1. Tutorial: Data Warehouse: Selecting the BI and ETL Products That Are Right for You!
Bill Hostmann
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U.S. Symposium/ITxpo Bill Hostmann
6–11 October 2002
Walt Disney World
Orlando, Florida
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2. Key Issues
Key Issues
1. What significant advancements and trends are occurring in data integration tools for building data warehouses and BI applications?
2. What are the key criteria to consider when evaluating data integration technologies?
3. What significant advancements and trends are occurring in BI tools?
4. What are the key criteria to consider when selecting BI tools and technologies?
1. What significant advancements and trends are occurring in data integration tools for building data warehouses and BI applications?
2. What are the key criteria to consider when evaluating data integration technologies?
3. What significant advancements and trends are occurring in BI tools?
4. What are the key criteria to consider when selecting BI tools and technologies?
Successful implementation of a data warehouse depends on management commitment, solid planning and appropriate resource allocation. Enterprises that adequately address these key points are still at significant risk of failure if they do not select the proper tools and technologies upon which to build and deploy their data warehouse. Selection of data integration and business intelligence (BI) products are difficult, and enterprises routinely experience drastic variations in cost, productivity and return on investment. At the back end of the data warehouse, data integration tools may be leveraged to assist in the effort of data acquisition from operational systems. Complexity of requirements and the dynamic nature of these markets make tool selection particularly challenging. Enterprises face a complex range of tool and platform options. In this presentation, we will discuss each of the critical data integration and BI tool/technologies selections enterprises will face in the course of delivering a data warehouse/BI project.
Copyright © 2002

3. A Successful BI Project Is Based on a Well-Defined Methodology
A successful data warehouse is the result of a well-defined and executed methodology. Selecting tools is one step within the methodology, and it’s not the first step.
7. Develop
Decision
Alternatives
6. Access,
Monitor,
Analyze
Facts
Majority of
construction effort
1. Definition
8. Share
and
Collaborate
Consume
2. Data
Identification
and
Inventory
5. Discovery
and
Exploration
Construct
9. Effect Change
4. Develop,
Implement,
Train
3. Tool
Evaluation/
Selection
Before we delve into the specifics of selecting tools, it is necessary to put the tool selection process into the context of an overall BI project methodology. The BI methodology ties together the phases, steps, roles and skills required to successfully develop, deploy and evolve a data warehouse and BI project. Selecting the right tools is just one step within the methodology. The entire project team, including those driving the tool selection process, need to understand the overall methodology and how each other’s steps/roles interrelate. Step 1 is definition. The ROI in BI tools is greatly increased by starting with a well-grounded definition of the end users and their business requirements. These requirements provide the foundation for each of the subsequent steps in the methodology. There are many techniques for gathering requirements. Pick one and use it. Without this set of defined requirements, the tools selection process and associated decisions will become an exercise in futility, since there will be no basis for making tradeoffs (and there will many) in the selection process. Step 2 is data inventory and identification. The data identification step is perhaps the most important and often the most underestimated in terms of time and effort needed. The source data is the raw material in a BI application’s information value chain. The number of data sources, content, complexity and access must all be inventoried and factored into the tools selection process.
Strategic Imperative: Use a BI implementation methodology to plan the project. Define requirements, architecture and data issues prior to tool selection.
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4. Management/Administration
A BI Solution Must Integrate and Align to Deliver Maximum Business Value BI
Applications
Data
Integration
BI Tools
Value
Data
Warehouse
Metadata
Security
Management/Administration
Business
Users
Data
Sources
Intranet/Extranet
Most BI projects and applications are motivated by a desire to derive more value from existing IT infrastructure and applications. The selection of data integration and BI tools should complement and leverage these investments as much as possible. It is therefore necessary to define a BI architecture and vision to complement the existing IT infrastructure. This means considering the issues of scalability, usability, security, metadata etc., from an overall system perspective not just a stand-alone tools perspective. Likewise, it’s important to have an overall BI applications road map that specifies the type of BI applications to be built and when. The architecture (both infrastructure and information) needs to be able to evolve over time to meet the new applications and changing business requirements. The tools need to be able to support and adapt to that evolution. A well-thought-out vision and architecture will go a long way to ensuring a higher return on investment (ROI) in your BI tools investment and delivering effective BI capabilities that meet the users’ requirements.
Tactical Guideline: Define the overall BI technical architecture and select the BI tools to leverage the existing IT infrastructure and deliver on the users’ BI requirements.
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5. Categories of Data Quality Technology
Market: Unlike many other technology markets, vendors in the data quality space are often dissimilar in nature, with differing strengths and functional competencies. Capabilities can be divided into four major data quality problem areas.
Relationship
Identification
Contact
Efficiency
Data
Re-Engineering
Data
Analysis
Key Issue: What technologies and services can be used to achieve data quality objectives?
The market for data quality tools contains not only a multitude of vendors, but products with dramatically different ranges of functionality and capability. This functionality range spans from pure data analysis (such as uncovering data anomalies related to domain integrity), to name-and-address integrity (such as consolidating redundant customer records), to data re-engineering (for example, repairing data elements). In addition, some tools focus on audits of operational data, while others focus on performing quality checks of data when entering the operational system.
Action Item: Determine the style of data quality technology most appropriate for the business requirements, and use this insight to drive vendor and product evaluation.
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6. Tools to Improve Data Quality
Market: The market for data quality tools continues to experience slow, but steady growth. Partnerships between data quality vendors and vendors in other markets (such as ETL and application integration middleware) and the advent of data quality service providers are starting to raise the general awareness of data quality issues.
Sample Vendor List
Contact
Efficiency
Relationship
Identification
Data
Analysis
Generic Data
Re-Engineering
Ascential Software
Avellino
DataFlux/SAS
Evoke Software
Firstlogic
Group 1 Software
Innovative Systems
Trillium Software
As of 08/02
Key Issue: What significant trends and advancements are occurring in tools for data integration?
In most cases, developing a comprehensive data quality program requires multiple tools in addition to the custom development of utilities. Most of these vendors have remained small, privately held companies, or spin-offs of larger companies, with product revenue of less than $15 million. Factors contributing to the slow adoption of these products include high prices, narrow functionality and steep learning curves. Although data-cleansing technology adds value, the value has diminishing returns. Once data is cleaned up, its quality should continue to be appropriately maintained through the owning application’s logic and data administration practices.
Action Item: Apply data-cleansing tools in cases where improvement of quality (such as name and address accuracy and de-duplication) cannot be achieved through other means.
= Strong
= Moderate
= Weak
(Blank) = No Functionality
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7. ETL ROI ‘Levers’ How Do You Estimate Your ROI? Number of data sources
Decision Framework: Many factors influence the benefits that enterprises can derive from deploying ETL tools. Although technical infrastructure issues are important, many other factors determine actual ROI.
Number of data sources
Type of data sources
Complexity of transformation
Complexity of integration
Resources
Skills
Metadata requirements
Intertool integration requirements
Environment stability
Data quality requirements
Data volumes
Time boundaries
Key Issue: What significant trends and advancements are occurring in tools for data integration?
Vendors specializing in ETL tools tout their substantial benefits, citing speed of delivery of data-integration projects and reduced cost of maintenance over the long term. Although many tools do deliver such benefits, results differ dramatically among enterprises. Infrastructure, business requirements and resources can cause these differences. Unfortunately, most ETL selection efforts limit their attention to basic infrastructure functionality (such as range of data source support) and do not consider the impact of the other "levers" on potential benefits and costs. This may result in unmet expectations and failure in the data acquisition phase of a data warehouse project. Enterprises must also consider business requirements and resource issues to clearly understand the potential costs and benefits of ETL tools. The business requirements and resource issues, such as data quality requirements, complexity of integration, interoperability concerns and the availability of staff and skills, have perhaps the greatest impact on the the success or failure of ETL deployments.
Action Item: When choosing ETL tools, be sure to consider more than just technical support for the specific sources from which data must be acquired — know the business requirements and the resources you can apply to the effort.
How Do You Estimate Your ROI?
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8. Data Integration: Selecting the Best Tool for the Job
Strategic Planning Assumption: Through 2004, because of major disparities in functionality between integration brokers and ETL tools, enterprises will be unsuccessful in applying only one of these technologies to all integration problems (0.9 probability).
Integration Brokers
ETL Tools
Adapters
Transformation
Business Process Mgmt.
Management Services
Intelligent Routing
Message Warehouse
Metadata Repository
Transport Mechanisms
Development Language
Real-Time Integration
Bulk Data Movement
Key Issue: What significant trends and advancements are occurring in tools for data integration?
Given the desire to more fully leverage infrastructure and work within budgetary constraints because of the challenging economic climate, many enterprises question the need for deploying integration brokers (IBs) and ETL tools. Most often, large enterprises with an application integration strategy raise this issue in light of new requirements for data warehousing or general data integration. Perceived similarities between the technologies, high prices and continued build-out of capabilities by IB and ETL vendors further fuel this debate. Although the proposition of leveraging a single technology for all integration needs is compelling, this approach is risky and problematic because of significant functional differences. Although IB and ETL products continue to move closer together with regard to data integration functionality, the overlap is not yet substantial enough to enable one style to be successfully applied to the traditional problem space of the other. Enterprises evaluating these technologies must carefully consider the nature of their business requirements, focusing specifically on the key differentiators of support for real-time integration and bulk data movement. Many enterprises will need to deploy ETL and IB products. Action Item: Enterprises should avoid the temptation to apply one integration product to the other’s problem domain. Doing so will require significant customization and extensions to supplement the tool’s off-the-shelf functionality.
Strong
Weak
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9. Completeness of Vision
ETL Magic Quadrant
Strategic Planning Assumption: By 2005, 50 percent of ETL processes will be implemented using custom-coded logic, 30 percent using database management systems (DBMS)-independent ETL tools and 20 percent using DBMS-vendor-provided ETL tools (0.8 probability).
Challengers
Leaders
Informatica
Oracle
Ascential Software
Microsoft
IBM
Ability to Execute
Embarcadero
Technologies
iWay Software
SAS
Computer
Associates
DataMirror
Acta Technology
Data Junction
Ab Initio Software
Hummingbird
Cognos
Evolutionary Technologies
International
Sagent
From “ETL Magic Quadrant Update: A Market in Evolution,” May 2002.
Key Issue: What significant trends and advancements are occurring in tools for data integration?
ETL functionality from the leading RDBMS vendors continues to improve and gain market visibility, although more slowly than expected. Therefore, a significant gap remains to be closed before it is considered “as good” as offerings from the pure ETL technology vendors. However, DBMS vendors continue to bundle ETL functionality closer to the database and at a very attractive price. This increasing pressure continues to cause ETL vendors to refocus energies toward new markets, such as analytic applications or application integration. We expect the ETL market will remain fragmented, with three types of vendors in the long term. DBMS vendors will continue to gain market share and become a stronger force. A set of niche ETL vendors with unique strengths or differentiations will survive. A small set of high-end DBMS-independent vendors will remain; however, ETL will not be their only product line. Given the relatively small number of ETL vendors, diversification of product lines will represent a continued challenge in focus and allocation of resources. Regardless of strengths, enterprises should ensure that the vendors/tools on their ETL shortlists are carefully evaluated to achieve a clear understanding of the potential costs and benefits.
Action Item: Enterprises should think strategically, even when buying tactically.
As of May 2002
Niche Players
Visionaries
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Completeness of Vision

10. Large Enterprises Typically Need Multiple BI Tools
Decision Framework: There are many types of BI tools on the market. Different tools deliver different types, interactivity and time frames of analysis. Most large organizations typically need multiple BI tools.
Most Common BI Needs
BAM
Event
CPM
Data
Mining
Ad Hoc
Query
Interactive
OLAP
Reporting
Advanced Analysis and
Forecasting
Scheduled
What
happened?
Why did it
happen?
What will
happen?
There is no such thing as a “silver bullet” BI tool. The most commonly used tools consist of production reporting or Enterprise BI Suites (EBISs), which typically consist of a rich set of combined functionality for reporting, ad hoc and online application processing (OLAP) analysis. Small and midsize businesses (SMBs) that cannot afford the resources or money to support multiple BI tools should try to concentrate on an EBIS first, because these tools cover most of what SMBs typically need. Large enterprises often need a portfolio of BI tools. For advanced analysis (as in the actuary department of an insurance company) or special tasks (like financial consolidation), specific packages or toolsets may be needed, because generic EBISs do not contain that functionality and it wouldn’t be efficient to develop everything oneself using a BI platform. A BI platform is often used for custom applications, but it may be too expensive or overkill to roll out to hundreds of users. Typically, an EBIS is efficient in servicing a broad set of users that have reporting and some query and analysis needs. During the next few years, this picture will become further complicated by the emergence of business activity monitoring (BAM), which will undoubtedly also have an impact on BI vendors. With a BI portfolio, enterprises can also apply a “hand-me-down” strategy. People that “outgrow” an EBIS can grow into a more heavyweight BI platform solution, handing the EBIS licenses down to a new, more inexperienced group of users.
Action Item: Plan to evaluate the BI tool types in developing your shortlist and recognize that multiple BI tools may be required. Minimize BI tool proliferation as much as possible.
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11. Consider the Needs of Multiple Types of Users
Decision Framework: When trying to create a BI solution of any kind, users and their role in the enterprise must be considered. Use the assessment model to match the different analytic requirements and types of user to the right types of tools.
Prioritize and match the user types and requirements
• Executives are among most strategic of users, with wide span of control, highly interruptive work style and ever-changing information needs. They need breadth of data, ease of use and customization. Their need for analytical complexity is modest, they have a balanced need for tactical and strategic decision making, and they rely on analysts for more in-depth analysis. Their need for user control and data depth is low. They do not expect to understand the inner workings of the system, and prefer to analyze information based on exceptions and trends rather than detailed data analysis and presentation. The Analyst: There are some differences between these two roles, but their similarities are significant. The analyst spends considerable time analyzing data for business managers. They analyze data for their own purposes and in support of their own business needs. As such, the depth of data, user control, analytical complexity and strategic application are the highest for this group. These people tend to be technologically savvy, and to explore different information domains to reveal deep insights. The Rank-and-File : the rank-and-file class of workers needs a one-to-many approach, where a single solution may be used to simultaneously support many users. These users tend to score highly in the need for ease-of-use and in the tactical application of information. Their need for data depth is modest. User control, customization, data breadth and analytical complexity rate extremely low.
Action Item: Use the assessment model to match the different analytic requirements and types of user to the right types of tools.
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12. BI Functionally — Selecting the Right Mix
Tactical Guidelines: Enterprises should provide BI services for “common” or “rank-and-file” users through a combination of static- and parameter-driven reporting and ad hoc query. Scalability should be a primary decision driver for large numbers of users.
High
High
Ad Hoc
Query – 10%
User
Functionality
and Flexibility
Required
Training
Investment
and Cost
Parameter-Driven
Reports – 30%
Static (Published) Reporting,
Including OLAP Viewing –
60%
Low
Low
More than 80 percent of BI users fall into the category of “common” or “rank-and-file.” Although BI is important and valuable for these users, it represents a small percentage of overall job activity (approximately less than 10 percent). This is in contrast to analyst/knowledge workers (30 percent or more) or analysts (80 percent or more), of which BI is a key/core component for their jobs. Common BI needs generally fall into the category of historical “reporting,” with users consuming information for basic awareness and simple analyses/comparisons. Often, this information may be considered “lowest common denominator” that is, information that may be of value to all, but not targeted toward any group in particular). These users make up the bulk of BI and should not be targeted with more costly and complex technologies/tools, such as data mining, executive information systems, balanced scorecards or other advanced BI technologies or applications. As a group, these users will consume the greatest percentage of overall BI spending, but the least on an individual basis. Nonetheless, holding true to the concept of information democracy, it is important to address these BI constituents. Failing to acknowledge and address their needs is to create an information “underclass,” elevating BI to elitist status.
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13. Enterprise BI Suites and Reporting Magic Quadrant
Market: Among the most active of all BI segments, the enterprise BI suites segment continues to be a high stakes game for vendors — with signs of a “packaged goods”-style marketplace.
Challengers
Leaders
Microsoft
Business
Objects
Cognos
Oracle
InfoBuilders
Ability to Execute
March 2002
Crystal
Decisions
August 2002
Actuate
Computer
Associates
MicroStrategy
From “Business Intelligence Magic Quadrants: Turbulent Waters,” August 2002.
Brio Software
Hummingbird
As of August 2002
Conclusion: Although demand remains strong, the BI market will be turbulent for the balance of 2002.
EBIS/reporting provides the technical underpinnings for delivering BI (often via the Web) to most users (approximately 75 percent). This includes reporting, ad hoc database query and basic OLAP viewing in a single environment. EBIS and reporting is the largest segment in terms of units and revenue. Often used aggressively for large, enterprise-scale BI projects, the recent decreased interest in these projects has caused a flattening in growth for many EBIS/reporting vendors. Among the more noteworthy: Actuate announced a decrease in growth from 89 percent in the first quarter of 2001 to 33 percent growth in the second quarter of 2001; Business Objects announced a decrease in growth from 45 percent in the first quarter of 2000 to 36 percent in the first quarter of 2001; Cognos, which helped define this segment, announced a negative impact upon its sales with disappointing second quarter of 2001 numbers (1 percent negative-revenue growth) and a 10 percent reduction of staff; Brio shocked the segment by announcing negative growth of 12 percent for the first quarter of 2001; Crystal Decisions (formerly Seagate Software) is showing new signs of life with a reinvigorated sales organization and updated products; and Information Builders continues to hold its own, having placed renewed emphasis upon its WebFocus products and business.
Niche Players
Visionaries
Completeness of Vision
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14. Convergence: Enterprise BI Suites and Robust Reporting Technologies
Strategic Planning Assumption: By year-end 2003, EBIS and robust reporting functionality will have converged into a single category of “enhanced” or “next-generation” EBIS products (0.8 probability).
Convergence
EBIS
Attributes
Robust
Reporting
Attributes
High
Usability
Sophisticated
Formatting
Oracle1
Oracle2
Brio1
Brio2
Semantic
Layer
Multiple
Output
Types
Microsoft
Information Builders
OLAP
Viewing
Enterprise
BI Suites
Robust
Reporting
Batch-Oriented
Ad Hoc
Query
Microstrategy
Crystal
Publishing/
Distribution
Cognos
Highly
Interactive
Hummingbird
Actuate
Highly
Scalable
Business
Objects
User-
Centric
High
Performance
“New EBIS”
Key Issue: How will BI software vendors react to ongoing market shifts and challenges?
Robust reporting and Enterprise BI Suites have historically remained technologically distinct. However, it is becoming clear that these two domains are quickly converging. This will result in a new or “next-generation” Enterprise BI Suites market, as EBIS is the larger of the two markets and because much of the convergence is being driven by EBIS vendors. The gradual move towards convergence has been underway for some time. EBIS products from Brio, Business Objects and Cognos have consistently improved reporting features with each new release. Likewise, production reporting products from vendors such as Actuate, Crystal and Information Builders have added more end-user features, such as spreadsheet connections, ad hoc "reporting" and even OLAP viewing. However, to obtain the complete set of EBIS and production reporting functionality today, you still will require multiple product sets. Reflecting this reality, most organizations have standardized on separate tools for these different feature sets.
Action Item: Evaluate vendors on their plans for consolidation and compare to your longer-term BI tool requirements..
Oracle1 — Oracle Discoverer
Oracle2 — Oracle Reports
Brio1 — Brio Intelligence
Brio2 — Brio Reports
Q403 Estimate
Q302
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15. BI Platforms Magic Quadrant
Market: An embryonic market in many ways, BI platform growth will be driven largely by third-party BI application vendors seeking technology through which they can deliver their domain expertise.
Challengers
Leaders
Ability to Execute
SAP
Microsoft
SAS
Oracle
PeopleSoft
March 2002
Hyperion
August 20/02
Microstrategy
Crystal Decisions
Arcplan
ProClarity
Computer
Associates
Sagent
Spotfire
From “Business Intelligence Magic Quadrants: Turbulent Waters,” August 2002.
AlphaBlox
WhiteLight Systems
As of August 2002
Conclusion: Although demand remains strong, the BI market will be turbulent for the balance of 2002.
BI platforms is a lower-volume market (in units) than EBIS/reporting, but has higher-value deals that often include a significant services component. Although many opportunities may exist for direct selling to user organizations, the more compelling (and emerging) business model is in support of value-added resellers (VARs) and independent software vendors (ISVs). This market segment will experience less change than EBIS/reporting because of its alignment with applications that support the business. Among the more notable changes are: Microsoft’s increasing ability to execute, with growing adoption of Analysis Server and related tools; SAS Institute’s improved execution, focusing more heavily upon third-party recruitment; and our inclusion of ProClarity. Microstrategy’s solid v.7.1 product, its refocusing upon BI platforms and the shedding of other interests have made it an interesting “dark horse,” while Hyperion’s lack of focus has hurt it, driving revenue downward and forcing a reduction of staff. In contrast, several smaller vendors continue to defy the current downward trend and are aggressively growing revenue. These include AlphaBlox, Arcplan and ProClarity. Finally, we removed vendors Gentia and Pilot Software from the Magic Quadrant (above) as we do not consider either to be viable as platform suppliers.
Niche Players
Visionaries
Completeness of Vision
Copyright © 2002

16. BI Technology Trends — Emerging Pattern Towards BI Networks
Strategic Planning Assumption: By 2007, XML, BI Web services, collaborative functionality and mobile/wireless functionality will have merged to a bigger picture: BI Networks, assisted by BAM (0.7 probability).
Visibility
Moving forward
At risk
No movement
CPM
Mobile and
Wireless BI
BI/aCRM
Financial BI
Applications
Alerting BI
Platforms
ERP/BI
OLAP
BAM
B2C BI
Extranets
EBIS
XML
Enablement
B2B BI
Extranets
Production/
Formatted/
Web Reporting
Thin Client
BI Web
Services
Data Mining
as part of BI
BI Portals
Collaborative BI
A number of things has changed from the first BI Hype Cycle (see The BI Hype Cycle; Major Innovation Ahead, SPA ). BI portals are now at the deep trough of disillusionment, and it is doubtful they will grow to the next phases. ERP-based BI is growing towards the trough. After the usual bad press associated with the trough, it is expected that ERP-based BI will climb to the plateau of productivity. Analytical CRM — from a BI perspective — is holding its place, vendors are shifting attention to other areas. A trend that was emerging last year is now rapidly climbing to the peak of inflated expectations: corporate performance management (CPM). Many software vendors are embracing and marketing the concept. Because of its high impact on management processes, we expect widespread adoption to be a relatively slow process. Business activity monitoring is another trend that is rapidly climbing the peak, but because of it technological challenges, not as fast as CPM. The more mature trends in BI are behaving in a rather stable manner. BI platforms, EBIS, OLAP and production reporting are still safe bets.
The BI Hype Cycle clearly shows that technology innovation precedes applications. Alerts and mobile BI may resurface as something completely different. The same goes for BI Web services, XML-based distribution of BI and collaborative BI. Although how these trends will materialize is uncertain, they all have one notion in common: that is “networks.” Although not all vendors are realizing this, “BI Networks” is the overarching trend, spanning multiple current developments.
Action Item: Recognize industry trends, but avoid products and vendors that lead with hype.
Technology
Trigger
Peak of
Inflated
Expectations
Trough of
Disillusionment
Slope of
Enlightenment
Plateau of
Productivity
BI Hype Cycle as of September 2002
Copyright © 2002

17. Build an Objective Evaluation Criteria Checklist
Tactical Guideline: Develop a shortlist. Evaluate the tools and the vendor. Prioritize both business and technical requirements. Avoid vendor hype.
Integration with Infrastructure
Functionality
Reliability
Scalability
Vendor Viability
Pricing
Customer References
Support/Services
Documentation
Partners
Scrutinize hype
Wireless
Web Services
Real Time
BI Portals
Having completed the definition of business and user requirements, the technical infrastructure, as well as a survey of the market, you are now well armed to develop a set of objective evaluation criteria for vendor selection. Evaluating the tools requires a commitment of adequate time and resources; vendors need time to respond to the request and tool evaluation usually has to be done in addition to everyone’s “day job.” Develop a shortlist of vendors with similar products; don’t try to compare vendors that have very different products — it will get very confusing when you try and do an apples and oranges comparison. This will make the task less daunting. Provide each of the vendors with a set of the business and user requirements as well as envisioned architecture. Also provide a typical business case example that you would like to see them address. Use the criteria checklist to stay focused on real requirements vs. hype. Whenever possible conduct a proof of concept that implements your case study example. You will learn a lot about the tools and the vendor in the process. Remember, the tools you buy are not stand-alone products; you will be building a relationship with the vendor. This relationship is in terms of the support and partnerships that you need to bring your unique set of requirements together within your BI project. Finally, check references and financial viability of the company.
Action Item: Develop an objective criteria and evaluate products and vendors against a case study example using business requirements.
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18. Intangible Factors Can Make or Break Tool Selection
Tactical Guideline: In a process of BI tool selection, enterprises should make intangible
factors explicit.
Rating
“We’d like to have you as a client. We offer you a 50 percent discount on a $1.5 million site license. Please decide now.” –
“We will be your strategic partner; we’ll do everything for $450,000.” –
+/–
“We sell solutions …”
“We have great stuff, but not everything. This is how
it fits in your overall framework. We also have some
partnerships for things we don’t do” +
Key Issue: Which objective and subjective factors influence BI tool selection?
In the end, people buy from people. Functionality issues aside, there should also be a level of trust and understanding. For vendors, it is hard to create that level of trust and understanding, as the end of the quarter is always near. This sometimes leads to schizophrenic discussions, where short-term revenue and a long-term customer relationship come into conflict. It is not surprising that two of the most stable vendors in BI, SAS and Information Builders, are private companies. Of course, when software needs to be purchased, enterprises should make use of the end of quarter to obtain additional discounts. This also helps the vendor’s account manager.
Many vendors talk about “solutions,” but this is the wrong word. First of all, it usually means the vendor offers a mix of applications, consultancy and custom development, and it is often not clear what exactly it is offering. Second, it places the vendor solution first, not the user problem. This is a fundamentally incorrect approach. Enterprises should challenge vendors that talk about “solutions” to make clear what they mean.
Vendors that are open about their strategy, what they do and, equally important, what they don’t do, are best able to establish long-term growth relationships with a loyal customer base.
Copyright © 2002

19. Recommendations
Recommendations
Before you evaluate tools and vendors, define your objectives, requirements, architecture and vision.
View data quality as a strategic business issue rather than just an IT problem.
As “data integration” markets continue to converge, heavily weigh strategic partnerships in your ETL evaluations.
Minimize BI tool proliferation, but recognize that today a single tool may not meet all your needs.
Overlook vendor hype; focus on objective criteria based on user requirements and business value.
Before you evaluate tools and vendors, define your objectives, requirements, architecture and vision.
View data quality as a strategic business issue rather than just an IT problem.
As “data integration” markets continue to converge, heavily weigh strategic partnerships in your ETL evaluations.
Minimize BI tool proliferation, but recognize that today a single tool may not meet all your needs.
Overlook vendor hype; focus on objective criteria based on user requirements and business value.
These are our key recommendations for how enterprises should approach vendor and tool/technology selections for their data integration and BI tools. First and foremost, follow a well-defined methodology that is shared by all those involved with the tool/technology selection process. Data quality tools, often overlooked, need to be included in the tools selection process. As the data-integration tool markets continue to converge, enterprises must focus on partnerships and alliances among the vendors, in addition to ensuring that the tools will truly add value, given the typically complex nature of most data acquisition/integration efforts. In the case of BI tools, enterprises should carefully evaluate not only the vendors’ technology, but also strategy and direction. Most important, in all cases, the role of vendors and products in the overall BI and data warehouse strategy of the enterprise must be considered, with the recognition that no single vendor or product will completely satisfy requirements. Enterprises must always measure vendors and technologies by their specific business requirements, vendor references and proof-of-concept work as a backdrop. Performing evaluations in this manner will maximize the chances of success in data warehouse and BI deployments. And most importantly have fun. Building BI applications is challenging but rewarding. And they deliver significant value to your organizations.
Copyright © 2002