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Full Contents
1 Introduction
1.1 Executive Summary
1.2 Vendor independence
1.3 Key findings
1.3.1 The market
1.3.2 The selection process
1.3.3 Achievement of business goals
1.3.4 Realizing benefits
1.3.5 The power of the brand
1.3.6 Applications
1.3.7 Products
1.3.8 Purchases
1.3.9 Customer loyalty
1.3.10 Platforms
1.3.11 Data sources
1.3.12 Implementation and rollout
1.3.13 Deployment issues and problems
1.3.14 Data volumes
1.3.15 Performance issues
1.3.16 OLAP and the Web
1.4 Charting conventions
1.5 Means, medians and modes
2 The sample
2.1 Objectives
2.1.1 Large sample
2.1.2 Well-distributed
2.1.3 Unbiased
2.2 Sample size
2.3 OLAP buyers compared with non-buyers
2.4 Respondents’ perspectives
2.5 Geographic distribution
2.6 Organization sizes by revenue
2.7 Organization sizes by employees
2.8 Vertical markets
3 Products included
3.1 Product list
3.2 Product notes
3.2.1 Analysis Services
3.2.2 PowerPlay
3.2.3 MIS
3.2.4 MicroStrategy
3.2.5 TM1
3.2.6 Essbase
3.2.7 BW
3.2.8 BusinessObjects
3.2.9 MIK OLAP and Board M.I.T
3.2.10 Oracle OLAP products
3.2.11 Hyperion Intelligence
3.2.12 Financial applications
3.3 Architecture
3.4 Trends
3.4.1 Arrivals and Departures
3.5 Client tools used with OLAP servers
3.5.1 Analysis Services client tools
3.5.2 Essbase client tools
3.5.3 SAP BW client tools
3.6 Comparing the front-end tools markets
4 Age profiles
4.1 Product age profiles
4.2 Median product ages
4.3 Changing product shares
5 Customer success
5.1 Business goals achieved
5.2 Business benefits enjoyed
5.2.1 Faster or more accurate reporting
5.2.2 Better business decisions through more thorough or timely analysis
5.2.3 Improved customer satisfaction through enhanced product and service
quality
5.2.4 Saved headcount in business departments
5.2.5 Saved other non-IT costs (eg, inventory, waste, financing)
5.2.6 Increased revenues through better sales and marketing analysis
5.2.7 Reduced external IT costs (hardware, support, consulting,
software licensing)
5.2.8 Saved headcount in IS
5.2.9 Other benefits
5.2.10 Benefits summary
5.2.11 Benefits trends
5.2.12 The Business Benefits Index
6 The purchase cycle
6.1 What influences the evaluation list?
6.1.1 Influences by organization type
6.1.2 Influences by license spend
6.2 Which industry analysts are influential?
6.3 The benefits of doing a formal evaluation
6.4 Why organizations choose products
6.4.1 Product vs company factors in product selection
6.5 ... and how they should have chosen
6.6 Does product choice affect the business benefits?
6.6.1 Success rates among client tools
6.7 License fees
6.8 Do you get what you pay for?
7 Vendor scorecard
7.1 Vendor marketing effectiveness
7.1.1 Getting on the short list
7.1.2 Shortlisting trend
7.1.3 Avoiding competitive evaluations
7.2 Sales success: winners and losers
7.2.1 Win rates by organization type
7.3 Buyer demographics
7.4 Seats purchased
7.5 Deployed seats
7.6 Prevalence rates
7.7 Shelfware
7.8 Future buying intentions
7.9 Product support
7.9.1 Product support methods
7.9.2 Overall product support ratings
7.9.3 Comparing vendor product support performance
7.9.4 Do big customers get better product support?
7.10 Customer loyalty
7.10.1 Product abandonment
7.10.2 Which would they standardize on?
7.10.3 Reasons for standardization
7.10.4 The loyalty Top Ten
8 Implementation
8.1 Implementers
8.2 External consulting spend
8.2.1 External consulting spend by primary implementation resource
8.2.2 External consulting spend by product
8.2.3 External consulting spend by input data volume
8.2.4 External consulting spend by platform
8.2.5 External consulting spend by organization size
8.2.6 External consulting spend by organization location
8.2.7 External consulting spend by product architecture
8.3 Median implementation fees
8.4 Implementation fees compared to license fees
8.5 Do you get what you pay for?
8.6 Which implementer is the most successful?
8.7 Implementation resource conclusions
8.8 Resources used to run and administer OLAP projects
9 Timescales
9.1 By organization size
9.2 By business language
9.3 By implementation leader
9.4 By input data volumes
9.5 By platform
9.6 By product
9.7 By product architecture
9.8 Median implementation times summary
9.9 Installed within three or six months
9.10 Implementation times conclusions
10 What goes wrong?
10.1 Problems encountered
10.2 People problems
10.3 Data problems
10.4 Product-related technical problems
10.5 Normalized product-related problem analysis
10.6 The problem mix in perspective
10.7 Minimizing deployment problems
10.8 Barriers to wider deployment
10.8.1 Barriers analyzed by product
10.8.2 Barriers analyzed by lead implementer
10.8.3 Trouble-free rankings
11 Applications
11.1 Applications by product
11.2 Applications by data volumes
11.3 Applications by organization size and location
12 OLAP and the Web
12.1 Web-deployment trends
12.2 Web-deployment rates by product
12.3 Web-deployment rates by region
12.4 Web-deployment rates by organization size
12.5 Web-deployment rates by lead implementer
12.6 Median Web-deployment rates
12.7 Effects of Web deployment on business success
12.8 Web deployment rates by application
12.9 Extranet usage
12.9.1 Extranet deployment rates
12.9.2 Extranet deployment trends
12.9.3 Current extranet deployment rate summary
12.9.4 Extranet target users
12.10 Browsers used for OLAP deployments
12.11 Preferred architectures
13 Server platforms
13.1 Server platform trend
13.2 Server platforms by region
13.3 Server platforms by organization size
13.4 Server platforms by data volume
13.5 Server platforms by product
13.6 Does the server platform affect business success?
13.7 The rise of 64-bit OLAP
13.8 Server platform conclusions
14 Source databases
14.1 Source databases
14.1.1 Source database trends
14.2 Data source mix by input data volumes
14.3 Data source mix by product
14.4 Most popular OLAP tools for major databases
14.4.1 The Microsoft Top Ten
14.4.2 The Oracle Top Ten
14.4.3 The IBM Top Ten
14.4.4 The Teradata Top Five
14.4.5 The Top Ten OLAP databases for manual data entry
15 Data volumes
15.1 Overall data volumes
15.2 Data volumes by product
15.3 Data volumes by platform
15.4 Data volumes by architecture
15.5 Data volumes by organization type
15.6 Median data volumes
15.7 License fees by data volumes
15.8 Is bigger better?
16 Performance at the speed of thought?
16.1 Does query performance impact business benefits?
16.2 How do you measure performance?
16.3 Reported query times
16.4 Query times vs input data volume
16.5 Query times by data volume bands
16.6 Complaints about poor query performance
16.7 Poor performance deterring wider deployment
16.8 Is MOLAP always faster than ROLAP?
16.9 Is OLAP faster on UNIX or Windows?
16.10 Data latency: load, build and pre-calculate times
16.11 Does latency impact business benefits?
16.12 Build/load time profiles by product
16.13 Data latency vs input data volume
16.14 Reported build times by data band
16.15 MOLAP vs ROLAP build/load times
16.16 UNIX vs Windows build/load times
16.17 Performance questions answered
17 Appendix: Survey questionnaire
Tables
Table 1 – Recommended Survey invitation wording
Table 2 – Respondents’ decision-making roles
Table 3 – Location of respondents’ parent organizations
Table 4 – Organization total revenues
Table 5 – Organization size by employees
Table 6 – Vertical markets by product
Table 7 – Vertical markets by data volumes
Table 8 – Products included in the sample
Table 9 – Client tools and applications used with Analysis Services
Table 10 – Third-party Excel add-ins used with Analysis Services
Table 11 – Client tools and applications used with Essbase
Table 12 – Client tools and applications used with SAP BW
Table 13 – Usage levels of SAP BW “Business Content”
Table 14 – Overall achievement of business goals
Table 15 – Weighting factors used to score benefit achievement levels
Table 16 – Achievement of faster or more accurate reporting
Table 17 – Achievement of better business decisions through more
thorough or timely analysis
Table 18 – Achievement of improved customer satisfaction through
enhanced product quality and/or service levels
Table 19 – Achievement of headcount savings in business departments
Table 20 – Achievement of savings of other non-IT costs (eg,
inventory, waste, financing)
Table 21 – Achievement of increased revenues through better sales
and marketing analysis
Table 22 – Achievement of reduced external IT costs (hardware,
support, consulting or software licensing)
Table 23 – Achievement of headcount savings in IS
Table 24 – Achievement of other benefits
Table 25 – Overall BBI calculation
Table 26 – How did you compile the list of products to evaluate?
Table 27 – Demographic effect on influences
Table 28 – Relative influence of industry analysts
Table 29 – Reasons given for choosing OLAP products
Table 30 – Reasons given for choosing particular OLAP products
Table 31 – Benefits-driven ranking of selection criteria
Table 32 – Detailed goal and benefit scores by product
Table 33 – Chances of products being evaluated
Table 34 – Win rates by organization demographics
Table 35 – Demographics of product buyers
Table 36 – Shelfware rates
Table 37 – Future buying intentions
Table 38 – Primary method of product support
Table 39 – Overall ratings of product support
Table 40 – Users’ ratings of major products’ support
Table 41 – Support ratings by license fees paid
Table 42 – Support ratings by license fees paid from 2004
Table 43 – Reasons cited for discontinuing use of products
Table 44 – The possible effects of standardization trends
Table 45 – Reasons cited for potentially standardizing on products
Table 46 – The product loyalty league table
Table 47 – All the implementation resources used
Table 48 – The primary implementation resource
Table 49 – Implementation spend
Table 50 – Ratio of consulting spend and license fees
Table 51 – Net resources used to run and administer projects
Table 52 – Most serious deployment problems
Table 53 – Analysis of technical problems
Table 54 – Most and least significant deterrents for each product
Table 55 – Deterrents to wider deployment, by product
Table 56 – Deterrents to wider deployment, by lead implementer
Table 57 – Primary purpose of applications
Table 58 – OLAP applications by product
Table 59 – OLAP applications by data volumes
Table 60 – OLAP applications by organization size and location
Table 61 – Percentage of Web-deployed seats from 2001 to 2005
Table 62 – Extranet targets
Table 63 – Preferred Web architectures
Table 64 – Preferred Web architectures by subgroup
Table 65 – Platforms by input data volume band
Table 66 – Data sources by input data volume bands
Table 67 – The top ten OLAP tools for Microsoft databases
Table 68 – The top ten OLAP tools for Oracle databases
Table 69 – The top ten OLAP tools for IBM databases
Table 70 – The top five OLAP tools for Teradata databases
Table 71 – The top ten OLAP products for manual data input
Table 72 – Reported input data volumes
Figures
Figure 1 – Sample chart, illustrating the charting conventions
Figure 2 – Illustrating means, medians and modes
Figure 3 – OLAP purchases, by total organization revenue
Figure 4 – OLAP purchases, by total employees in the organization
Figure 5 – Distribution of respondents’ roles
Figure 6 – Geographic makeup of the five editions of The OLAP Survey
Figure 7 – Geographic scope of respondents’ organizations
Figure 8 – OLAP usage rates by vertical market
Figure 9 – Architectural mix of products
Figure 10 – The primary product mix in each edition of The OLAP Survey
Figure 11 – SAP ERP data proportion in BW
Figure 12 – Client tool markets
Figure 13 – Age profiles by product
Figure 14 – Median times in months since software purchase
Figure 15 – Comparing product mixes in mature and recent sites
Figure 16 – Goal achievement rates
Figure 17 – Likelihood of achieving different business benefits
Figure 18 – Trends in reported benefits
Figure 19 – Differing influences by deal size
Figure 20 – Analyst influence trends in North America
Figure 21 – Product mixes in different analysts’ customer bases
Figure 22 – Goals and benefits achieved vs selection method
Figure 23 – Product compared to company factors in product selection
Figure 24 –Business Benefits Index (BBI) by product
Figure 25 –Comparing the 2004 and 2005 BBI scores by product
Figure 26 – Goal and business benefits achievements scores vs site
maturity
Figure 27 – BBI scores for different client tool groups
Figure 28 – License fee distribution
Figure 29 – Median license fees paid
Figure 30 – License fee distribution
Figure 31 – Goals and benefits vs license fees
Figure 32 – Evaluation frequency trend
Figure 33 – Likelihood of a formal evaluation
Figure 34 – Selection rates in evaluations
Figure 35 – Win rates in purchases within the last two years
Figure 36 – Proportion of unlimited seat deals
Figure 37 – Median named user seats, excluding server licenses
Figure 38 – Average licensed named users per site, excluding
server licenses
Figure 39 – Median number of seats deployed per site
Figure 40 – Percentage of sites with over 1000 deployed seats
Figure 41 – Mean number of seats deployed per site
Figure 42 – Prevalence rates in organizations
Figure 43 – Inclination to buy more seats
Figure 44 – Analysis of primary support methods
Figure 45 – Support ratings by method
Figure 46 – Support quality ratings by product
Figure 47 – Product support rating vs license fees paid
Figure 48 – Reported rates of discontinued usage
Figure 49 – Preferred products to retain when standardizing
Figure 50 – Lead Implementer mix
Figure 51 – External consulting spend by lead implementation resource
Figure 52 – External consulting spend by product
Figure 53 – External consulting spend by input data volumes
Figure 54 – External consulting spend by platform
Figure 55 – External consulting spend by organization size
Figure 56 – External consulting spend by organization location
Figure 57 – External consulting spend by product architecture
Figure 58 – Median consulting fees summary
Figure 59 – Banded ratio of consulting spend to license fees
Figure 60 – Benefits vs external consulting spend
Figure 61 – Benefits vs primary implementation resource
Figure 62 – Project success rates by implementation time
Figure 63 – Problem rates by implementation time
Figure 64 – Implementation times by organization size
Figure 65 – Implementation times by organization language
Figure 66 – Implementation times by project leader
Figure 67 – Implementation times by input data volumes
Figure 68 – Implementation times by platform
Figure 69 – Implementation times by product
Figure 70 – Implementation times by product architecture
Figure 71 – Median implementation times in months
Figure 72 – Percentage of rollouts within three and six months
Figure 73 – Problem trends over time
Figure 74 – Problem trends in The OLAP Surveys
Figure 75 – Most-serious problem trends in The OLAP Surveys
Figure 76 – People problems by rollout times
Figure 77 – People problems by organization characteristics
Figure 78 – Reported incidence of data problems by product
Figure 79 – Average serious technical problems reported per site
Figure 80 – Ratio of technical to environmental problems
Figure 81 – Comparing the problem mix by product
Figure 82 – Comparing the problem mix by organization type
Figure 83 – Comparing the problem mix by lead implementer
Figure 84 – Deterrents to wider deployment, if any
Figure 85 – No deterrents to wider deployment
Figure 86 – Web-deployment trend vs forecasts
Figure 87 – Web-deployment rates by product
Figure 88 – Reported Web-deployment rates by region
Figure 89 – Reported Web-deployment rates by organization size
Figure 90 – Reported Web-deployment rates by lead implementer
Figure 91 – Median Web deployment rates across multiple dimensions
Figure 92 – Reported success rates by Web deployment rates
Figure 93 – Median Web deployment rates by application
Figure 94 – Extranet deployment by product
Figure 95 – Extranet deployment by country
Figure 96 – Extranet deployment by server platform
Figure 97 – Extranet rate trends: perception vs reality
Figure 98 – Current extranet deployment rates summary
Figure 99 – Extranet targets by product
Figure 100 – OLAP browser shares
Figure 101 – Server platform shares
Figure 102 – Overall server platforms trend
Figure 103 – Detailed platform trends, 2001-2005
Figure 104 – Detailed platform trends by geographic region
Figure 105 – Detailed platform trends by organization size
Figure 106 – Server platforms vs input data volumes
Figure 107 – Server platforms by product
Figure 108 – Goals and benefits scores by platform
Figure 109 – 64-bit server penetration
Figure 110 – Source databases for OLAP applications
Figure 111 – Database sources for OLAP data
Figure 112 – Database sources for by OLAP product
Figure 113 – Reported input data volumes by product
Figure 114 – Product shares vs input data volumes
Figure 115 – Reported input data volumes by platform
Figure 116 – Reported input data volumes by architecture
Figure 117 – Reported input data volumes by organization characteristic
Figure 118 – Median reported input data volumes (in Gb)
Figure 119 – Median reported input data volumes in Gb – log
scale
Figure 120 – License fees by data volume
Figure 121 – License and implementation fees by data volume
Figure 122 – Goals and benefits vs input data volumes
Figure 123 – Performance problems compared to other product-related
problems
Figure 124 – Goals and business benefits vs query performance
Figure 125 – Total reported problems vs query performance
Figure 126 – Comparing OLAP query times from 2002–2005
Figure 127 – Reported OLAP query times
Figure 128 – Median query times
Figure 129 – Query time trend
Figure 130 – Median query times vs median input data volumes
Figure 131 – Reported mean typical query times by size band
Figure 132 – Reported median typical query times by size band
Figure 133 – Performance complaints
Figure 134 – Poor query performance as a deterrent to wider deployment
Figure 135 – Network bandwidth as a deterrent to wider deployment
Figure 136 – Reported mean typical query times by architecture
Figure 137 – Reported median typical query times by architecture
Figure 138 – Reported mean typical query times by platform
Figure 139 – Reported median typical query times by platform
Figure 140 – Goals and business benefits vs load/calculate times
Figure 141 – Load/pre-calculate times by product
Figure 142 – Median load/calculate times
Figure 143 – Median load/pre-calc times vs median input data volumes
Figure 144 – Reported mean typical load/calculate times by size band
Figure 145 – Reported median typical load/calculate times by size
band
Figure 146 – Reported mean typical load/calculate times by architecture
Figure 147 – Reported median typical load/calculate times by architecture
Figure 148 – Architecture split by platform
Figure 149 – Reported mean typical load/calculate times by platform
Figure 150 – Reported median typical load/calculate times by platform
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