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Transcript of Entire BI Solution
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