Visualization? Wayne D. Van Sluys Lead Consultant & Oracle ACE - - PowerPoint PPT Presentation

What is Oracle Data Visualization?

Wayne D. Van Sluys Lead Consultant & Oracle ACE info@interrel.com epm.bi/videos

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interRel Highlights and Company Overview

Disclaimer

• These slides represent the work and opinions of the

presenter and do not constitute official positions of Oracle

• r any other organization.
• This material has not been peer reviewed and is presented

here with the permission of the presenter.

• This material should not be reproduced without the written

permission of interRel Consulting.

Intro to DV

Think About This

• Your eye will focus on about

50 things per second

• In an average lifetime, eyes see

24 million different images.

• 80% of our memories are

determined by what we see

A plane figure with four equal sides and four right angles

90% 60,000x 65% 40% 36,000 .25 6

Why Data Visualization?

• We must be more agile
• We need a solution to help us find trends,

patterns and relationships across a number

• f data sources
• We need a visual graphical interface (no

programming please)

• We don’t want to have to rely on super

technical resources to access information

• No more big huge expensive data

warehousing projects

• Sources of data can change and need to be

able to quickly update

• Change the standard; change

expectations

What is Data Visualization?

• Self service visual representations of data

̶ Enables people to see data presented in a visual format ̶ Use charts and graphs for more detail

How is Data Visualization Being Used?

• Comprehend

information quickly

• Pinpoint trends
• Identify patterns
• Communicate a story

Benefits of Data Visualization

• Vivid information – not just a spreadsheet for upper management
• Review information in a new, constructive way
• Enables users to visually see connections between business

processes

• Ability to detect shifts in customer behaviors across multiple data

sets faster

• Brings actionable insights to the surface
• Tell a story with data

Data Visualization

• Self service data exploration and analytics in

visual, modern BI interface

• Connect to Essbase cubes as well as well as

many other data sources for data exploration, analyses, and dashboards

• Click and drag UI to create visualizations
• Create multiple canvases / insights
• Presentation and story telling mode
• Use Data Flows to Wrangle and Mashup Data

to create new Datasets

• Ever expanding library of visualizations
• Any user / no tech skills required
• Machine Learning
• Artificial intelligence
• Natural language
• Custom visualization plug-ins available

from online library

Demo: Self Service Analysis – Gross Sales

Cool DV Features

Not Just Visualizations – You can Model with Data Flows

• You can use data flows to

produce curated (combined,

• rganized, and integrated)

data sets

• Data Flows are available in

the DV UI

• Build data sets from a

predefined sequence

• Refresh your data regularly
• n a schedule
• Load data into an Essbase

cube

• Load to relational table
• Data flow results are available

in BI & Published Reporting

Related: OAC BI Guided Analyses & Dashboards

• Guided dashboards and analytics for “click the button” users
• Model sources for BI consumption
• Users can consume data and interact with data through guided prompts, controls, and

action links

• Mobile devices can be used to create analyses and interact with the data
• Essbase, relational, or any data source

OAC DV vs. OAC BI

OAC DV OAC BI

Old school tried and true Analyses and Dashboards Guided analyses and dashboards Cool new solution DV Projects Self service exploration and visualization

Data Visualization (DV) ▪ Create DV projects ▪ Data exploration and analytics ▪ Modern interface ▪ Self service analytics ▪ Single OAC Essbase cube per project ▪ Click and drag UI to create powerful visualizations ▪ Create multiple canvases and insights ▪ Presentation and story telling mode ▪ Any user / no tech skills required BI Cloud ▪ Create analyses and dashboards ▪ Guided user experience through pre-existing dashboards ▪ Traditional BI interface ▪ Report and dashboard designers create content for audience ▪ Reusable / repeatable presentations with updated data ▪ More modeling capabilities / RPD ▪ Relational data ▪ Slightly more technical requirement

DV Connections & Data Sets

Create a DV Data Set

Data Prep for File Based Project

• DV accepts file of type: .xlsx, .xls, .csv, and .txt (custom delimited)
• Currently, files have a maximum size of 50 MB
• One or more files can be uploaded with “numbers” (measures).

Numerical data is considered to be the “facts” (like in the relational world).

̶ If uploading data files from multiple sources, it is best if the data across the files matches exactly (i.e. don’t have “TX” in one file and “Texas” in another)

• (Though there are other cool features to assist with this later on)
• The other columns are considered the “attributes” of the facts

̶ Avoid null values in these columns

Data Flows

• A Data Flow is similar to an Extract Transform and Load (ETL)

process used in data warehouse systems, but simpler.

̶ Data Flows allow power users to combine data from a number of different sources and create a new data set for use in DV projects

• Right click and Add Step to add another spreadsheet

̶ It’s easiest to use a data flow if the two source files aren’t exactly the same, to use some fun transformation options ̶ Can be opened to edit or run to refresh the data file that has been created by the flow

Data File

• Once a data file is uploaded, it is displayed in DV

̶ Attributes (dimensions/attributes) and Measures (numbers) will need to be tagged accordingly. Data Flows read the file to the best of their ability, but it’s best to review each column and confirm it is correct (and fix it if not).

Data Wrangling and Calculated Columns

• Data Wrangling and Calculated Columns

̶ Data Wrangling is the process of manually mapping data from its original format to another that can be read easier ̶ Create a calculated column to replace a symbol so metadata matches ̶ Join the data sets ̶ Remove the excess columns

Blended Data

• Blending is the joining of two or more files or sources and this can

also be done within a project

Creating a Data Connection & Data Set (Essbase example)

Must Grant Security for Data Sources

Creating a Project in DV: Basics

Overview

• When you create data visualizations, you group them by “project”
• For some source types, you can combine them with other data

sources into a single DV project

̶ One exception is Essbase Cloud cubes; you can only choose one cube per DV Project in the current version

• From Data Sources screen, in the bottom of the left pane, there are
• ptions for creating new Data Sources, Connections, and Data

Flows.

̶ You can either add them from here or you can add them from within a project (shown in next slide)

Basic Steps

• From the DV home screen under Create, click “Project”
• Select the appropriate data source
• Visualize mode to create graphs and charts
• Drag and drop members and generations from the left panel.

DV Project Window

Left Navigation Bar: Data Elements

• Data Elements tab

̶ Lists the data elements from all data sources ̶ Select one or more data elements or columns and drag into the Canvas area

Left Navigation Bar: Analytics

• Analytics tab

̶ Lists available analytics highlights that can be added to visualizations like trend lines or outlier highlights

Left Navigation Bar: Visualizations

• Visualizations tab

̶ Lists the types of visualizations that can be added to the Canvas ̶ Visualizations are grouped for easier access

Explain Feature

• Explain on Measures:

̶ Basic Facts about Measure:

• What are the values of the measure and how do they relate to each other?

̶ Anomalies of Measure

• What groups in the data exhibit unexpected results for measure ?
• Explain on Attributes:

̶ Basic Facts about Attribute

• What are the values of Region and how do they relate to each other?

̶ Key Drivers of Attribute

• What elements in this data best explain the values of Attribute?

̶ Segments that Explain Attribute

• What hidden groups in the data can predict outcomes for Attribute?

̶ Anomalies of Attribute

• What groups in the data exhibit unexpected results for Attribute?

Demo: Explain

Explore Panel

• Grammar Panel

̶ The main properties screen for the visualization that is displayed

Main Canvas Window

• Where you drag and drop the project elements
• There can be multiple charts or graphs in the area
• You can also update the visualizations via icons within the chart or

graph by hovering over the chart or graph here

Prepare, Visualize, and Narrate

• Prepare

̶ Make changes to the data sources within a project

• Visualize

̶ Primary work area within a DV project ̶ Create charts and graphs to analyze data

• Narrate

̶ Save visuals you want to share as a Presentation to help tell a story of the data

Share Projects with Others

• To allow updates from others, must provision
• Move to the shared folder, everyone can view

DV Features

Adding a Canvas

• Add a canvas while in Visualization mode of a project to create

another visual or dashboard

Filters

• Filters can be added to visualizations to reduce the amount of data

shown (good for performance) and focus on just what you need to see

̶ Can be created at the Project level or at the visualization level

Copy Features

• Copy & paste visuals within the same canvas or to different canvas
• You can make a copy of the entire canvas by doing a right-click,

Duplicate Canvas on the canvas tab

Calculated Data Elements

• Manually build a calculation expression or use the function to help

create a formula

̶ Right click My Calculations and Add Calculation

Adding Advanced Analytics

• DV has a variety of built in analytics that work with different

visualizations

̶ Accessed via the side menu by dragging them into the canvas OR ̶ Activated via the properties of a selected visualization OR ̶ Accessed by right clicking within a visualization

Aesthetic Options

• Change which type of graph you want to use by hovering over the

visual, and select the Change Visualization Type icon in the upper- right hand corner

• Sort visual results by right-click on them and selecting the sort
• ption

Manage Color

• Change the colors on Visuals

Contrasting Attributes = Contrasting Colors Complementary Attributes = Complimentary Colors

Hovering

• Hover over portions of the visual to see the exact value

̶ Use Tooltip to add more information

Brushing

• Data Visualization Feature: Brushing

̶ Allows for selecting a data value in one visualization and seeing related data highlighted in other visualizations on the same canvas

Presenting Visualizations

• Narrate Mode:

̶ Select Canvases to include in Presentation ̶ Add Notes

Sharing Visualizations

• As we know, a project can be saved by clicking the Save Project icon
• n the menu bar
• Once saved, select either print or export the project
• Export an entire project or a single visualization

Demo: Creating a Visualization from a Spreadsheet

Demo: Create Visualization with Multiple Files

Essbase & DV

Considerations

Likely Facts about the Essbase Cube You Want to Visualize

• Financial Reporting cube with large accounts dimension (P&L,

Balance Sheet)

̶ Accounts dimension tagged as “Accounts” to take advantage of Essbase financial intelligence / features (time balance, variance reporting)

• Taking advantage of Essbase consolidation tags (+, -, ~, ^)
• Ragged structures all over the place (sweet spot of Essbase)
• Haven’t named any generations
• Years and periods are in two separate dims
• Variance calcs could reside in a few different places
• Alternate hierarchies
• Users love member selection, drill, keep only, remove only

What This Looks Like in DV

Drill and Zoom Behaviors Supported

DV / Essbase Observations

• Awesome

̶ Easy click and drag interface ̶ Some “Essbase” like features – Keep Selected / Remove Selected, Drill to next level, Drill to Attribute ̶ Lots of charting options ̶ Filtering options ̶ Insights, storytelling, sharing

• Considerations

̶ Sort of turns Essbase into a more relational view ̶ If generation names are not defined, it will be a generic Gen#, dim name

• If you don’t know the generation where a member exists, you might have a hard time

finding

̶ Account flat list as metric ̶ Set your expectations – this is not Smart View on the web

• No member selection

̶ Might tweak Essbase design to better support DV

• Misc. Thoughts

Installing DVD

• Do not have spaces in installation path

Install to c:/Oracle/DVD

• Install DVML

Install to c:/Oracle

Further Reading:

Berinato, Scott. (2016). Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Harvard Business Review Press. Cairo, Alberto. (2016). The Truthful Art: Data, Charts, and Maps for Communication, 1st Edition. New Riders. Evergreen, Stephanie. (2016). Effective Data Visualization: The Right Chart for the Right Data, 1st Edition. SAGE Publications, Inc. Evergreen, Stephanie. (2017). Presenting Data Effectively: Communicating Your Findings for Maximum Impact, 2nd

• Edition. SAGE Publications, Inc.

Few, Stephen. (2009). Now You See It: Simple Visualization Techniques for Quantitative Analysis. Analytics Press. Few, Stephen. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten, 2nd Edition. Analytics Press. Nussbaumer Knaflic, Cole. (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals, 1st

• Edition. Wiley.

Tufte, Edward. (2001). The Visual Display of Quantitative Information, 2nd Edition. Graphics Press. Ware, Colin. (2008). Visual Thinking. Morgan Kaufmann. Ware, Colin. (2012). Information Visualization, 3rd Edition. Morgan Kaufmann. Wexler, Steve. (2017) The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios. Wiley. Wong, Dona M. (2013). The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures, 1st Edition. W. W. Norton & Company.

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