Enhancing BI: The Impact of Analytical Databases
Analyzing large amounts of data has challenged many organizations, hindered by poor database performance and organization. But analytical databases have solved these issues by efficiently collecting and storing data, enabling businesses to run analyze it and run queries, which enhances business intelligence. In this article, you will learn what an analytical database is, its impact on business intelligence (BI), and why you should consider implementing one.
What are analytical databases?
An analytical database refers to a data management platform designed to store and organize data for business intelligence and analytics applications. Analytics databases are read-only platforms with a focus on quickly returning queries and easier scalability. They are often integrated as a component of a larger data warehouse system.
The benefits of analytical databases for BI
Analytical databases offer several benefits, including storing historical data for trend identification and precise predictions. They also help businesses by:
Faster query response time
Users can quickly evaluate large volumes of data since analytical databases are designed for speedy query performance.
Columnar data storage
A design that uses columns for organization rather than rows enables very quick querying of enormous numbers of data points. Column-based designs scale better for large amounts of data than conventional row-based designs.
Improved data compression
The columnar design of analytical databases also allows them to compress the data more efficiently.
Advanced analytics capabilities
Advanced analytics capabilities like data mining, predictive modeling, and statistical analysis are frequently included in analytical databases. Business users may derive greater insights from their data using these models and make more informed decisions.
Scalability
Analytical databases are highly scalable thanks to their columnar design and designed to manage large volumes of data. Since the performance won’t be affected, organizations may add additional data and users as needed.
Deeper insights
Data mining, predictive modeling, and statistical analysis are examples of the advanced analytics found in analytical databases.
How analytical databases work
Large numbers of complex queries can be handled effectively by analytical databases due to their design. They achieve this through:
Columnar Storage: Analytical databases store data in a columnar format rather than the traditional row-based format. This enables faster query processing and greater compression of data.
Indexing: To speed up the processing of queries, analytical databases employ indexing techniques. They create indexes on columns that are frequently used in queries, allowing the database to find relevant data quickly.
Parallel Processing: Some analytical databases are built to take advantage of the parallel processing capabilities of modern hardware. They can divide queries across numerous processors or nodes, which speeds up query processing.
Aggregation: To expedite the execution of queries, analytical databases can precompute aggregations of data. For instance, they can compute the sum or average of a column in advance and store it separately, enabling them to quickly deliver the precomputed answer when a query calls for that aggregate.
Data Partitioning: To enable quicker data retrieval and processing, analytical databases might split data across different disks or nodes. They can also divide up the data according to factors like geography or time, which will speed up searches that just need a portion of the data.
In general, analytical databases are designed to query and analyze huge volumes of data fast and effectively, which makes them perfect for business intelligence and data warehousing applications.
Real-world examples of industries using analytical databases
For data warehousing, business intelligence, and advanced analytics, analytical databases are extensively utilized across a wide range of industries. Following are some actual instances of sectors that make use of analytical databases:
Retail: Analytical databases are used by retail businesses to store and analyze customer, sales, inventory, and other company data. Analyzing it enables them to make wiser choices regarding pricing, promotions, inventory control, and client interaction.
Healthcare: Healthcare facilities use analytical databases to store and evaluate patient, clinical, and operational data. Analyzing this allows them to improve patient outcomes, reduce costs, and organize their business more effectively.
Finance: Transactional data, market data, and other financial data are stored and analyzed by financial organizations using analytical databases. Running analytics on this data lets them quickly see trends, dangers, and opportunities as well as make investment judgments.
Manufacturing: Manufacturing businesses store and analyze production data, quality data, and supply chain data in analytical databases. They can optimize production processes, raise quality, and cut costs with the insights gained from analyzing this data.
All things considered, analytical databases are employed in a variety of industries to assist businesses in deriving meaning from their data and making fact-based decisions.
Common types of analytical databases
The most common types of analytical databases:
Columnar database: Databases that use columns instead of rows allow for the organization of data. As a result, the database engine needs to read fewer data points to process queries, which improves performance.
Data warehouse appliance: For efficient handling of analytical workloads, this is a combination of hardware, BI tools, and a database.
In-memory database: To allow the database engine to simplify the data for query processing, the nonrelational source data is loaded into system memory in a compressed format.
Massively parallel processing (MPP) database: When the source data is spread over a cluster of servers, the workload is shared for quicker processing.
Online analytical processing (OLAP) database: Aggregated source data is kept in multidimensional cubes to make it simple to analyze it using various data properties.
Choosing the right analytical database depends on a company’s specific needs. After understanding the types of analytical databases, it’s important to consider the challenges and best practices for effective business intelligence.
Analytical database challenges
There are several challenges to achieving effective business intelligence with analytical databases.
- Data quality: Ensuring the quality and consistency of the data is one of the biggest challenges in developing a successful analytical database for business intelligence. Making sure the data is correct, whole, and current is important for accurate results. Inconsistent data might cause erroneous analysis and judgment.
- Data integration: Data integration from several sources is another challenge when creating an analytical database. Spreadsheets, CRMs, and other systems are just a few ways businesses store data. Building a comprehensive view of the business may require a lengthy and complex process of integrating this data into one database.
- Data security: Security is important since analytical databases frequently hold sensitive company information. The database needs to be secured from illegal access and information leaks. Strong access controls, encryption, and other security measures must be implemented to achieve this.
- User training: Lastly, user adoption and training are necessary to create an effective analytical database. To make wise judgements, business users must be able to access and use the data. Training in database tools and analytics methods, along with clear data and meaning transmission, is necessary for this.
By addressing these challenges, businesses build an effective analytical database for business intelligence, unlocking the full potential of their data to make better-informed decisions.
Analytical databases best practices for effective business intelligence
Here are some best practices for using an analytical database effectively:
- Ensure data quality by implementing a data validation and cleansing process.
- Implement appropriate security measures to protect your database.
- Regularly perform maintenance tasks such as database backups, performance tuning, and indexing optimization to ensure efficient performance.
- Foster collaboration and communication among your data analysts, IT team, and business stakeholders to ensure effective use of the analytical database.
Future trends and developments in analytical databases and business intelligence
In the near future, it is likely that we will see a rise in the use of cloud-based analytical databases for their increased scalability, adaptability, and cost-efficiency. An increase in the application of artificial intelligence and machine learning algorithms for advanced data analysis and prediction is also on the horizon.
Analytical databases and Bold BI
To make informed business decisions based on your data, organizations require a front-end tool that can quickly retrieve and visualize data through dynamic user interactions. Utilizing a data visualization tool such as Bold BI, alongside a high-performance analytics database like Exasol on the backend, enables the unlocking of valuable insights from data.
Bold BI is a powerful business intelligence and visualization tool that can fetch data efficiently from analytical databases, making it easy to visualize and analyze. It provides a wide range of visualization options to present data meaningfully, making it easier to identify trends, patterns, and outliers.
You can easily share data and dashboards with colleagues and stakeholders using collaboration and sharing features in Bold BI. By embedding this business intelligence solution within your organization’s everyday application, you can transition your workforce more smoothly and access your KPIs without opening a separate program.
I hope you now have a better understanding of analytical databases and how they can help your organization to unlock the value of its data by speeding up the process of analysis.
Originally published at https://www.boldbi.com on May 3, 2023.
