5 Critical Data Warehousing Best Practices

A data warehouse is a scalable and flexible repository for centralizing data and providing analytics to organizations. A data warehouse enables organizations to create a single source of truth by integrating data from multiple disparate sources.

An enterprise data warehouse provides many benefits, such as breaking down data silos, consolidating data from different applications, and detecting gaps in data that would otherwise go unnoticed.

With businesses adopting data warehouses at a rapid pace, it is imperative that you are familiar with data warehouse best practices before building your first data warehouse.

Ensure That Stakeholders Are Engaged Early On

Stakeholders such as business analysts, department managers, and data scientists use the warehouse’s information for analysis and reporting purposes. Organizations that incorporate feedback from these parties are more likely to make informed decisions and are less likely to require substantial changes in the future.

For example, by working closely with senior management, data warehouse projects stay aligned with the organization’s overall business strategy. Getting the attention of these key decision-makers from the start is also an instrumental part of ensuring their commitment to the project. Data warehouse projects may never be implemented or may be terminated without the support of management.

Establish Solid Master Data Management (MDM) Practices

In MDM, the goal is to establish a controlled process for collecting and organizing accurate, consistent and verified master data as the enterprise’s system of record. A key challenge in MDM is ensuring that the data warehouse is fed reliable and accurate master data.

Data quality must be ensured across all of your data sources, records must not be lost or deleted in the warehouse, and anomalies must be tracked.

When done correctly, this will eliminate a large part of the transformation effort involved in the process of populating warehouses.

Determine How Frequently Data Needs to Be Loaded

Establishing how frequently your company needs to load data will help you define data use cases. In case several transactions are collected over a period of time, a process known as batch processing can be used to process large volumes of data all at once. The batch processing process does not require the assistance of data entry personnel, which reduces the operational costs of the business.

Another method is real-time data processing, which involves continuous input, processing, and output of data. With real-time data processing, companies are able to analyze data in real time and take immediate action when time is of the essence. Even though batch processing is often appropriate in the majority of organizations, real-time processing can have various uses within an organization.

Prepare A Data Flow Diagram

The Data Flow Diagram provides a comprehensive overview of all the company’s data repositories and how data moves within the organization. It is essential that you have a thorough understanding of the potential sources of data when building a warehouse. When you list your data inputs and their locations within the company data structure, you are ready to collect all those threads at the right time in the warehouse.

Consider Cloud Data Warehouses

Cloud data warehouse solutions offer all the advantages of the cloud, such as flexibility, scalability, and high availability, which can assist companies in fulfilling their data warehousing and business intelligence needs.

The cloud integrates seamlessly with a wide range of commonly used business applications, offers users self-service capabilities, and eliminates hardware costs and administrative burdens. Most providers offer their own query engines and pipeline tools, and some can connect to data stored anywhere within an IT network.

Driving Business Intelligence with Data Warehousing

To gain a deeper understanding of your data, it is important to centralize it. As a result, creating a data warehouse that addresses this need has become a strategic requirement for businesses. Creating a data warehouse requires careful planning and keeping in mind the build data warehouse best practices outlined above.

Using a well-structured data warehouse facilitates the management and analysis of data, which helps businesses make more informed decisions and improve business processes.