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Best Practices to Remember When Implementing Data Warehouses

Following best practices is a sensible approach in every industry; data warehousing best practices are no exception.

Examples of best practices are methods or techniques considered the best or optimal way to perform a particular activity, process, or practice. It is best to start with best practices and then adapt them to the specific needs of your business. Companies that continuously develop their best practices based on feedback from the industry, customers, and the internal environment gain a strategic, tactical, or operational advantage over their competitors.

It allows assets, skills, and resources to add value to the business, stakeholders, and customers. A data warehouse can be such a strategic asset for any business. Developing best practices for storing information in a single data warehouse requires the data warehouse to be optimally aligned with the company’s objectives.

Data held in a business is sometimes not used to the extent that it could be. In most cases, many companies make decisions based on their best knowledge and beliefs or experts’ choices. However, these decisions can be even more effective and meaningful if they are supported by intelligent data, insights, and an understanding of data users’ needs. This can be achieved through an optimally built and distributed data warehouse.

To function as efficiently as possible, companies rely on many best practices. These best practices are related to management methods, processes, and technologies. Below are some examples of best practices and considerations when using data warehouses. These should be used by businesses to maintain and maximize the value of the data warehouse.

Decide Whether You Need a Data Warehouse


A lot of organizations struggle to implement a data warehouse because they have yet to find a clear business justification for it. Companies that identify their data problems and focus on finding solutions are more likely to succeed. To gain a complete picture of the future potential of any business, for example, many companies make decisions without analyzing their data, while successful companies create plans and strategies based on the data. Business can standardize their data from different sources to reduce the risk of error and improve the overall accuracy. Data warehouses make accessing data faster and more efficient, allowing managers to develop data-driven strategies and stay ahead of the competition.

Decide Whether You Want a Local Or Cloud Data Warehouse


Should you set up a local or cloud data warehouse? A data warehouse consolidates business data from on-premises and cloud applications and acts as a single repository to support analysis and decision-making. Many businesses are choosing to replace on-premises data warehouses with cloud-based alternatives.

A local data warehouse provides complete control over a set of technologies. However, all hardware and software components must be purchased, deployed, and maintained. On-premises data warehouses have the advantage of better governance and regulatory compliance, as all data is stored locally.

Modern cloud data warehouses offer on-demand scalability and cost-efficiency through included features such as identity and access management and analytics. The initial investment is meager, and the cloud is responsible for data security. Another advantage of cloud data warehouses is that they provide better uptime and system and system availability. Outsourcing the maintenance and management of the data warehouse to a supplier frees up valuable time and resources. In turn, you can use them for analytics or other strategic initiatives.

Anticipate The Data to Build a Data Model


Data is ideally derived from an integrated organization-wide data model. In a data model you should use all the data you collect and analyze and all available services and products. The data model helps the company understand the value chains between different data processors and realize which data needs to be reviewed and how. In addition, creating an enterprise-wide data model reduces the development and maintenance time of the data warehouse.

Analysis Of Data Loading Frequency


Processing in batches is an efficient way to simultaneously process large amounts of data, where several actions are recorded over time. This method helps companies reduce operational costs, as it does not require specialized staff for data entry. In contrast, real-time processing involves continuous data entry, processing, and posting. During batch processing suits most businesses, some organizations require real-time data processing for specific applications. Real-time data processing and analysis enables organizations to take immediate and helpful action when it is crucial to act quickly. With real-time data management, stakeholders have the proper knowledge at the right time to take the right move.

Preference For the ETL Tools Over ELT Tools


In data warehouses, either extract, transform, load (ETL) or extract, load, transform (ELT) is commonly used as the data integration method. ETL and ELT are the two most widely used methods to aggregate and store data from different sources in a data warehouse. The main advantage of ELT over ETL is the flexibility and simplicity of storing new, unstructured data. It also provides instant access to all information, saving BI analysts time when processing new data.

Build A Good Team


Build a good team of experts in planning, designing, and implementing data warehouses. Building a good team is one of the essential best practices for data warehouse design, development, and implementation. No matter how good the technology is, without the right team members, the overall result will be disappointing. Data architects, data engineers, business analysts, and data analysts are vital individuals who can contribute to a successful data warehouse.

Summary Of the Best Data Warehouse Practices


Best practices in business intelligence and data warehouse go hand in hand. The better the technical infrastructure of a data warehouse, the better a company can collect, store, analyze, and visualize data for customer intelligence. Companies must ensure good data quality is maintained for business intelligence. A data warehouse can easily support any tool or application that needs data for business intelligence.

In the article, we have discussed several best practices for data warehouses. Depending on your organization and the challenges you face, you can add more best practices to the above best practices. Depending on your organization’s experience, challenges, and general market dynamics, these best practices can be applied across your organization.

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