Dimensional Lifecycle Road Map
As we know, data warehousing requires the technological knowledge as well as the knowledge of business processes to build an efficient data warehouse, we follow certain life cycle to build our data warehouse. These steps manages our workforce to develop an efficient data ware house and most likely yield greater productivity out of our data warehouse, if followed wisely. Therefore, we can conclude that the dimensional lifecycle to build data warehouse acts as a road map to successfully build an efficient data warehouse (Kimball, Page 332).
The lifecycle starts with the project planning and goes through numerous process of figuring out the requirements that contains technical as well as analytical requirements to develop and constantly maintain and grow the data warehouse.
The first step in data warehousing life cycle starts with project planning. We need to access readiness of our organization if they are ready to proceed ahead for the project (Kimball, Page 334).
If we take an example of a video store that is growing day by day and we felt as if we need to create a data warehouse for the business then we also need to figure out if creating a data warehouse is feasible or not. We need to consider culture and scoping of the video store (Kimball, Page 336). We need to see the cost of building the data warehouse and benefit that we get out of it. Then, we need to see if it is worth investing money on data warehouse for our store. We can hire individuals that are passionate about videos as well as sound on the database and data warehouse technology.
We need to develop a project plan which would ensure our progress on the project life cycle that has certain time frame to accomplish the task and the task that we already accomplished.
The second important phase of our life cycle is requirement definition. We need to find out the information that we need from our data warehouse, so that, the top level executives would find it easier to make a smart decisions to be productive and stay at the top in the competitive market. We can look at the current database of the video store and gather information for the questions that would not be answered correctly by our current database which would hamper our productivity for the store.
We can also set the requirements by asking questions to the dimensional model that we create for our store. The question would be the question that the top level executives would want the answer for the store to be more productive. Example of one requirement for our store would be, "How many people come to our store with our ongoing marketing promotions?".
We need to document the requirements we come up with. We need to have the requirements for architecture we would have for our data warehouse. We need to create a dimension table and a fact table for the requirement we come up with. We should also find out the analytical requirements for our video store.
We finally deploy the data warehouse and keep on performing the maintenance and growth because with time, our requirements and business process changes and we need to accommodate the change. This is a never ending process and there is always a scope for change in the data warehouse.
Basically, data warehousing process seems to be more organized with the application of dimensional lifecycle road map that only helps the team responsible to build the data warehouse of the organization. This life cycle doesn't have a hard and fast rules and could be different for different organizations. It is just a guideline for the project team members and the project managers which proves to be yielding productivity and they are followed by many organizations. I personally, feel that this is very helpful for the members of the data warehouse team to accomplish the milestone and deliverables on time and with greater efficiency.
Kimball, Ralph, and Margy Ross. The Data Warehouse Toolkit: the Complete Guide to Dimensional Modeling. New York: Wiley, 2002. Print.