Once the dust settles from the panic surrounding the new millennium, enterprises the world over will still be faced with the requirement to provide an easily accessible, integrated, scrubbed source of historical and current data. While data warehousing is becoming an established science and a burgeoning economic segment, it still lacks a clear historical perspective. This lack of perspective prevents practitioners, IT and business management from fully understanding the organizational evolution required for enterprises implementing data warehousing.

Three distinct phases of data warehousing organizations have evolved, each with its own unique distinguishing characteristics. If you have implemented, are in the process of implementing or are planning a data warehousing effort, it is critical to know where you are along the organizational evolutionary path of data warehousing. It is often fatal to let the business cultivate Phase 3 expectations, while you are struggling to deliver Phase 1.

Phase 1 organizations are consumed by the data warehouse methodology of "build it, and they will come." While wise organizations have achieved successes by building to solve specific business pain, the "buy to build" mind-set still pervades this segment. These organizations are primarily trying to "automate the past" and tend to conduct user interviews based on a data- driven scenario, instead of a business process-driven scenario. Other defining characteristics are:

* "One version of the truth" mission statement;

* Build teams consumed with integrating tabular data;

* "Buy to build" mind-set, ignorant of, or in denial of the systemic nature of a data warehouse or data mart system;

* Still on a very steep and high learning curve;

* Building a system to almost exclusively serve a knowledge worker/power user audience;

* Limited business scope of the project;

* End-user access and analysis tools primarily in the Q&R (query and reporting) and LowLAP (low-end, on-line analytical processing) segments;

* Highly likely to be building the system manually;

* Limited to no meta data;

* Limited to no system monitoring capability;

* Little to no mission-critical status for anything that has been built to date;

* Little to no "Butler Factor." (This measurement is named for Tim Butler, a marketing manager for GE Medical Systems, whose critical evaluation factor for any technology is, "What will be different on a Tuesday afternoon?") Phase 1 organizations' efforts usually have little impact on the day-to-day life of the organization.

Phase 2 organizations have moved beyond their initial efforts. They have learned from the early failures of the "build it, and they will come" crowd and have built specific solutions to specific business pain. In doing so, they have constructed a system of architected data resources, both data warehouses and data marts, that provide high-level impact throughout their organizations. These organizations have built and cultivated a clear vision of the systemic nature of data warehouse and data mart systems and have created and sustained all the necessary technical and human processes required for sustenance and growth. Other defining characteristics are:

* "Change the business" mission statement;

* Data warehouse and data mart systems are fully integrated into the processes of the business;

* Multiple, closed-loop OLTP/data warehouse systems;

* Enterprise-class EMT (extract, mapping and transformation) tools;

* Very scalable technology and processes;

* Highly developed monitoring systems for all aspects of the data warehouse and data mart systems including load and utilization processes;

* Multiple, fully integrated, non-OLTP data sources including third-party data sets and data islands;

* Independent data warehousing team responsible for core competencies for all data warehouse and data mart initiatives;

* Sophisticated analysis capabilities, including data mining and statistical analysis;

* Data warehouse steering committee fully involved in prioritization and air cover;

* Industrial-strength OLAP tools;

* 100 percent server-centric tools;

* Access and analysis tools that are fully capable of resource sharing, scheduling and distribution;

* Integrated, open and extensible meta data repository, with a single point of entry for all users;

* Almost all members of the enterprise are users;

* Fully functional thin-client system access;

* Fully mission critical;

* Large business scope;

* Multiple architected data warehouses and data mart systems;

* 100 percent Butler Factor (i.e., everything is different on a Tuesday afternoon for every user).

Phase 3 organizations have moved beyond their own internal audiences and are using their data warehouses to leverage partner and public relationships. They have fully leveraged the power of integrated information resources to fundamentally change not only their internal world, but also those of other stakeholders, such as customers and the public at large. Technically, they have moved beyond mere tabular data and have incorporated multiple data types. Phase 3 organizations operate outside the borders of their own organizations and use their warehouses to change the lives of everyone. Other defining characteristics are:

* "Manage and share knowledge to change the world of all stakeholders" mission statement;

* Internal, partner and external closed-loop systems;

* Internal, partner and external user audience for all levels of access and analysis, from reporting to industrial-strength OLAP;

* 100 percent thin-client enabled for all processes;

* Multiple data types, including text, image, sound and video;

* Data warehouses and data marts that are only one element of the Phase 3 system;

* Fully integrated, multiple internal and external data warehouse and data mart systems;

* Fully replicable, extensible core competencies;

* Networks of build and sustenance teams;

* Enterprise business scope for internal and partner organizations;

* Mission-critical status for internal, partner and external organizations;

* 100 percent Butler factor for all internal, partner and external users.

If you're involved in data warehousing, it is imperative to be honest and realistic about where you are along the path. If you are solidly in Phase 1, it is suicidal to take on another subject area, another data mart or another data warehouse. It takes a significant amount of time for the organization to realize a reasonable "Butler Factor" from your initial efforts; and until people's lives start changing on a Tuesday afternoon, you are not going to have the widespread political impact you'll need to start or sustain further efforts.

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