“predictive analytics” contents index the author · 2020-05-27 · predictive analytics while...

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

https://www.sap-press.com/sap-analytics-cloud_5026/#utm_source=AWS&utm_medium=readingsample&utm_campaign=Browse%20the%20Book&utm_content=1934

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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 Predictive Analytics

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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Figure 7.1 Smart Discovery

Figure 7.2 Smart Insights

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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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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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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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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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7.2 Smart Assist

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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

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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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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

https://www.sap-press.com/sap-analytics-cloud_5026/#utm_source=AWS&utm_medium=readingsample&utm_campaign=Browse%20the%20Book&utm_content=1934

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