AFSUG Cafe BI - Durban 8 Nov 2011

World Café. Social Business Intelligence.
World Café Social Business Intelligence
#CafeBI (www.twitter.com/afsug)
Facilitated by Manti Grobler (SAP) and Charles de Jager (SAP)

Data Categories

Supports automated processing
–C f
Conforms with d t models associated with d t b
ith data d l i t d ith databases and
d
Structured
spreadsheets
– Granular data stored in fields
Generally does not support automated processing
– No data model or not easily understood
Unstructured – Insufficient metadata
– Noisy data communications such as an email message, blog or
document
High Volume of small data bits
– Huge volume
Event
– Only act on exceptions
– Captured at source

© 2011 SAP AG. All rights reserved. 2

Common Structured Data


Data Categories

–C f
d
Structured
spreadsheets
document
High Volume of small data bits
– Huge volume
Event


Common Unstructured Data

A press
release
communication


Common Unstructured Data

Forum
p
postings
g


Data Categories

–C f
d
Structured
spreadsheets
document
High Volume of small data
– Huge volume
Event


Common Event Data


What vs. Why and When
vs

It’s generally said that…

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


From the Business Perspective

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

-- Seth Grimes, Alta Plana


Text Analytics Boosts Business Results

“Organizations embracing text
analytics all report having an
epiphany moment when th
i h t h 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
relational tables. Th reason i th t
l ti l t bl The is that
data that is structured is easy to mine
and analyze.”

-- Prabhakar Raghavan, Yahoo
Research


Knowledge

Strategy
telligence
e
External
Information
Int

PP
formation
n

FI

Plan
HR

CO
SD
Inf

PM
MM

Operate / Generates Data


Business Intelligence Typically Runs Off Structured Data


Business Intelligence Reporting off Structured Data

How can you extend
your BI investments to
unstructured text data?
t t dt td t ?


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

Or do you innovate
with intelligence?

Workers Lose Productivity from Inadequate
Information Access

54%
Lose Productivity

Source: Economist, ‘Enterprise Knowledge Workers Study


The Goal: Be a Best Run Business

77%

“77% of high
performers have
above average
analytical
y 23%

capability”

Low High
Source: Competing on Analytics, Thomas Davenport Performers Performers

IT Is Looking for Flexibility in Sharing Relevant
Information

Organizations require:
• Trusted, consolidated, and
, ,
actionable information

• From a variety of data
y
sources

• Self-service access


RELEVANT INFORMATION
Mobile Large
Device Scale

Business
Suite

Microsoft
Self Office
Service

LESS RELIANCE ON IT
© SAP AG 2010. All rights reserved. / Page 21

Everything
The Social Media MasterClass 2011

@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 rages
Twitter explodes Debate rages
about whether Qaddafi had been
Q f
killed or Bin Laden tracked down.

2900 Tweets per second.
2900 Tweets per second

@keithurbahn: “So I’m told by a
reputable person they have killed
bl h h kill d
Osama Bin Laden. Hot damn.
Osama Bin Laden Hot damn ”
‐ 1 May 2011, 10.25pm

Keith Urbahn
Chief of staff for Donald Rumsfeld

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

@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
dead usama 
d d osamabinladen”
bi l d ”

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

From 10.45pm – 2.20am on
p
1st and 2 nd May 2011, there was an

average of 3000 Tweets per second.

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

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

Why?

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

This is changing
This is changing everything…

From the way we discover
y
information, to the way we share
information, to the way we
consume i f
information and most
ti d t
importantly, the way we connect
importantly the way we connect
with others.

Meme. Noun.
M N

An idea, behavior or style that
, y
spreads from person to person in a
culture.

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

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

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

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

Technology is only an enabler
But the power is in the patterns
p p

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

http://www.whatdoestheinternetthink.net/

http://twendz.waggeneredstrom.com/

How do you visualize your information?

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

Text Data Processing Defined

Structured
ructured Text

Database
1.Extract meaning g
d

2.Transform into structured Once structured it can be…
data for analysis Integrated
3.
3 Cleanse and match
Unstr

Queried
Analyzed
Visualized
Vi li d
Reported against

Unlocks Key Information from Text Sources to
Drive Business Insight


Automate 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 sentence


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

SAP BusinessObjects Data Services 4.0
ata
Business UI Technical UI
ructured Da

(Information (Data Services)
Steward)
Str

Unified M t d t
U ifi d Metadata

One Runtime
Architecture &
Services ETL

Data Quality
uctured

Profiling
Unstru

Text Analytics
Data

One Administration Environment
(Scheduling, S
(S h d li Security, U
it User M
Management)
t)
One Set of Source/Target Connectors

Provides access to all critical business data (regardless of data source, type,
( g , yp ,
or domain) enabling greater business insights and operational effectiveness


Text Data Processing on the Data Services Platform

Native Text Data Processing on the Data Services p
g platform
with the Entity Extraction transform to extract :
 Predefined entities (like company, person, firm, city, country, …)
 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
 Japa ese
Japanese
 Simplified Chinese
 …
(expanding to 31 languages in next releases)


Supported Entity Types for Extraction

Who: people, job title, and national Where: addresses, cities, states,
identification numbers countries, facilities, internet
What:
Wh t companies, organizations, fi
i i ti financial
i l addresses,
addresses and phone numbers
indexes, and products How much: currencies and units of
When: dates, days, holidays, months, measure
years, 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)
Chinese Spanish

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:
Sentiments: strong positive, weak Such as person-organization, person-
positive, neutral, weak negative, alias, travel events and security
strong negative, problems
Requests: customer requests Enterprise:
Mergers and acquisitions, as well as
M d i iti ll
executive job changes

Language Support: E li h F
L S t English, French,
h Language Support: E li h
L S t English,
German, Spanish Simplified Chinese

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


Understanding Sentiment

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

-- Wikipedia


Voice of the Customer

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


Social Media is Noisy

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

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

socialimplications.com


Your 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 failed
resolutions
 Betrayal – primary driver of what causes
customers to complain online
p

-- Thomas M. Tripp and Yany
Grégoire,
G é i MIT Sloan Management
Sl M t
Review


Opinions Do Matter

“78% of consumers trust peer
recommendations.”

-- The Broad Reach of Social Technologies,
Forrester Research


Web Intelligence reports in the BI Launch Pad


Opened WebI report


Searching on “computer”
computer


“Computer” in the Most Mentions Concepts report
Computer


“Enjoy” stance in the Positive Sentiments
Enjoy


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


Drilling down to further understand the complete context


The data flow in the Data Services Designer


AFSUG Cafe BI - Durban 8 Nov 2011

Recomendados

Recomendados

Más contenido relacionado

Destacado

Destacado (18)

Similar a AFSUG Cafe BI - Durban 8 Nov 2011

Similar a AFSUG Cafe BI - Durban 8 Nov 2011 (20)

Último

Último (20)

AFSUG Cafe BI - Durban 8 Nov 2011