Entire BI Solution

Ferhat İşyapan

SCOPE

Why You Should Consider a BI Solution?

How We Do This?

What We Will Do With Your Data?

PART I

Why You Should Consider A BI Solution?

Do You Face Any of These Problems ?

“We have mountains of data in this company, but we can’t access it.”

“We need to slice and dice the data every which way.”

“We must make it easy for business people to get at the data directly.”

“Just show me what is important.”

“It drives me crazy to have two people present the same business metrics

at a meeting, but with different numbers.”

“We want people to use information to support more fact-based decision

making.”

Main Aspects of a Business Intelligence Solution

One Reliable Data Source – Single Point of Truth

Quick and Easy Data Access

Be Able to Reach Historical Data

Minimizing IT Dependency for End Users

For Business People; They Will Be Able to Make Their Own Reports

To Get Rid of Reporting Burden on Your Production Systems

Efficient and Data Driven Decision Support System

Gain Money, Time and Effort in The Long Term

PART II

How We Do This?

What is Business Intelligence – Actually?

BI is neither a product nor a software.

It is an architecture and a collection of integrated operational as

well as decision-support applications and databases that provide the

business community easy access to business data

The Business Intelligence Process

Aggregate Data

Database, Data Mart,

Data Warehouse, ETL Tools,

Integration Tools

Present Data

EnrichData

Inform a Decision

Reporting Tools, Dashboards,

Static Reports, Mobile Reporting,

OLAP Cubes

Add Context to Create Information,

Descriptive Statistics,

Benchmarks

Data-driven,Fact-based decisions

Technologies That We Use Data Warehouse

An OLAP data base modeled by dimensional modeling to store our data.

Staging AreaIt’s our kitchen to prepare Data Marts which we use to generate the DWH.

Operational Data StoreThe data base which stores our near-real time data for online reports

Column Store TechnologyAs a part of our DWH, we will store our fact based tables in column oriented format to access huge amounts of data quickly

ETLExtract-Transform-Load processes to integrate data from different sources to our DWH

M-OLAP CubesMultidimensional OLAP cubes for quick access the data by end-users and taking advantage of analytical functionality.

BI Reporting SystemThe reports which answer the questions that is asked by business people often. It includes semantic layers, prepared reports, dashboards, balanced score-cards and self service BI.

Self-Service BI Prepared data from OLAP cubes and denormalized summary tables that end-users can make their own reports and analysis.

Data Science Data Mining & Machine Learning Models to make decisions and explorations by using the data from the DWH and Big-Data.

Journey Of Your Data

OLTP

CRM

OLTP

Staging Area

Operational Data Store

Data Warehouse

TabularData Mart

TabularData Mart

OLAP

OLAPReal Time Service Tracker

Log DB

Operational Reporting

Daily ETL

Daily ETL

Near Realtime

ETL

Source Layer Staging &ODS Layer DWH Layer Presentation Layer

PART III

What We Will Do With Your Data?

What We Will Do With Your Data?

After Getting Data From Market to Your Plate...

Reports

Data Mining

Knowledge Management

Advanced Decision Making Systems

Reports

Analytical Reporting

About Big PicturePeriodicFor Decision MakersBI Reports, Dashboards, Balanced Scorecards

EX: Monthly Ticket Sales Revenue Trend

Operational Reports

Detailed Reports for Operators

EX: Daily Top 20 Complaint Transactions

Ad-Hoc Reports

Anything You Want One-Time Urgently

Data Mining

Discovery of Hidden Informations

Finding Business Opportunities

Finding The Truth And What Is Really Important

Examples:

Analytical CRM Segmentation Models Descriptive Statistics Prediction Models

The figure demonstrates the groups of passengers that we classified by using K-Means Clustering before.

Group 2 : Goes Same Places With Their Family. The One Place TravellersGroup 1 : Goes Different Places With Different People. The Explorers.

Knowledge Management

Make Data Can Be Shared

Ensure Everyone Speak The Same Language

Which Information, Where And How ?

Advanced Systems

Machine Learning - Learning from data

Big Data – (Social Media, Machine logs...)

Data Science

Examples:

Sales Predicitons with Linear Regression Recommendation Engines Route Optimization Rules Future Profitability Forecasts

Thank You