We are witnessing advanced & dramatic growth in Business Intelligence and how individuals & businesses are taking advantage of it. Though for that to happen it is essential to be able to analyze and utilize data. And, for that, all the data needs to be stored in one place & structured to harness & analyze it.
And, implementing an Enterprise Data Warehouse (EDW) can be a great way to support your digital transformation journey. A data warehouse stores large data sets from multiple data sources to help derive valuable insights for informed decision-making. But building a warehouse is not simple.
Through this article, we will take a deep dive into the details of why & how to build a data warehouse. So, if you are looking to get outlines on the fundamental approach to EDW design & development, this post will be worth reading.
Here’s what we will cover.
Table of Contents:
Having your business data gathered in one place is one of the biggest advantages of having a DWH. Though it is not the only one. Data warehouse development enables you to leverage additional benefits such as:
Listed below are critical components you need to consider while developing your future data warehouse. It would include how many data sources will connect to DWH, the amount of information segregated by their nature & complexity, analytics objectives & more.
A typical data warehouse architecture has the following layers:
Note: Data analytics & Business Intelligence tools are further integrated on the top of the data storage layer.
Your data warehouse must adapt to the requirements of your business users from varied functional areas like HR, Supply Chain, Finance, etc. We understand that every data warehouse is unique and requires a different approach to development. The traditional approaches used to create a data warehouse include top-down & bottom-up.
Referred to as Bill Inmon’s approach, it deals with designing the centralized storage first and then the creation of data marts from summarized DWH data.
Referred to as Ralph Kimball’s approach it suggests building data marts first followed by incremental development of the data warehouse from independent data marts.
As different business areas may not follow the same rules & references for data management, the data marts
While it is not always feasible to rely on any one approach because both approaches have their pros & cons.
As an experienced data warehouse consulting and development services company, we customize the approaches to meet business needs. And follow a hybrid or federated approach, wherever applicable.
Combines the best of both approaches while utilizing;
This approach emphasizes developing a normal enterprise model and the first dependent data mart. Enables designing first several independent data marts in the normal form while using the star schema physical models to deploy them. Further, an ETL tool stores & manages the dependent & independent mart models. This is while synchronizing the variances in data. It also extracts & loads data from source systems into the independent data marts at atomic & summary levels.
It rationalizes using any means possible to integrate analytical resources & meet changing business needs. This approach acknowledges the reality that organizations & systems change over time to implement a formalized architecture. It emphasizes;
Financial firms may collect every client interaction with data warehouses. It allows them to;
DWH plays a significant role in offering you predictive & real-time analytics. They help with;
By utilizing machine learning algorithms, DWH helps automate the process of risk management. It allows;
To work as a single source of truth for the following data points;
To identify gaps & discover opportunities for cost-reduction & quality improvement across.
It is vital to set a pricing strategy by implementing a rate shopping software (for rooms & travel) integrated with a data warehouse.
The CRM platforms integrated with DWH allow the creation, access & analysis of guest profiles while obtaining a true “picture” of each customer. The list of valuable attributes recorded in the said profile would include;
An essential feature for retailers that enables them to scale operations
DWH assists retailers with getting a deeper look at customer & their interests
Enables performance tracking available across department, process & employee levels
We can answer all your questions about the DWH implementation plan, project cost, DWH architecture & technology stack.
Based on our experience in DWH development, here’s a suggestive plan to create a data warehouse from scratch. However, some steps may vary depending on project complexity, data quality, data analytics objectives & more.
We utilize reliable platforms from industry leaders like AWS and Azure to build high-performing data warehouses.
Some of them include:
Explore how we utilize Azure Analytics Services to help organizations leverage cloud-based analytics.
While all data warehouses are unique in their own way it becomes difficult to attribute a fixed cost to build one. Though typically for data warehouse development listed below are the factors that influence the cost:
We hope this guide clears all your questions & doubts about how to build a data warehouse. At Rishabh Software, we have the experience & expertise to provide custom DWH development to meet your business & technology needs.