Post on 16-Jan-2016
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DO YOU NEED A DATA WAREHOUSE?
WHY PROPERLY STAGED DATA IS CRITICAL TO BI SYSTEM SUCCESS
• Introduction
• The Quick Answer
• Why Business Intelligence (BI)
• Challenges & Basic Requirements of BI Systems
• Reporting Direct from Source Systems
• Technical Solution Alternatives
• Data Warehouse Benefits
• How to Build a Data Warehouse (20,000 foot view)
• Additional Resources & Upcoming Events
• Q & A
TODAY’S AGENDA
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GOTOWEBINAR CONTROL PANEL
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PRESENTATION SLIDE DECK ON WWW.SENTURUS.COM
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CRITICAL SUCCESS FACTORS IN BI
• Architectures & Data Transformation
• BI Tools
• Methodologies & Techniques
• People & Processes
Chapters in the BI Demystified Series
CRITICAL SUCCESS FACTORS IN BI
• Architectures & Data Transformation
• Data Marts & Data Warehouses
• BI Tools
• Methodologies & Techniques
• People & Processes
Chapters in the BI Demystified Series
John
Peterson CEO & Co-Founder
Senturus
TODAY’S PRESENTER
7
WHO WE ARE
SENTURUS INTRODUCTION
Our Team:
Business depth combined with technical expertise. Former CFOs, CIOs, Controllers, Directors, BI Managers & Enterprise BI/DW Architects
SENTURUS: BUSINESS ANALYTICS ARCHITECTS
9Copyright 2014 Senturus, Inc. All Rights Reserved
Business Intelligence Enterprise Planning Predictive Analytics
Creating Clarity from Chaos
750+ CLIENTS, 1600+ PROJECTS, 14+ YEARS
10Copyright 2014 Senturus, Inc. All Rights Reserved
• Former Head of BI/ Lead Architect – VISA
• Former BI Architect – Jamba Juice
• Former Head of BI – Dole
• Former Chief BI Architect – Cisco
• Former BI Architect – Daimler AG
• Former Lead of IT Architecture – Paramount Pictures
• Former Head of BI – Experian
• Former Head of BI – Robert Half International
• Former Head of Training (IBM Cognos, Southern California)
• Former Controller – The GAP
• Two former CFO’s
• Several former Vice Presidents of Marketing
• Several former COO’s
• Several Former CIO’s
• Former Partner - PWC ($50million+ projects)
• Average experience = over 20 years
A Few of Our Team Members (former roles)
Deep & Pragmatic Experience
Copyright 2014 Senturus, Inc. All Rights Reserved.11
WHAT DO YOU USE FOR BI DATA
“STORAGE” TODAY?
QUICK POLL
DO YOU INTEND TO DEPLOY AN ENTERPRISE
DATA WAREHOUSE AT SOME POINT?
QUICK POLL
DO YOU REALLY NEED A DATA WAREHOUSE?
QUICK ANSWER
The short is answer is:
Almost always, YES
DO YOU REALLY NEED A DATA WAREHOUSE*?
15Copyright 2014 Senturus, Inc. All Rights Reserved.
* or Conforming Data Marts
The rest of this presentation will focus
on why…
WHY?
16Copyright 2014 Senturus, Inc. All Rights Reserved.
DATA-DRIVEN INSIGHT LEADS TO
BOTTOM LINE RESULTS
WHY BUSINESS ANALYTICS?
BUSINESS INTELLIGENCE DRIVES COMPETITIVE ADVANTAGE
18Copyright 2014 Senturus, Inc. All Rights Reserved.
11.3%
14.0%
12.1%
0.5%
9.4% 9.3% Value Integrators
All other enterprises
EBITDA5-year CAGR, 2004-2008
Revenue5-year CAGR, 2004-2008
ROIC5-year average, 2004-2008
49% more 30% more> 20x more
Source: IBM Institute for Business Value, The Global CFO Study 2010
GETTING THE RIGHT INFORMATION TO THE
RIGHT DECISION MAKERS AT THE RIGHT TIME
THE CHALLENGE
SOURCE DATA IS NOT ACTIONABLE INFORMATION
20Copyright 2014 Senturus, Inc. All Rights Reserved.
Standard
Reports (Push-Pull)
Dashboards/
Scorecards
Self-service Reporting
& Ad-Hoc Analysis
Alerts
The
Chasm ERP, CRM Data
Planning Data
De
cis
ion
s &
Acti
on
s
So
urc
e S
yste
ms o
f R
eco
rd
Other Sources
“What do you want?”
“What do you have?”
THE TYPICAL SOLUTION
21Copyright 2014 Senturus, Inc. All Rights Reserved.
Standard
Reports (Push-Pull)
Dashboards/
Scorecards
Self-service Reporting
& Ad-Hoc Analysis
Alerts
ERP Data
CRM Data
Planning Data
De
cis
ion
s &
Acti
on
s
So
urc
e S
yste
ms o
f R
eco
rd
Other Sources Or more specifically….
THE TYPICAL SOLUTION* (DETAILED)
22Copyright 2014 Senturus, Inc. All Rights Reserved.
Standard
Reports (Push-Pull)
Dashboards/
Scorecards
Self-service Reporting
& Ad-Hoc Analysis
Alerts
ERP Data
CRM Data
Planning Data
De
cis
ion
s &
Acti
on
s
So
urc
e S
yste
ms o
f R
eco
rd
Other Sources * Often coupled with individual acts of Macro & VLOOKUP
heroism, done infrequently and inconsistently
Excel
Powerpoint
Access
Solu
tions
Manually process in Excel
Combine multiple sources
Find, organize and align
data
Filter non-relevant data
Calculate missing
measures
Publish and distribute
reports
Use BI Tools toproduce reports
(scheduled and on-demand)
Use ETLto populate a mart/DW
(write once, run daily)
OR
But, most reports require business logic be applied to data
Pro
ble
mWHY IT PAYS TO BUILD A AUTOMATED BI SYSTEM
23Copyright 2014 Senturus, Inc. All Rights Reserved.
Save money, make money
“We just want a report”
Need
Repeat for everyreport, everymonth
Build Once
Build Once
THE REAL CHALLENGE IN A NUTSHELL
The Data has to be Transformed somewhere between the source systems and the end-user
The question is simply – WHERE ?
1. By the End-User (In Excel, etc)
2. By the Front-end BI Tool (with live queries)
3. By an Intermediate process & staging area (ETL, DW)
BUT FIRST, SOME BUSINESS INTELLIGENCE
MUST-HAVES & GIVENS
BASIC REQUIREMENTS
• Deliver a stable & user-friendly data structure– Reports will not break if source system files change– Foundation for true “Self-service” reporting and analytics
• Provide fast performance
– Especially for ad hoc reporting and interactive dashboards
• Handle multiple sources of data
– Cross-functional facts (metrics) and dimensions
• Deliver high quality, validated data
• Maintain historical data in a common format – Even if source systems change or grow– Also, maintain historical context of data (SCD’s) – Allows for trending and “as-of” analysis
A FEW UNIVERSAL BI SYSTEM REQUIREMENTS
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• Provide additional ways to “roll-up” data
– Hierarchies, attributes, defined metrics
• Provide field, table & measure names that make sense to business users
• Enable pre-calculations for commonly used measures
– E.g Gross margin, ratios, special qualities (pounds, gallons, etc)
• Provide user & role based security
– Often different than authentication within OLTP environment
A FEW UNIVERSAL BI REQUIREMENTS (CONT.)
27Copyright 2014 Senturus, Inc. All Rights Reserved.
WHY NOT SIMPLY POINT THE BI TOOLS AT THE
SOURCE SYSTEMS?
ARCHITECTURAL OPTIONS
DIRECT CONNECTION TO SOURCE SYSTEM
ERP Data
Labor Data
StandardReports
Web P
ort
al
Other SourcesAd h
oc
Query
ingPlanning Data
Slic
ing &
D
icin
g
Dash
board
Auth
oring
Report
Auth
oring
Dashboards/Scorecards
Sourc
e S
yst
em
s of
Reco
rd
Thre
shold
Ale
rtin
g
Self-service Reporting& Analysis
Threshold-basedAlerts
Excel
Planning “Data Set”
Sales “Data Set”
Finance “Data Set”
HR “Data Set”
Other “Data Set”
…
• Transaction processing (OLTP) systems are optimized for Data Entry, not Reporting
– Highly normalized, atomic level data
– Few indexes
– Cryptic naming (tables, columns)
– Odd formats (e.g. Julian dates, non-decimal numbers
– Priority often given to transaction processing
• OLTP systems change over time
– System upgrades, inducing structural changes
– System migrations
– Company acquisitions bring new sources
• OLTP systems not designed for rich metadata and hierarchies
– Limited fields and flex (UD) fields
– Little to no control over uniqueness of rollups
– Dimension maintenance is tedious at best
OTHER CHALLENGES OF DIRECT CONNECTION
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Performance
Usability
Stability
Usability
• Reporting queries can adversely impact OLTP data entry
– Queries are often intensive
• OLTP systems lack historical data and context
– Deleted records
– Legacy data often lost
– Only current values stored
• OLTP systems not capable of storing data from other/all sources
– Despite claims, source systems are not good repositories of other system data
– Multiple sources often don’t have common keys, structures relationships, granularity, etc.
• OLTP system security typically does not match BI needs
– Additional users and roles
– Extra licenses
– Unnecessary (& risky) access and complexity
CHALLENGES OF DIRECT CONNECTION OR REPLICATION (CONT.)
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Performance
Usability
Usability
Secure
OPERATIONAL DATA SOURCE: SAP EXAMPLE
* Just a few of the over 70,000 tables in SAP R/3
A FEW OTHER PROBLEMS WITH REPORTING
DIRECT FROM SOURCE (OLTP) SYSTEMS
MORE CHALLENGES
ERP, plus…
CRM
Master data of all types
Plans, forecasts, budgets
Security data
POS or channel data
Shop floor (or equiv) data
3rd party data
Big Data
…
THERE IS ALWAYS MORE THAN ONE SOURCE
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ONE PROBLEM WITH TRYING TO REPORT ACROSS SUBJECTS
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Cross joins (between query subjects [Reporting View, DEPT BUDGET,
Reporting View, DEPT EXPENSE] are not permitted in the identity.
Without conformed dimensions
Upgrades
Migrations
Re-implementations
Acquisitions
…
SOURCE SYSTEMS CHANGE OVER TIME
36Copyright 2014 Senturus, Inc. All Rights Reserved.
Yet, good BI relies on historical data
trending and context
My Big Epiphany:
Business Intelligence Success Hinges on Dimensional Data
Source systems NEVER support all the rollups, attributes & hierarchies
Rollups, attributes & hierarchies changeALL the time
ROLLUPS & HIERARCHIES MUST BE ADDED
Date & Time
Financial - Departments
Financial - Chart of Accounts
Product (often multiple)
Brand
Sales Territory
Customer
Employees/Management
Supplier
Asset
Geography/Location
Etc.
A FEW HIERARCHY EXAMPLES
Company reorgs
Multiple product hierarchies
Finance version vs. Marketing version
Sales territory realignment
Management hierarchies vs. geographic territories
Pre- and post- acquisition rollups
Multiple division rollup disparities
External supplier and third-party data hierarchies vs. internal
Temporary groupings (promos, tiger teams, etc.)
A FEW CLASSIC EXAMPLES OF CHANGE
SO WHAT DO WE NEED TO DO…
TECHNICAL SOLUTION
• Separate intensive query and reporting tasks from servers & disks used by transaction processing (OLTP) systems
• Create data models and technologies optimized for query and reporting that are NOT appropriate for transaction processing.
– E.g. bit-mapped indexes, denormalized tables…
• Transform data and embed “knowledge,” roll-ups and business logic into the data structures so that non-IT users can perform “self-service BI”
• Create a single location where information from multiple source systems can be accessed and combined for reporting purposes.
SO WHAT DO WE NEED TO DO (TECHINICALLY)
41Copyright 2014 Senturus, Inc. All Rights Reserved.
• Provide a validated repository of data that has been cleaned of inaccurate or spurious data quality issues.
• Maintain a repository of historical data gathered from prior and legacy sources, as well as data that would otherwise be purged from the current transaction processing system(s).
• Allow for secured access to data for analytics without opening up access to systems where data might inadvertently be modified, or transaction processing performance hindered.
• Provide a stable platform upon which end-users can build customized reports, dashboards and analytics
– Regardless of source system gyrations over time
SO WHAT DO WE NEED TO DO… (CONT.)
42Copyright 2014 Senturus, Inc. All Rights Reserved.
Create a
Data Warehouse
IN OTHER WORDS…
43Copyright 2014 Senturus, Inc. All Rights Reserved.
THE REAL SOLUTION
1. Properly staged data Extracted
Transformed
Enhanced & Combined
Validated
Delivered
2. Good tools to “consume” and use the information Report
Monitor
Analyze
Properly Staged Data BI Tools
The Real Solution
45Copyright 2014 Senturus, Inc. All Rights Reserved.
Sourc
e S
yst
em
s of
Record
Single Version of the Truth
Data
Abst
racti
on M
odelInformation Security
ReportAuthoring
DashboardAuthoring
Slicing &Dicing
Ad HocQuerying
ThresholdAlerting
ERPData
LaborData
OtherSources
PlanningData
StandardReports
Dashboards/Scorecards
Self-ServiceReporting &Analysis
Threshold-based Alerts
Web P
ort
al
WHAT IS A DATA WAREHOUSE?
DEFINITIONS
“ A data warehouse is a subject oriented, integrated, nonvolatile, time variant collection of data in support of management's decisions"
Bill InmonBuilding the Data WarehouseJohn Wiley & Sons, Inc., 1992
Classic Definition: Data Warehouse
47Copyright 2014 Senturus, Inc. All Rights Reserved.
WHAT IS NOT A DATA WAREHOUSE?
DEFINITIONS (PART 2)
DATA WAREHOUSE = COPY OF SOURCE SYSTEM?
ERP Data
Labor Data
StandardReports
Web P
ort
al
Other SourcesAd h
oc
Query
ingPlanning Data
Slic
ing &
D
icin
g
Dash
board
Auth
oring
Report
Auth
oring
Dashboards/Scorecards
Sourc
e S
yst
em
s of
Reco
rd
Thre
shold
Ale
rtin
g
Self-service Reporting& Analysis
Threshold-basedAlerts
ERP Data“Warehouse”
Labor Data“Warehouse”
Other Data“Warehouse”
Planning Data“Warehouse”
Excel
Planning “Universe”
Sales “Universe”
Finance “Universe”
HR “Universe”
Other “Universes”
…
Replication
DATA WAREHOUSE = NON-INTEGRATED DATA MART SILOS?
ERP Data
Labor Data
StandardReports
Web P
ort
al
Other SourcesAd h
oc
Query
ingPlanning Data
Slic
ing &
D
icin
g
Dash
board
Auth
oring
Report
Auth
oring
Dashboards/Scorecards
Sourc
e S
yst
em
s of
Reco
rd
Thre
shold
Ale
rtin
g
Self-service Reporting& Analysis
Threshold-basedAlerts
Excel Spreadmart
Planning Datamart
Sales Datamart
Finance Datamart
HR Datamart
Other Datamarts
…
ETL Processes
Integrated Data BI Tools
TRUE INTEGRATED DATA WAREHOUSE
51Copyright 2014 Senturus, Inc. All Rights Reserved.
* Also known as a Star Schema
Sourc
e S
yst
em
s of
Record
ConformingBusiness ProcessDimensional Models*
Single Version of the Truth
Data
Abst
racti
on M
odelInformation Security
ReportAuthoring
DashboardAuthoring
Slicing &Dicing
Ad HocQuerying
ThresholdAlerting
ERPData
LaborData
OtherSources
PlanningData
Data
Inte
gra
tion (
ETL)
StandardReports
Dashboards/Scorecards
Self-ServiceReporting &Analysis
Threshold-based Alerts
Web P
ort
al
WHY INTEGRATED DATA MARTS OR WAREHOUSE?
Enhances Reporting Performance and Flexibility– Data Marts are organized as denormalized data structures for speed and
ease of Reporting vs. Transactional system.
– Offloads the transactional system of reporting requests
– Drill-to-detail (regardless of data location)
Enables Data Integration or Cross Business Analysis– Enables analysis across business processes and functional areas
– Allows data from multiple sources to be integrated into one source of truth with common dimensionality (GL, Planning, Payroll, Sales)
– Discussion of conformed dimensions
– Example: Budget vs. Actuals
Allows Historical Data and Trend Analysis– Captures historical perspective vs. snapshot in time. (ex.Sq ft)
– Allows shifts in sources systems seamlessly
WHY INTEGRATED DATA MARTS OR WAREHOUSE? (CONTINUED)
4. Allows for Automation of business rules & transformations to human-readable information
– Insulates Business Users from cryptic structures and changes in the source systems
– Discussion of Transformation Layers
5. Allows for Additional org/hierarchy rollups & groupingsnot provided by source systems
– ALWAYS needed, never 100% supported by sources
– Should be table driven
Manual
EffortVlookups
Fragile
Macros
WHY INTEGRATED DATA MARTS OR WAREHOUSE? (CONTINUED)
6. Flexible Architectures enables reporting flexibility, i.e. the right tool for the right job
– Robust Reports for operational needs (plus, automatic delivery)
– Cubes for analytics, what if and scenarios
– Ad Hoc Reporting
– Dashboards & Scorecards for Management
7. Empowers Business User Self Service through any of the avenues from above
– Provides the ability to drill into the “Why?”
SPECIFIC EXAMPLES OF WHEN A DATA
WAREHOUSE ADDS VALUE
BENEFITS
Aggregation
Pre-calculation
Fewer joins
Simple joins
Incremental loads
Indexing and Optimization for DW
Less logic at the reporting layer
PERFORMANCE ENHANCEMENT EXAMPLES
56Copyright 2014 Senturus, Inc. All Rights Reserved.
Nomenclature transformation
Both measures and dimensions
Lookups - Measures (e.g. cost)
Lookups – Dimensions (rich master data)
Date conversions
“Pre-computed” Date logic
Granularity matching (e.g. plan vs. actual)
Business logic application
e.g. Definition of Revenue
USABILITY & VALIDITY ENHANCEMENT EXAMPLES
ENABLE QUERIES ACROSS SUBJECT AREAS
Product ProductType
ProductCategory
ProductClass
SuperBallpoint Pen
Ballpoints Pens Education
Metal Writer Pen
Ballpoints Pens Business
Felt Great Felt Tips Markers Education
Product Supplier MaterialType
Product Category
Product Class
Super Ballpoint Pen
Acme Ballpoints Pens Plastic
Metal Writer Pen
XYZ Ballpoints Pens Metal
Felt Great Acme Felt Tips Markers Hybrid
Marketing’s Product Dimension table: Manufacturing’s Product Dimension table:
Product Product Type Product Category
Marketing Product Class
Manufacturing Product Class
Supplier
Super BallpointPen
Ballpoints Pens Education Plastic Acme
Metal WriterPen
Ballpoints Pens Business Metal XYZ
Felt Great Felt Tips Markers Education Hybrid Acme
Conformed Product Dimension table:
Befo
reAft
er
CONFORMING DIMENSIONS (EXAMPLE)
Source:
The Data Warehouse Toolkit
© Ralph Kimball, Margy Ross
John Wiley & Sons, Inc.
Maintaining accurate historical context (SCD’s)
Snapshots and balances (e.g. inventory)
Transactionless Facts (e.g. promo periods)
Trending
EXAMPLES OF SPECIAL CHALLENGES
ACCURATELY MAINTAIN HISTORICAL CONTEXT
20092010
Store #23: 8,000 sq ft
Store #23: 20,000 sq ft
Year Store ID Store Size
Revenue Rev/sq ft
2009 23 8,000 $500,000 $63
2010 23 20,000 $1,300,000 $65
Year Store ID Store Size
Revenue Rev/sq ft
2009 23 20,000 $500,000 $25
2010 23 20,000 $1,300,000 $65
Accurate Historical Context:
Report that uses Store Size attribute from ERP table:
Store gets remodeled
Incorrect !
Rich, built-in date functionality (MTD,QTD…)
Pre-calculated time intervals, and other derived metrics
Data-driven security
Reduced licensing costs
And lots more….
A FEW MORE EXAMPLES
HOW TO BUILD A DATA WAREHOUSE
-- A 20,000 FOOT VIEW
FINAL TIP
DATA WAREHOUSES WITHOUT THE NEGATIVES
• Recommendation: Don’t set out to build a data
warehouse [i.e. “Boil the ocean”]
• Instead, build a series of business process
dimensional models with conformed
dimensions
• The result will provide the benefits of a data
warehouse without you ever having done a
data warehouse project.
Business Process Dimensional Models
Date Time Store Product, etc. Qty Ext Cost Ext Amount Margin,.
Dimensions (Attributes) Measures (Metrics)
Store Key
Store ID
Store Name
Store Loc
Store
Region
Store Size
Store Age
…
Product ID
Product Name
Product Class
Product Line
Product Weight
Shipping Cost
…
Date
Year
Quarter
Month
Week
Day
Day name
…
Added (Rolled-Up)
Averaged
Calculated
…
CONFORMED DIMENSIONS = KEY TO INTEGRATION
Date Time Store Product Qty, Revenue, Gross Margin
Dimensions (Attributes) Measures (Metrics)
Store ID
Store Name
Store
District
Store
Region
Store Mgr
Store Age
…
Product ID
Product Name
Product Class
Product Line
Product Weight
Shipping Cost
…
Date
Year
Quarter
Month
Week
Day
Day name
…
Added (Rolled-Up)
Averaged
Calculated
…
Date Time Store Product Plan Qty, Plan Rev, Plan Margin
Product
Line
Measures / Facts
Amount, Quantity
Units = 10
Amount = $17,525
Cost = $8,000
District
StoreProduct
(SKU)
Product
Subclass
Channel
Calculations &
Consolidations
Margin, Roll-ups…
Quarter
Year
Month
Week
Day
Period 1
Versions
Scenarios
Actuals
Forecast
MTD
QTD
YTD
WTD
Season
Period 2
Product
Class
Territory
Sales Rep
Old RegionNew Region
DIMENSIONAL MODEL (SIMPLIFIED)
Source:
The Data Warehouse Toolkit
© Ralph Kimball, Margy Ross
John Wiley & Sons, Inc.
COMMON, CONFORMING DIMENSIONS
CLOSING ARGUMENTS
CONCLUSION
We agree that some Additional Repository (other than the source system) is needed.
We agree that some Transformations should be done once during ETL, not live in every query.
We agree that Rich Dimensionality and Transformation adds tremendous value to data.
We agree that it is critical to lay the Proper FoundationBEFORE you start building tons of reports, etc.
CLOSING ARGUMENTS
70Copyright 2014 Senturus, Inc. All Rights Reserved
Therefore, we agree that:
We need a Real Integrated Data Warehouse
We need to do it Right
We can & should build it Incrementally
And we need to do it Now
CLOSING ARGUMENTS
71Copyright 2014 Senturus, Inc. All Rights Reserved
FROM IBM AND SENTURUS
ADDITIONAL RESOURCES
PRESENTATION SLIDE DECK ON WWW.SENTURUS.COM
Copyright 2014 Senturus, Inc. All Rights Reserved 73
www.senturus.com/events
• Sept 11 Beginning Authoring Tips & Tricks in Cognos BI
• Sept 17 Houston Cognos Users Group
• Sept 18 Improving the Planning Cycle for Sophisticated Business Needs
UPCOMING EVENTS
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Save $400
By registering before October 3rd and using
Senturus’ preferred customer code
G14SNTURUS, you’ll get an additional $100
off the early bird discount for a total of $400
savings.
PLUS $200 Training Credit
Receive $200 in Senturus training credit, good toward any of our 20 live
instructor-led Cognos online training classes.
PLUS $25 in Gambling Chips
At Senturus, we’re betting on you! Registrants will enjoy a welcome gift of
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http://www.senturus.com/ibm-insight-iod-2014/
*Custom, tailored training also available*
COGNOS TRAINING OPTIONS
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Q & A
THANK YOU