Analytics has advanced at a rapid rate and dramatically in recent years, and many organizations are taking advantage of it.
To be fully able to utilize ALL your valuable data it is essential that stakeholders within an organization are able to harness and analyze the relevant data.
Enter the modern Data Warehouse.
Don’t worry you can jump to the step by step process of how to build a data warehouse in case you are already comfortable with the basics.
A data warehouse plays an integral role in an organizations analytics system.
Initially data is collected from operational departments including but not limited to Sales, HR, Finance and External sources.
All of this data is consolidated in an ETL process that extracts data from the different data sources; transforming it in a common format and writes & loads it in a single destination database.
This single repository is the data warehouse.
Once the data is collected in single repository, it can be used for reporting and analysis by business stakeholders.
The best part is the trust you get out of using a single data warehouse – TRUST. Yes, you can be sure that all business stakeholders dealing with this data are 100% certain that all employees and external players are utilizing the same data source. So there is no disparity!
So what are the key elements of a data warehouse?
Enterprises often wonder what are the benefits of deploying a data warehouse. We have you covered!
All organizations have a common objective – to ensure they make the best business decisions.
A data warehouse once incorporated can benefit your enterprise greatly.
Some of the major benefits of a data warehouse are:
Data warehouse architectures are as complex as the information system that contains commutative and historical data from multiple sources.
Broadly speaking, there are three main approaches to constructing Data Warehouse layers: Single Tier, Two Tier and Three Tier.
The main focus of a single tier data warehouse structure is creating compact data sets and to minimize the amount of data stored.
This is essentially more useful in cases where the primary goal is to reduce redundancies but it is not as effective for enterprises with large data needs and multiple streams of data.
The two tier data warehouse structure is a client-server application.
In such an architecture there is direct communication between the client and data source server, we call it a data layer/database layer.
Normally, there is no intermediary application between the client and server in a two tier data warehouse structure.
The three tier data warehouse model is the most common architecture of data warehouses.
The three tier data warehouse architecture is the most commonly used data warehouse design in order to build a data warehouse by incorporating the most required Data Warehouse Scheme Model, OLAP server type and the required front end tools for reporting or analysis purposes.
It comprises of the Bottom, Middle and Top Tier.
As a preliminary process, before the data is loaded onto the repository, all the relevant data is identified from different sources of the system. The data is then cleaned up, to avoid repeating form its current storage point.
The next step involves transforming all these data into a single format of storage. The final step includes loading the data on the repository. Few commonly used ETL tools are:
ROLAP: Relational online analytical processing is a model of online analytical processing which executes an active multidimensional breakdown of data stored in the relational database, instead of redesigning the relational database into a multidimensional database.
This is applied when the repository comprises of only the relational database system in it.
MOLAP: Multidimensional online analytical processing is a model of online analytical processing that catalogs directories directly on its multidimensional database system.
This Is applied when the repository comprises of only the multidimensional database system in it.
HOLAP: Hybrid online analytical processing is a hybrid of both the relational and multidimensional database systems.
HOLAP allows the storing of data in both the relational and multidimensional formats.
The type of tool depends purely on the form of outcome expected. It could be an analysis tool, a query tool, reporting tool or a data mining tool.
Some of the commonly used Top Tier tools are:
Ok – So by now you have a clearer picture of what a data warehouse is, what are the benefits of a data warehouse and the architecture of a data warehouse.
In short a data warehouse is a business analyst’s dream – all the information about the enterprises activities gathered in one place, open to a single set of analytical tools.
So now that the basics are out of the way; let’s talk about the 7 primary steps in building a data warehouse in 2021.
How do you gather the requirements for a data warehouse/analytics project?
In most typical analytics projects, if you ask an enterprise stakeholder “what do you want” – normally the response would be either “We don’t know” or “We want everything”. In essence both responses are one in the same.
As much as it pains technology professionals to hear such responses, if you think about it, in most cases they are the right answers.
Gathering requirements for a data warehouse project is different to operational systems.
Designing a data warehouse is a business wide journey. Data Warehouses do touch all areas of the business, so every department needs to be on board with the structure and design.
Since the data you require is used by humans within the organization and those too from different departments, it is essential for aligning the functional and departmental goals with the overall project.
For example, if your marketing and sales data are not combined and aligned, your overall query is going to miss out on important components.
Each and every department needs to understand the purpose of the data warehouse, how it will benefit that department, and what kind of results including reporting and analytics can they expect from your warehousing solution.
The Requirements and Designing a data warehouse should focus on the following core objectives:
Once the requirements from the data warehouse are clear, it is essential to setup the databases and servers.
At the bare minimum it is important to setup a development environment and a production environment.
In most data warehousing projects there are three environments: Development, Testing and Production.
It is not accurate to think that it’s enough to simply have different physical environments set up. The different processes (such as Reporting, ETL, OLAP Cube) also need to be setup properly for each environment.
Having different environments setup is important for the following reasons:
Data warehouse modeling is the process of structuring and designing the schemas of the detailed information for the data warehouse.
The objective of data warehouse modeling is developing schemas describing the reality to the best of the system’s ability, which the data warehouse is needed to facilitate and support.
There are two main reasons why data warehouse modeling is a really important component and step as part of building a data warehouse in 2021 & beyond.
The process of extracting and organizing raw data, transforming it to make it much more understandable, and loading it onto a database is known as the ETL process.
Data coming from different data sources have a distinct structure, every data set needs to be transformed distinctly before using it for analytics.
Since, ETL process is responsible primarily for the in-between work, choosing a subpar or developing a poor ETL process can break your entire warehouse.
If you’re a CXO making the decisions you should primarily be targeting good visualization, optimal speeds and the ability to build consistent data pipelines between our new warehouse solution and your existing architecture.
Online Analytical Processing is a software category which allows users to analyze information from multiple DB’s at the same time.
It facilitates viewing business data from different points of views and is an important technology enabler for analysts.
Most business analysts need to frequently group, join and aggregate data.
With OLAP data can be pre-aggregated and pre-calculated which makes analysis faster.
OLAP databases are divided into one or multiple cubes.
OLAP DB’s are divided into multiple cubes to allow for faster analysis of data according to the multiple dimensions that define a business and its problems.
The advantage of OLAP Cubes is that it can be thought of as an extension of the modelling structure provided by a spreadsheet, which accommodates data in columns and rows i.e. two dimensional array of data.
The best part is that an OLAP Cube can accommodate any number of arrays, or Dimensions.
Regardless of the strength of the OLAP, if you can’t easily visualize the reports the data warehouse brings zero value to enterprises.
Hence, as part of the data warehousing initiative it is essential to focus on front end development as well.
What are the things to look for when developing the front end as part of building a data warehouse?
Well, by this point the business requirements have been captured, physical environment complete, data model decided and the ETL process has been documented.
The next logical step in this process is how business users will be able to access the data warehouse.
Front end development is in a nut shell how users will access the data for analysis and run the respective reports.
There are many options available, including building your front end through in house experts or purchasing it off the shelf. Primary drivers into a decision should include:
Once you’re ready and have done a rigorous quality assurance of the data warehouse, it’s time to educate and train your enterprise stakeholders who will be making important business decisions based on these data points.
So once you have crossed all these avenues – you’re ready to design and build a data warehouse.
This is the general route which most businesses use when building a data warehouse. We would recommend do a deep dive into understanding your specific requirements for a data warehouse development process.
See how Al Intisar’s consulting services can elevate your data and push your clean data to your data warehouse.