turning your warehouse data into business intelligence ...– analysis: it should be possible to...

Turning your Warehouse Data into Business Intelligence:

Reporting Trends and VisibilityMichael Armanious; Vice President Sales and Marketing

Datex, Inc.

Overview

• Introduction• What is Business Intelligence?• Why use it?• What are the benefits?• How does it work?• How to turn 3PL warehouse data into decision

making information• How do we get there?

Introduction

• You want to:– Understand your business– Identify and exploit areas of strength– Address areas that are not performing well– Identify areas of opportunity– Understand the consequences of decisions– Reward staff who perform well– Educate staff performing less well– Identify areas of business growth

Introduction

• Today users have:– A suite of standard reports– Excel, Crystal Reports, SQL Reporting Services

• But you may need to:– Analyze, cross check and interrogate a number of

reports to obtain the data you need.

5

Common Pain Points

Data everywhere, information no whereDifferent users have different needs

Excel versus PDFPull versus pushOn demand – on scheduleYour format – my format

Takes too long – wasted resources/effortsSecurityTechnical “mumbo jumbo”

6

Why Business Intelligence?

• What happened?• What is happening?• Why did it happen?• What will happen?• What do I want to happen?

ERP CRM TMSSCM WMS

Past

Present

Future

Data

Business Intelligence Vision

• Improving business insight to all employees:– Advanced analytics– Self service reporting– End-user analysis– Business performance

management

– Operational applications

OLAP (Online Analytical Processing) • OLAP tools support interactive analysis and

exploration of large and complex dimensional data sets

• Much of the power of OLAP comes from the use of a standard data model (cubes) and offline processing, aggregation, and analysis of data

• To use OLAP tools effectively, you need to have a basic understanding of how and why data is structured in cubes and the kinds of analyses that this structure makes readily available to you

What Are OLAP Tools?• OLAP tools provide a mechanism for interactive analysis

and exploration of dimensional data

– Interactive: users need to be able to easily specify queries

– Analysis: it should be possible to perform (and reuse) complex analyses of the dimensional data

– Exploration: answering one question with an OLAP tool frequently raises numerous subsequent questions

• A good OLAP tool allows the user to quickly pose follow-on queries

– Dimensional: OLAP tools operate on dimensional data – data structured as facts and dimensions

OLAP’s Role In Decision Making

OLAP excels at exploring complex, structured questions

OLAP Sweet-Spot

Why Not Just Write SQL Queries?

• Performance• Complexity• Exploration• Presentation• Difficulty in dealing with hierarchies• Difficult or impossible to specify some desired

queries

Traditional Analysis Structure

Why Not Just Use Spreadsheets?

• Complexity (with > 2 dimensions)• Presentation is tied to representation• Does not scale to large data sets or many

dimensions– Storage and representation is ill-suited to the task

• Inability to deal with hierarchies

Traditional Analysis Structure

OLAP’s Place In A BI Solution

Reconcile Data

Derive

Data

OLAPCube

OLAPTools

What are cubes?

• An analytical tool not a reporting tool• Adopt Business Intelligence (BI)

– To analyze data– Understand the health of the company– Collaboration – Single point to share knowledge– Reduce decision time– Impact the bottom line by measuring operations– Enhance competitive advantage– Provide key performance indicators (KPI)

3PL Sample Data Cube

Date

War

ehou

se

sum

sumAccesorials

HandlingStorage

1Qtr 2Qtr 3Qtr 4QtrWH1

WH2

WH3

sum

Cube of Measures and Dimensions

Diagram Source: Hoffer, Prescott, McFadden, Modern Database Management, 7th ed.

Service

Storage Storage

Month

Measure

Slicing• The slicing operation selects specific values for one or more

dimensions of a cube and renders measures for those dimensions in a two-dimensional table

Filtering• Filtering reduces the elements included in a calculation• Filtering can cross multiple slices• Example: filter previous results to only show February, April, May


What are the Benefits

• Provide high performance queries– Get information quickly– Supports interactive analysis over large volumes of

data

– Can interrogate multiple data sources –multiple branches

– Enables drill down on your information– No report writing skills required– Does not impact the performance of your system

Dimensional Databases as Cubes• OLAP tools represent dimensional data as cubes

– Cubes are also sometimes referred to as hypercubes

• Dimension tables are represented as cube dimensions

• Facts are represented using measures– Measures can be thought of as the values stored in

individual cells of the cube– Measures consist of two parts:

• A numerical value that represents the basic fact• A formula for combining multiple measures into a single measure

Standard vs Analytical Reporting

• What’s the difference between standard versus analytical reporting?– Standard reporting

• On line transactional processing• Multiple reports needed to ensure you get a complete

picture of the business• Once the report has finished processing the criteria is

set, you would need to re-run the report to view other criteria

• Data is structured for information creation and editing NOT reporting cause performance issues

Analytical Reporting

• Analytical Reporting (OLAP)– On line analytical processing– Data is snapshot at pre-determined times of the

day. Therefore, no impact on database after initial load

– Data is structured for ad-hoc reporting

How does it work?

Dimensional Databases as Cubes• OLAP tools represent dimensional data as cubes

– Cubes are also sometimes referred to as hypercubes

• Dimension tables are represented as cube dimensions

• Facts are represented using measures– Measures can be thought of as the values stored in

individual cells of the cube– Measures consist of two parts:

• A numerical value that represents the basic fact• A formula for combining multiple measures into a single

measure

Dimensional Modeling Example

Dimension tables provides details on stores, products, and time periods


Quick Review: Dimensional Example With Data

Product (dimension) Period (dimension)

Store (dimension)

Sales(fact)


Multiple Fact Tables

• It is frequently useful to store more than one type of fact in a single multidimensional database (star schema)

• This can be handled by using multiple fact tables that share dimensions

• Example: modeling products sold and products purchased


Factless Fact Tables – Tracking Events

• “Factless” fact tables store only foreign keys, no facts• Factless fact tables allow the tracking of what types of events

happened, and under what circumstances they happened


Conformed Dimensions• When dimensions are shared across multiple fact tables

they must be conformed dimensions

• Conformed dimensions– One or more dimension tables associated with two or more

fact tables for which the dimension tables have the same business meaning and primary key with each fact table

• Conformed dimensions allow users to:– Query across multiple fact tables– Improve consistency of meaning and structure for derived and

retrieved information

Tabular Representation of Measures and Dimensions

• Simple example of viewing OLAP data in a grid:– Row headings (Warehouse) represent dimension members– Columns represent different measures

Warehouse Services Sales Data for 2006

Warehouse Gross Sales Quota Profits Sales vs. Quota

Chicago $3,250,000 $2,750,000 $624,352 + $500,000

New York $4,500,000 $3,550,000 $100,000 + $950,000

Pittsburgh $1,600,000 $1,700,000 $250,000 - $100,000

Measures

Dimension

Tabular Representation of Measures and Dimensions

• Example 2: Warehouse sales by year and warehouse location– Column and row headings represent dimension values in this case– Cells represent measures, Name of table describes measure

Warehouse Services Sales Data 2004-2010

Store 2004 2005 2006 2007

Chicago $3,250,000 $3,500,000 $3,000,000 $3,900,000

New York $4,500,000 $4,350,000 $5,100,000 $5,450,000

Pittsburgh $1,600,000 $1,700,000 $1,800,000 $1,650,000

Dimensions

Measures

The End Result

Cold Storage

3PL

Report customers

Business users

Report Viewer

Report Builder

Power usersDevelopers Report Designer

Why Report Builder?

What is Report Builder?

• A new ad-hoc report design tool for Microsoft SQL Server Reporting Services

• Targeted at business users who want to find and share answers to interesting questions

• Driven from a business model of the data, so users do not need to understand the underlying data structures

• Not a full analytical client or replacement for Microsoft®Office Excel® Pivot Tables

• Fully integrated with Reporting Services and delivered in SQL Server 2010

Microsoft®SQL Server™

Management Studio

Report Builder Architecture

SQL Server catalog

Web services interface

Report Server

Report BuilderClient

ModelDesigner

Data sources(SQL Server,

Analysis Services)Drillthrough report generation

Query generation

ReportManager

ReportDesigner

Report Builder versus Report Designer

Report Builder Report DesignerTargeted at business users Targeted at IT professionals and

developers

Ad hoc reports Production reports

Auto-generates queries using semantic layer on top of the source

Native queries (SQL, OLE DB, XML/A, Open Database Connectivity (ODBC), Oracle)

Reports built on templates Free-form (nested, banded) reports

ClickOnce application, easy to deploy and manage

Integrated into Microsoft® Visual Studio® development system

Cannot import Report Designer reports Can work with reports built in Report Builder

Summary• OLAP tools support interactive analysis and

exploration of large and complex dimensional data sets

• Much of the power of OLAP comes from the use of a standard data model (cubes) and offline processing, aggregation, and analysis of data

• To use OLAP tools effectively, you need to have a basic understanding of how and why data is structured in cubes and the kinds of analyses that this structure makes readily available to you

Summary

• Business Intelligence– “It is the application of knowledge derived from

analyzing an organizations data to effect a more positive outcome” –Ralph Kimball (Ph.D.)

– Collaborate on a shared view of the business– Reduce the time to decision– Provides flexibility in the ways you access data– Low time to impact; low latency query results– No reporting writing skills

Turning your Warehouse Data into Business Intelligence: �Reporting Trends and VisibilityOverviewIntroductionIntroduction Common Pain PointsWhy Business Intelligence?Business Intelligence VisionOLAP (Online Analytical Processing) What Are OLAP Tools?OLAP’s Role In Decision MakingWhy Not Just Write SQL Queries?Traditional Analysis StructureWhy Not Just Use Spreadsheets?Traditional Analysis StructureOLAP’s Place In A BI SolutionWhat are cubes?3PL Sample Data CubeCube of Measures and DimensionsSlicingFilteringWhat are the BenefitsDimensional Databases as CubesStandard vs Analytical ReportingAnalytical ReportingHow does it work?Dimensional Databases as CubesDimensional Modeling ExampleQuick Review: Dimensional Example With DataMultiple Fact TablesFactless Fact Tables – Tracking EventsConformed DimensionsTabular Representation of Measures and DimensionsTabular Representation of Measures and DimensionsThe End ResultWhy Report Builder?What is Report Builder?Report Builder ArchitectureReport Builder versus Report DesignerSummarySummary

turning your warehouse data into business intelligence ...– analysis: it should be possible to...

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