a presentation on business intelligence june 10 th 2003 by paul balacky & richard fayers

A Presentation on Business Intelligence June 10th 2003

by

Paul Balacky & Richard Fayers

Topics

Introductions

Characteristics of a Business Intelligence Application

Demonstration

Design Issues

Introductions – Thorogood Associates Ltd

Established 1987 as Independent Business Intelligence Specialists

45 people

We are located in London, High Wycombe, Manchester and Princeton USA

Microsoft Gold Certified Partner for Business Intelligence

15 years experience in the application of Business Intelligence/OLAP technologies

We partner with key players in the market

www.thorogood.com

Characteristics of BI

Business Intelligence

The term Business Intelligence (BI) is relatively new but the it is synonymous with a range of applications that have been around for years;– Decision support systems

– Executive Information Systems

– On-line Analytical Processing (E.F Codd early 90’s) or multi-dimensional modelling

It is the conversion of data into information in such a way that the business is able to analyse the information to gain insight and take action

BusinessIntelligence

AN

AL

YS

IS

INSIGHT

AC

TIO

N

MEASUREMENT

The BI Cycle

Source: Business Intelligence, Elizabeth Vitt

BI Questions

What happened?– What were our total sales this month?

What’s happening?– Are our sales going up or down, trend analysis

Why?– Why have sales gone down?

What will happen?– Forecasting & What If Analysis

What do I want to happen?– Planning & Targets

Source: Bill Baker, Microsoft

Where is Business Intelligence applied?

ERP Reporting

KPI Tracking

Product Profitability

Risk Management

Balanced Scorecard

Activity Based Costing

Global Sourcing

Logistics

Sales Analysis

Sales Forecasting

Segmentation

Cross-selling

CRM Analytics

Campaign Planning

Customer Profitability

Operational Efficiency Customer Interaction

OLTP v OLAP

OLTP systems model processes

OLAP focuses on output and user reporting and analysis requirements– Data warehouses support business decisions by

collecting, consolidating, and organizing data for reporting and analysis with tools such as online analytical processing (OLAP) and data mining.

(Microsoft)

OLAP still requires a very formal approach

Business Intelligence Software

Integration of– OLAP multi-dimensional technology

– Relational database technology

– Web technology

Scalability for warehousing

Flexibility, performance and business views

Web deployment

Major BI\OLAP Vendors

Oracle 9i OLAP

SAP BW

Microsoft SQL Server 2000 & Analysis Services

Hyperion Essbase\IBM

Microstrategy

Cognos

Business Objects

State of BI at the present time

Robust, scaleable, web deployable BI technologies are available

Problems are likely to lie in data complexity, process and people

Successful implementation demands very close working between the business and the system providers

Choosing products is as hard as ever– There’s no such thing as a green field site (OLAP, Query &

Reporting, RDBMS, ETL, Data Mining)

– ERP vendors are offering BI

The BI market has been turned upside down in the last 4 years

Microsoft has entered the market with dramatic impact

Oracle has lost momentum

The products best able to work with Microsoft’s platform were unknown 4 years ago

BI in Action

How Many Matches?

How Many Matches Now?

Concept of a Cube or Pivot Table

Date

Product

Region

Product – Chocolate

Date – May 2003

Region – South East

Measure – Sales

How much Chocolate did we sell in the South East in May 2003?

SQL Server 2000

Analysis Services

Relational Database

DTS

MDX

InformixText Excel OracleAccess Sybase SQL Server

Front-End Tools

Client Server

Web

SQL

Client Server

Web

Design Considerations

Things to get right at design stage

Scope of project– Better to phase project than big bang

Business unit buy-in

– Ownership within the BU and clear goals

User Focus– Management of user expectations becomes very important

Things to get right at design stage

Source data– Do we have access?

– Often data in disparate sources and not always accessible

– Is it at the same level– Budget data may be formulated at a higher summary level than

actual data is sourced at

– Process– How and when does the data get into the Warehouse?

– What level of data cleansing & transformation will be required

– Who is responsible?

Things to get right at design stage

Source data– Are we able to match outputs to inputs

– Merging and matching of data sources – Requirement for company wide data standards and definitions

– Are there common keys?

– Hierarchy movements over time

– the need to restate or retain historic view?

– Timeliness of data

– Data volumes

– Handling of missing values and relationships

Things to get right at design stage

Can you deliver the user/business requirements with the tools/skills available– Some things that look easy are sometimes not

– Dimension changes

– Things that do not seem important to the developer are important to the business user

– Format

– Performance– Some things will be slow because they are slow

– Manage expectations

– Product limitations

Things to get right at design stage

Reporting vs Analysis– They may seem the same but they are not

– Different tools

– Different approach

– Different audience

BI Design Parameters

Cubes– Number of cubes – possibly defined by business functions

or security

– Number of dimensions per cube, shared or private

– Partitions relating to data volumes and update speeds (cube processing times)

– Virtual cubes – cross functional analysis

– Data storage options

Dimensions– Types of hierarchies - multiple, ragged, parent\child,

balanced\unbalanced

– Size, number of members

– Member properties and how these could be used (attributes)

– Number of levels, children within each level

– Hierarchy changes over time

– Reporting views, scenarios

BI Design Parameters

Time Dimension– Alternative time hierarchies – calendar, financial

– 13 period year – weeks to period

– Number of levels

BI Design Parameters

Timeliness of Data– Real-time

– Next day

– Weekly reviews (possible weekend to process)

– Monthly reviews (month end processing)

BI Design Parameters

Measures– Methods of aggregation

– Data entering cubes at differing levels required for comparisons

– Custom rollups

– Non additive data

– Precision, format

BI Design Parameters

Calculated Measures– Time series calculations– SQL vs OLAP calculations (pre cube build vs post cube

build)– Calculated cells– Nature of equations required to derive the calculated

measures– Currency exchange rates– Distributed processing opportunities (server calcs vs client

side calcs)– Application of MDX

BI Design Parameters

Write-Back requirements– Allocations\break back requirements, level of data entry

– Audit log

– Validation

BI Design Parameters

Output requirements – User report definitions – format, layout, precision– Types of adhoc analysis– Actions– Requirements for printed output – Quantitative vs Qualitative data output– Browser\Office delivery– OLAP database drill-through to SQL Server– Number of users– Report maintainability– Security

BI Design Parameters

Security– Cube

– Dimension

– Cell level

BI Design Parameters

To consider when building BI applications..

Users can fail to realise how much info they requested – leads to poor perceived performance

Complexity due to a large number of dimensions – users don’t understand the model/numbers

Hard to test because they are conceptually complex

Performance vs storage – consider MOLAP/HOLAP/ROLAP, on-the fly versus pre-aggregated data

There is a strong case for a BI strategy

BI can drive significant value

It is an agile technology– crosses functional boundaries

– crosses organisational boundaries

– Implementation can involve many stakeholders

Tactical BI applications may deliver significant value (and prove BI’s worth) …

In a post boom business climate, BI offers a pragmatic way of delivering high return in the short term without major upheaval