AFSUG Cafe BI - Charles de Jager

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. 1

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


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 and event
t t d d t
information?


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


http://archivist.visitmix.com/

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

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

How do you visualize your information?

http://www.whatdoestheinternetthink.net/

So what can you do for me?

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


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 - Charles de Jager

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