Data Warehouse building has as many ways as a company can be built. The data warehouses are built in accordance with the business users’ needs in different functional areas, the different business conditions & circumstances, and the competitive pressures. Hence, every other data warehouse is different from each other. The purpose of these data warehouse is to gather and store all the information about the business activities in one place which is easily accessible to analytical tools.

But how can you build or create a Data Warehouse from the ground? The answer to this question has been discussed below in this article. It explains four different ways and 7 steps in which you can build a data warehouse.

Building a Data Warehouse from Scratch

Basics of Data Warehouse Building:

If you want to build a data warehouse for your organization then you must first plan the system for your data warehouse. You need to understand the questions and information which the “decision makers” and “analysts” require to make a smart and wise decision. You must try to adopt a warehouse system which can help the decision makers and analysts to access the accurate and timely information so that they can make the right decisions for the business.

Approaches to Architect the Data Warehouses:

To architect an appropriate data warehouse for gathering and storing the business data, the organizations can adopt four different approaches which are:

  • Top-Down Approach
  • Bottom-up Approach
  • Federated Approach
  • Hybrid Approach

Top-Down Approach:

With the top-down approach, the organization can build a “Centralized” Data Warehouse which acts as a Center Point for the whole analytic environment. In this system, the data is extracted from single or multiple sources and it holds the atomic/transaction data which is then integrated within a normalized data model of the enterprise. In the data models, the data is then summarized, dimensionalized, and distributed to data marts. The data marts are dependent due to the fact that the data inside marts is derived from a centralized data warehouse system.

Bottom-Down Approach:

The bottom-down approach helps to architect a “Star Schema” designed warehouse. This approach enables a quick deployment of data in dimensional data marts. The data marts in this system are not dependent and gather both atomic and summary data. The data marts in this architecture are built on the next and reuse the dimensions & facts which the users can use across the entire data mart to either get a single version analysis or the summary plus the single analysis of the entire atomic data.

Hybrid Approach:

The Hybrid approach uses a combination of both ‘top-down’ and ‘bottom-up’ approaches to architect a data warehouse. It uses the best features of both the approaches such as capitalization of speed & user-orientation (bottom-up) without compromising integration of data (top-down).

Federated Approach:

“Federated Approach is an Architecture of Architecture,” said Doug Hackney. This approach is not an architecture itself but it helps to lay down the plans to deploy a data warehouse system.  This approach suggests multiple integrations of ‘Heterogeneous’ data warehouses, data marts, and packaged applications.

Step-By-Step Guide to Build Data Warehouse:

The SQL Server Magazine has issued a “7 Steps to Data Warehousing”. To initiate the process of data warehouse building, first, you need data that can describe this process and select the KPI’s which will characterize the process.

The 7 steps to build the data warehouse are listed below using the example of training lone of businesses

Step1: Determine and Set Business Objectives

To start with data warehouse building the organization must determine and prioritize their goals and objectives, whether they want to increase the revenue by reducing the overall production cost per unit or whether increasing the overhead staffing is returning values to the business as determined. This will help to collect the appropriate data which the decision makers will need.

Step 2: Define the Processes

Once you have determined the goals and objectives of your business you must identify and define the processes that the system will require to operate. These processes will then correlate the key performance indicators identify earlier by the organizations. All the information generated by the correlation of KPI’s, factors which generates them and the processes. To make this system work and track the entire business process you must identify the entities and the structures which operate together.

Step 3: Collect and Analyze Information

Now you know the processes and other factors of the warehouse systems, you need to start collecting the raw information for these processes. You can collect the information for the respective processes from reports. After all the information has been collected, analyze the information and process it into the useful data. Furthermore, you need to understand the way people gather and process the information.

Step 4: Construct a Conceptual Data Model

Create a conceptual model for data in which you can put the processed data. Determine the subjects which you want to express in these data models. You can add up the data in the form of facts, tables, and dimensions which relate and coordinate these facts.

Step 5: Locate Data Sources and Plan Data Transformation

By reaching these steps the organizations know what they need and why they need it. At this stage, the organization starts looking for the ways and places from where it can gather the crucial information and how to transfer this information into the data warehouse. To move the data in the warehouse, you must have a consolidated data warehouse structure.

In addition, you can also move the data from one data warehouse structure to another. It is also possible to transform the data in-between the warehouse structure, to do this first you need to plan the whole data movement process when you need it and when will this take place.

Step 6: Set up the Tracking-Duration

You need to keep track of the data which you need to archive with the time because the data warehouses consume a huge amount of storage. These data warehouses retain data at different granularity levels. These granularity levels are same within the same warehouse structure but different in other structures. So when the data ages, you can move the old data to another level with less detail in a summary form.

Step7: Implement the Plan

Now that you have created the data warehouse structure and integrated all the data in it, all you need is to plan which is viable for scheduling and estimating the work and projects. The effective strategy for this is to plan the entire data warehouse and then implement a small part of it as a data mart. This will demonstrate the capabilities of the system.

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