Data is a business’s most valuable asset. When given the ability to harness its power, organisations can transform their data into meaningful business intelligence (BI)—and act on this to stay ahead in an ever-more competitive landscape.

This is where the enterprise data warehouse (EDW) comes in. An EDW centralises and consolidates data from many sources—storing it in a denormalised structure called a star schema—making it easy to analyze, visualise, and forecast essential business metrics.

As such, a well-designed data warehouse is a desirable asset for all businesses. However, the lifecycle of data warehouse development has traditionally been long, frustrating, and expensive. Automating the process is a natural solution, but many business leaders need convincing automation isn’t something to fear.

So, can automation really relieve the burden of EDW development and allow organisations of every size to benefit from quality BI?

Challenges in traditional data warehouse development

Data warehousing projects have typically required massive investments of time and money, meaning that having a data warehouse and good BI has been limited to companies with deep pockets.

The process involved tedious methods of data scoping, modelling, transforming, designing, testing, data QAing, and deploying. And it took highly trained, technical SQL developers and subject matter experts in the business’s data to devise and code the best data sources for the company.

Traditional data warehouse development can also take many months to complete, and there’s been little standardisation of the process. While BI tools have attempted to simplify it, the multitude of data sources and business rules make development inherently challenging.

These struggles are compounded when technical skills are insufficient in-house, where the approach has been to outsource data warehouse development to a team of consultants. But this has often left IT teams responsible for something they don’t fully understand.

Automating the process

A good solution to the challenges of data warehouse development is automation. Data warehouse automation (DWA) accelerates and automates the data warehouse development cycles and reduces the time and error rate of traditional BI projects. It efficiently addresses the time-intensive, repetitive work involved in developing a data warehouse design project—including eliminating the need for hand coding.

Using DWA, teams can fast-track delivery by employing auto-generated data warehouse designs that fulfill business requirements. This automation can also help standardise data discovery, development, testing, and change management, providing consistency from project to project.

Like with many areas of IT, cloud providers have simplified DWA. Data warehouse as a service (DWaaS) vendors offer industry-specific templates for data, as well as storage and predefined schemas to shape data warehouses in the cloud.

Extensive project timelines can be significantly reduced by tapping into the straightforward data tools available within these ready-to-use platforms. Additionally, these services integrate into data management and data catalogue services to maintain data quality over time.

How does DWA work?

Modern DWA platforms can generate code using simple drag-and-drop interfaces. This slashes the time it takes to produce background code from hours to minutes.

Over time, requirements change. DWA allows developers to quickly make workload and design changes as the project progresses while eliminating the laborious task of understanding the existing code, determining where to make the changes, and then making and QAing them.

The emergence of big data systems, their evolution into data lakes, and the prevalence of software as a service (SaaS) have resulted in an ever-growing number of data sources. Modern DWA tools use hundreds of built-in APIs and connectors, making it far easier for a developer to extract data in real time—with a mere click of a button.

Traditional mapping exercises are extremely repetitive and time-consuming, but DWA software accelerates this part of the project’s evolution too. DWA software also includes data-mapping functionality to simplify data integration.

Additionally, metadata analysers are built in to reduce data redundancy, discrepancies, and errors usually encountered during the extract, transform, load (ETL) phase of a data warehouse project.

Addressing the lack of standardisation in traditional data warehouse build projects, DWA also gives developers the opportunity to set rules, standards, and methodologies every subsequent project should follow. This makes building a data model meeting regulatory compliance and data governance requirements more straightforward.

The case for automation

Automation enables businesses to bypass much of the complex and time-consuming data warehouse development lifecycle. By automating tedious tasks and seamlessly integrating data sources, businesses of all sizes can reap the rewards of actionable business intelligence. Once an organisation has developed its new enterprise data warehouse, it’s vital to monitor usage and optimise any performance issues to ensure a flawless experience for end users.

IT teams must keep a close eye on available database insights and data warehouse monitoring tools—which provide a detailed view of the data warehouse environment and help secure the biggest return on investment.