Information is the most valuable resource of the 21st century. With the rapid development of data science and data mining algorithms, companies are able to know more about their clients, optimize their workflows, and understand each department's strengths and weaknesses.
How Enterprise Data Warehouse Impacts Businesses: Concept and Benefits Explained
However, the data should be extracted, transformed into a suitable format, and stored for further operations before the analysis. To automate this routine and labor-intensive process, companies use enterprise data warehouses. In this post, we’re going to figure out what enterprise data warehouse is, consider the components of a data warehouse, and how to build one for your business.
Table of contents
What is EDW?
Data warehousing is a process of collecting, storing, and analyzing data from different sources. Basically, a data warehouse is a relational database that contains various data types from different sources. Data warehouses are meant to store and analyze data rather than transmit it like ordinary databases do.
Enterprise data warehouses keep historical information for a very long period of time. With this data, you’re able to get business insights and draw up reports with ease. Think of it as a pie recipe. When you have only a recipe, you should go to the store (your data source) to get all the ingredients (your information). However, your ingredients may be unique, so you have to visit several stores situated in different parts of the city (your sales and marketing department). It takes much more time and money to bake such a pie. If you had a cupboard staffed with prepared and organized ingredients, you’d waste less time on cooking. This cupboard is your enterprise data warehouse.
The most significant benefit of EDW is that your analysts can focus on their tasks. They don’t have to figure out complex SQL queries and understand data structure because engineers have already done it for them. Now, analysts work with a user-friendly interface that doesn’t leave chances for confusion.
What Does EDW Serve For?
Entrepreneurs from various fields can benefit from data analysis these days. Businesses that rely on information in the decision-making process can use EDW to assess the information gathered years ago and come up with the smartest business moves.
Furthermore, EDW software provides insights into the workflow of every company’s department. With comprehensive statistics about your employees’ workflow, you can optimize their work, reallocate workforces to other assignments, and even automate some repetitive tasks.
In case you rely on data visualization, EDW solutions are great for building charts based on large amounts of data. Business analysts will work efficiently having all the data at hand. So, where can you apply enterprise data warehouses?
Let’s take a look at its use cases in several industries:
- Retail industry. With data warehousing, analysts can predict the demand for certain goods, conduct market research, and find new distribution channels. Analyzing internal data about purchases and customer preferences, you can improve the retail chain's efficiency.
- Healthcare. An enterprise data warehouse in healthcare stores electronic health records (EHRs) and saves queries for every patient that may be necessary in case of an emergency.
- FinTech. Banks can use EDW technology to store client data. With data mining algorithms, analytics can assess clients’ credit history and decide whether to approve or decline their loan requests.
Data Warehouse Types
As we’re already clear with the concept of enterprise data warehouse it’s high time to dive into more technical details of EDW and get acquainted with different types of data warehouse.
Enterprise Data Warehouse
EDW is the first and most common type of data warehouse. Companies use an EDW database to keep heterogeneous data from various sources in one place. After the extraction, transformation, and loading (ETL process), the data can be used for trend analysis and reporting. The enterprise data warehouse is the only type of data warehouse that stores extensive information from a variety of subject areas.
Operational Data Store
Unlike enterprise data warehouse solutions, operational data stores gather and keep short-term data. The ODS information is updated in real-time (every minute, every hour), and the data retention period becomes much lower compared to EDW. This type of storage is suitable for analysis of everyday tasks and superficial personnel performance analysis.
Data Mart is the same as an enterprise data warehouse except for one detail. While EDM saves all the data related to the company, Data Mart stores only a certain department's information. Thus, marketing and sales departments will have two different data marts. This type of storage can be used for highly accurate performance overviews of separate departments.
Enterprise Data Warehouse Architecture
EDWs have a complex architecture because they need to transmit, cleanse, store, and analyze the data. Each of these functions requires a separate layer that deals with its task. Now, we’ll single out each architecture type and figure out their levels.
This is the most widely used enterprise data warehouse architecture type. It includes three tiers responsible for different assignments.
A schematic representation of three-tier EDW architecture
This level is a database itself. Developers usually use relational databases for EDW solutions. Here, the ETL process happens, and the system prepares data for further analysis.
Here we have an online analytical processing (OLAP) system. OLAP is a data discovery and analysis system that helps your business analysts to work with the data effectively. All predictions, reports, and charts are processed at this stage.
There are two types of OLAP systems:
- MOLAP. An analytical system that works with multidimensional databases. MOLAP solutions are usually easy to master because of short queries and top-notch optimization.
- ROLAP. The same system that works with relational databases. ROLAP systems have complex queries and require an experienced employee to work with the data warehouse. However, it has a strong advantage over MOLAP. A ROLAP-based EDW software is more scalable because it handles larger volumes of data.
The middle tier is a kind of mediator between the EDW database and the end-user. Still, it’s hard for non-tech users to deal with the database without the interface. That’s why we need a section responsible for user interaction.
The top tier is a layer that simplifies the lives of ordinary users. Here, developers take care of the interface for end-users. Mind that the interface should be intuitive and clear for your employees.
Two-tier, or also known as client-server architecture, is less popular among database engineers than three-tier architecture. The characteristic feature of this type is the direct communication between the user and the server. The two-tier system consists of client applications and server databases.
This enterprise data warehouse architecture is easier to create and maintain. Without the OLAP layer, the data transmission gets faster. Still, two-tier EDW software is hard to scale. It makes this architecture less cost-effective with the growth of users. On top of that, a lack of OLAP level makes employees spend more time on data analysis.
It’s the least popular approach for enterprise data warehouse development. Single-tier architecture implies that the client, database, and server resides on the same machine. Because of the limited resource capacity, this strategy requires users to save as little information as possible. That’s why this architecture is suitable for minor projects or business logic testing.
How to Create EDW?
As you already know, a full-fledged enterprise data warehouse is a complex project that includes many components and steps you need to take. Database engineering, ETL process, data analysis algorithms, and other parts of your system take a lot of development time. On top of that, you have to find a skillful software development company that can handle this task.
Now, let’s figure out the components of the data warehouse that should be thought over during the development.
EDW database is the core of the whole structure. First things first, you have to decide on the database type. Usually, developers use relational databases for enterprise data warehouses. Relational or SQL databases grant high scalability potential and minimize the chance of anomalies. However, this approach works with precise and structured data.
Three advantages of SQL databases
If that’s not your case, you can go with the NoSQL database. NoSQL gives you more flexibility. If you can’t set the role for your data in the database, just save it with NoSQL. While relational databases require each piece of data to have a type, NoSQL lets you save separate files to folders. For example, you can save a whole blog page to the database as a flat file. Afterward, you can extract the data you need and manipulate it.
Main advantages of NoSQL databases
Still, if you’re working with sensitive data like users’ payment details, relational databases are required. Unorganized storing of sensitive data is forbidden by data security protocols.
To put all the data in order, developers use data sourcing, transformation, and migration tools. Let’s single out each step of the extraction, transformation, and loading process.
Three steps of the ETL process
Here, your enterprise data warehouse extracts the data from various sources. The data can be in different formats like SQL, NoSQL, XML files, flat files, and more. The main requirement is to store extracted data into the waiting area. You can’t place the data directly into the EDW database because it should be transformed into a unified format first. Mixing data types may ruin the whole structure of your repository and corrupt already existing information.
In the second stage of the ETL process, the system transforms your data into a unified format. Here are some examples of transformations:
- Filtering. Filtering the data by attributes. If the attribute doesn’t match with the required ones, it won’t be loaded into the data warehouse.
- Merging. Unifying separate attributes into one (for example, make one “First/Last Name” out of two “First Name” and “Last Name” attributes).
- Sorting. Sorting data based on some criteria (sorting employees’ salaries from minimal to maximal in a certain department).
- Cleaning. Filling up the NOT-NULL variables with some data, converting acronyms to full names.
After dealing with all the steps, the system is ready to load data into the warehouse. Depending on the data warehouse type, you can set the update frequency of your storage. For example, operational data stores can refresh the data every few minutes, while enterprise data warehouses can load new data daily or even weekly.
Metadata is the information about the data inside your EDW database. It may be confusing at first, but the example will clear things out. Let’s take a look at the following data:
At first, this seems like a useless combination of letters and digits. But the metadata will explain everything:
- Ph2458 - the physician’s ID
- Pt8234 - the patient’s ID
- 129.00 - the cost of the visit
- 00:34:23 - the length of the visit
That’s how healthcare establishments keep the data about each visit to a certain doctor. Thus, metadata keeps information that helps to understand the data inside the warehouse. Metadata may also contain details about the origin of data, the last attempt of modification, where it is stored, and more.
The primary aim of enterprise data warehouses is to provide comprehensive reports and clear information for analysts. But it’s impossible without correctly configured query tools.
Data mining algorithms are a primary concern for EDW software developers. Data mining lets analysts find new correlations, trends, and patterns in large volumes of data. With these algorithms, analysts can detect new opportunities and improve the decision-making process.
This article explains the concept of machine learning, data science, and Artificial Intelligence.
Steps to Create an EDW
It’s obvious that such a labor-intensive project as an enterprise data warehouse requires a whole team of developers that’ll take care of this job for you. But as a project owner, you still have your responsibilities that are crucial for the whole development process.
Clearly define the requirements
Before searching for a tech partner, you should clearly understand the purpose of a data warehouse in your company. Analyze the amount of data you need to store and what profits will automation of analytics processes bring to your business. After the assessment, it may appear that a simple database and business intelligence tools like SAP or Microsoft Power BI will be enough for you.
If you’re still sure that an enterprise data warehouse is your choice, you have to decide on the subject field, warehouse type, and architecture. These issues are hard to manage for a non-tech person, so you should find a software development company that has an experienced database engineer. Together, you can figure out what solution will be the best match for your business. Don’t forget about the feature list for your data warehouse. Without clear requirements, your development team can’t get to work.
Find a reliable tech partner
The success of the whole project depends on the experience of the software development company. When searching for a tech partner, pay attention to the number of successful projects, their complexity, and what technologies were used during the development processes.
Here, you can learn how to interview and hire reliable software developers if you don’t have a tech background.
Besides, keep an eye on the company’s set of services. If the team focuses on IoT devices or mobile development, this company may not have experience in managing complex EDW databases. That’s why you should search for a company that has an in-depth knowledge in the field you need.
Finally, the development cost also plays a vital role in the choice of your partner. Developers from different regions have different rates. Here are hourly rates in the most well-known outsourcing tech hubs.
How Can Cleveroad Help You?
At Cleveroad, we know how to create projects that matter. We have extensive expertise in various niches, and for more than five years on the market, we’ve delivered 170+ successful projects. Our database engineers are proficient with relational and NoSQL databases. They know how to create database structures and queries to build enterprise data warehouses with top-notch performance.