Predictive Analytics 3.1 – Adding a Dataset & Visualization

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Lesson Two: Adding A Dataset & Visualization
SAP BusinessObjects
Predictive Analytics 3.1

Course Overview
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SAP BusinessObjects – Predictive Analytics 3.1
Lesson Two: Adding A Dataset & Visualization

Agenda
What Will Be Covered:
1. SAP Predictive Analytics – BusinessObjects 3.1
2. Adding A New Dataset
3. Visualization – Measures & Dimensions
1. Geo Bubble Chart/Time & Geographic Hierarchies/Monthly Animation
4. Summary & Next Lesson
5. Contact us

▪ In this lesson of our three part SAP BusinessObjects Predictive
Analytics 3.1 course, we’ll introduce an in-depth look at the various
features of SAP’s BusinessObjects, using our real world business use
case of a web application crawling actual data from crime reports &
arrests in a specific region during the year 2009.
▪ Lesson three investigates the possible real world predictions made with
this data for 2010 via SAP BusinessObjects Predictive Analytics 3.1.
SAP BusinessObjects Predictive Analytics

▪ In our real world business use case, we had a web application crawl actual
data from crime reports & arrests in a specific region during the year 2009.
▪ Crawler gathered police press announcements regarding crime, arrests, & occurrences.
Now use the data gathered from 2009 to predict crime rates & arrests for the year 2010.
▪ Our data source file is in Excel spreadsheet text format; showing the data structure of
each column entry.
ID Column The Crime Identifier
Time Column Time Crime Was Committed
City Column City Where Crime Was Committed
Street Column Street Where Crime Was Committed
Latitude Column Latitude Where Crime Was Committed
Longitude Column Longitude Where Crime Was Committed
Arrest Column Whether Or Not An Arrest Was Made

▪ Begin by opening SAP Predictive Analytics 3.1 SAP BusinessObjects.
▪ Click the Expert Analytics option on the left-hand side of the screen.

▪ The Expert Analytics screen appears.
▪ Click the Expert Analytics link, highlighted in the red box.

▪ The initial Expert Analytics home dashboard appears.

▪ Follow menu path File > New to add a new dataset.

▪ The Add new dataset screen appears.
Adding a New Dataset

▪ For this lesson, begin using your Microsoft Excel crime report
spreadsheet as your data source.
▪ Select Microsoft Excel as your data source and click the Next button.

▪ The Add new dataset selection screen appears.
▪ Upload the Microsoft Excel spreadsheet from your computer.
▪ Select Add Files.
▪ Options: Text file, Copy from Clipboard, Connect to SAP HANA, Download from SAP
Business Warehouse, Download from SAP HANA, Universe, & Query with SQL.

▪ You’ll be prompted to select the Excel spreadsheet from your local computer.
▪ Locate the Excel spreadsheet; click Open.
▪ Predictive Analytics 3.1 will auto-populate the dataset for you.

▪ The data is now displayed; all the empty dataset fields have also been
auto-populated, such as Dataset Name, Sheet, Table Header Type.
▪ The Excel spreadsheet values are also auto-populated for you, such as id, time, city,
street, lat, lng, & arrest. Leave all fields & values as-is.
▪ Click the Create button.

▪ The Visualize screen appears, displaying the populated dataset
information, such as Measures, Dimensions, & the chart to the right.
Visualization – Measures & Dimensions

▪ Now create a Geo Bubble Chart to easily visualize the selected dataset
information. Select the Geo Bubble Chart option.
▪ Other options provided are: Bar Chart, Line Chart, Pie Chart, Scatter Plot, Heat Map,
Crosstab, Numeric Point, & Box Plot.

▪ The Geo Bubble Chart Visualization appears.

▪ In the Dimensions section, convert the lat & lng dimensions
from the letters ABC into numbers.
▪ The import parsed the lat & lng columns as text.
▪ Keep the original length without any cutting due to any data formatting
issues. So parse those columns as text & convert them into what you
can calculate w/: a number.
▪ Hover your cursor over lat & the Options wheel appears.
▪ Left-click it; the options drop-down appears.
▪ Select the Convert To Numbers option.

▪ The ABC lat now appears below as the numbers 123 lat (2).

▪ Now repeat those same steps for lng.
▪ The ABC lng will appear as the numbers 123 lng (2).

▪ To enrich analytical data add the Measures Time &
Geographic Hierarchies.
▪ Measures allow for easy calculation manipulation.
▪ Hierarchies allow for natural groupings of related columns.
▪ When acquiring data, SAP Lumira detects potential measures
& hierarchies.
▪ Detected Measures are displayed in the Measures &
Dimensions panel on the left side of the Visualize
screen.
▪ Dimensions identified as potential hierarchies are
presented w/ a Globe icon; select the Options icon
next to any dimension to manually create a hierarchy.

▪ Geography & Time Hierarchies are automatically detected when a dataset is
acquired; hierarchies can be custom made any time to enrich a dataset.
▪ Time Hierarchies can be created on date & number columns, while Geographical Hierarchies
can only be created on columns containing values compatible w/ geography data values in the
NAVTEQ database of the Predictive Analytics application.
▪ Contextual menus of dimensions & column headers can be used to create hierarchies.
https://en.wikipedia.org/wiki/Navteq

▪ For Time Hierarchies, new columns selected as levels are added
automatically to datasets.
▪ New time hierarchies appear on the Hierarchies Semantics tab.
▪ In Geographic Hierarchies, the Geographical Data dialog displays columns
that match internal data in green, columns that match ambiguously are
displayed in orange, & non-matching columns are shown in red.
▪ New Geography Columns that are selected as levels are automatically
added to the dataset, & the new Geography Hierarchy appears on the
Measures & Dimensions panel.
▪ Matched levels of a geography hierarchy can be modified at any time.

▪ When datasets consist of latitude & longitude data, Geographic
Hierarchies can be customized to use the data.
▪ Predictive Analytics 3.1 creates a measure for each column
during data acquisition.
▪ Please note again that latitude & longitude data must be presented numerically
(featured earlier in this presentation).

▪ Create a Geographical Hierarchy for the City Dimension.
▪ Select the Options icon to the right of the City Dimension.
▪ Follow menu path city > Create a geographical hierarchy - >
By Latitude / Longitude.

▪ Select the Geographical Data.
▪ The Targeted Dimension should be city, the Latitude: lat (2),
the Longitude: lng (2), and the Geographical level: Other.
▪ Click the Next button to continue.

▪ Now generate the parent levels.
▪ The Geographical levels of Country, Region, and Sub-Region
should all be selected.
▪ Click the Finish button.

▪ Again, Measures are used to enrich datasets.
▪ They can be created manually from a column or
dimension, or you can allow the application to detect
them for you automatically in numeric column data
types when a dataset is acquired.
▪ Create a Measure for the city.
▪ Select the Options icon to the right of the city
Dimension again. Follow menu path city >
Create a measure.

▪ Now begin selecting your Measures & Dimensions.

▪ First drag & drop the city measure into the Measures Value box.

▪ Drag & drop the city_LongLat (geoOtherLevel) into the
Dimensions Geography box.

▪ Date & time functions return date data or time data.
▪ You might need to convert the format of your source data.
▪ Let’s create a Time Hierarchy.
▪ Select the Options icon to the right of the Time
Dimension; follow menu path time > Create a time
hierarchy… .

▪ The visual below appears.

▪ Drag & drop the Month (Month Level) Dimension into the Animation box.
▪ It is best to use the month option due to the limited amount of collected source
data; only a few months of data can be properly assessed.
▪ The amount of days does not make sense in this case, so use Months to
animate the data.

▪ The below Month Animation visual now appears.
▪ Click the Play button at the bottom of the screen to see the monthly visual
breakdown of crime rates & events collected by the crime web crawler.
▪ When you click Play, a month by month visual breakdown of various crime
activity will play on your screen like a time lapse video.

▪ Additional features of the Geo Bubble Chart display by hovering your mouse
curser over any location & left-clicking it.
▪ Features include: Filter, Drill Up, Drill Down, & Exclude.
▪ Drill Up & Down to see the city by Sub-Region, Region, & Country.
▪ As you Drill Up & Down, Measures on the left side of the screen adjust
according to the Dimensions; you can also drag & drop the desired Dimensions
into the Dimensions box on the left-side of the screen.
▪ Reverse the drilling to cycle back upwards through the Geography Dimensions

▪ In this lesson, you learned about the basic features of SAP’s Predictive
Analytics 3.1, including how to Add a New Dataset and how to work with
Visualization techniques such as Measures & Dimensions.
▪ In the next lesson, you’ll learn about the unique Prediction Properties of
Predictive Analytics 3.1, using our data from real world crime reports & arrests.
Summary & Next Lesson

Success – You Completed The Lesson!
10/10Copyright © Blackvard Management Consulting – All rights reserved www.blackvard.com
Congratulations, you completed the lesson!
In this lesson you successfully learned:
1. SAP Predictive Analytics – BusinessObjects 3.1
2. Adding A New Dataset
3. Visualization – Measures & Dimensions
a) Geo Bubble Chart/Time & Geographic
Hierarchies/Monthly Animation
4. Summary & Next Lesson
5. Contact us

Want to learn more about
Predictive Analytics?
Contact us today for your FREE
consultation with our experts.
Email: info@blackvard.com
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Predictive Analytics 3.1 – Adding a Dataset & Visualization

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