 44, 290, 330
events ........................................................ 331
reminders ................................................. 333
task owners ............................................. 333
task settings ............................................ 331
Canvas ........................................... 36, 229, 354
Canvas pages ..................................... 188, 275
Card view ...................................................... 148
Catalogs ........................................................... 91
CDS views ....................................................... 54
Cell references ............................................ 322
Chart filters ........................................ 236, 239
Charts ............................................................. 193
action bar ................................................. 214
adding ....................................................... 207
adjust size ................................................ 206
axis label .................................................. 221
colors ......................................................... 219
copy to story ........................................... 196
create ............................................... 194, 201
custom color palettes .......................... 220
dimensions .............................................. 205
display options ...................................... 195
formatting ............................................... 220
granularity .............................................. 206
IBCS ............................................................. 212
interactions ............................................. 248
legend ........................................................ 221
moving ...................................................... 207
ranking ...................................................... 218
reference line .......................................... 216
select type ................................................ 195
sorting ....................................................... 215
time series ................................................ 205
type selection .......................................... 202
types ........................................................... 204
Checkbox groups ....................................... 377
add values ................................................ 378
create ......................................................... 378
script .......................................................... 379
Choropleth/drill layer .............................. 230
Classification scenario ............................. 364
Cloud connector .......................... 69, 81, 117
Cluster properties ...................................... 231
Code libraries ................................................ 63
Columns ........................................................ 148
hide ............................................................. 154
Company network ............................... 66, 69
Comparison chart ...................................... 204
Conditional formatting ................. 208, 220
rules ............................................................ 209
Connections ................................................. 124
create ......................................................... 125
interface .................................................... 125
Content network ........................................ 123
transport .................................................. 124
Content search .............................................. 89
439
Index
Context menu ................................... 395, 405
jumps ........................................................ 411
Conversion rates ....................................... 293
Copy action ................................................. 329
Correlation chart ....................................... 204
Cross-origin resource sharing (CORS) . 72
configuration ............................................ 74
Currency conversion ...................... 295, 310
Currency conversion table .................... 292
D
Dashboards ................ 20, 27, 183–184, 398
create ........................................................ 407
jumps ........................................................ 411
launching ................................................ 412
library ....................................................... 407
topic filters .............................................. 412
topics ................................................ 398, 407
Data access control ............... 178, 180, 307
Data acquisition ........................................... 30
Data actions ......................................... 42, 328
create ........................................................ 328
types .......................................................... 329
use .............................................................. 330
Data audit ..................................................... 307
Data changes ............................................... 110
Data cleansing ............................................ 149
Data distribution ...................................... 149
Data entry ................................. 282, 309, 312
Data exploration ................................... 34–35
Data exploration mode ................. 191, 193
access ........................................................ 196
Data import jobs ....................................... 302
Data integration .................................... 24, 51
connection types ..................................... 72
Data locking ................................................ 307
Data management ........................... 174, 300
Data mapping ............................................. 302
finalize ...................................................... 304
Data models ....................................... 129–130
authorizations ....................................... 169
blank ......................................................... 144
create ............................................... 143, 161
data sample ............................................ 145
data source ............................................. 161
Data models (Cont.)
draft data ................................................. 144
edit columns ........................................... 148
editing ....................................................... 175
export ........................................................ 111
expose data ................................................ 97
finalize ...................................................... 160
import ....................................... 71, 131, 141
justification ............................................ 130
layout ........................................................ 148
live data sources ................................... 161
requirements .......................................... 147
sample data ............................................ 141
saving ............................................... 160, 169
scheduling ............................................... 132
transporting .............................................. 98
types ................................................. 131, 133
verify .......................................................... 159
Data source node ...................................... 326
Data sources .............................. 51, 74, 84, 94
change ...................................................... 202
connections ............................................ 124
import connections ......................... 58, 61
import on-premise ............................... 117
live .............................................................. 161
live connections ................................ 52, 56
non-SAP ....................................................... 61
select ................................................. 143, 192
Data transfer .................................................. 65
Data wrangling 30, 32, 131, 138, 141, 146
formulas ...................................................... 31
screen areas ............................................ 146
Datasets ......................................................... 134
creation .................................................... 134
from SAP S/4HANA .............................. 136
import ....................................................... 134
name/location ....................................... 136
source ........................................................ 134
Date columns .............................................. 165
prerequisites ........................................... 165
Date format ................................................. 151
Date hierarchy ............................................ 166
Demo files .................................................... 129
Development environment .................. 365
applications ............................................ 369
areas .......................................................... 369
440
Index
Dimensions ....................... 34, 139, 203, 294
add account ............................................ 295
change ....................................................... 150
convert to measure .............................. 258
create ......................................................... 154
data access .............................................. 181
details ........................................................ 177
distribute .................................................. 314
duplicates ................................................ 247
fill ................................................................ 315
formula help ........................................... 382
generic ....................................................... 298
group ............................................... 169, 173
input controls ......................................... 246
modify ....................................................... 168
overview ......................................... 172, 297
required .................................................... 168
search ........................................................ 179
Distribution ................................................. 285
Distribution chart ..................................... 204
Dropdowns .................................................. 374
add values ............................................... 375
create ......................................................... 374
script .......................................................... 376
Dynamic date filters ................................. 242
Dynamic text .............................................. 234
E
Elements ............................................. 232, 373
button ....................................................... 382
checkbox group ..................................... 377
create ......................................................... 374
dropdown ................................................ 374
filter line ................................................... 383
other .......................................................... 385
radio button ........................................... 379
Embedded models .................................... 141
Error bar ........................................................ 218
Esri ArcGIS server ...................................... 230
Exception aggregation ............................ 168
Explorer ...................................... 204, 252, 401
enable ........................................................ 252
use ............................................................... 253
Export jobs ................................................... 111
create ......................................................... 111
trigger ........................................................ 113
F
Facets .............................................................. 424
Feature layer ................................................ 230
Files ................................................................. 120
Filter line ....................................................... 383
create ......................................................... 384
set up .......................................................... 384
use ............................................................... 385
Filters ....................... 195, 203, 216, 236, 320
advanced controls ................................ 245
criteria ....................................................... 349
nested ........................................................ 245
Fiscal year settings .................................... 308
Fixed date dimension filter ................... 242
Fixed time filter .......................................... 243
Flat files ...................................... 134–135, 143
Flat tables ...................................................... 352
Flow layer ...................................................... 230
Folder structures ....................... 95, 120–121
sharing ...................................................... 122
Forecasts ....................................................... 361
copy ............................................................ 310
data ............................................................ 305
version ....................................................... 305
vs. actual .................................................. 361
Formula editor ............................................ 176
Formulas ............................................. 158, 323
actions ....................................................... 159
create ......................................................... 254
help .......................................... 255, 372, 381
input control ........................................... 255
G
Gantt view .................................................... 331
Geo maps ...................................................... 229
content layers ......................................... 229
create layers ............................................ 229
zoom .......................................................... 232
Geographical enrichment ...................... 153
Geographical hierarchy ........................... 152
Geolocations ...................................... 153, 164
live data sources .................................... 164
Global dimensions .................................... 139
Grid pages ........................... 40, 188, 191, 322
tables ......................................................... 322
Grid view ............................................. 149, 298
441
Index
H
Heatmap ....................................................... 230
Hierarchy .................................. 222, 298, 314
tables ......................................................... 228
Hierarchy management ......................... 178
drag and drop ........................................ 179
interface ................................................... 178
moving members ................................. 179
Hybrid solutions .......................................... 21
Hyperlinks .......................................... 219, 250
pages ......................................................... 251
types .......................................................... 250
I
Identity provider ......................... 76–77, 118
requirements ............................................. 93
Import connections .................................... 68
cloud ............................................................. 83
credentials ............................................... 127
data sources .............................................. 58
integration scenarios ............................ 80
on-premise ................................................. 81
scenario ....................................................... 69
setup ...................................................... 81, 84
Import jobs .................................................. 114
In-cell charts ............................................... 227
Indicator chart ........................................... 204
Info panel ..................................................... 370
InfoSet queries .............................................. 59
Input controls ......................... 240–241, 247
data dimensions ................................... 242
measures ................................................. 243
Input field .................................................... 385
International Business Communication
Standards (IBCS) ................ 212–213, 226
J
JavaScript ..................................................... 369
JDBC drivers ................................................... 60
Joins ................................................................ 275
Jumps .................................................... 395, 411
page to page ........................................... 411
to chart ..................................................... 412
K
Key influencers .......................................... 343
L
Lanes ..................................................... 189, 268
adjust size ................................................ 268
create ............................................... 269–271
formatting ............................................... 268
Level-based hierarchies .......................... 151
Library .............................. 403–404, 407, 410
Licenses ......................................................... 106
in use ......................................................... 114
Lifecycle content management ........... 428
Link dimensions ........................................ 276
Linked analysis .................................. 215, 248
Live connections ................................... 63, 96
advantages ................................................ 66
analytical models ................................. 139
authorizations .......................................... 94
cloud ............................................................. 77
credentials ............................................... 127
data models ............................................ 132
data sources .............................................. 52
direct connection ..................................... 73
example ............................................. 64, 126
integration scenarios ............................. 72
limitations .................................................. 67
measures .................................................. 167
multiple instances ................................... 97
on-premise ................................................. 74
recommended scenarios ....................... 68
reverse proxy ............................................. 78
SAP HANA views ................................... 165
Logos .............................................................. 426
M
Machine learning ...................................... 336
algorithms ............................................... 338
Maintenance mode .................................. 421
Master data .................................................. 138
Master data model .................................... 294
Mean absolute percentage error
(MAPE) ...................................................... 360
442
Index
Measure-based dimensions .................. 260
Measures .................................... 155, 203, 224
attributes ................................................. 167
calculated ................................................ 167
create ............................................... 166, 176
deviation over time ............................. 257
edit .............................................................. 175
hide ............................................................. 175
input controls ............................... 246, 248
select .......................................................... 224
smart discovery ..................................... 342
variance .................................................... 212
Metadata ................... 64, 118, 140, 388, 417
Modeler ........................................ 30, 162, 170
action bar ...................................... 163, 172
areas .......................................................... 172
authorizations ....................................... 180
data source ............................................. 163
editing models ....................................... 175
measures .................................................. 167
open ........................................................... 171
overview ................................................... 171
rebuild ....................................................... 172
sidebar ...................................................... 173
validation ................................................ 172
N
Navigation ...................................................... 87
home screen .............................................. 88
main menu ................................................ 90
Nodes ................................................... 229, 325
create ......................................................... 327
types ........................................................... 326
Notifications ................................. 29, 89, 120
NVARCHAR .................................................. 165
O
OData ............................................................... 60
OData services ............................................ 385
create ......................................................... 386
Operational concept .................................. 95
Organizational structures ........................ 94
P
Package transportation ........................... 123
Page filters .................................................... 239
create ......................................................... 239
member selection ................................. 239
Pages ..................................................... 185, 188
background color .................................. 261
comments ................................................ 279
formatting ............................................... 261
types ........................................................... 188
Parent-child hierarchies ............... 151, 298
create ......................................................... 151
Pareto principle .......................................... 129
PATH ................................................................. 78
Permissions ................................................. 107
Planning ................................... 20, 26, 39, 281
calendar .................................................... 289
edit models ................................................ 40
functionality ........................................... 308
integrations ............................................... 39
licensing .................................................... 281
models ......................................................... 39
multistep .................................................... 44
tools ............................................................ 287
within stories .......................................... 285
workflows ................................................... 39
Planning model .......................................... 140
access and privacy ................................ 306
actual data .............................................. 301
append data ............................................ 305
comments ................................................ 279
create ......................................................... 290
data ............................................................ 283
data import method ............................ 302
data mapping ......................................... 302
data privacy ............................................ 307
demo data ................................................ 291
fiscal time ................................................. 308
forecasted data ...................................... 305
preferences .................................... 295, 299
set up .......................................................... 306
upload transactional data ................ 300
user data ................................................... 140
writing data ............................................ 181
Point of interest ......................................... 230
443
Index
Predictive analytics ............. 20, 26, 45, 335
boardroom .............................................. 401
overview ................................................... 336
Predictive model
configure ................................................. 358
dataset ...................................................... 363
evaluate ................................................... 361
list ............................................................... 359
publish ...................................................... 362
status ........................................................ 362
train ........................................................... 358
Predictive scenario ................................... 357
Private dimensions .................................. 139
Private forecast .......................................... 318
Private versions ......................................... 284
Product help .................................................. 29
Profile settings .............................................. 89
Public dimensions .................................... 297
Public versions ........................................... 283
Q
Quarterly release cycle .............................. 24
Queries .......................................................... 132
R
R programming language ............ 338, 350
R servers .............................................. 119, 350
packages .................................................. 353
R visualizations .......................... 45, 338, 350
builder ....................................................... 351
create ........................................................ 351
dataset ...................................................... 353
Radio button groups ................................ 379
add values ............................................... 380
create ........................................................ 380
script .......................................................... 381
Range slider ................................................. 386
Reference lines ........................................... 215
Regression scenario ................................. 364
Reports .......................................... 36, 184, 416
links ........................................................... 418
Responsive pages ...................................... 189
Restricted export ...................................... 307
Restricted measures ................................ 256
Reverse proxy ........................................ 78–79
Roles ........................................................ 95, 105
assign ........................................................ 109
authorizations ....................................... 108
create ......................................................... 106
custom ...................................................... 107
full data access ...................................... 109
licenses ...................................................... 105
overview ................................................... 105
permissions ............................................. 107
requests .................................................... 109
standard ................................................... 106
Root topics ......................................... 407–408
RSS reader ..................................................... 235
S
SAML 2.0 ................................................... 76, 93
SAP Analytics Catalog .............. 27, 415, 429
adding content ...................................... 429
authorizations ....................................... 430
browsing .................................................. 432
external content ................................... 430
filters .......................................................... 432
licensing ................................................... 429
publishing content ............................... 430
text search ............................................... 432
SAP Analytics Cloud
architecture ............................................... 49
data integration ...................................... 50
functional areas ....................................... 25
home screen ............................................... 28
initial activites plan ............................... 92
overview ...................................................... 24
user interface ............................................ 27
SAP Analytics Cloud agent ................ 70, 81
setup .......................................................... 117
SAP Analytics Cloud Agent Simple
Deployment Kit ....................................... 82
SAP Analytics Cloud User and Team
Provisioning API ................................... 103
SAP Analytics Hub ............................. 27, 415
adding sections ..................................... 423
authorizations ....................................... 418
branding .................................................. 426
cockpit ...................................................... 419
444
Index
SAP Analytics Hub (Cont.)
content management ......................... 428
data ............................................................ 427
edit mode ................................................. 427
facets ......................................................... 424
favorites ................................................... 417
fields ........................................................... 423
home page ............................................... 426
language .................................................. 422
launching ................................................. 419
layout ........................................................ 423
licensing ................................................... 415
list of values ............................................ 424
maintenance .......................................... 421
overview ................................................... 416
setup .......................................................... 419
SAP BEx Query Designer .......................... 53
SAP BPC, version for SAP
BW/4HANA ............................................... 59
SAP BPC, version for the Microsoft
platform ..................................................... 59
SAP Business Planning and Consolidation
(SAP BPC) ...................................... 22, 54, 82
SAP Business Suite ...................................... 54
SAP Business Warehouse (SAP BW) ..... 52,
97, 130, 133
connectors ................................................. 55
data source ............................................... 81
queries .................................................. 53, 58
SAP BusinessObjects BI platform ... 22, 54
SAP BW/4HANA ........................................... 53
SAP Cloud Platform Identity
Authentication ........................................ 93
SAP Data Warehouse Cloud .................... 55
SAP Digital Boardroom .................... 27, 391
example .................................................... 393
hardware recommendations ........... 413
navigation ............................................... 394
overview ................................................... 392
responsive pages ................................... 393
SAP ERP ........................................................... 59
SAP HANA ............................................. 52, 161
SAP HANA Info Access .............................. 52
SAP HANA Live ............................................. 54
SAP HANA smart data integration ....... 55,
118
SAP HANA views .............................. 162, 164
SAP Java Connector ..................................... 70
SAP Lumira, designer edition .......... 22, 48
SAP Predictive Analytics ........................... 22
SAP S/4HANA ................................ 54, 59, 136
SAP S/4HANA Cloud ................................... 77
SAP SuccessFactors ..................................... 59
SAP Web Dispatcher ................................... 79
Scheduling ...................................................... 71
Script editor . 352, 371, 375, 379, 381–382
environment ........................................... 353
formula help ........................................... 372
syntax check ........................................... 371
Search to insight ................ 45, 89, 338, 347
automatic proposals ........................... 349
open ............................................................ 347
search screen .......................................... 348
Security ................................................ 101, 118
data changes .......................................... 109
Segments ...................................................... 361
Self-service BI ................................................ 25
Semantics ........................................... 130–132
additional ................................................ 132
Signal analysis ............................................ 362
Simple calculation node ......................... 326
Simulation .......................................... 288, 345
Single sign-on (SSO) ..... 66, 76, 92, 95, 101,
118
Slider ............................................................... 386
Smart assist ....................... 45, 335–336, 340
Smart discovery ............... 45, 187, 336, 340
advanced options ................................. 341
charts ......................................................... 343
configure .................................................. 341
overview ................................................... 342
pages .......................................................... 342
simulation ............................................... 345
unexpected values ................................ 344
Smart grouping .......................................... 355
activate ..................................................... 356
scatterplot ............................................... 356
Smart insights ............................ 45, 336, 346
accessing .................................................. 346
add to story ............................................. 346
sidebar ....................................................... 346
Smart predict ..................... 45, 335, 340, 357
scenarios .................................................... 46
Software development kit (SDK) ......... 388
445
Index
Spreading .................................. 285, 313, 315
Statistical key figures .............................. 362
Storage consumption ............................. 114
Story ........................................................ 32, 184
add charts ............................................... 196
back export ............................................. 266
boardroom view ................................... 401
catalog ...................................................... 267
collaboration ............................................ 37
comments ............................................... 278
convert ..................................................... 268
create ........................................................ 186
creators .................................................... 185
data ........................................................... 199
design ........................................................ 260
device preview ....................................... 190
dynamic text .......................................... 234
elements ................................................... 233
embedded ................................................ 264
embedded model .................................. 273
environment .......................................... 366
example ................................................... 261
export ............................................... 111, 264
file section ............................................... 198
filters ......................................................... 246
import into library ...................... 402, 407
initiate ...................................................... 187
insert section .......................................... 198
interface ................................................... 197
linking .......................................................... 38
live data ...................................................... 64
main area ................................................ 198
models ......................................................... 98
overview page ....................................... 404
pages ............................................................ 33
planning .................................................. 285
preferences .............................................. 261
publish to mobile ................................. 267
publishing .................................................. 37
responsive page .................................... 269
SAP Digital Boardroom ..................... 400
save ............................................................ 200
schedule publication ........................... 266
scope .......................................................... 366
screen adjustments ............................. 190
share .......................................................... 263
Story (Cont.)
templates ................................................. 274
text box .................................................... 232
text element ............................................ 233
tools ........................................................... 199
top bar ...................................................... 198
URL ............................................................. 264
viewer interactions .............................. 235
viewers ...................................................... 185
Subtopics ...................................................... 409
Syntax check ............................................... 256
System configuration .............................. 116
System landscape ............................... 96, 100
multiple systems ...................................... 96
System monitor ......................................... 114
System usage .............................................. 114
T
Tables ............................................................. 222
action bar ................................................ 225
create ......................................................... 223
drilldown .................................................. 225
expand ...................................................... 224
formatting ............................................... 226
freeze ......................................................... 225
hierarchies ............................................... 228
in-cell charts ........................................... 226
mass data entry .................................... 226
measures .................................................. 224
predefined calculations ..................... 227
sidebar ...................................................... 226
view ............................................................ 154
Target dimension ...................................... 315
Teams ............................................................. 103
assign via SSO ........................................ 104
create ......................................................... 103
Templates ............................................ 187, 274
Text operations .......................................... 259
Threshold-based coloring ...................... 208
Time dimensions ...................................... 165
Time series chart ....................................... 205
Time series forecast ........................ 354, 357
activate ..................................................... 355
example .................................................... 355
Tooltips ......................................................... 217
446
Index
Topics ............................................................. 403
add content ............................................. 404
additional settings ............................... 405
create ......................................................... 410
details ........................................................ 406
filters .......................................................... 405
moving ...................................................... 408
relationships ........................................... 408
Tracing ........................................................... 116
Transactional data .................................... 300
Transform log ...................................... 31, 159
Transformations ................................. 31, 146
create ......................................................... 158
execution ................................................. 157
history ....................................................... 159
R programming language ................ 339
Translation ................................................... 422
Tree structure ................................... 396, 410
Trellis .............................................................. 218
Trend chart .................................................. 204
U
Unexpected values ................................... 344
Union node .................................................. 326
Universes ........................................................ 54
Usage statistics ........................................... 421
Users ................................................................. 92
attributes ................................................. 102
create ......................................................... 102
delete ......................................................... 102
import list ................................................ 103
management ................................ 101, 419
V
Value driver trees ..................... 42, 287, 324
calculation rules .................................... 288
create ......................................................... 325
generation ............................................... 327
nodes .......................................................... 325
simulation ............................................... 288
use ............................................................... 328
versions ..................................................... 326
Value lock management ......................... 199
Variances ....................................................... 210
color-coding ............................................ 212
types ........................................................... 211
Version management .......... 282–283, 309
interface .................................................... 309
Versions ......................................................... 155
add to table ............................................. 310
filter ............................................................ 318
mapping ...................... 156, 163, 303, 305
publishing ................................................ 310
verifying .................................................... 156
Virtual private network (VPN) ................ 74
Visualization layers .................................. 391
Visualizations ....................................... 32, 183
W
Weights ................................................ 285, 314
Widget ............................................................ 220
Widgets ....................................... 249, 384, 411
custom ....................................................... 388
requirements .......................................... 388
select .......................................................... 412
Word cloud ................................................... 354
Workflows ..................................................... 368
Y
Year-on-year node ..................................... 326
First-hand knowledge.
We hope you have enjoyed this reading sample. You may recommend or pass it on to others, but only in its entirety, including all pages. This reading sample and all its parts are protected by copyright law. All usa-ge and exploitation rights are reserved by the author and the publisher.
Abassin Sidiq is a product manager of analytics at SAP. He has worked at SAP since 2012 and has been a part of product management for various analytics solutions since 2015. He has been part of SAP Analytics Cloud develop-ment since the product‘s inception.
Abassin regularly represents SAP at conferences such as the DSAG Annual Congress, DSAG Technology Days, and
SAP TechEd, where he hosts sessions about analytics and associated topics. He studied economics and business informatics at the Universität Mannheim and the Technische Universität (TU) Darmstadt. Before working in product manage-ment, he was a part of marketing and sales teams.
Abassin Sidiq
SAP Analytics Cloud446 Pages, 2020, $79.95
ISBN 978-1-4932-1934-6
www.sap-press.com/5026
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