Analytics for dw professionals Dr Jay B Simha abiba 20110527

© Abiba Systems | www.abibasystems.com

Dr. Jay B. Simha

CTO, ABIBA Systems

TDWI India Chapter


• About ABIBA

• Analytics

• Technology

• Case study

• Lessons learned

• Open house


A year spent in artificial intelligence is enough to make one believe in God

Alan J. Perlis

Epigrams in programming

http://www.cs.yale.edu/quotes.html

Most of the managers today are trying to do things right. But the question is are they finding right thing to do?

Stephen Covey

Noted Management Guru

http://www.cs.yale.edu/quotes.html


• Technology firm focused on being a Business Intelligence & Analytics partner of choice for Telecom Operators Globally

• We develop advanced analytics tools customized to meet industry specific requirements.

Our solution highlights

• Advanced B.I and Analytics solution

• Rapid deployment for quick wins

• User friendly solutions to improve end user product utilization

• Lower TCO to ensure higher ROI

ABIBA Management

• Promoter team have several decades of experience in Telecom Companies globally .

• Highly Qualified Management Team with extensive domain knowledge and

technological know how.



Section Business Intelligence Business Analysis

Data• Relational• Large N

• Flat• Large C

Users• All• High

• Mostly tactical• Few

Complexity• Large data• multiple source

• Large no of variable• correlation

Utility Decision support• Decision support• decision management

Mode Process driven• Objective• Model driven


Analytics is the science of examining business data to identify historic trends and patterns, predict potential trends and to analyze the effects of certain decisions or events.

The goal of analytics is to improve the quality of decision making in business by gaining in-depth knowledge of past, current and future performance.

Type of Analytics :

Descriptive Analytics: Descriptive Analytics provides the insights into the distribution of the data

Predictive Analytics: Predictive Analytics tries to predict the value of one or more responses in a data set

Prescriptive Analytics: Prescriptive Analytics tries to optimize the process outcome


Optimization

Predictive Modeling

Segmentation

Reporting and Analysis



BI and

Segmentation

CLV Survival

analysis

Churn

prediction

Profiler

Recommendation

Response

model



StatisticsOp. Research

Decision treesNeural

networksCubes

Optimization

ForecastingClassification/

Response modelingSegmentation

Visualization What-if Slice/dice/drill/Roll

Business

Technology


Analytics disciplines

Database

Artificial intelligence

Business modeling

Statistics

Psychology PerformanceVisual

Hypothesis verification

Inference

Multi variant testing

?

A | BCD

OLAP IN-MemoryETL

Rule based

Decision tree

Fuzzy logic

Neural network

If - then


Function Data Reference Module

Analysis• Column store

• In memoryOLAP

Modelling• In database

• In - memoryNeural networks

Optimization• In memory

• Disk basedLinear programming

Storage• Relational

• Big table


Analytics

Cubes

Customer data mart

Data Integration

DashboardsAnalysisReports

Analysis

Churn Modeler

Data sources

Segmentation & Profiling

Customer LTV

Cross Sell

Up Sell



Target Definition

•Define/Select Campaign targets.

•Target based sampling list

•Analyze success of Campaign

Campaign Definition

Campaign Execution

Campaign Analysis

•Objective•Validity•Media•Message

•Send Messages•Default Assign

Campaign Management System


Customer Lifetime Value

Churn

Profiler

Target Modeler

Dat

a re

po

sito

ry

Security

Dat

a In

terc

han

ge

pro

toco

lD

ata

Inte

rch

ange

p

roto

col

EDW Reports

Dashboards

Analysis

Alarms

Historical

data

Historical

data

Predictive

data

Security

Analysis

cubes

Ap

plic

atio

n s

erv

ice

s

CMS data mart


Define targets

Sample targets

Run sample campaigns

Analyze the effectiveness

Select the best campaign for the whole set

Campaign Analytics

EDW



New Somewhat recent

1000

500

499

0

D(Profitability)

LongVery long

C1

Commit

Customer

C2

Improve

Loyalty

C3

Augment

Turnover

C4

Don’t

Invest

Recent customers

Old customers

high

low


New Somewhat recent

1000

500

499

0

D(Profitability)

LongVery long

Recent customers

Old customers

high

low







• Team competence

• Crossing the chasm

• Business User involvement

• Need to show ROI

• Scientific frameworks for business function

• Data, data, data

• Statistics helps

• Analytics is effective

• Data Integration is essential

Business

Technical
