Before any data can bring its value for a business, it must be analyzed. But before in-depth analysis, all data must be extracted from the data source, transformed into the proper format, and stored someplace. These complicated actions can be boosted with the use of an enterprise data warehouse or EDW. Let’s take a closer look at what an enterprise data warehouse is, break down its components, and discover the benefits it can bring to businesses.
Enterprise Data Warehouse. What is it all About?
Data warehousing stands for collecting, storing, and analyzing data from various sources. To keep it simple, a data warehouse is a database made for storing data from different sources, and its primary goals are storing and analyzing data, but not transmitting (like ordinary databases do).
Benefits of EDW in Business
It’s impossible to make data-driven decisions without information. Enterprises that utilize gathered data receive a significant advantage in the competition race and make wise business decisions.
Moreover, using EDW software, companies can get useful insights into the overall company’s workflow and workflow of each separate department. These stats help to optimize the workflow of the company and get the most out of current resources.
And the main cherry on top is an over-the-top data visualization. EDW tools help to make various charts and progress dashboards based on massive data arrays. This visualization helps to present data more conveniently and prepare it for analysis.
But what industries can benefit from using EDW? Let’s consider several use cases:
- Retail industry. With the use of an enterprise data warehouse, It would be easier for analysts to oversee future demand for goods, search for additional distribution channels, and make market researches more efficient.
- Healthcare industry. Within the EDW, all-important medical data can be stored along with electronic health records (EHRs).
- FinTech. Banks utilize enterprise data warehouses to store their client’s data. Moreover, with EDW, it’s much easier to get access to the client’s credit history, which boosts the loan acceptance process’s speed.
Types of Data Warehouse
All data warehouses can be split into three types that differ from each other in various aspects.
Enterprise Data Warehouse
This data warehouse type is the most popular one since many companies use it to store various data types in one place. To make the use of data, it must come through the ETL process. ETL is an abbreviation which means:
- E for Extraction
- T for Transformation
- L for Loading
When the ETL process is set and done, business data can be used for business purposes.
Operational Data Store
The main difference between ODS and EDW is that the operational data store’s main use case is to keep short-term data since all data in ODS is updated in real-time. This type of data warehouse is very useful for easy analysis of daily tasks and staff performance analysis.
Datamart is very much alike EDW except for one significant difference. EDW saves all company data from all sources, while data mart saves only specific data from a specific department. For example, marketing and sales departments will have two separate data marts filled with specific data related to each department.
EDW is a complicated structure with various layers that fulfill different tasks. Moreover, there are several types of EDW architecture. Let’s unblur all aspects and consider all EDW architecture types.
This is the most popular architecture type, which includes three tiers responsible for different tasks.
- Bottom tier. This layer is basically a database where all data stored.
- Middle tier. Within this tier, you can find an online analytical process system (or simply, OLAP). This system helps analysts to work with business data more efficiently.
- Top tier. This layer is made to simplify the work of the end-user. Users can see an interface and work with business data without inconveniences.
This architecture is often called client-server architecture. Two-tier architecture can be set up and maintained with great ease. Moreover, there is no OLAP layer, so data transmitting is much quicker.
But there are huge drawbacks. This architecture is not that popular since it’s harder to scale. Besides, without the OLAP layer, it becomes harder for analysts to work with business data. This makes this architecture type less cost-effective.
This type of architecture is the least popular. The main idea of single-tier architecture is that client, database, and server run on the same machine. Since resources are very limited, the users must save little data arrays. That’s the reason why this architecture type is an optimal choice for small projects and business logic testing.
How to Make Enterprise Data Warehouse?
Let’s consider each aspect of the enterprise data warehouse that should be taken into account.
This is one of the most important aspects of any EDU, and it is very important to decide on the type of database. Relational databases are commonly used. These SQL databases are designed to work with accurate and structured data, and this system has its undeniable advantages.
There is also an option to use the NoSQL database. This option allows you to save data in folders without applying a specific role to each data type. This greatly simplifies data storage. In addition, NoSQL databases have the advantages listed in the picture below.
This process is used to more clearly organize all the data in the database.
- Extraction. Data extracts from the data source to the waiting area.
- Transformation. Data transforms into a proper format (filtering by data attributes, unifying separate attributes into one, sorting data, and so on)
- Loading. All transformed data ready to be loaded into the data warehouse.
Metadata stands for information about the data in an enterprise data warehouse. Sounds complicated, right? To clarify all things about metadata, let’s take a look at the example.
Ph3269 Pt 4453 234.00 00:43:12
Looks odd, right? Right now, metadata comes to the rescue.
- Ph3269 – Physician’s ID
- Pt4453 – Patient’s ID
- 234.00 – visit cost
- 00:43:12 – the length of the visit
That’s the way hospitals store information about each visit of a patient to a physician. Metadata contains information that helps to understand the data within the EDW.
Properly created and configured query tools are a must-have for every enterprise data warehouse. Thanks to them, analysts have easy access to data stored in EDW.
Useful Steps to Make EDW
EDW creation is a complicated process that requires a big team of professional developers to manage this task fulfillment. Consider these vital steps before starting EDW creation.
Jot Down Your Requirements
To begin with, you must clearly define why your business needs a data warehouse. Roughly define the amount of data you want to store in it. It depends on whether you should use a huge data warehouse created from scratch, or you will have enough ready-made solutions.
Find a Tech Vendor That’ll Help You Out
Find a reliable software development partner that has extensive experience in the field that you need help with. If the company is inexperienced, then there is a high probability that difficulties may arise during development, and the product may come out of poor quality. Therefore, pay attention to the portfolio of completed projects, industries, and the list of technologies that the vendor uses.
We’ve covered all aspects of enterprise data warehouse creation. From now on, you’re armed with knowledge and ready to make your own enterprise data warehouse.