how to design a better bi and analytics experience for everyone

© 2016 Forrester Research, Inc. Reproduction Prohibited 2

We work with business and technology leaders to develop customer-obsessed strategies that drive growth.

How to Design a Better BI and Analytics Experience for Everyone Boris Evelson, VP and Principal Analyst

April 19, 2016


A B C


A B C D

Data visualization is the tip of the BI

iceberg


Why data visualization? 1.Visual information is more powerful than

any other type of sensory input. › When we hear something, we remember 10% of it three days later; if

we add a picture, we remember 65% of it.

› 80% to 90% of information received by the brain comes through the eyes, and about half of your brain function is dedicated directly or indirectly to processing vision.

Source: October 27, 2015 Forrester Brief: Data Visualization Unlocks The Value Of Business Insights


Why data visualization?

2. We can’t see patterns with numbers alone



Why data visualization? 3. It's often the only way to fit all the data

points onto a single screen (deep data sets, too many rows of data)

4. It's hard to analyze broad data sets without it (too many columns of data)



ADV differs from static graphs and charts by

› Dynamic data content

› Visual querying

› Multiple-dimension, linked visualizations

› Animated visualizations

› Infographics

› Visual story telling

What is advanced data visualization (ADV)?

Process flow for designing SOI apps with data visualization components

Checklist for designing SOI apps with data visualization components


What type of data visualization are you planning to build?


› Identify your specific goals and objectives.

› Define your metrics and key performance indicators (KPIs).

› Map your data visualization design to your target audience.

› Consider different decision categories.

› Find the right design for your delivery channel.

Define business requirements


The Implications Of Designing Dashboards For Operational, Tactical, And Strategic Decisions


› Quantitative — numbers that can be counted or measured

› Discrete — a finite number of possible quantitative values, such as the number of employees in the office

› Ordinal — a finite number of possible values with an order, such as days of the week

› Nominal or Categorical — data that you can group and sort, such as product types.

Categorize your data to identify the most appropriate method of visualization.


Use the most suitable chart for each analysis type


› Bar charts. Use horizontal labels; space the bars appropriately; use consistent colors; and order the data appropriately.

› Pie charts. Visualize no more than 5 categories per chart; don't use multiple pie charts to make comparisons; make sure all data adds up to 100%; and order slices clockwise by size. Remember, we are only able to gauge the size of pie slices if they are in familiar percentages, such as 25%, 50%, or 75%.

› Line charts. Plot no more than 4 lines; label lines directly; and plot all data points so that the top one takes up approximately two-thirds of the Y axis.

› Area charts. Position more variable data at the top; display no more than four data categories; and use transparent colors for overlapping areas.

Follow best practices for each visualization type


› Show causality. Do not choose X and Y (or more axes and dimensions) simply to show data on a 2D plane; use them to demonstrate the cause-and-effect relationship. Ask yourself if the X and Y axes help the users understand how the changes in one measure cause changes in another.

› Avoid 3D graphics. While they add another dimension, they also create occlusions, make orientation more complex, and can suggest a physical metaphor that may not relate to the data set.

› Use parallel scales and encode from zero. Using two charts side by side where the difference in scale is significant — using linear and logarithmic scales, for example — may be confusing. If you must use different scales, label them very clearly and make sure the viewer zeroes in on that information. Avoid using charts with dual scales for similar reasons. Remember that if a scale does not start at zero, it may not correctly reflect the proportions of change or difference; for example, 10 will appear twice as high as 6 if the starting point of the Y axis is 2.

Follow best practices for axis and scales


Avoid audience misinterpretation of results because of color choice › Color blindness. As many as 8% of men and 0.5% of women are color blind.

› Certain colors carry connotations. Red usually carries a negative connotation of alarm, while green is usually viewed as positive

› Consider afterimage effects. For example, staring at a green spot for a few seconds will leave an impression of a red spot when looking at a white background.

› Avoid bright, intense, or otherwise different color that stands out. It may inadvertently call attention to that data point, giving a false impression that the data point is somehow distinct and that you've specifically called it out.


Use colors consistently and deliberately

› Deploy a standard color palette. Make sure that the same color always signifies the same purpose across all data visualizations by creating a standard color palette for your company.

› Be deliberate in your use of colors. For example, deeply saturated colors stand out from pastels, so use them to highlight a certain attribute or to call your audience's attention to a particular element.

› Avoid using red, amber, and green. Do not use these specific colors unless you want them to represent the universally accepted connotations of stop, be alert, and go.


Ensure that a chart and its colors are telling the same story › Create the same dashboard with black and white or shades of grey palette.

› Make sure that your colorless data visualization tells the same story as the one with color or that your colors add the particular dimension to your story that you intended.


Don’t forget text and labels

› Apply text and labels appropriately. Do not assume that a user of a data visualization application will know what individual axes and data points mean. Make them as easy to read as possible by sticking to single lines of horizontal text. Avoid unusual fonts; use standard fonts that will render similarly on different browsers and devices.

› Sequence similar objects consistently. Keep all text labels close to their visual component. Don't confuse the viewer by ordering data points on a bar chart or a trend line in a way that doesn't correspond with the order of the labels in the legend.


Provide context

› Target vs actuals. Showing a timeline or a regional pattern of actual revenues won't tell the whole story unless you supply the context, such as budget or target numbers to compare the actuals.

› Magnitude of change. Similarly, showing only percentage changes won't reflect the full implications of that change if the audience doesn't have the context of the actual amount — is it a few dollars or a few millions of dollars?

› Proportion of change. Conversely, just showing amounts without relating them to a percentage won't demonstrate the whole magnitude of the change.


Understand and follow the key Gestalt principles of visual perception › Proximity — the distance between objects dictates whether they appear in groups that belong

together or independently

› Similarity — objects that share visual characteristics like shape, size, color, texture, value, or orientation will appear to belong together;

› Continuity — our visual preference for continuous figures means that two intersecting curved lines will appear as that, not as four lines meeting at the intersection

› Closure — we tend to close simple figures, so that a circle with a small gap in the arc will still appear as a circle;

› Area — we perceive the smaller of two overlapping figures as a figure and the larger as the background;

› Symmetry — in a symmetrical pattern, we perceive the whole pattern, rather than the individual parts that make it up.


The level of human perception of each visualization technique


Viewing patterns


Reduce clutter. Fit all data points on one screen

› Minimize non data objects. Use a large proportion of screen real estate for data points rather than non-data objects

› Ink-to-data-ratio. Ensure that very few items or objects on a screen — no more than 20%,— could be erased without your visualization losing its meaning.

› Fit all of the key data points on a single screen. Remember the age-old dilemma of "I don't know what I don't know" — as you may not know what is hidden outside the visible screen


Use Navigation, Animation, And Icons To Interact With Data Visualizations

› Apply both instrumented and gestural interactions.

› Consider alternative UIs, such as search, faceted navigation, and NLP.

› Ensure that all key visualization interaction and navigation features are available.

› Minimize the use of icons.

› Use popups for explanations when possible (NA on mobile)

› Apply animation where appropriate.

› Consider data entry functionality for what-if analysis.

› Provide collaboration functionality.

› Go beyond data visualization with data wrangling.


Forrester “story arc”


Recommendations › Don’t give up ownership of SOI and data visualization to IT …

› … But leverage tools that IT pros can use to productionalize your BI and data visualization applications

› Standardize data visualizations across the enterprise (standard color palettes, standard fonts, same chart types for similar analysis, similar sequences in storyboards, etc.)

› Use Forrester Data Visualization Checklist to standardize data visualization apps before they go into production

forrester.com

Thank you Boris Evelson [email protected] http://www.forrester.com/Boris-Evelson http://blogs.forrester.com/boris_evelson https://twitter.com/bevelson https://www.linkedin.com/in/bevelson https://www.facebook.com/ForresterBI

Information Builders’ Strategy for Broadest Adoption

44

Michael Corcoran Sr. Vice President & CMO

About Us Information Builders

Our goal is to deliver the industry’s best engineered software and top customer service to ensure customer success.

1,350 employees

Millions of users

Global market presence

10,000 customers

Better Data. Better Analytics.

Award winning service

Award winning software

Our Mission Information Builders

To provide our customers with Better Data and Better Analytics to help them...

Self-Service BI & Analytics for Everyone Business Opportunities

Pervasive (Operational) BI and Analytics Employees Operational Reporting & Alerts,

Decision Support, Performance

Operationalize Insights, Change Culture

Performance Benchmarks, Alerts

Partners, Suppliers & Distributors

Operational Optimize Supply Chain, Meet SLA’s

Customers, Citizens, Patients & Students

Vital Self-service Experience, e-Statements

New Revenue, Loyalty/Retention, Public Awareness

Back-Office BI and Analytics

Executive Management

Strategic Metrics Dashboards Scorecards

High-level Decision-making

Analysts Analytical Analytics Advanced Analytics

Generate Insight, Identify Problems & Trends

Organizational Role

Analytical Requirements

Analytical Users

Results

Information Builders’ 3i Platform Better Data. Better Analytics.

Self-Service BI and Analytics for Everyone Information Builders’ WebFOCUS Platform

49

� Self-service for Business Analysts

� The most complete single tool for all forms of analytics and reporting

Business User Edition Application Edition Enterprise Edition

� Self-service for all your employees

� Tools for analysts � InfoApps for everyone � Stronger governance

� Self-service for your entire universe of users

� All employees � Customers � Partners � Massive scale

Visualize Monetize Operationalize

Broadest Self-Service BI & Analytics Offering WebFOCUS Business User Edition feat. InfoAssist+

Visual Data Discovery

Robust Reporting

In-Document Analytics

Rapid Dashboards

Data Preparation

InfoAssist+

IT Developers Management Operational Workers Business Partners Customers

Tools for Business Users & IT; InfoAppsTM for the Masses Self-Service BI and Analytics for Everyone

51

InfoAppsTM

Dashboards & Scorecards

Governed Self-service Reporting

Mobile InfoApps E-Statements

Data Discovery InfoApps

Predictive InfoApps Search & Social Analytic InfoApps

Portal Create your own App Store experience

Operationalize Insights & Monetize Data

Business Analysts

App Studio InfoAssist+

Data Discovery

Predictive Analytics

Social Intelligence

Search

Location Intelligence

Reporting

Dashboards

Generate & Deploy Insights

SocialHot

BadFeedback

InfoAppsTM Extend Insights to Everyone Driving Pervasive BI and Analytics Adoption

52

Descriptive

Diagnostic

Predictive

Prescriptive

Discovery

Value

Skill

s Lev

els

InfoAppsTM Extend Insights to Everyone Driving Pervasive BI and Analytics Adoption

53

Descriptive

Diagnostic

Predictive

Prescriptive

Discovery

Value

Skill

s Lev

els

I. Executive Summary Page 10

Management Dashboards & Scorecards

Business Analysts Simple to Advanced Analytics Tools

Operational Workers Real-time Reporting & Decision

Support Apps

Supply Chain Partners Performance Metrics & Reporting Apps

Customer & Citizens

Self-service Reporting Portals & Analytic e-Statements

WebFOCUS Self-Service BI and Analytics for Everyone

Customer Results

55

In Their Own Words…

Thank You and Follow Us…

56

Twitter @mpcorcoran and @infobldrs Facebook www.facebook.com/informationbuilders LinkedIn www.linkedin.com/company/information-builders

Q&A Session

57

how to design a better bi and analytics experience for everyone

Data & Analytics