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Analytical Processing of Data
Prasad Chitta
Agenda or TOC
• The Analytical
• Data
, where there is no data to analyse to
where we have too much of data to analyse

• “
” used till date for this
•
we have a single structure for OLTP
and Analytics?
• The ‘data’
of data
• Data
Analytical Processing of Data
Operational
Reporting /
MI

Analytics

OLAP / BI / ETL
Descriptive (Uni
or bivariate)

Analytics
Diagnostic or
Inquisitive
Content
(Unstructured)

Discovery

Predictive
Structured

Predictive
Statistical Techniques

Machine Learning
Data Scenarios…

• New product design
• Simulation
• Knowledge
representation

No Data

Structured
Data
• From normalized
OLTP systems
• Variables , mostly
numbers

• Unstructured
• Quickly varying
• Mostly non-numeric

BIG data
Data analysis technology “names”
• Adhoc Data
& Queries
• Management Information
, Business Intelligence
• Business Analytics
• Real-Time BI
• Artificial
Analytics
• In-memory
Why can’t we analyze OLTP data
•

•
•

•

OLTP schema
Optimized for handling
transactions i.e., updates
Short and quick transaction
are catered
Multiple concurrent users
read small amounts of data
Key based, index lookups
used to access data

•

•
•

•

OLAP schema
Optimized for large loads of
data in ETL mode
Large summarizations to be
performed
Less number of users read
huge amounts of data
Full scans through data are
often required
The Data lifecycle
Sensing

ETL,
Warehousing

OLAP reporting

Acquiring,
Validating

Operational
Reporting,
Dashboarding

Analytics

Storing

Transactional
Update

Archiving,
Purging
Aspects of ‘Data’
Integration,
Migration
Reference
Data

Master Data

Meta data

Data

Quality

Visualization
Business Value

Business Value - Analytics Matrix
What is the best that can happen?

What will happen?

Optimization
Linear/Non-linear
programming & Simulations

Predictive Modeling
Baseline Demand
Impact of Causal Factors

Descriptive Modeling

Why something happened?

Describe historical event

Insights/Limited What-if

A
n
a
l
y
t
i
c
s

Actionable insights

What happened?

R
T
B
I

OLAP Reporting
Drill-thru
Drill-Across

Standard Reporting
Sales, Inventory, Business
Performance

Data Management
Internal, Syndicated,

Decision Support

DSS
Decision Guidance  Advanced analytics
DSS – Decision Support Systems,
RTBI – Real Time Business Intelligence

9
http://www.linkedin.com/in/prasadchitta

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Analytical processing of data

  • 1. Analytical Processing of Data Prasad Chitta
  • 2. Agenda or TOC • The Analytical • Data , where there is no data to analyse to where we have too much of data to analyse • “ ” used till date for this • we have a single structure for OLTP and Analytics? • The ‘data’ of data • Data
  • 3. Analytical Processing of Data Operational Reporting / MI Analytics OLAP / BI / ETL Descriptive (Uni or bivariate) Analytics Diagnostic or Inquisitive Content (Unstructured) Discovery Predictive Structured Predictive Statistical Techniques Machine Learning
  • 4. Data Scenarios… • New product design • Simulation • Knowledge representation No Data Structured Data • From normalized OLTP systems • Variables , mostly numbers • Unstructured • Quickly varying • Mostly non-numeric BIG data
  • 5. Data analysis technology “names” • Adhoc Data & Queries • Management Information , Business Intelligence • Business Analytics • Real-Time BI • Artificial Analytics • In-memory
  • 6. Why can’t we analyze OLTP data • • • • OLTP schema Optimized for handling transactions i.e., updates Short and quick transaction are catered Multiple concurrent users read small amounts of data Key based, index lookups used to access data • • • • OLAP schema Optimized for large loads of data in ETL mode Large summarizations to be performed Less number of users read huge amounts of data Full scans through data are often required
  • 7. The Data lifecycle Sensing ETL, Warehousing OLAP reporting Acquiring, Validating Operational Reporting, Dashboarding Analytics Storing Transactional Update Archiving, Purging
  • 8. Aspects of ‘Data’ Integration, Migration Reference Data Master Data Meta data Data Quality Visualization
  • 9. Business Value Business Value - Analytics Matrix What is the best that can happen? What will happen? Optimization Linear/Non-linear programming & Simulations Predictive Modeling Baseline Demand Impact of Causal Factors Descriptive Modeling Why something happened? Describe historical event Insights/Limited What-if A n a l y t i c s Actionable insights What happened? R T B I OLAP Reporting Drill-thru Drill-Across Standard Reporting Sales, Inventory, Business Performance Data Management Internal, Syndicated, Decision Support DSS Decision Guidance  Advanced analytics DSS – Decision Support Systems, RTBI – Real Time Business Intelligence 9