AFSUG Cafe BI - Durban 8 Nov 2011

World Café Social Business IntelligenceWorld Café. Social Business Intelligence.#CafeBI (www.twitter.com/afsug)

Facilitated by Manti Grobler (SAP) and Charles de Jager (SAP)

Data CategoriesData Categories

Supports automated processingC f ith d t d l i t d ith d t b d–Conforms with data models associated with databases and spreadsheets

–Granular data stored in fields

Structured

Generally does not support automated processing–No data model or not easily understood–Insufficient metadata–Noisy data communications such as an email message, blog or

document

Unstructured

document

High Volume of small data bits–Huge volumeHuge volume–Only act on exceptions–Captured at source

Event

© 2011 SAP AG. All rights reserved. 2

Common Structured DataCommon Structured Data





Structured


document

Unstructured

document

High Volume of small data bits–Huge volumeHuge volume–Only act on exceptions–Captured at source

Event


Common Unstructured DataCommon Unstructured Data

A press releaserelease communication


Common Unstructured DataCommon Unstructured Data

Forum postingsp g





Structured


document

Unstructured

document

High Volume of small data–Huge volumeHuge volume–Only act on exceptions–Captured at source

Event


Common Event DataCommon Event Data


What vs Why and WhenWhat vs. Why and When

It’s generally said that…

structured data tells us “what” and t d t t ll “Wh t” d “Wh ”event data tells “What” and “When”and

unstructured data tells us “why”unstructured data tells us why


From the Business PerspectiveFrom the Business Perspective

“If you are not analyzing text – if you’re analyzing only transactional i f ti ’ i iinformation – you’re missing opportunity or incurring risk.”

-- Seth Grimes, Alta Plana


Text Analytics Boosts Business ResultsText Analytics Boosts Business Results

“Organizations embracing text analytics all report having an

i h t h thepiphany moment when they suddenly knew more than before.”

-- Phillip Russom, The Data Warehousing Institute


Text Analytics Expands Your Vision of Business Intelligence

“The bulk of information value is perceived as coming from data in

l ti l t bl Th i th trelational tables. The reason is that data that is structured is easy to mine and analyze.”

-- Prabhakar Raghavan, Yahoo ResearchResearch


KnowledgeS

e

Knowledgetrategy

telli

genc

eExternal

Information

Int

n FIPP P

lan

form

atio

n FI HR

COSDIn

f SDPMMM


Operate / Generates Data

Business Intelligence Typically Runs Off Structured DataBusiness Intelligence Typically Runs Off Structured Data


Business Intelligence Reporting off Structured DataBusiness Intelligence Reporting off Structured Data

How can you extend your BI investments to

t t d t t d t ?unstructured text data?


Do you report just for the sake f ti ?of reporting?

Or do you innovate with intelligence?

Workers Lose Productivity from InadequateInformation Access

54%54%Lose Productivity


Source: Economist, ‘Enterprise Knowledge Workers Study

The Goal: Be a Best Run BusinessThe Goal: Be a Best Run Business

77%

“77% of high77% of high performers haveperformers have above average

23%

above average analyticalycapability”

Low High


Source: Competing on Analytics, Thomas Davenport

LowPerformers

HighPerformers

IT Is Looking for Flexibility in Sharing Relevant Information

Organizations require:

• Trusted, consolidated, and, ,actionable information

• From a variety of dataysources

• Self-service access


RELEVANT INFORMATIONRELEVANT INFORMATIONLargeScale

MobileDevice

BusinessSuite

MicrosoftOfficeSelf

Service

LESS RELIANCE ON IT© 2011 SAP AG. All rights reserved. 21

© SAP AG 2010. All rights reserved. / Page 21

Discussion Session 1

EverythingEverything© 2011 SAP AG. All rights reserved. 23

The Social Media The Social Media MasterClassMasterClass 20112011EverythingEverything

@pfeiffer44: “POTUS to address the nation@pfeiffer44: POTUS to address the nation tonight at 10.30pm eastern time”

1 May 2011 9 45pm‐ 1 May 2011, 9.45pm,

Dan Pfeiffer, Communications director at the White House

Twitter explodes Debate ragesTwitter explodes. Debate rages about whether Qaddafi had been Q fkilled or Bin Laden tracked down.

2900 Tweets per second2900 Tweets per second.

@keithurbahn: “So I’m told by a bl h h kill dreputable person they have killed

Osama Bin Laden Hot damn ”Osama Bin Laden. Hot damn.‐ 1 May 2011, 10.25pm

Keith UrbahnChief of staff for Donald Rumsfeld

The rumor turns out to be true.i l 10 45‐ approximately 10.45pm

@nytimes: “NYT NEWS ALERT:@nytimes: NYT NEWS ALERT: Osama bin Laden Is Dead, White ,

House Says.”

@foxnews: “FoxNews’ Chad@foxnews: FoxNews Chad Pergram confirms Osama bin Laden g is dead usama osamabinladen”

@cnnbrk: “Osama bin Laden is d d bi l d ”dead usama osamabinladen”


Just before Obama makes his dd 11 30address at 11.30pm…


From 10.45pm – 2.20am on p1st and 2nd May 2011, there was an average of 3000 Tweets per second.

The highest sustained rate ofThe highest sustained rate of Tweets. Ever.

Everything is going “real time”.Everything is going real time .

Why?

Because the mobile has squashedBecause the mobile has squashed time and space.time and space.

This is changing everything…This is changing everything…

From the way we discoveryinformation, to the way we share

information, to the way we i f ti d tconsume information and most

importantly the way we connectimportantly, the way we connectwith others.

M NMeme. Noun.

An idea, behavior or style that , yspreads from person to person in a

culture.

Copyright 2011 All Rights Reserved

I’ “ i t” thi d ’t ff t ?I’m a “giant” this doesn’t effect me?

Get practical about it

But never forget the number one l f h i l brule of the social web…

It’s all about balance and common sense at the end of the day.

We want to authentic, transparent, ti ! W t t !conversations! We want to engage!

Technology is only an enablerBut the power is in the patternsp p

One tweet does not a pattern make. So do you t t it?trust it?

http://www.tweetreach.com

http://archivist.visitmix.com

http://www.whatdoestheinternetthink.net/

http://twendz.waggeneredstrom.com/

How do you visualize your information?

http://maps.linkfluence.net/vc/

Information is Beautiful

Discussion Session 2

Text Data Processing DefinedText Data Processing Definedd

Text

1.Extract meaning

Structured Database

ruct

ured

Once structured it can be… Integrated

g2.Transform into structured

data for analysis3 Cleanse and match

Uns

tr QueriedAnalyzedVi li d

3.Cleanse and match

VisualizedReported against

Unlocks Key Information from Text Sources to


Drive Business Insight

Automate Research AnalysisAutomate Research Analysis

Text data processing semantically understands the meaning and context of information, not just the words themselves. Applies linguistic and statistical

techniques to extract entities, concepts and sentiments Discerns facts and relationships that

were previously unprocessable Allows you to deal with information

overload by mining very large corpora of words and making sense of it without having to read every sentencehaving to read every sentence


SAP BusinessObjects Data Services Data integration, data quality, data profiling, and text data processing

ata Business UI

(InformationTechnical UI(Data Services)

SAP BusinessObjects Data Services 4.0ru

ctur

ed D

a (InformationSteward)

U ifi d M t d t

(Data Services)

Str

One Runtime Architecture &

Services

Unified Metadata

ETL

uctu

red Data Quality

Profiling

Uns

tru

Dat

a Text Analytics

One Administration Environment (S h d li S it U M t)

Provides access to all critical business data (regardless of data source, type,

(Scheduling, Security, User Management) One Set of Source/Target Connectors


( g , yp ,or domain) enabling greater business insights and operational effectiveness

Text Data Processing on the Data Services PlatformText Data Processing on the Data Services Platform

Native Text Data Processing on the Data Services platformg pwith the Entity Extraction transform to extract : Predefined entities (like company, person, firm, city, country, …) Sentiment Analysis (e.g. Strong positive, Weak positive,Sentiment Analysis (e.g. Strong positive, Weak positive,

Neutral, Weak Negative, Strong Negative) Custom entities (customized via dictionaries)

Languages supported (for version 4.0) English German French Spanish JapaneseJapa ese Simplified Chinese …

(expanding to 31 languages in next releases)(expanding to 31 languages in next releases)


Supported Entity Types for ExtractionSupported Entity Types for Extraction

Who: people, job title, and national identification numbers

Wh t i i ti fi i l

Where: addresses, cities, states, countries, facilities, internet addresses and phone numbersWhat: companies, organizations, financial

indexes, and productsWhen: dates, days, holidays, months,

addresses, and phone numbersHow much: currencies and units of

measureyears, times, and time periods Generic Concepts: “text data”, “global

piracy”, and so on

Current Languages supported with Data Services 4.0: English, French, German, Simplified Chinese Spanish Japanese (concepts only)Simplified Chinese, Spanish, Japanese (concepts only)

Some of the additional Languages coming: Arabic, Dutch, Farsi, Italian, Korean, Japanese (with concepts), Portuguese, Russian


Pre-defined Extraction of Sentiments, Events, and Relationships

Voice of Customer Public Sector:Voice of CustomerSentiments: strong positive, weak

positive, neutral, weak negative,

Public Sector: Such as person-organization, person-alias, travel events and security

strong negative, problemsRequests: customer requests Enterprise:

M d i iti llMergers and acquisitions, as well as executive job changes

L S t E li h F h L S t E li hLanguage Support: English, French, German, Spanish

Language Support: English, Simplified Chinese

These are starter packs that can be built upon for a specific deployment


Understanding SentimentUnderstanding Sentiment

“Sentiment analysis or opinion mining refers to the application of natural language processing,natural language processing, computational linguistics, and text analytics to identify and extract subjective information in sourcesubjective information in source materials.”

-- Wikipedia


Voice of the CustomerVoice of the Customer

Apply text data processing to enhance customer service and satisfaction by understandingsatisfaction by understanding customer opinions on blogs, forum postings, and social media.


Social Media is NoisySocial Media is Noisy

“The challenge lies in identifying statistically valid data related to specific b i i iti f th t i fbusiness priorities from the mountain of available content. You don’t want to overthrow a key marketing campaign b f bl it idbecause a few bloggers write snide things. ”

-- Leslie Owens, Text Analytics Takes Business Insight To New Depths

socialimplications.com


Your Best Customer May Be Your Worst EnemyYour Best Customer May Be Your Worst Enemy

When Unhappy Customers Strike Back on the Internet

Double Deviation – customers have been victims of not only a product or service failure, but also failedservice failure, but also failed resolutions Betrayal – primary driver of what causes

customers to complain onlinep

-- Thomas M. Tripp and YanyG é i MIT Sl M tGrégoire, MIT Sloan Management Review


Opinions Do MatterOpinions Do Matter

“78% of consumers trust peer recommendations.”

-- The Broad Reach of Social Technologies, Forrester ResearchForrester Research


DemoDemo

Web Intelligence reports in the BI Launch PadWeb Intelligence reports in the BI Launch Pad


Opened WebI reportOpened WebI report


Searching on “computer”Searching on computer


“Computer” in the Most Mentions Concepts reportComputer in the Most Mentions Concepts report


“Enjoy” stance in the Positive SentimentsEnjoy stance in the Positive Sentiments


“False” and “Issue” stances in the Negative SentimentsFalse and Issue stances in the Negative Sentiments


Drilling down to further understand the complete contextDrilling down to further understand the complete context


The data flow in the Data Services DesignerThe data flow in the Data Services Designer
