To have a proper data warehouse is the dream that many business analysts have. All the information about an organization’s activities is made available in one place and accessible to the analytical tools. But, when it comes to actualizing a data warehouse, many challenges are coming into the picture.
At the first point, you need to plan your customized data warehouse system by keeping the organizational goals in mind. You should also understand the questions that users may ask to ensure proper upkeep of the data. As the primary purpose of a data warehouse is to offer decision-makers the most accurate and timely information, you have to make the right choices while setting up a data warehouse.
To establish a data warehouse, we need to understand the organization’s business model and sales practices. To facilitate the data warehouse planning, you have to talk to the senior management staff to the organization’s bottom-level employees. First, we need to determine the organization’s business objectives, and then we should collect and analyze all information about the enterprise operations. Identify the core business processes which the enterprises need to track and construct a structural model of the data. Finally, we have to locate all data sources and plan various data types and transformations. Let us explore this process step by step.
Here’s How To Set Up a Data Warehouse
Step 1: Determining the business objectives
Every organization is in one or another phase of its growth and may require a proper mix of sales, administration, production, operations, and support personnel. The key decision-makers always want to know whether increasing the staffing’s overhead is returning the desired value to the organization. To answer the decision makers’ questions, we need to understand what is defined as success for a particular business. For this, in the first phase of establishing a data warehouse, you have to work with the management team and understand the business activities’ quantitative measurements. Identify what the decision makers use as the performance measures. These measurements are considered as key performance indicators (KPIs), which are numeric measures of the company’s activities like:
1. Net profit
2. Number of units sold
3. Gross profit
4. Total spent etc.
Collect all key performance indicators to put into a table known as the fact table. For support in terms of data warehouse setup, you can contact the RemoteDBA experts.
Step 2: Analyze the information
To gather performance indicators, you have to ask the top leaders questions as they will have various sources of information used to make informed decisions. Start with these data sources. The summary and analytical reports prepared by the analysts and supervisors are ideal examples of this. The major challenge to data warehouse designers is finding ways to collect information and write off certain types of information as inaccurate or unimportant. However, remember that nothing exists without reason. Before you disregard any source of information, you have to understand why it exists and its importance.
Step 3: Identify the core business
Once you have a clear understanding of the KPIs, you need to consider which business processes you need to correlate. For this, identify the exact entities to create the KPIs to be factored into the data warehouse. A data warehouse is ideally the collection of all these interrelated data structures.
Each of such data structures stores the KPIs for a specific business process, which further relates to the KPIs of other related processes and so on. To design a structure for tracking the business processes, you have to identify the entities that work together and create the KPI.
Step 4: Make a conceptual model
After identifying the business processes and the KPIs, you can next create a conceptual model of your data. For this, you have to determine the actual subjects, which should be expressed in the fact tables, and the dimensions related to these facts.
Decide the format for each key performance indicator for each business process. This will ultimately be aggregated to the form of LLP cubes, where the data should be in a consistent unit of measure.
This may seem to be very simple, but it is not so in the actual scenario. Next, you need to relate the dimensions of the KPIs. The interactions of various entities generate every row in the fact table. To add these facts, you have to populate all dimensions and correlate each activity.
There are many data systems where incomplete data exist. You have to correct these deficiencies before you can use these in a data warehouse. After making all the corrections, you can construct the dimensions and fact tables. The fact table’s major key is a composite key, which is made of a foreign key for each dimension table.
Step 5: Define the data sources and plan the transformations
Once you exactly know what you need from a data warehouse, next to you have to attain it. For this, you have to identify the critical information stored and how to move them into the data warehouse structure. Company data usually comes from different sources.
There may be CRM packages tracking the sales information, a time reporting system to keep track of the time, a sales management system to track sales, and operations management practice to record all operational data, and so on. You have to move all these data into a consistent and consolidated data structure.
The most difficult task here is to correlate information between the CRM and time reporting databases etc. At this phase of the data warehouse, you have to plan how to reconcile the data in various databases to be easily correlated and copied into the data warehouse tables.
You need to transform the data as you try to move it from one structure to another. Some transformations can be made through simple mapping to the database columns based on names.
Some other transformations may involve the complete conversion of the data storage types. Some may be unit of measure conversions, and some other transformations may summarize the data. Sometimes, the transformation may require complex programming and the usage of sophisticated algorithms to identify the values. So, you have to choose the right tools to perform data transformation in an ideal manner.
Once you reach up this stage, you can move ahead with setting the tracking duration and implementing a data warehouse plan. Data warehouse systems can provide the decision makers consistent and consolidated data about the organizational activities. With proper planning, the system can offer vital information on how various factors are interrelated to offer profit or loss to the organization.
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