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DOYOUNEEDADATAWAREHOUSE? 
WHYPROPERLYSTAGEDDATAISCRITICALTOBI SYSTEMSUCCESS
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Resource Library 
Senturus’ whole purpose is to make you successful with Business Analytics. Thus, we offer a series of technology-neutral webinars, training on specific software, demonstrations, and no-holds-barred reviews of new software releases. We host dozens of live webinars every year and we offer a comprehensive library of recorded webinars, demos, white papers, presentations and case studies on our website--a wealth of learning resources. Most of our content is custom created and constantly updated, so visit us often to see what’s new in the industry. 
www.senturus.com/resources/ 
3 
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•Introduction 
•The QuickAnswer 
•Why Business Intelligence (BI) 
•Challenges & BasicRequirements of BI Systems 
•Reporting Direct from SourceSystems 
•Technical Solution Alternatives 
•Data Warehouse Benefits 
•How to Build a Data Warehouse (20,000 foot view) 
•Additional Resources & Upcoming Events 
•Q & A 
TODAY’SAGENDA 
4 
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CRITICALSUCCESSFACTORSINBI 
•Architectures & Data Transformation 
•BI Tools 
•Methodologies & Techniques 
•People & Processes 
Chapters in the 
BI Demystified Series
CRITICALSUCCESSFACTORSINBI 
•Architectures & Data Transformation 
•Data Marts & Data Warehouses 
•BI Tools 
•Methodologies & Techniques 
•People & Processes Chapters in the BI Demystified Series
John Peterson CEO & Co-FounderSenturusTODAY’SPRESENTER 
7
WHOWEARE 
SENTURUSINTRODUCTION
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Our Team: 
Business depth combined with technical expertise. Former CFOs, CIOs, Controllers, Directors, BI Managers & Enterprise BI/DW Architects 
SENTURUS: BUSINESSANALYTICSARCHITECTS 
10Copyright 2014 Senturus, Inc. All Rights ReservedBusiness IntelligenceEnterprise Planning 
Predictive Analytics 
Creating Clarity from Chaos
750+ CLIENTS, 1600+ PROJECTS, 14+ YEARS 
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•Former Head of BI/ Lead Architect –VISA 
•Former BI Architect –JambaJuice 
•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. 
12
WHATDOYOUUSEFORBI DATA“STORAGE” TODAY? 
QUICKPOLL
DOYOUINTENDTODEPLOYANENTERPRISEDATAWAREHOUSEATSOMEPOINT? 
QUICKPOLL
DOYOUREALLYNEEDADATAWAREHOUSE? 
QUICKANSWER
The short is answer is: 
Almost always, YES 
DOYOUREALLYNEEDADATAWAREHOUSE*? 
16 
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* or Conforming Data Marts
The rest of this presentation will focus on why… 
WHY? 
17 
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DATA-DRIVENINSIGHTLEADSTOBOTTOMLINERESULTS 
WHYBUSINESSANALYTICS?
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BUSINESS INTELLIGENCE DRIVES COMPETITIVE ADVANTAGE 
Copyright 2014 Senturus, Inc. All Rights Reserved. 20 
11.3% 
14.0% 
12.1% 
0.5% 
9.4% 9.3% Value Integrators 
All other enterprises 
EBITDA 
5-year CAGR, 2004-2008 
Revenue 
5-year CAGR, 2004-2008 
ROIC 
5-year average, 2004-2008 
> 20x 49% more 30% more more 
Source: IBM Institute for Business Value, The Global CFO Study 2010
GETTINGTHERIGHTINFORMATIONTOTHERIGHTDECISIONMAKERSATTHERIGHTTIME 
THECHALLENGE
SOURCEDATAISNOTACTIONABLEINFORMATION 
22 
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Standard 
Reports (Push-Pull) 
Dashboards/ 
Scorecards 
Self-service Reporting 
& Ad-Hoc Analysis 
Alerts 
The Chasm ERP, CRM Data 
Planning Data 
Decisions & Actions 
Source Systems of Record 
Other Sources 
“What do you want?” 
“What do you have?”
THETYPICALSOLUTION 
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Standard 
Reports (Push-Pull) 
Dashboards/ 
Scorecards 
Self-service Reporting 
& Ad-Hoc Analysis 
AlertsERP Data 
CRM Data 
Planning Data 
Decisions & Actions 
Source Systems of Record 
Other Sources 
Or more specifically….
THETYPICALSOLUTION* (DETAILED) 
24 
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Standard 
Reports (Push-Pull) 
Dashboards/ 
Scorecards 
Self-service Reporting 
& Ad-Hoc Analysis 
AlertsERP Data 
CRM Data 
Planning Data 
Decisions & Actions 
Source Systems of Record 
Other Sources 
* Often coupled with individual acts of Macro & VLOOKUP heroism, done infrequently and inconsistently 
Excel 
Powerpoint 
Access
Solutions 
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 to 
produce reports 
(scheduled and on-demand) 
Use ETL 
to populate a mart/DW 
(write once, run daily) 
OR 
But, most reports require business logic be applied to data 
Problem 
WHYITPAYSTOBUILDAAUTOMATEDBI SYSTEM 
25 
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Save money, make money 
“We just want a report” 
Need 
Repeat for everyreport, everymonth 
Build OnceBuild Once
THEREALCHALLENGEINANUTSHELL 
The Data has to be Transformedsomewhere 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)
BUTFIRST, SOMEBUSINESSINTELLIGENCEMUST-HAVES& GIVENS 
BASICREQUIREMENTS
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•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 multiplesources of data 
–Cross-functional facts (metrics) and dimensions 
•Deliver high quality, validateddata 
•Maintain historicaldata 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 FEWUNIVERSALBI SYSTEMREQUIREMENTS 
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•Provide additional ways to “roll-up” data 
–Hierarchies, attributes, defined metrics 
•Provide field, table & measure names that make senseto business users 
•Enable pre-calculationsfor commonly used measures 
–E.gGross margin, ratios, special qualities (pounds, gallons, etc) 
•Provide user & role based security 
–Often different than authentication within OLTP environment 
A FEWUNIVERSALBI REQUIREMENTS(CONT.) 
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WHYNOTSIMPLYPOINTTHEBI TOOLSATTHESOURCESYSTEMS? 
ARCHITECTURALOPTIONS
DIRECTCONNECTIONTOSOURCESYSTEM 
ERP Data 
Labor Data 
Standard 
Reports Web Portal 
Other Sources Ad hoc Querying 
Planning Data Slicing & Dicing 
Dashboard Authoring 
Report Authoring 
Dashboards/ 
Scorecards 
Source Systems of Record 
Threshold 
Alerting 
Self-service Reporting 
& Analysis 
Threshold-based 
Alerts 
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 
OTHERCHALLENGESOFDIRECTCONNECTION 
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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 
CHALLENGESOFDIRECTCONNECTIONORREPLICATION(CONT.) 
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PerformanceUsabilityUsability 
Secure
OPERATIONALDATASOURCE: SAP EXAMPLE 
* Just a few of the over 70,000 tables in SAP R/3
A FEWOTHERPROBLEMSWITHREPORTINGDIRECTFROMSOURCE(OLTP) SYSTEMS 
MORECHALLENGES
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ERP, plus… 
CRM 
Master data of all types 
Plans, forecasts, budgets 
Security data 
POS or channel data 
Shop floor (or equiv) data 
3rdparty data 
Big Data 
… 
THEREISALWAYSMORETHANONESOURCE 
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ONEPROBLEMWITHTRYINGTOREPORTACROSSSUBJECTS 
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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 
… 
SOURCESYSTEMSCHANGEOVERTIME 
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Yet, good BI relies on historical data trending and context
My Big Epiphany: 
Business Intelligence Success Hinges on Dimensional Data 
Source systems NEVERsupport all the rollups, attributes & hierarchies 
Rollups, attributes & hierarchies changeALL the time 
ROLLUPS& HIERARCHIESMUSTBEADDED
Date & Time 
Financial -Departments 
Financial -Chart of Accounts 
Product (often multiple) 
Brand 
Sales Territory 
Customer 
Employees/Management 
Supplier 
Asset 
Geography/Location 
Etc. 
A FEWHIERARCHYEXAMPLES
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 FEWCLASSICEXAMPLESOFCHANGE
SOWHATDOWENEEDTODO… 
TECHNICALSOLUTION
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•Separate intensive queryand 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, denormalizedtables… 
•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 locationwhere information from multiple source systemscan be accessed and combined for reporting purposes. 
SOWHATDOWENEEDTODO(TECHINICALLY) 
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•Provide a validatedrepositoryof 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 accessto 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 
SOWHATDOWENEEDTODO… (CONT.) 
47 
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Create a 
Data Warehouse 
INOTHERWORDS… 
48 
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THEREALSOLUTION 
1.Properly staged data 
Extracted 
Transformed 
Enhanced & Combined 
Validated 
Delivered 
2.Good tools to “consume” and use the information 
Report 
Monitor 
Analyze
Properly Staged DataBI Tools 
The Real Solution 
50Copyright 2014 Senturus, Inc. All Rights Reserved. 
Source Systems of Record 
Single Version of the Truth 
Data Abstraction Model 
Information Security 
Report 
Authoring 
Dashboard 
Authoring 
Slicing & 
Dicing 
Ad Hoc 
Querying 
Threshold 
Alerting 
ERP 
Data 
Labor 
DataOtherSourcesPlanningData 
Standard 
ReportsDashboards/ ScorecardsSelf-ServiceReporting & Analysis 
Threshold- 
based Alerts 
Web Portal
WHATISADATAWAREHOUSE? 
DEFINITIONS
“ A data warehouse is a subject oriented, integrated, nonvolatile, time variant collection of data in support of management's decisions" 
Bill Inmon 
Building the Data Warehouse 
John Wiley & Sons, Inc., 1992 
Classic Definition: Data Warehouse 
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WHATISNOT ADATAWAREHOUSE? 
DEFINITIONS(PART2)
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DATAWAREHOUSE= COPYOFSOURCESYSTEM? 
ERP Data 
Labor Data 
Standard 
Reports 
Web Portal 
Other Sources 
Ad hoc Querying Planning Data 
Slicing & DicingDashboard AuthoringReport Authoring 
Dashboards/ 
Scorecards 
Source Systems of Record 
Threshold 
Alerting 
Self-service Reporting 
& Analysis 
Threshold-based 
Alerts 
ERP Data 
“Warehouse” 
Labor Data 
“Warehouse” 
Other Data 
“Warehouse” 
Planning Data 
“Warehouse” 
Excel 
Planning “Universe” 
Sales “Universe” Finance “Universe” 
HR “Universe” Other “Universes” … 
Replication
DATAWAREHOUSE= NON-INTEGRATEDDATAMARTSILOS? 
ERP Data 
Labor Data 
Standard 
Reports 
Web Portal 
Other Sources 
Ad hoc Querying 
Planning Data 
Slicing & DicingDashboard Authoring 
Report Authoring Dashboards/ Scorecards Source Systems of Record 
Threshold 
Alerting 
Self-service Reporting 
& Analysis 
Threshold-based 
Alerts 
Excel Spreadmart 
Planning Datamart 
Sales Datamart 
Finance Datamart 
HR Datamart 
Other Datamarts 
… 
ETL Processes
Integrated DataBI ToolsTRUEINTEGRATEDDATAWAREHOUSE 
57 
Copyright 2014 Senturus, Inc. All Rights Reserved. 
* Also known as a Star Schema 
Source Systems of Record 
Conforming 
Business Process 
Dimensional Models* 
Single Version of the Truth 
Data Abstraction Model 
Information Security 
Report 
Authoring 
Dashboard 
Authoring 
Slicing & 
Dicing 
Ad Hoc 
Querying 
Threshold 
AlertingERPDataLaborData 
Other 
Sources 
Planning 
Data 
Data Integration (ETL) 
Standard 
Reports 
Dashboards/ 
Scorecards 
Self-Service 
Reporting & 
Analysis 
Threshold- 
based Alerts 
Web Portal
WHYINTEGRATEDDATAMARTSORWAREHOUSE? 
Enhances Reporting Performance and Flexibility 
–Data Marts are organized as denormalizeddata 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 HistoricalData and TrendAnalysis 
–Captures historical perspective vs. snapshot in time. (ex.Sqft) 
–Allows shifts in sources systems seamlessly
WHYINTEGRATEDDATAMARTSORWAREHOUSE? (CONTINUED) 
4.Allows for Automation of business rules & transformations to human-readable information 
–Insulates Business Users from cryptic structuresand 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 drivenManual EffortVlookups 
Fragile 
Macros
WHYINTEGRATEDDATAMARTSORWAREHOUSE? (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?”
SPECIFICEXAMPLESOFWHENA DATAWAREHOUSEADDSVALUE 
BENEFITS
questions 
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Aggregation 
Pre-calculation 
Fewer joins 
Simple joins 
Incremental loads 
Indexing and Optimization for DW 
Less logic at the reporting layer 
PERFORMANCEENHANCEMENTEXAMPLES 
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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. Definitionof Revenue 
USABILITY& VALIDITYENHANCEMENTEXAMPLES
ENABLEQUERIESACROSSSUBJECTAREAS 
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 WriterPen 
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 ProductClass 
Supplier 
Super BallpointPen 
Ballpoints 
Pens 
Education 
Plastic 
Acme 
Metal WriterPen 
Ballpoints 
Pens 
Business 
Metal 
XYZ 
Felt Great 
FeltTips 
Markers 
Education 
Hybrid 
Acme 
ConformedProduct Dimension table: 
Before 
After
CONFORMINGDIMENSIONS(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) 
TransactionlessFacts (e.g. promo periods) 
Trending 
EXAMPLESOFSPECIALCHALLENGES
ACCURATELY MAINTAIN HISTORICAL CONTEXT 
2009 
2010 
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-calculatedtime intervals, and other derived metrics 
Data-driven security 
Reduced licensing costs 
And lots more…. 
A FEWMOREEXAMPLES
HOWTOBUILDADATAWAREHOUSE--A20,000 FOOTVIEW 
FINALTIP
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DATAWAREHOUSESWITHOUTTHENEGATIVES 
•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 ModelsDateTime StoreProduct, etc.QtyExt Cost Ext Amount Margin,. 
Dimensions (Attributes) 
Measures (Metrics) 
Store Key 
Store ID 
Store Name 
Store Loc 
Store Region 
Store Size 
Store Age 
… Product IDProduct NameProduct ClassProduct LineProduct WeightShipping Cost… 
Date 
Year 
Quarter 
Month 
Week 
Day 
Day name 
… 
Added (Rolled-Up) 
Averaged 
Calculated 
…
CONFORMEDDIMENSIONS= KEYTOINTEGRATION 
DateTime StoreProductQty,Revenue, Gross Margin 
Dimensions (Attributes) 
Measures (Metrics) 
Store ID 
Store Name 
Store District 
Store Region 
Store Mgr 
Store Age 
… Product IDProduct NameProduct ClassProduct LineProduct WeightShipping Cost… 
Date 
Year 
Quarter 
Month 
Week 
Day 
Day name 
… Added (Rolled-Up) AveragedCalculated… 
DateTime StoreProductPlanQty,Plan Rev, Plan Margin
Product 
Line 
Measures / Facts 
Amount, Quantity 
Units = 10 
Amount = $17,525 
Cost = $8,000District 
Store 
Product (SKU) 
Product Subclass 
Channel 
Calculations & Consolidations 
Margin, Roll-ups… 
Quarter 
YearMonth 
WeekDay 
Period 1 
Versions 
Scenarios 
Actuals 
Forecast 
MTDQTD 
YTD 
WTD 
Season 
Period 2 
Product Class 
Territory 
Sales RepOld Region 
New Region 
DIMENSIONALMODEL(SIMPLIFIED)
Source: 
The Data Warehouse Toolkit 
© Ralph Kimball, Margy Ross 
John Wiley & Sons, Inc. 
COMMON, CONFORMINGDIMENSIONS
CLOSINGARGUMENTS 
CONCLUSION
questions 
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We agree that some Additional Repository (other than the source system) is needed. 
We agree that some Transformationsshould 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 FoundationBEFOREyou start building tons of reports, etc. 
CLOSINGARGUMENTS 
79 
Copyright 2014 Senturus, Inc. All Rights Reserved
Therefore, we agree that: 
We need a RealIntegrated Data Warehouse 
We need to do it Right 
We can & should build it Incrementally 
And we need to do it Now 
CLOSINGARGUMENTS 
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FROMIBM ANDSENTURUS 
ADDITIONALRESOURCES
PRESENTATIONSLIDEDECKONWWW.SENTURUS.COM 
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www.senturus.com/events 
•Sept 11Beginning Authoring Tips & Tricks in Cognos BI 
•Sept 17Houston Cognos Users Group 
•Sept 18Improving the Planning Cycle for Sophisticated Business Needs 
UPCOMINGEVENTS 
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Do You Really Need a Data Warehouse? Avoid the Downsides Typically Associated with Data Warehouses

  • 2. questions here Copyright2014Senturus,Inc. AllRightsReserved This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Hear the Recording
  • 3. Resource Library Senturus’ whole purpose is to make you successful with Business Analytics. Thus, we offer a series of technology-neutral webinars, training on specific software, demonstrations, and no-holds-barred reviews of new software releases. We host dozens of live webinars every year and we offer a comprehensive library of recorded webinars, demos, white papers, presentations and case studies on our website--a wealth of learning resources. Most of our content is custom created and constantly updated, so visit us often to see what’s new in the industry. www.senturus.com/resources/ 3 Copyright 2014 Senturus, Inc. All Rights Reserved
  • 4. •Introduction •The QuickAnswer •Why Business Intelligence (BI) •Challenges & BasicRequirements of BI Systems •Reporting Direct from SourceSystems •Technical Solution Alternatives •Data Warehouse Benefits •How to Build a Data Warehouse (20,000 foot view) •Additional Resources & Upcoming Events •Q & A TODAY’SAGENDA 4 Copyright 2014Senturus, Inc. All Rights Reserved
  • 5. CRITICALSUCCESSFACTORSINBI •Architectures & Data Transformation •BI Tools •Methodologies & Techniques •People & Processes Chapters in the BI Demystified Series
  • 6. CRITICALSUCCESSFACTORSINBI •Architectures & Data Transformation •Data Marts & Data Warehouses •BI Tools •Methodologies & Techniques •People & Processes Chapters in the BI Demystified Series
  • 7. John Peterson CEO & Co-FounderSenturusTODAY’SPRESENTER 7
  • 9. questions here Copyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 10. Our Team: Business depth combined with technical expertise. Former CFOs, CIOs, Controllers, Directors, BI Managers & Enterprise BI/DW Architects SENTURUS: BUSINESSANALYTICSARCHITECTS 10Copyright 2014 Senturus, Inc. All Rights ReservedBusiness IntelligenceEnterprise Planning Predictive Analytics Creating Clarity from Chaos
  • 11. 750+ CLIENTS, 1600+ PROJECTS, 14+ YEARS 11 Copyright 2014 Senturus, Inc. All Rights Reserved
  • 12. •Former Head of BI/ Lead Architect –VISA •Former BI Architect –JambaJuice •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. 12
  • 16. The short is answer is: Almost always, YES DOYOUREALLYNEEDADATAWAREHOUSE*? 16 Copyright 2014 Senturus, Inc. All Rights Reserved. * or Conforming Data Marts
  • 17. The rest of this presentation will focus on why… WHY? 17 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 19. questions here Copyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 20. BUSINESS INTELLIGENCE DRIVES COMPETITIVE ADVANTAGE Copyright 2014 Senturus, Inc. All Rights Reserved. 20 11.3% 14.0% 12.1% 0.5% 9.4% 9.3% Value Integrators All other enterprises EBITDA 5-year CAGR, 2004-2008 Revenue 5-year CAGR, 2004-2008 ROIC 5-year average, 2004-2008 > 20x 49% more 30% more more Source: IBM Institute for Business Value, The Global CFO Study 2010
  • 22. SOURCEDATAISNOTACTIONABLEINFORMATION 22 Copyright 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 Decisions & Actions Source Systems of Record Other Sources “What do you want?” “What do you have?”
  • 23. THETYPICALSOLUTION 23 Copyright 2014 Senturus, Inc. All Rights Reserved. Standard Reports (Push-Pull) Dashboards/ Scorecards Self-service Reporting & Ad-Hoc Analysis AlertsERP Data CRM Data Planning Data Decisions & Actions Source Systems of Record Other Sources Or more specifically….
  • 24. THETYPICALSOLUTION* (DETAILED) 24 Copyright 2014 Senturus, Inc. All Rights Reserved. Standard Reports (Push-Pull) Dashboards/ Scorecards Self-service Reporting & Ad-Hoc Analysis AlertsERP Data CRM Data Planning Data Decisions & Actions Source Systems of Record Other Sources * Often coupled with individual acts of Macro & VLOOKUP heroism, done infrequently and inconsistently Excel Powerpoint Access
  • 25. Solutions 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 to produce reports (scheduled and on-demand) Use ETL to populate a mart/DW (write once, run daily) OR But, most reports require business logic be applied to data Problem WHYITPAYSTOBUILDAAUTOMATEDBI SYSTEM 25 Copyright 2014 Senturus, Inc. All Rights Reserved. Save money, make money “We just want a report” Need Repeat for everyreport, everymonth Build OnceBuild Once
  • 26. THEREALCHALLENGEINANUTSHELL The Data has to be Transformedsomewhere 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)
  • 28. questions here Copyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 29. •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 multiplesources of data –Cross-functional facts (metrics) and dimensions •Deliver high quality, validateddata •Maintain historicaldata 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 FEWUNIVERSALBI SYSTEMREQUIREMENTS 29 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 30. •Provide additional ways to “roll-up” data –Hierarchies, attributes, defined metrics •Provide field, table & measure names that make senseto business users •Enable pre-calculationsfor commonly used measures –E.gGross margin, ratios, special qualities (pounds, gallons, etc) •Provide user & role based security –Often different than authentication within OLTP environment A FEWUNIVERSALBI REQUIREMENTS(CONT.) 30 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 32. DIRECTCONNECTIONTOSOURCESYSTEM ERP Data Labor Data Standard Reports Web Portal Other Sources Ad hoc Querying Planning Data Slicing & Dicing Dashboard Authoring Report Authoring Dashboards/ Scorecards Source Systems of Record Threshold Alerting Self-service Reporting & Analysis Threshold-based Alerts Excel Planning “Data Set” Sales “Data Set” Finance “Data Set” HR “Data Set” Other “Data Set” …
  • 33. •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 OTHERCHALLENGESOFDIRECTCONNECTION 33 Copyright 2014 Senturus, Inc. All Rights Reserved. Performance Usability Stability Usability
  • 34. •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 CHALLENGESOFDIRECTCONNECTIONORREPLICATION(CONT.) 34 Copyright 2014 Senturus, Inc. All Rights Reserved. PerformanceUsabilityUsability Secure
  • 35. OPERATIONALDATASOURCE: SAP EXAMPLE * Just a few of the over 70,000 tables in SAP R/3
  • 37. questions here Copyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 38. ERP, plus… CRM Master data of all types Plans, forecasts, budgets Security data POS or channel data Shop floor (or equiv) data 3rdparty data Big Data … THEREISALWAYSMORETHANONESOURCE 38 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 39. ONEPROBLEMWITHTRYINGTOREPORTACROSSSUBJECTS 39 Copyright 2014 Senturus, Inc. All Rights Reserved. Cross joins (between query subjects [Reporting View, DEPT BUDGET, Reporting View, DEPT EXPENSE] are not permitted in the identity. Without conformed dimensions
  • 40. Upgrades Migrations Re-implementations Acquisitions … SOURCESYSTEMSCHANGEOVERTIME 40 Copyright 2014 Senturus, Inc. All Rights Reserved. Yet, good BI relies on historical data trending and context
  • 41. My Big Epiphany: Business Intelligence Success Hinges on Dimensional Data Source systems NEVERsupport all the rollups, attributes & hierarchies Rollups, attributes & hierarchies changeALL the time ROLLUPS& HIERARCHIESMUSTBEADDED
  • 42. Date & Time Financial -Departments Financial -Chart of Accounts Product (often multiple) Brand Sales Territory Customer Employees/Management Supplier Asset Geography/Location Etc. A FEWHIERARCHYEXAMPLES
  • 43. 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 FEWCLASSICEXAMPLESOFCHANGE
  • 45. questions here Copyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 46. •Separate intensive queryand 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, denormalizedtables… •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 locationwhere information from multiple source systemscan be accessed and combined for reporting purposes. SOWHATDOWENEEDTODO(TECHINICALLY) 46 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 47. •Provide a validatedrepositoryof 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 accessto 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 SOWHATDOWENEEDTODO… (CONT.) 47 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 48. Create a Data Warehouse INOTHERWORDS… 48 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 49. THEREALSOLUTION 1.Properly staged data Extracted Transformed Enhanced & Combined Validated Delivered 2.Good tools to “consume” and use the information Report Monitor Analyze
  • 50. Properly Staged DataBI Tools The Real Solution 50Copyright 2014 Senturus, Inc. All Rights Reserved. Source Systems of Record Single Version of the Truth Data Abstraction Model Information Security Report Authoring Dashboard Authoring Slicing & Dicing Ad Hoc Querying Threshold Alerting ERP Data Labor DataOtherSourcesPlanningData Standard ReportsDashboards/ ScorecardsSelf-ServiceReporting & Analysis Threshold- based Alerts Web Portal
  • 52. “ A data warehouse is a subject oriented, integrated, nonvolatile, time variant collection of data in support of management's decisions" Bill Inmon Building the Data Warehouse John Wiley & Sons, Inc., 1992 Classic Definition: Data Warehouse 52 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 54. questions here Copyright 2014Senturus,Inc.AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 55. DATAWAREHOUSE= COPYOFSOURCESYSTEM? ERP Data Labor Data Standard Reports Web Portal Other Sources Ad hoc Querying Planning Data Slicing & DicingDashboard AuthoringReport Authoring Dashboards/ Scorecards Source Systems of Record Threshold Alerting Self-service Reporting & Analysis Threshold-based Alerts ERP Data “Warehouse” Labor Data “Warehouse” Other Data “Warehouse” Planning Data “Warehouse” Excel Planning “Universe” Sales “Universe” Finance “Universe” HR “Universe” Other “Universes” … Replication
  • 56. DATAWAREHOUSE= NON-INTEGRATEDDATAMARTSILOS? ERP Data Labor Data Standard Reports Web Portal Other Sources Ad hoc Querying Planning Data Slicing & DicingDashboard Authoring Report Authoring Dashboards/ Scorecards Source Systems of Record Threshold Alerting Self-service Reporting & Analysis Threshold-based Alerts Excel Spreadmart Planning Datamart Sales Datamart Finance Datamart HR Datamart Other Datamarts … ETL Processes
  • 57. Integrated DataBI ToolsTRUEINTEGRATEDDATAWAREHOUSE 57 Copyright 2014 Senturus, Inc. All Rights Reserved. * Also known as a Star Schema Source Systems of Record Conforming Business Process Dimensional Models* Single Version of the Truth Data Abstraction Model Information Security Report Authoring Dashboard Authoring Slicing & Dicing Ad Hoc Querying Threshold AlertingERPDataLaborData Other Sources Planning Data Data Integration (ETL) Standard Reports Dashboards/ Scorecards Self-Service Reporting & Analysis Threshold- based Alerts Web Portal
  • 58. WHYINTEGRATEDDATAMARTSORWAREHOUSE? Enhances Reporting Performance and Flexibility –Data Marts are organized as denormalizeddata 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 HistoricalData and TrendAnalysis –Captures historical perspective vs. snapshot in time. (ex.Sqft) –Allows shifts in sources systems seamlessly
  • 59. WHYINTEGRATEDDATAMARTSORWAREHOUSE? (CONTINUED) 4.Allows for Automation of business rules & transformations to human-readable information –Insulates Business Users from cryptic structuresand 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 drivenManual EffortVlookups Fragile Macros
  • 60. WHYINTEGRATEDDATAMARTSORWAREHOUSE? (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?”
  • 62. questions here Copyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 63. Aggregation Pre-calculation Fewer joins Simple joins Incremental loads Indexing and Optimization for DW Less logic at the reporting layer PERFORMANCEENHANCEMENTEXAMPLES 63 Copyright 2014 Senturus, Inc. All Rights Reserved.
  • 64. 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. Definitionof Revenue USABILITY& VALIDITYENHANCEMENTEXAMPLES
  • 65. ENABLEQUERIESACROSSSUBJECTAREAS 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 WriterPen 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 ProductClass Supplier Super BallpointPen Ballpoints Pens Education Plastic Acme Metal WriterPen Ballpoints Pens Business Metal XYZ Felt Great FeltTips Markers Education Hybrid Acme ConformedProduct Dimension table: Before After
  • 66. CONFORMINGDIMENSIONS(EXAMPLE) Source: The Data Warehouse Toolkit © Ralph Kimball, Margy Ross John Wiley & Sons, Inc.
  • 67. Maintaining accurate historical context (SCD’s) Snapshots and balances (e.g. inventory) TransactionlessFacts (e.g. promo periods) Trending EXAMPLESOFSPECIALCHALLENGES
  • 68. ACCURATELY MAINTAIN HISTORICAL CONTEXT 2009 2010 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 !
  • 69. Rich, built-in date functionality (MTD,QTD…) Pre-calculatedtime intervals, and other derived metrics Data-driven security Reduced licensing costs And lots more…. A FEWMOREEXAMPLES
  • 71. questions here Copyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 72. DATAWAREHOUSESWITHOUTTHENEGATIVES •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.
  • 73. Business Process Dimensional ModelsDateTime StoreProduct, etc.QtyExt Cost Ext Amount Margin,. Dimensions (Attributes) Measures (Metrics) Store Key Store ID Store Name Store Loc Store Region Store Size Store Age … Product IDProduct NameProduct ClassProduct LineProduct WeightShipping Cost… Date Year Quarter Month Week Day Day name … Added (Rolled-Up) Averaged Calculated …
  • 74. CONFORMEDDIMENSIONS= KEYTOINTEGRATION DateTime StoreProductQty,Revenue, Gross Margin Dimensions (Attributes) Measures (Metrics) Store ID Store Name Store District Store Region Store Mgr Store Age … Product IDProduct NameProduct ClassProduct LineProduct WeightShipping Cost… Date Year Quarter Month Week Day Day name … Added (Rolled-Up) AveragedCalculated… DateTime StoreProductPlanQty,Plan Rev, Plan Margin
  • 75. Product Line Measures / Facts Amount, Quantity Units = 10 Amount = $17,525 Cost = $8,000District Store Product (SKU) Product Subclass Channel Calculations & Consolidations Margin, Roll-ups… Quarter YearMonth WeekDay Period 1 Versions Scenarios Actuals Forecast MTDQTD YTD WTD Season Period 2 Product Class Territory Sales RepOld Region New Region DIMENSIONALMODEL(SIMPLIFIED)
  • 76. Source: The Data Warehouse Toolkit © Ralph Kimball, Margy Ross John Wiley & Sons, Inc. COMMON, CONFORMINGDIMENSIONS
  • 78. questions here Copyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 79. We agree that some Additional Repository (other than the source system) is needed. We agree that some Transformationsshould 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 FoundationBEFOREyou start building tons of reports, etc. CLOSINGARGUMENTS 79 Copyright 2014 Senturus, Inc. All Rights Reserved
  • 80. Therefore, we agree that: We need a RealIntegrated Data Warehouse We need to do it Right We can & should build it Incrementally And we need to do it Now CLOSINGARGUMENTS 80 Copyright 2014 Senturus, Inc. All Rights Reserved
  • 82. PRESENTATIONSLIDEDECKONWWW.SENTURUS.COM Copyright 2014 Senturus, Inc. All Rights Reserved 82
  • 83. www.senturus.com/events •Sept 11Beginning Authoring Tips & Tricks in Cognos BI •Sept 17Houston Cognos Users Group •Sept 18Improving the Planning Cycle for Sophisticated Business Needs UPCOMINGEVENTS 83 Copyright 2014 Senturus, Inc. All Rights Reserved
  • 84. Save $400 By registering before October 3rdand 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 $25 in Mandalay Bay Hotel gambling chips upon arrival. Save $600 on IBM Insight (formerly IOD) 84 Copyright 2014 Senturus, Inc. All Rights Reserved. http://www.senturus.com/ibm-insight-iod-2014/
  • 85. *Custom, tailored training also available* COGNOSTRAININGOPTIONS Copyright 2014 Senturus, Inc. All Rights Reserved 85
  • 86. questions hereCopyright 2014Senturus,Inc. AllRightsReserved Hear the Recording This slide deck is part of a recorded webinar. To view the FREE recording of the entire presentation and download the slide deck go to www.senturus.com/resources/do-you-really-need-a- data-warehouse/ Senturus’ comprehensive library of recorded webinars, demos, white papers, presentations and case studies is available on our website. www.senturus.com
  • 87. Thank You!! www.senturus.com 888-601-6010 info@senturus.com Copyright2014bySenturus, Inc. Thisentirepresentationiscopyrightedandmaynotbereusedor distributedwithoutthewrittenconsentofSenturus,Inc.