The 21st century opened its doors to advanced technologies and developments that shape the future of modern societies. One of these developments is data. Data is the lifeblood of any organization. It facilitates the ease of the decision-making process, helps them strategize effectively and provides easy means to analyze company data to measure organizational growth.
However, while data holds considerable importance, understanding and leveraging its vast information reservoir can be daunting, especially without the right tools and know-how.
This is where databases and data warehousing come in. These essential data-management systems will organize and store data while also transforming it into meaningful insights that can drive business success.
St. Bonaventure University understands the intricacy of business analytics and offers students pursuing a career in this department an online business analytics master’s program. This program helps prepare students for future roles with big data. It gives an in-depth knowledge of databases and data warehousing and how students can navigate data warehouses from large datasets in their careers in two years.
This article will also discuss database and data warehousing — their significance, differences and uses.
What are databases and data warehousing?
Databases are primarily used to manage data and ensure these data are secured, which supports business operations and transaction processing. They handle the day-to-day operations and are the source of truth for most operational systems.
Databases are also essential for organizations. It ensures they have access to timely and accurate information. Banks use them to keep track of customer information, balances and transactions. Universities use databases to keep track of student records, course registrations and grades. And healthcare providers implement them to store patient information and medical records.
It’s also a reference godfather that can help businesses make a quick decision that enables smooth business processes. Proper data management guarantees data security and integrity, significantly risking data inconsistencies and breaches that can negatively impact the organization.
This is why databases are designed to handle data-management tasks, including data insertion, deletion and updating. They can store various data types, from text and numbers to dates and binary data.
There are numerous types of databases, including relational, object-oriented and hierarchical databases. One commonly used model is the relational database, created based on the proposed model by E.F. Codd in 1970. Relational databases compose a set of tables with data fitting into predefined categories.
Each table in this set has a minimum of one data category in a column, and each row contains specific data instances for the categories in the column. Some examples of relational databases include Oracle database and MySQL.
Another common type of database is the Object-oriented database (OOB), which classifies data as objects and classes. Here’s how it works. OOB groups data into classes of objects. For instance, books in a library are grouped into categories (such as fiction, history and science).
These books are often sub-grouped within those categories by factors, like the author’s name, title and publication year. Now, the different categories of these books are called classes, and objects are the individual books in the library. An example of a database that uses this model is PostgreSQL.
While a database is an excellent tool for maintaining data in a structured and accessible manner, it can also flop when storing massive data volumes from various sources. This is where data warehousing shows its prowess.
Data warehouses are large-capacity depots that can aggregate data from different sources to provide meaningful insights. Unlike databases that record daily transactions and operations, data warehouses are optimized for data analysis and querying large data sets. Databases are the duplexes, and data warehouses are the palaces.
Data warehousing involves data cleaning, integration and consolidation. This data-management system handles complex queries over large amounts of data, and these data are often denormalized to improve performance.
Data warehouses also summarize and store the data to improve query speed. This is why data warehouses can better handle complex analysis, such as data mining, that could be challenging or even impossible with a standard database.
Data warehouses are usually built using a process called ETL (Extract, Transform, Load). This process involves extracting data from numerous sources, transforming it into a constant format and loading it into the data warehouse. Examples of data warehousing technologies, include Amazon Redshift, Google BigQuery and Microsoft Azure.
Use of databases in different industries
Databases are the cornerstone of any data-driven industry, from education to telecommunications. They are primarily designed for recording and tracking daily transactions, ensuring data is available and consistent. Some of the industries that implement databases include:
- Healthcare industry
The healthcare sector implements databases to maintain patient records, track their treatments, manage inventory and schedule appointments. For example, a hospital might have a relational database where each table represents different aspects of patient care. One table might store patient demographics, another might record medical histories, and another might track prescriptions and treatments.
Databases give healthcare organizations an all-in-one method of handling all their responsibilities. Another way the healthcare industry can utilize databases to improve patient care is to ensure healthcare professionals have quick access to accurate patient data to help them make enlightened decisions on the best treatment path.
Finance and banking
Banks and financial institutions rely on databases for various operations, including managing customers’ accounts, processing their transactions and complying with regulations. A bank might use the database to store customer account information, such as account numbers, balances and transaction histories.
This way, when a customer logs into their online banking account or makes any transaction, the system queries the database to retrieve and update the necessary information. This ensures adequate transaction processing and accurate data, which is crucial to gain customers’ trust and complying with regulations.
Online retailers use databases to manage their inventory and provide personalized shopping experiences. A company’s system interacts with its database in cases where a customer places an order on their e-commerce website. This interaction checks if the product is available, updates the inventory and processes the purchase. Their database might also store user activity data, which the company can use to provide personalized product recommendations to their consumers.
Use of data warehousing in different industries
Some of the industries that utilize data warehousing include:
- Retail industry
Retail businesses can use data warehouses to analyze sales trends and observe customers’ buying habits and market patterns. This approach helps them inform their future business strategies to suit their target audience. How?
A company might extract data from various operational databases, including its sales, customer service and inventory databases, and then load this information into a data warehouse. That data warehouse transforms and consolidates the data to facilitate complex queries and analysis.
Now, the company might use this data compounded in the warehouse to identify which of their products is most popular, when their peak sales period is and discover trends in their customers’ behavior.
Knowing all this information can significantly impact how they’ll handle future business decisions, including when they should run their sales, which products they should promote and the best ways to manage their inventory.
The telecommunications industry utilizes data warehouses to analyze call detail records and network performance and realize customer churn rates. Telecom companies can use this data-management system to extract data from various sources, including customer and network databases, and load it to their data warehouse.
The warehouse analyzes the data to identify patterns and trends, like the common network issues their customers face or factors, contributing to their customer churn rate. This information can help them better shape their customer retention strategies and establish more efficient market positioning.
Databases and data warehouses play crucial roles in modern businesses and organizations. These data-management systems are integral to organizations, but what differentiates them? Discover the difference between the two data-management models and how each is essential to how an organization implements them.
Difference between databases and data warehouses
While these two serve similar functions, they have practical differences. These are five differences between databases and data warehousing:
The purpose of getting a data-management system is the first approach to seeing the differences between these two. This also depends on the work they’re designed to do. Databases are primarily used to manage daily transactions and operations. For instance, a bank’s database would record each deposit or withdrawal a customer makes in real time.
Each customer’s transaction must be recorded accurately and immediately, and the bank’s database must be able to handle numerous transactions concurrently, ensuring that the balance of each account is always up to date. This process makes the database an operational tool.
On the other hand, data warehouses store, integrate and analyze large volumes of historical data to support decision-making. The bank might load its daily transaction data into a data warehouse, and the warehouse helps them know how deposits and withdrawals vary by region or customer demographics.
It can also help them identify trends, such as an increase in withdrawals during the holiday season or a correlation between the size of deposits and the customer’s age.
How an organization intends to use the data management system heavily determines which system should be used. However, both models are integral to a company’s data infrastructure, each serving its unique purpose.
Data organization in databases and data warehouses is tailored to suit their distinct roles within an organization’s data architecture. This tailoring is evident in the use of Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP)systems.
Databases primarily employ OLTP systems. OLTP is a type of data processing that manages transaction-oriented applications online. For example, the OLTP system is designed to process many short, atomic transactions, meaning it can handle multiple transactions simultaneously in real time.
This is vital for businesses like e-commerce platforms, where speed, efficiency and real-time processing are critical. When customers place orders on an e-commerce site, the OLTP system updates the inventory, processes the order, adjusts the accounts and confirms the purchase, all in a fraction of a second.
Data warehouses, in contrast, use OLAP systems. OLAP processes fewer transactions but is usually more complex and involves large volumes of data. OLAP system’s strength is its capacity to analyze relationships between multiple business elements, including time, product and location, and still efficiently analyze and summarize the data.
For example, an e-commerce business might use an OLAP system to analyze its sales trends, such as understanding seasonal buying patterns — do customers buy jackets and boots during winter? Or what periods do people buy kitchen items the most? They can also use it to recognize the most profitable customer segments to help them tailor their marketing strategies to the right audience.
OLAP’s capacity to perform quick complex calculations on large amounts of data means it can provide business intelligence that informs strategic decision-making. While the OLTP systems in databases manage the day-to-day running of the business, OLAP systems in data warehouses offer insights that can shape a company’s future direction.
The design and structure of the data storage, also known as schema, is a fundamental difference between databases and data warehouses. Each system employs a schema design tailored to suit the work it’s meant to do.
Databases commonly use an Entity-Relationship (ER) model in their schema design. An ER model represents data as entities, attributes of entities and relationships between entities. How? If a university database has an entity for ‘Students’ and attaches features, like student ID, name and major, the same university might have another entity titled ‘Courses’, with attributes, like course ID, name and department.
Now, these entities might nurture certain relationships. In the case of this university, ‘Students’ might enroll in ‘Courses’. ER model is intuitive and aligns well with real-world constructs. This makes it perfect for databases where the organization is focused on managing transactions and maintaining integrity.
On the other hand, data warehouses often use star schema, known for OLAP systems. Star schemas divide facts and dimensions. Here’s how they work. Fact tables are the data to be analyzed (like the student’s grades). Dimension tables consist of descriptive details about the data, such as the students’ details, their course details and faculty details.
For example, the university data warehouse might have a fact table for ‘Exam Results’, with entries for each exam taken. The dimension table could provide details about each student and their courses.
Since these two data-management processes serve different purposes and volumes, their performance focus is also inherently different. They’re designed to handle distinct operations.
While databases are primarily transactional systems designed to handle a high frequency of write operations, data warehouses run the read operation. What does this mean? The read operation goes through previously recorded data in the memory to study new patterns and trends, while the write operation stores new information or value in memory.
An airline system is a good example. The airline’s database records thousands of transactions daily, such as bookings, cancellations and flight updates. Each of these transactions is a write operation, as data is being recorded or updated in the database.
The database is responsible for sorting these write operations quickly without compromising the data’s integrity. However, the constant write-up operations in a database can make it challenging to run complex analytical queries. These queries often require scanning large amounts of data, which can be slow and resource-intensive with databases. This is where data warehouses save the day.
Data warehouses are analytical systems designed to support read-heavy operations. This makes them perfect for the job. Warehouses are optimized for complex queries and aggregations across large volumes of data and still efficiently extract meaningful observations from the data.
The airline’s strategic planning team might use data warehouses to analyze previous booking data and evaluate the impact of their pricing strategies. These tasks involve reading and processing large volumes of data with relatively few write operations, and what system does it better than a data warehouse management system?
Data in a database is updated in real time. For instance, a news website database must be updated instantaneously as new articles are published, a comment is posted or a user logs in. This real-time update ensures that the website reflects the most current state of data, which is crucial for maintaining the operational efficiency and relevance of the site.
Data warehouses, however, are typically updated on a batch basis. The data is collected over time and then loaded into the data warehouse simultaneously. The frequency of these batch updates can vary based on how the analysis is performed, but it’s usually less frequent than the real-time updates of a database. For instance, the data warehouse for the news website might be updated daily or weekly with data about user behavior on the site.
Databases and data warehouses are vital in modern information management as they facilitate efficient storage, retrieval and analysis of vast data. These systems are essential to various industries and data-driven processes, including business intelligence and predictive analytics.
However, their use demands expertise, selection based on organizational needs and adherence to data security and privacy regulations. These systems will also advance with technology, offering enhanced data management and analytics capabilities, which is why business analytics experts must arm themselves with the necessary educational knowledge.