Moss L.T., Atre S. Business intelligence roadmap: The complete project lifecycle for decision-support applications
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Data naming and abbreviation standards for BI applications provide consistency and
a common look and feel useful for both developers and business people. Proven
standards can be applied (such as the convention of name compositions using prime,
qualifier or modifier, and class words), or new organization-specific standards can be
created. The data administration group usually has been trained in the various
industry-standard naming conventions.
Abbreviations are part of naming standards, but they apply only to physical names
(e.g., column names, table names, program names), not to business names. The
organization should publish a standard enterprise-wide abbreviations list that
includes industry-specific and organization-specific acronyms. Every BI project team
should use these abbreviations and acronyms.
Meta Data Capture
Meta data is a world unto itself. Large amounts of descriptive information can be
collected about business functions, business processes, business data objects,
business data elements, business rules, data quality, and other architectural
components. The organization needs standards or guidelines that govern who
captures which meta data components and how, when, and where to capture them.
The meta data repository should be set up in such a way that it supports the
standards for meta data capture and usage.
Logical Data Modeling
Logical data modeling is a business analysis technique (not to be confused with
logical database design). Every business activity or business function uses or
manipulates business data in some fashion. A logical data model documents those
logical data relationships irrespective of how the functions or the data are
implemented in the physical databases and applications.
Project-specific logical data models should be merged into one cohesive, integrated
enterprise logical data model. This activity usually is—and should be—included in the
job description for the data administration department, which may be part of the
enterprise architecture group. The enterprise logical data model is the baseline
business information architecture into which physical systems (operational or
decision-support, including BI applications) are mapped. The organization should
establish standards for creating the project-specific logical data models for BI
projects and for merging the models into the enterprise logical data model.
Data Quality
Information can be only as good as the raw data on which it is based. Most
organizations have a lot of dirty data—too much to cleanse it all. Each organization
must establish guidelines about triaging (categorizing and prioritizing) dirty data for
cleansing. In addition, the organization must create standards that define acceptable
quality thresholds and specify how to measure data quality during database loads.
Instructions for error handling and suspending dirty data records should also be part
of the standards.
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Testing
Testing standards specify what types of tests should be performed and who should
participate in the various types of testing. The organization should provide guidelines
that describe the types of test cases required at a minimum, how much regression
testing to perform, and under what circumstances to regression test. A brief
description of a test plan, perhaps even a template, as well as instructions for how to
organize and manage the various testing activities should be included.
Reconciliation
The BI decision-support environment will have multiple target databases and
multiple BI applications. Since BI applications are not stand-alone systems, their
development must be coordinated and reconciled to guarantee consistency across
the BI decision-support environment. That includes having one (logical) central
staging area with reconciliation programming for every input-process-output module
regardless of whether the module is written in native code or produced by an ETL
tool.
Security
BI data is derived from operational data. Therefore, security guidelines that apply to
the operational data also apply to the BI data. However, if data is summarized and
the ability to drill down to the details is not enabled, some of the security features
can be relaxed. But rather than allowing the members of each project team to make
up the rules as they please, the data owners should establish security standards to
guide the project teams on what types of security measures are mandatory for what
types of data exposure. These standards should include guidelines for categorizing
security risks. Security risks should be considered for data sensitivity, application
security, network security, and security against intrusions, hacks, viruses, and other
nuisances on the Web.
Service-Level Agreements
Organizations function according to explicit or implicit business principles. Business
principles are explicit if stated in mission or vision statements, implicit if they are just
"understood" by the staff. For example, if an organization rewards project managers
for meeting deadlines even though their applications are full of errors while it
punishes project managers for missing deadlines even though their applications are
flawless, the implicit business principle is "speed before quality." Service-level
agreements (SLAs) ordinarily support the explicit as well as the implicit business
principles. Therefore, SLA standards should state the business principles and outline
the minimum acceptable SLA measures to support those principles. For example, "All
projects must meet a 98 percent data quality threshold for financial data." SLA
measures can also apply to query response time, timeliness, availability, and level of
ongoing support.
Policies and Procedures
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Standards and guidelines should also cover the policies and procedures of an
organization, such as operating procedures, project change-control procedures,
issues management procedures, and dispute resolution procedures. Additional topics
(e.g., communication processes, estimating guidelines, roles and responsibilities,
standard document format) should also be part of the policies and procedures. The
purpose of having policies and procedures, along with standards and guidelines, is to
help streamline and standardize the BI decision-support environment. In other
words, policies, procedures, standards, and guidelines must add value for the
organization as a whole—or they should not exist.
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Nontechnical Infrastructure Evaluation Activities
The nontechnical infrastructure activities need to be performed linearly, as indicated
in Figure 2.11. The list below briefly describes the activities associated with Step 2,
Section B, Nontechnical Infrastructure Evaluation.
Assess the effectiveness of existing nontechnical infrastructure
components.
The policies, procedures, guidelines, and standards, which are all part of the
nontechnical infrastructure, exist to assist in the coordination and management
of the BI decision-support environment. They should not hinder the project
teams or slow them down unnecessarily. Therefore, review the appropriateness
and effectiveness of all nontechnical infrastructure components at the beginning
of each BI project. Expand, reduce, or revise any inadequate components as
necessary.
Eliminate unnecessary activities or tasks from the development
methodology or add missing activities or tasks.
Ensure that naming standards and abbreviations make sense and are
comfortable to the business community.
Review the logical data modeling and meta data strategies, and ensure
that the data administration and meta data administration groups are
adequately staffed.
Refine the organization's data quality initiative.
Examine the testing standards, and ensure that a sufficient amount of
reconciliation is being performed.
Review the guidelines for SLAs and security.
Tasks within this activity can be performed concurrently.
1.
Write the nontechnical infrastructure assessment report.
Once you have assessed all the components of the existing nontechnical
infrastructure, prepare a report that outlines your findings and gives
recommendations for improvement. If there are missing nontechnical
infrastructure components, prioritize which ones to include in the next BI
project and which ones to defer.
2.
Improve the nontechnical infrastructure.
In the project plan, give time estimates for modifying or improving nontechnical
infrastructure components as well as for establishing new components. If the
improvements must be completed prior to starting the BI project, create a
separate infrastructure project with a separate team and a separate project
plan.
3.
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Figure 2.11. Nontechnical Infrastructure Evaluation Activities
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Deliverable Resulting from These Activities
Nontechnical infrastructure assessment report
This report should document the deficiencies of the existing nontechnical
infrastructure and should cover the following items:
- Standards
- Use of a development methodology
- Estimating guidelines
- Scope management procedure
- Issues management procedure
- Roles and responsibilities
- Security process
- Meta data capture and delivery
- Process for merging project-specific logical data models into the
enterprise logical data model
- Data quality measures and triage process
- Testing process
- SLAs
- Support function
- Dispute resolution procedure
- Communication process
Include a section for proposed improvements for those selected nontechnical
infrastructure components that will be included in the BI project.
1.
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Roles Involved in These Activities
BI infrastructure architect
In some organizations, the BI infrastructure architect may have responsibility
over the nontechnical architectural components of the BI decision-support
environment. In other organizations, he or she works closely with the data
administrator, meta data administrator, and data quality analyst. Occasionally
the BI infrastructure architect oversees the activities of the data administrator,
meta data administrator, and data quality analyst. It is up to the organization
to select the enterprise architecture reporting structure that is most appropriate
for its organizational culture.
Data administrator
In many organizations, data administration has the responsibility for most of
the nontechnical infrastructure components, in particular logical data modeling,
data quality, naming standards, and meta data. However, since the area of
nontechnical infrastructure involves so many disciplines, the traditional data
administration responsibilities should be divided among the data administrator,
the meta data administrator, the data quality analyst, and sometimes even the
BI infrastructure architect. All of these roles are usually staffed by members of
the enterprise architecture group.
Data quality analyst
The data quality analyst takes charge of finding and analyzing dirty data in the
source files. Since it is impossible to cleanse all the dirty data, the organization
must establish triaging procedures and prioritization guidelines. The data
quality analyst is the steward of those data quality standards.
Meta data administrator
The meta data administrator is responsible for the meta data repository. He or
she must create it (or buy and install it), maintain it, and populate it. During
the BI project, the data administrator will provide the business meta data, and
the database administrator and data quality analyst (with the help of the ETL
and application lead developers) will provide the technical meta data. The meta
data administrator must then merge all the meta data into the meta data
repository and make it available to IT staff and to the business people. The
meta data administrator should therefore establish the standards related to the
meta data repository activities.
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Risks of Not Performing Step 2, Section B
Business intelligence is all about creating an enterprise architecture solution to the
decision-support chaos that exists today. It is a cross-organizational initiative.
Therefore, cross-organizational activities are of critical importance. The absence of
those activities will lead to stovepipe development and will add to the "spaghetti
chart" more data marts and more stand-alone BI applications that are neither
integrated nor reconciled. As a result, the organization would continue to lose the
opportunity to enhance its business decisions and competitive advantages.
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Bibliography and Additional Reading
Technical Infrastructure Evaluation
Bischoff, Joyce, and Ted Alexander. Data Warehouse: Practical Advice from the
Experts. Upper Saddle River, NJ: Prentice Hall, 1997.
Devlin, Barry. Data Warehouse: From Architecture to Implementation. Reading, MA:
Addison-Wesley, 1997.
Inmon, William H. Building the Data Warehouse. New York: John Wiley & Sons,
1996.
Inmon, William H., Claudia Imhoff, and Greg Battas. Building the Operational Data
Store. New York: John Wiley & Sons, 1996.
Jarke, Matthias, Maurizio Lenzerini, Yannis Vassiliou, and Panos Vassiliadis.
Fundamentals of Data Warehouses. New York: Springer, 2000.
Kelly, Sean. Data Warehousing: The Route to Mass Customization. New York: John
Wiley & Sons, 1996.
Kimball, Ralph, and Richard Merz. The Data Webhouse Toolkit: Building the Web-
Enabled Data Warehouse. New York: John Wiley & Sons, 2000.
Linthicum, David S. Enterprise Application Integration. Boston, MA: Addison-Wesley,
2000.
Moeller, R. A. Distributed Data Warehousing Using Web Technology: How to Build a
More Cost-effective and Flexible Warehouse. New York: AMACOM American
Management Association, 2001.
Nontechnical Infrastructure Evaluation
Adelman, Sid, and Larissa Terpeluk Moss. Data Warehouse Project Management.
Boston, MA: Addison-Wesley, 2000.
Brackett, Michael H. Data Resource Quality: Turning Bad Habits into Good Practices.
Boston, MA: Addison-Wesley, 2000.
———. The Data Warehouse Challenge: Taming Data Chaos. New York: John Wiley &
Sons, 1996.
Bruce, Thomas A. Designing Quality Databases with IDEF1X Information Models. New
York: Dorset House, 1992.
English, Larry P. Improving Data Warehouse and Business Information Quality:
北斗成功社区 BeiDouWeb.com 教育音视频/电子书/实用资料文档/励志音乐影视 仅供免费试用/版权原著所有
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Methods for Reducing Costs and Increasing Profits. New York: John Wiley & Sons,
1999.
Hoberman, Steve. Data Modeler's Workbench: Tools and Techniques for Analysis and
Design. New York: John Wiley & Sons, 2001.
Inmon, William H. Building the Data Warehouse. New York: John Wiley & Sons,
1996.
Inmon, William H., Claudia Imhoff, and Greg Battas. Building the Operational Data
Store. New York: John Wiley & Sons, 1996.
Inmon, William H., Claudia Imhoff, and Ryan Sousa. Corporate Information Factory.
New York: John Wiley & Sons, 1997.
Inmon, William H., John A. Zachman, and Jonathon G. Geiger. Data Stores, Data
Warehousing and the Zachman Framework: Managing Enterprise Knowledge. New
York: McGraw-Hill, 1997.
Kuan-Tsae, Huang, Yang W. Lee, and Richard Y. Wang. Quality Information and
Knowledge Management. Upper Saddle River, NJ: Prentice Hall, 1998.
Reingruber, Michael C., and William W. Gregory. The Data Modeling Handbook: A
Best-Practice Approach to Building Quality Data Models. New York: John Wiley &
Sons, 1994.
Ross, Ronald G. Business Rule Concepts. Houston, TX: Business Rule Solutions,
1998.
Simsion, Graeme. Data Modeling Essentials: Analysis, Design, and Innovation.
Boston, MA: International Thomson Computer Press, 1994.
Zachman, John. The Zachman Framework: A Primer for Enterprise Engineering and
Manufacturing. La Canada, CA: Zachman International, 2002.
Zachman Institute for Framework Advancement: http://www.zifa.com
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