Top 10 Requirements for Visual Analytics

Anthony DeightonCTO & SVP of Products

September 8, 2015

A Shift in Gravity

To Business-Driven

To External Data

To Analysis-Centric

To Platform Providers• Most “new” data is generated outside

the organization (ie: Cloud)

• Diversity of data sources:

• Shifting from System Centricity to Business Unit Centricity

• Classic Market Disruption

DataGravity

AnalyticGravity

BuyerGravity

SellerGravity

IT

Developer

Data Scientist

Business Analyst

Information Worker

Stack Vendors

Specialist Tools

Platforms

Descriptive

DiagnosticPredictive

Prescriptive

• Mainstream shifting from “report-centric w/ some analysis” to “analysis-centric w/ some reporting”

• Business value will rise

Volume Velocity Variety

Data Acquisition

Data Storage

Data Preparation

Automated Pre Analysis

High Performance Databases

Reporting Analytic Apps Embedded Analytics

MobilityCollaboration &

Sharing

Visualization

Visualization alone isn’t the answer

Top 10 Requirements for

Visual Analytics

#1: Correct Results!Full-Outer Join

across multiple data sources

*** No Data Left Behind ***

Data Left Behind

No Data Left Behind

Immune to Fan-Trap & Chasm-Trap

*** No Data Double-Counted ***

Double Counted

TripleCounted Correct

In Query-Based Visualization Tools:- measures are double-counted if mapped to two categories- measures are triple-counted if mapped to three categories- …

#2: Unified Experience

Operating System

Hardware

User Experience

Calculation Engine

#3: Embedded Best Practices

Smart Visualizations• All charts are connected by default;

No need to wire individual objects.• Touching an object effects all charts.

User-Driven Creation• Drag-and-Drop Creation• Shared & Governed Libraries• Progressive Creation• Bring Your Own Data

#4: Flexible Data ModelsQuery Based Visualization

and Traditional BI

vs.

Qlik’s Associative Model

• Pre-defined joins• Pre-aggregated hierarchies• Pre-conceived notions of

how data should be related• Only part of the story

• Dynamic associations• Explore freely in any direction• Understand how data is

actually related• The whole story

#5: Search

• Global keyword search across all data, dimension and measures

• Multi-value search in the same string exposing dimension matches and relationships within the data

• Elimination of cumbersome navigation, and prompt pages

• Faster time to insight

• Relevance ranking based on strength of associations

#6: Scalability and Performance

• Sub-second-response that enables interactive data analysis

• User and Data Scalability

• Support for concurrent users and large amounts of complex, multi-source data

• Robust deployments via elastic scaling architecture.

• Hybrid approach:• In-Memory Data Indexing• Live Connection to ‘Big Data’ sources

#7: Enterprise-Class Governance

• Data-Level Security without having to trade-off performance and self-service flexibility

• Centralized management, monitoring, control, disaster recovery and security.

• Governed centralized libraries and flexible rules-based security to ensure consistency of data and analytics.

#8: Open Platform• Extend the out-of-the-box capabilities

by adding custom visualizations that leverage standard web-

technologies to support specialized analysis.

• Integrate with statistical tools, such as R.

• Embed Analytics capabilities in 3-rd party portals or custom

apps using Mashups and Open APIs.

#9: User Driven Data

• Business user data ingestion unifying local and governed data.

• Automatically profiling of data to suggests associations.

• Extensive library of third party data sources available as a service for easy integration into analysis

• Built-in ETL layer; No need for external tools or data repositories. Big Data

SAPSalesforce Excel

#10: Mobile-First, Cloud-First Design• No need to install any software to create or

consume content

• Explore, analyze, create and collaborate anywhere, anytime on any device.

• Gesture based touch user interface that intuitive user experience on touch devices

• Responsive design adapts visualizations, data, and functionality to create the best user experience

• Build once consume anywhere model eliminates overhead to customize content for mobility

#11: Data Story Telling & Reporting

• Communicate insights and analyses using rich, interactive data stories

• Create and present rich stories with narrative and graphics

• Direct, in-context linking to live analysis to answer follow-up questions

• Immediately answer questions that arise in discussion, reducing delays in decisions

• Print and Export to .PDF and PowerPoint for broad sharing

Top 10 11 Requirements for Visual Analytics

1. Correct Results – No Data Left Behind, No Data Double-Counted

2. Unified Experience – Beautiful design AND powerful calculations

3. Embedded Best Practices – Do the “right thing” by default

4. Flexible Data Models – Not joins, associations

5. Search – All data indexed all the time

6. Scalability and Performance – The two second rule

7. Enterprise-Class Governance – Control at the center, freedom at the edges

8. Open Platform – Mash-ups, open APIs

9. User Driven Data – Make data integration as easy as analytics

10. Mobile-First, Cloud-First Design – No need to install software, touch gestures

11. Data Story Telling & Reporting – Communicate Insights

Demo