the big picture of business intelligence: goals, concepts, and...
TRANSCRIPT
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The Big Picture of Business Intelligence: Goals, Concepts, and the Platform
Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd [email protected]
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Objectives
Overview state of Business Intelligence in 2010
Discuss the technology platform
Introduce fundamental BI concepts
The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions © 2010 Project Botticelli Ltd & entire material © 2010 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE.
This seminar is based on a number of sources including a few dozen of Microsoft-owned presentations, used with permission. Thank you to Chris Dial, Tara Seppa, Aydin Gencler, Ivan Kosyakov, Bryan Bredehoeft, Marin Bezic, and Donald Farmer with his entire team for all the support.
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Overview of BI and PM
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Business Intelligence BI - Improving Business Insight
“A broad category of applications and technologies for gathering, storing, analyzing, sharing and providing access to data to help enterprise users make better business decisions.” – Gartner
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1. BI and Power of Visualisation 2. Balanced Scorecards
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Business Intelligence Today Low end-user adoption rates and high reliance on IT
Analyst Issues: Hard to access organizational data
Reliant on IT for reporting
Difficult to share insight
IT Pro Issues: No time for ad-hoc BI requests
Lack of control
Organizational BI often expensive
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From Organizational BI to Personal BI Enabling managed self-service BI
IT
Unm
anaged
IT
Managed
IT In
vo
lve
me
nt
Self Service
Easy to use
On and Offline
Collaborative
Empowered, Managed, Accurate
Accurate
Secure
Scalable
Up to date
Rogue “Spreadmarts”
Data Sources
Data Marts
BI and LOB Apps
Portals and Dashboards
Corporate BI
User Context
Empowered Reliant on IT
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Microsoft BI Strategy Democratizing Business Intelligence
Familiar environment
Integrated into Microsoft Office
Built on SQL Server
Improving organizations by providing business insights to all employees leading to better, faster, more relevant decisions
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FY1 FY2 FY3 FY4 FY5
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FY1 FY2 FY3 FY4 FY5
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FY1 FY2 FY3 FY4 FY5
Classic Business Intelligence
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FY1 FY2 FY3 FY4 FY5
Self-Service Business Intelligence
Classic Business Intelligence
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Microsoft Business Intelligence
Shar
ed
Pers
on
al
Sco
pe
Organic Intentional Development
Self-Service
Performance Management
Easy discovery of data
Simple, intuitive tools
Ad-hoc
Creative and agile
Consistent corporate definitions, KPIs
Corporate policies and processes
Contextual and accountable
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Three Contexts of BI Use
Personal BI Built by me, for me, used only by me
Team BI Built by someone on the team, shared inside a team
Organizational BI Built and maintained by IT, for use across company
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2
3
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Technology Platform
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Business User Experience
Microsoft Business Intelligence You may already have these products
Data Infrastructure and BI Platform Analysis Services
Reporting Services
Integration Services
Master Data Services
Data Mining
Data Warehousing
Integrated Content and Collaboration Thin client experience
Dashboards & Scorecards
Search
Content Management
Compositions
Familiar User Experience Self-Service access & insight
Data exploration & analysis
Predictive analysis
Data visualization
Contextual visualization Business Collaboration Platform
Data Infrastructure & BI Platform
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Big Picture: Managing Information
Data
Warehouse
Analysis Services
Master Data Services
ERP
CRM
HRMS
BI Developer or Analyst
Integration Services
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Big Picture: Dashboards, Scorecards
Knowledge Worker Cubes, Warehouse
Analysis Services
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Big Picture: Reporting
End User Cubes, Warehouse
Analysis Services Reporting Services
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PowerPivot for Excel PowerPivot for SharePoint
Analysis Services
Big Picture: BI Analyst, Power User
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Fundamental Concepts
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Enterprise Data
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BI & PM in an Enterprise
Data Sources
Master Data
Services
Cleansing
Data Marts
Data Warehouse
Client
Access
Client
Access
1: Clients need access to data 2: Clients may access data sources directly 3: Data sources can be mirrored/replicated to reduce contention 4: The data warehouse manages data for analyzing and reporting 5: Data warehouse is periodically populated from data sources 6: Master Data Services may simplify the data warehouse population 7: Manual cleansing may be required to cleanse dirty data 8: Clients use various tools to query the data warehouse 9: Delivering BI enables a process of continuous business improvement
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Silo Integration Challenge
Data Warehouse
Call Center
Web Apps
Inventory
ERP HR
Finance
CRM
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Source Systems
Process real-time transactions
Optimized for data modifications Normalized
Limited decision support
Commonly called: Online transaction processing (OLTP) systems
Operational systems
HR Finance Inventory
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Data Warehouse
Provides data for business analysis Grouped in subject-specific stores called Data Marts
Optimized for rapid ad-hoc information retrieval
Integrates heterogeneous source systems
Consistent historical data store
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ETL: Extract, Transform, and Load
1. Extract data from the source systems
2. Transform data into desired form
3. Load data into the warehouse
ETL
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Dimensions and Facts Basis of All BI
Fact – something that happened Sale, purchase, shipping...
Transaction or an event
Verb
Essentially a Measure
Dimension – describes a fact Customer, product, account...
Object
Noun
A fact (measure) is expressed in terms of dimensions 42 footballs sold to John on 20100115.
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Dimensions
Describe business entities
Contain attributes that provide context to numerical data
Present data organised into hierarchies
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Predictive Analysis
Presentation Exploration Discovery
Passive
Interactive
Proactive
Role of Software
Business Insight
Canned reporting
Ad-hoc reporting
OLAP
Data mining
Predictive Analysis
Self-service Analysis
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OLAP or Multidimensional Data
Online Analytical Processing = Multidimensional Data
Measures and Dimensions
Uses a calculation engine for fast, flexible transformation of base data (such as aggregates)
Supports discovery of business trends and statistics not directly visible in data warehouse queries
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Cube (UDM) Unified Dimensional Model
Combination of measures (from facts) and dimensions as one conceptual model
Rich data model enhanced by Calculations
Key Performance Indicators (KPIs)
Actions
Perspectives
Translations
Partitions
Formally, cube is called a UDM
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2009
Q1
Jan
Feb
Mar
Accessories Parts
Cars
Measures
Dates
Products
Ритейл
Cube
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Dicing a Cube
1
3
2
6
25
Ритейл
2009
Q1
Jan
Feb
Mar
Accessories Parts
Cars
Measures
Dates
Products
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Ad-hoc Self-Service Analysis
Interactive, Excel-style column-oriented and multidimensional analysis of extremely large volumes of data
Pivots, advanced filtering (slicers), and tabular expressions
+
OLAP-style analytics
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Data Mining
Discovery of (very) hidden patterns in mountains of data
Correlation search engine
Combination of statistics, probability analysis, database technologies, machine learning, and AI
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Key Performance Indicator (KPI)
Quantifiable measurement comparing performance to goals
Measure of organizational health when grouped into a business scorecard
Ideally, with a balanced perspective onto groups of KPIs
Built with:
Using OLAP (enterprise-level KPIs)
In SharePoint Server PerformancePoint Services (often team KPIs)
Using data mining (predictive KPI)
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KPI Characteristics
Value
Goal
Status
Trend
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Dashboards and Scorecards
Scorecard Table (pivot-like) of KPIs
Dashboard Contains scorecards, analytical reports, and other analytical visualisations
Create them: DIY: PowerPivot
Quickly: SharePoint 2010 PerformancePoint Services
Bespoke: custom SharePoint, Silverlight, and .NET development
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Organizational Visibility Track key metrics
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Conclusions
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Microsoft BI and PM Solutions At: www.microsoft.com/casestudies
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Summary
Business Intelligence is a top IT priority for businesses
Self-service analytics are quickly becoming crucial tools enhancing employees’ performance
Good data warehouse design, master data management, data integration, and multidimensional design enable rich BI use
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© 2010 Microsoft Corporation & Project Botticelli Ltd. All rights reserved. The information herein is for informational purposes only and represents the opinions and views of Project Botticelli and/or Rafal Lukawiecki. The material presented is not certain and may vary based on several factors. Microsoft makes no warranties, express, implied or statutory, as to the information in this presentation. Portions © 2010 Project Botticelli Ltd & entire material © 2010 Microsoft Corp. Some slides contain quotations from copyrighted materials by other authors, as individually attributed or as already covered by Microsoft Copyright ownerships. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The information herein is for informational purposes only and represents the current view of Project Botticelli Ltd as of the date of this presentation. Because Project Botticelli & Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft and Project Botticelli cannot guarantee the accuracy of any information provided after the date of this presentation. Project Botticelli makes no warranties, express, implied or statutory, as to the information in this presentation. E&OE.