Data warehouses are complex systems that can be difficult to implement. The reason for this is that a data warehouse is made up of numerous smaller, individual systems. Each of these smaller systems must be planned, designed, and implemented correctly to create a larger whole. A data warehouse is a repository of raw data that has been organized and structured so it can be easily analyzed and understood by people who might not regularly analyze large datasets.
A data warehouse strategy needs to be created as part of any implementation because it will help you identify your objectives, discover the best way to achieve those objectives while minimizing costs and risks, and make sure you’re keeping track of important milestones along the way.
How to Plan and Execute a Data Warehouse Strategy?
Step 1: Determine Your Data Warehouse Strategy Goals
Before creating a data warehouse is even an option, you need to decide what your specific goals are. This will help you create a strategy that is specifically tailored to accomplishing those goals. For example, if your goal is to understand customer behavior, then you will want to choose specific ways to analyze the data that is being collected in the warehouse to determine what information is being used and how customers are interacting with your products or services. The tables and charts you put into your data warehouse might also be determined by your goals.
You might decide to use different types of charts and tables based on how you want your organization to analyze data. For example, if you want your organization to be able to answer questions about its business, you might decide to use tables that include metrics like transaction counts and frequency. On the other hand, if you want to be able to answer questions about customers, then you might decide to use charts with metrics like the lifetime value (LTV) of customers or the number of transactions performed by customers.
Step 2: Decide On Data Warehousing Architecture
After you have decided on your goals, you need to decide how you want to organize your data. This will be determined by the type of architecture you choose for your data warehouse. For example, you can have a star or rectangular architecture. There are also hybrid architectures that combine two or more different styles of architecture together. Your strategy will also help you determine the type of architecture to choose for your data warehouse.
The next decision you need to make is about your data storage technology. This choice will be determined by the quantity of data that you have and the type of data that you have. You may have data that is primarily textual (like product details or customer actions), data that is primarily numerical (like sales figures or click rate), or data that is both textual and numerical (like geolocation data).
For example, if your data consists primarily of product details, you will want to choose a storage medium that is better suited to handling textual data like text files. However, if your data consists primarily of numerical data, you might want to choose a storage medium that is better suited to handling numerical data like tables or tables that can be indexed.
Step 3: Build a Roadmap of Tasks and Events
Next, you will need to create a roadmap that outlines your timeline for implementing the data warehouse, the tasks that will be performed during the implementation, and the events that will take place along the way. This roadmap should include dates for each task, who will be responsible for performing that task, and the length of time the task will take. Furthermore, the roadmap for your data warehouse strategy should include dates for the key milestones that will allow you to track your progress.
For example, you might include dates for when you will conduct a pilot run to test the operation of the new data warehouse when you will bring in an expert to conduct a baseline analysis of the data you have now, and when you will allow your team to start using the new data warehouse for real analysis.
Step 4: Determine the People, Skills and Tools You’ll Need
Picking people, skills, and tools for your data warehouse implementation is a crucial part of the implementation process. This is because the people who will be performing the implementation tasks will determine which tools they will use, so finding the right people is critical to the success of the project. Furthermore, picking the right people for the implementation will also determine which tools you will use. For example, if your project is being done by a team of analysts, then you might decide to use a tool designed for managing an analysis team and the analysis work they do.
On the other hand, if your project is being done by a business analyst, then you might decide to use a tool designed for managing a BI implementation project. If you’re unsure whether your project is being done by a business analyst, then you can use the project maturity model (PMM) to help you determine your project’s maturity level.
The PMM was created by the Software Engineering Institute (SEI) at the University of Pittsburgh to help organizations decide how mature their projects are. The PMM uses five categories to classify project maturity, and each category has a level of maturity. At the midpoint of each category, there is a level of maturity that is most appropriate for critical projects like a data warehouse strategy project.
Step 5: Estimate Task Time and Task Costs
The time it takes to complete each task will determine the feasibility of completing the entire project. In other words, if some of the tasks take longer than expected to complete, then the entire project will be delayed. You should also consider the costs of each task to help you estimate how much time and money each task will take. Some of the things you might want to consider for each task are the time it will take to perform that task, the skill level required to perform that task, and the tools required to perform that task.
Step 6: Confirm Your Strategy and Roadmap
Once you have created a roadmap and roadmap dates, you need to confirm that the roadmap is accurate. This can be done by comparing the dates on the roadmap to the dates when the actual tasks were completed. You should also check your assumptions against what has actually happened to confirm that your data warehouse strategy is working as expected.
Furthermore, you should also compare the dates on your data warehouse strategy roadmap with the implementation dates for your other organization initiatives to make sure your data warehouse implementation timelines aren’t being delayed by other projects. If your strategy and roadmap dates don’t match up, you should investigate why that is the case. Perhaps there has been a delay to your other initiatives, or perhaps there is a new way of doing business that has resulted in other initiatives being delayed.
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