Presents a webinar on Visual Analysis and The Rise of Data Discovery

©2009 ADVIZOR Solutions® 1advizorsolutions.com

The Rise of Data Discovery and Analysis Tools-- Enabling Better and Faster Decisions

Presenter: Doug Cogswell, President & CEO, ADVIZOR Solutions, Inc.

Email Questions To: [email protected]

©2009 ADVIZOR Solutions® 2

Agenda

The Landscape: Data tells “stories” End-users often stuck “Cycle of Pain”

New Technologies: In-memory-data-management Data Visualization Predictive Analytics

Examples and Use Cases

Q & A



Agenda


New Technologies: In-memory-data-management Data Visualization Predictive Analytics


Q & A


Higher EdFinancial ServicesTransportationHealthcare


Business Intelligence

“The term business intelligence (BI) dates to 1958.[1] It refers to technologies, applications, and practices for the collection, integration, analysis, and presentation of business information and also sometimes to the information itself.

The purpose of business intelligence is to support better business decision making.” *

[1] http://www.research.ibm.com/journal/rd/024/ibmrd0204H.pdf* http://en.wikipedia.org/wiki/Business_Intelligence


Management

Business Analysts

Frontline Staff

Strategic Decisions

Tactical Decisions

Information Needs Vary


Focus on Management: (1) operational reports, and (2) scorecards / dashboards.

However, staff need to slice and dice data, look at trends, etc. to make decisions

Reports, Scorecards and Dashboards fall short: Summary, not Detail, Time consuming and difficult to implement Consume a lot of core IT resource

Key staff largely unsupported

And, when they then go to IT or central reporting for custom reports they often get: The wrong data late, or at best . . . . . . the right data through a time consuming iterative process

Today . . .


There are 4 key aspects to Business Intelligence

Get Data

Store Data

Understand Information

Make Decisions

Most effort has been here

Most value still to come!!


Many organizations have two layers of this “stack”, and use them to attempt to fill the other two.

SAS, SPSS, Filemaker, etc.

Cognos, Hyperion, Business Objects,

etc.

?

Understanding Information . . .

Operational Reporting

Performance Reporting

Discovery & Analysis

Advanced Analytics


Problem: the “Cycle of Pain”

Custom Report Requests: Custom query submitted to IT Backlog – 5 days to get answer Begs another question, back through the cycle Frustration on both sides

Excel: Download “extracts” Slice and dice in Excel Time consuming and challenging Don’t have all the data Hard to show to management “Shadow data systems”


New technologies to the rescue!!

SAS, SPSS, Filemaker, etc.

Cognos, Hyperion, Business Objects,

etc.

• In-Memory-Data• Data Visualization• Predictive Analytics

Operational Reporting

Performance Reporting

Discovery & Analysis

Advanced Analytics


Management

Business Analysts

Frontline Staff

Strategic Decisions

Tactical Decisions

Not “One Fits All”

Operatio

nal Rep

orting

Perfo

rman

ce Sco

reca

rds/

Dashboa

rds

Data D

iscov

ery &

Analy

sis

Advance

d Analy

tics


More people More decisions More often

Strategy is implemented through tactics

Underfunded: Why??

Staff Tactical Decisions: Critically Important


Management Strategy gets focus “Clean Summary” NOT “Tactical Detail”

Leverage Legacy Systems “The Reporting System will be ready in six months”

Fear of “information democracy” Aka “The Unknown”

Why Underfunded?


From Gartner BI Summit:

#1 IT priority (for 4th year)

Penetration of end-users will increase 2.5x (by 2012)

4 Key driving technologies: (1) in-memory-data-management

(2) data visualization

(3) social software

(4) search

New models are emerging (the “Information Buffet”)

Structured Decision Making Autonomous Decision Making Controlled / Qualified Access Open / Unqualified Access

• Gartner BI Summit keynote address, Kurt Schlegel and Bill Hostmann


In-Memory-Data-Management


In-Memory-Data-Management

“Supports the concept of end user analysis and visual discovery™ by enabling very fast interaction, slicing and dicing, and calculation.

No predetermined structure is required, so the analysis can be completely ad hoc against any combination of any elements in any table in the memory pool.

Since the detail is all in memory, as the end user slices and dices the data the detail list is constantly changing. When done, the end-user can easily export his / her list (of customers, products, underperforming employees, etc.) to another system for action.”


In-Memory Advantages:

Ad Hoc

Fast

Flexible

Cross Table

Simple Query

Summary AND Detail


Lots of Information, but Fragmented and Hard To Access . . .

Call Records

Alumni Data

Email Appeals

Student Caller Data


First Step: Knit the Information Together . . .

Affinity

Capacity

Activities

DemographicCall Records

Alumni Data

Email Appeals

Student Caller Data


. . . Enabling Easy Answers to Key Questions.

Q: Who is not doing well with my top donors?

Call Records Alumni Email Student Caller Data


Example #1: pre-built call center “project” Team of users “Pool” of ~70 tables from 3 systems:

Call record details Alumni Data Student Data

Loaded and refreshed each night Access by either:

Client application (used in the demo) Web portal

No pre-set hypothesis. Will have to: “Fish” Let the data tell its story Collaborate

Let’s Take a Look . . .


Demo . . .


Key Benefit: Collaboration


Data Visualization


“Recognizing that humans have a keen ability to process visual information, data visualization tools have been developed that allow people to interpret and analyze vast amounts of data. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. People use visual analytics tools and techniques to: Synthesize information and derive insight from massive, dynamic, ambiguous,

and often conflicting data. Detect the expected and discover the unexpected. Provide timely, defensible, and understandable assessments. Communicate assessment effectively for action.” *

Interactive Data Visualization

* National Visualization and Analytics Center (NVAC™), http://nvac.pnl.gov/about.stm


Let’s take a look . . .

Example #2: ad hoc analysis Desktop analyst

Portfolio of mutual funds . . .

. . . and find and categorize the “dogs”

10 minutes

No pre-set hypothesis. Will have to:

“Fish”

Let the data tell its story

Share findings


Demo . . .


Data Visualization

Drill Down . . .


Charts > Pages > Projects

ADVIZORADVIZORChartsCharts

15 interactive15 interactiveChartsCharts

solve anysolve anydata displaydata display

needneed

ADVIZORADVIZORPagesPages

Combinations ofCombinations ofChartsCharts

that addressthat addressspecific businessspecific business

issuesissues

ADVIZORADVIZORProjectsProjects

Combinations ofCombinations ofPages Pages

that address thethat address theperformance needsperformance needsof a business unitof a business unit


15 Visualization Charts

Bar Chart:


Pie Chart:

Line Chart:


Map:


Heat Map:


TextFilter, DataSheet, and Counts:


ScatterPlot:

TimeTable:


Parabox:

Multiscape: Histogram:


DataConstellation:


SummarySheets:


Visualization: Finding Patterns in Data

Example #3: Airline Network Overbooked Flights

Changing data

Single flights don’t matter . . .

. . . groups of them do.


Oversold Flights . . .


Oversold Flights . . . Grouping Matters


Boston . . .


Flight Details . . . Take Action


Predictive Analytics



“Uses mathematical tools and statistical algorithms to examine and determine patterns in one set of data . . .

. . . in order to predict behavior in another set of data

Integrates well with in-memory-data and data visualization”



Concepts: Target Base Population (“excluded” data not used)

Explanatory Fields Core Table Calculated and joined from other tables*

Regression Based Model Examine results against original data* Predict*

* Key advantage of an in-memory application since it has all of the underlying data


Example #4: Medical Claims Data Who are our high cost members?

What makes them unique?

Who else fits the same profile?

Let’s take a look . . .


1.8mm Claims; 21k Members


Wide Range of Behaviors


95 Members Lots of Claims, and High $$


Build Model


Predict Others


List of 220 Other Members with similar profile



Fast Answers

Complements Visual Discovery™

Don’t need to know statistics

Integrates well with In-Memory

Highly collaborative

Business Staff Can do This!!!


What We Covered:


New Technologies BREAK THE CYCLE OF PAIN!!! In-memory-data-management Data Visualization Predictive Analytics




What We Covered:


New Technologies BREAK THE CYCLE OF PAIN!!! In-memory-data-management Data Visualization Predictive Analytics



Higher EdFinancial ServicesTransportationHealthcare


Questions and Answers
