A SURVEY ON IMPLEMENTING

DATA WAREHOUSING IN AN ORGANIZATION

The purpose of this survey is to assess the factors that contribute to implementing data warehousing in an organization. All information collected will be kept confidential, and the results will be presented in summarized form only. As used in this questionnaire, the term data warehouse refers to an enterprise-wide data warehouse, while datamart refers to a smaller warehouse developed for a functional area or department; infrastructure refers to the physical computing infrastructure in an organization, including hardware, telecommunications assets, networks, etc.; and star schema refers to a database schema supporting multiple views of data based on the business processes that the database supports.

This questionnaire lists a variety of possible items that may pertain to implementing a data warehouse. From your own observations during the implementation of data warehousing in your organization, please rate the extent that each possible factor ACTUALLY CONTRIBUTED to implementing your data warehouse by circling one of the numbers in the scale to the right of each item. Please respond to each item using both scales. The numbers in both of these scales mean:

1 = Not At All 4 = Some Extent

2 = Very Little Extent 5 = Great Extent

3 = Little Extent 6 = Very Great Extent

Recognizing the benefits/business value of implementing and using a data warehouse	1 2 3 4 5 6
Getting an initial commitment from senior management for a data warehousing project	1 2 3 4 5 6
Being aware that the first data warehouse project typically takes longer than subsequent projects because of other changes it requires in the infrastructure	1 2 3 4 5 6
Understanding the business problems to be solved using data warehousing	1 2 3 4 5 6
Having a disciplined approach to data warehouse development	1 2 3 4 5 6
Having a complete project plan to steer the data warehouse development effort	1 2 3 4 5 6
Having a data warehouse project leader who is knowledgeable of all the business processes of the organization	1 2 3 4 5 6
Understanding that an enterprise-wide data warehouse is not built all at once; it is grown in incremental steps over a period of time	1 2 3 4 5 6
Having commitment by a high-level sponsor who is widely respected in the company	1 2 3 4 5 6
Training to help people better understand data warehousing so that they become more efficient in using the data warehouse	1 2 3 4 5 6
	Extent Item SHOULD CONTRIBUTE To Implementing Data Warehouse (1 = not at all .... 6 = very great extent)
Having someone in top management who is a visionary or a leader who recognizes that data warehousing provides an opportunity to exploit data for business purposes	1 2 3 4 5 6
Recognizing that a data warehouse containing external data provides the ability to compare and contrast industry-wide external data with an organization s internal data	1 2 3 4 5 6
Recognizing that the data warehouse will ultimately support both operational people and informational people (decision makers)	1 2 3 4 5 6
Recognizing that all requirements may not emerge until near the end of a data warehousing project	1 2 3 4 5 6
Understanding that implementing a data warehouse is driven by a continually expanding logical data model that leads to reusable data contained in the data warehouse	1 2 3 4 5 6
Using innovative ways to get people to tell you about their thought processes, problems that need to be solved, measures to use, and the data needed for those measures	1 2 3 4 5 6
Recognizing that the support for building a data warehouse typically comes from the departmental level, not top management	1 2 3 4 5 6
Understanding that data warehouse data is reusable, but does not produce system procedures that are any more reusable than traditional environments	1 2 3 4 5 6
Understanding the importance of maintaining historical data in the data warehouse	1 2 3 4 5 6
Having a fundamental methodology from which one can select the portion(s) that are useful for a particular project	1 2 3 4 5 6
Using prototyping tools	1 2 3 4 5 6
Having a developmental approach that may be the reverse of the traditional life-cycle development methodology	1 2 3 4 5 6
Training people on what can or can not be reused from one data warehouse project to another	1 2 3 4 5 6
Understanding internal company politics	1 2 3 4 5 6
Having an adequate organizational infrastructure	1 2 3 4 5 6
Understanding that tools of today are easier for people to master and may reduce the amount of training required	1 2 3 4 5 6
Insuring that data in the data warehouse is properly integrated	1 2 3 4 5 6
Strictly adhering to the steps specified in the chosen methodology and the sequence specified by that methodology	1 2 3 4 5 6
Having an enterprise-wide perspective without feeling the need to create the enterprise-wide data warehouse in a single project	1 2 3 4 5 6
Starting with some initial requirements by having someone tell the project team what types of data are important to the business, the history that should be kept, and other high-level requirements	1 2 3 4 5 6
Recognizing that a data warehouse containing external data coupled with internal organizational data yields a more potent way to look at business realities	1 2 3 4 5 6
Seeing the potential for consolidating multiple datamarts to build the enterprise model of the data warehouse from the bottom-up	1 2 3 4 5 6
	Extent Item SHOULD CONTRIBUTE To Implementing Data Warehouse (1 = not at all .... 6 = very great extent)
Realizing that the star schema method makes it possible for datamarts to be created so that individual functional areas get access to the data in a way that helps people understand it	1 2 3 4 5 6
Acknowledging that creating information architectures and performing enterprise-wide data modeling are very expensive, long-term types of activities	1 2 3 4 5 6
Recognizing that the star schemas created for different datamarts are typically very different because they are based on different processes	1 2 3 4 5 6
Recognizing that the first generation of data warehouse users typically supply a lot of the requirements for the data warehouse	1 2 3 4 5 6
Using CASE tools for modeling processes while implementing a data warehouse	1 2 3 4 5 6
Understanding the importance of cleaning up the data from disparate sources by integrating different versions of data to help people see and understand different pictures of reality that occur over time	1 2 3 4 5 6
Having an overall data warehousing architecture based on one or more of the following: business objectives and drivers, information architecture, application architecture, and technology architecture	1 2 3 4 5 6
Recognizing that people may not be able to specify all the data they will need for decision-making purposes at the beginning of the data warehouse project.	1 2 3 4 5 6
Having a project plan to use in analyzing how well you did in completing the project	1 2 3 4 5 6
Realizing a high percentage of data warehouse data is internally generated data	1 2 3 4 5 6
Creating or obtaining methodology training materials/resources	1 2 3 4 5 6
Recognizing that the more technologically sophisticated people need less training on how to get data from the data warehouse	1 2 3 4 5 6
Using external consultant support in an orderly fashion that progresses from helping set up policies and procedures on the first project(s), overseeing the next one, helping assure quality in the third one, and then eliminating the use of consultants altogether	1 2 3 4 5 6
Understanding of and belief in data warehousing by key functional and information systems managers	1 2 3 4 5 6
Managing resistance to change	1 2 3 4 5 6
Having a clearly defined scope for a data warehouse project so that peoples expectations will not exceed what can actually be delivered by the data warehouse	1 2 3 4 5 6
Prototyping the incrementally progressing design in a data warehouse project	1 2 3 4 5 6
Understanding that the traditional data dictionary supplied with most CASE tools may be of little use in implementing and using a data warehouse	1 2 3 4 5 6
Recognizing that cleaning up all the data warehouse data at the source requires going into old legacy code and addressing the lack of integration of the data	1 2 3 4 5 6
Realizing that smaller data warehousing projects may be more readily funded because they deliver something that is useful more quickly and require fewer resources for shorter time periods	1 2 3 4 5 6
Being disciplined in doing the right things in the correct sequence	1 2 3 4 5 6
Having a good set of query tools to support people using the data warehouse	1 2 3 4 5 6
Acknowledging that process modeling, work-flow modeling, and event modeling are optional in building and implementing a data warehouse	1 2 3 4 5 6
	Extent Item SHOULD CONTRIBUTE To Implementing Data Warehouse (1 = not at all .... 6 = very great extent)
A data warehouse that includes industry specific external data that can be shared between competitors, such as that you might find in industry specific data models	1 2 3 4 5 6
Understanding that senior managers may not be enthusiastic about data warehousing projects until they recognize the capabilities of data warehousing in helping people use, understand, and analyze data	1 2 3 4 5 6
Using public training on data warehousing provided by a university, professional development seminar, or vendor seminars	1 2 3 4 5 6
Giving consideration to technological advances like database management system designs that allow the star schema, data extraction and transformation tools, metadata repositories and tools, etc.	1 2 3 4 5 6
Determining inadequacies in the organizational infrastructure in supporting the possible increased loads across networks after implementing data warehousing	1 2 3 4 5 6
Recognizing that organizational personnel may perceive that implementing a data warehouse for decision support may be a threat to their job security	1 2 3 4 5 6
Good business savvy, technology savvy, motivation, and commitment to get the right combinations of things required in a data warehouse project	1 2 3 4 5 6
Knowing that security, data archiving, and back-up and recovery may not be adequately provided for when datamarts are created in functional departments	1 2 3 4 5 6
Understanding that traditional economic measures such as ROI may not reflect the full value of a data warehouse	1 2 3 4 5 6
Using an iterative data warehouse project life-cycle that includes building something, testing it, letting people use it, and then redesigning what was created before moving on to another phase of the project	1 2 3 4 5 6
Acknowledging that data warehousing puts tremendous pressure on external data providers to put their data in a useful format, very detailed, very accurate, etc.	1 2 3 4 5 6
Demonstrating the new capabilities of the data warehouse to aid people in identifying new decision-support requirements that they might not have originally envisioned	1 2 3 4 5 6
Training people about strategic decision support	1 2 3 4 5 6
External consultant support to provide significant leverage in implementing a data warehouse from the standpoint of experience across companies and experiences with different techniques	1 2 3 4 5 6
Using a steering committee to help set priorities for the data warehouse project team	1 2 3 4 5 6
Understanding that implementing and using a data warehouse requires typical commitments to change, i.e. accepting and preparing for change, etc.	1 2 3 4 5 6
Designing the data warehouse to support both pre-canned data acquisition and impromptu or ad hoc data discovery for decision making purposes	1 2 3 4 5 6
In modest size organizations, starting with a central data warehouse and then building outward toward functional departments or datamarts	1 2 3 4 5 6
Having support from senior management to increase infrastructure capabilities where needed	1 2 3 4 5 6
Recognizing that a table of data important to one datamart or business functional area may be exactly the same table used for another datamart or different functional area	1 2 3 4 5 6
Recognizing the possible need to modify available CASE tools to support the chosen data warehouse project methodology	1 2 3 4 5 6
	Extent Item SHOULD CONTRIBUTE To Implementing Data Warehouse (1 = not at all .... 6 = very great extent)
Including company-related external data in the warehouse to help out-compete a company s competitors through data mining of information about customers, suppliers, and other external environment aspects	1 2 3 4 5 6
Earning the commitment of top management as initial data warehousing projects are completed by demonstrating data warehousing capabilities through answering questions that could not be readily answered in traditional decision support environments	1 2 3 4 5 6
Providing continuous training to make sure that all potential users are adequately trained and a data warehouse becomes viewed as an important corporate asset	1 2 3 4 5 6
Using external consultant support to assist in transferring new technologies into the data warehouse infrastructure	1 2 3 4 5 6
Having a complete understanding of the capabilities of the existing organizational infrastructur	1 2 3 4 5 6
Building a data warehouse on an all at once basis rather than using a process of incremental builds	1 2 3 4 5 6
Determining chargeback mechanisms for creating and maintaining the data warehouse	1 2 3 4 5 6
Having good metadata to provide a detailed attribute map of all data contained in the data warehouse	1 2 3 4 5 6
Insisting on certain standards for products from the development cycle, such as the completeness of the metadata or completeness of the data model for that business subject area	1 2 3 4 5 6
Tuning and honing each datamart through the support of end-users for the particular function that it is trying to provide for that area of the business	1 2 3 4 5 6
Using a CASE tool to add attributes as they are needed, as the data warehouse is incrementally built to produce an enterprise-wide view of data	1 2 3 4 5 6
Training information systems personnel to help them understand that a data warehouse is completely different from existing structures	1 2 3 4 5 6
In very large corporations, building a data warehouse from the departments inward towards a centralized data warehouse (integration of departmental datamarts)	1 2 3 4 5 6
Understanding what data is available, what data is needed, and where to find that data	1 2 3 4 5 6
Realizing that data warehousing completely changes the way people (decision support people, analysts, and even executives) do their work	1 2 3 4 5 6
Understanding that data warehousing requires a lot of the right culture, the right level of people, and an organization that has appreciated the value of information as a corporate resource for some time	1 2 3 4 5 6
Depending on vendors who have been a positive influence on the company in the past in other ways, but who may not be helpful in building, implementing, and using a data warehouse	1 2 3 4 5 6
Allowing people to test drive a prototype to help them identify latent requirements and more readily see what is possible with a data warehouse	1 2 3 4 5 6
Using tools that support text search functionality for automatic indexing and retrieval to use in finding occurrences of phrases which are near other phrases in textual data in the data warehouse	1 2 3 4 5 6
	Extent Item SHOULD CONTRIBUTE To Implementing Data Warehouse (1 = not at all .... 6 = very great extent)
Understanding that from the perspective of decision analysis, the data warehouse data model is fundamentally unstable because query outcomes that answer "what if questions" continue to spawn a series of better what if questions until the answers to subsequent questions cannot be answered with data currently in the warehouse	1 2 3 4 5 6
Having reusable metadata in the data warehouse	1 2 3 4 5 6
Using a steering committee to help resolve integration issues that the project team can not or should not resolve	1 2 3 4 5 6
Dealing with perceptions about the high volume of data needed for the data warehouse	1 2 3 4 5 6
Demonstrating data warehousing capabilities by showing the capability to answer questions that could not be readily answered in traditional decision support environments	1 2 3 4 5 6
Recognizing that the firm s competitors are implementing data warehousing	1 2 3 4 5 6
Managing people s expectations about the capabilities of the data warehouse	1 2 3 4 5 6
Understanding what is reusable in the data warehouse and what is not	1 2 3 4 5 6
Realizing that a company can customize a commercially-available industry-specific data model to more closely fit its organization and its environment rather than building its own data model	1 2 3 4 5 6
Realizing that commitment to building and implementing a data warehouse must initially focus on developing a business case for the value of data warehousing	1 2 3 4 5 6
Realizing that having a data warehouse may change people's jobs, which means that they must be trained on how to perform their jobs as they have changed	1 2 3 4 5 6
Acknowledging that the business model of the organization may be of little value in the implementation of a data warehouse	1 2 3 4 5 6
Using prototyping in the initial stages of a data warehouse project to show managers how having the warehouse can impact the business	1 2 3 4 5 6
Having a cross-functional data warehouse project team that includes functional personnel, IS personnel, managers, and trainers	1 2 3 4 5 6
Having project team members with effective communications skills	1 2 3 4 5 6
Recognizing that different training needs will exist for personnel experienced in data warehousing, novices to data warehousing, and new hires in the company	1 2 3 4 5 6
Recognizing that holding the line on the project scope is not so critical if people understand the ramifications of a scope change and its effect on the total project	1 2 3 4 5 6
Recognizing that the first generation of data warehouse users generally receive better training than second or subsequent generations of users	1 2 3 4 5 6
Including an overall architecture in the long-term plan	1 2 3 4 5 6
Recognizing that IS personnel may feel threatened by data warehousing since it may provide better responsiveness to people than traditional systems developed by IS personnel	1 2 3 4 5 6
	Extent Item SHOULD CONTRIBUTE To Implementing Data Warehouse (1 = not at all .... 6 = very great extent)