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The Big Picture of Business Intelligence: Goals, Concepts, and the Platform

Rafal Lukawiecki Strategic Consultant, Project Botticelli Ltd rafal@projectbotticelli.com

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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.