“Hybrid environments are complex and the need for consistency of design, development standards and documented process controls is crucial”
Migrating to a cloud data warehouse can be a very successful endeavour for many organisations. One critical success factor ensuring a satisfactory conversion is the utilisation of data warehouse automation technology. Here are a few of the many justifications you can use to warrant the usage of automation technology.
The first thing to think about is that hybrid environments – those consisting of on-premises and cloud implementations – are much more complex than single location ones. The need for consistency of design, development standards and documented process controls across all environments is much greater.
We know that analytics environments must be built iteratively – that is, each project is built upon the foundation of the previous projects, reusing the designs, standards and knowledge. These projects combine into a data warehouse program and to ensure consistency, the implementers must use the same standards and conventions for all projects.
Automation technology employs the best practices and strengths from leading data warehouse methodologies, making it the best way to support necessary consistency and reliability across the program, regardless of the cloud infrastructure platform selected.
Second, migration to the cloud can involve the movement of massive volumes of data. The team must ensure that all migration mechanisms preserve the structure and integrity of data. Automation again can rapidly guarantee that all structures follow documented guidelines. It also sets up the proper data quality and integrity processes in a repeatable and reusable way.
A third rationalisation is support for the ever-changing data and analytics requirements. One thing that is guaranteed for implementers creating an analytical environment – it will change.
Changes are a sign of healthy analytics usage but the implementation team must be equipped to handle the changes quickly without disrupting other analytic functions. Data warehouse changes require agility in terms of fast prototyping and multiple iteration support. Automation makes the implementers so much more efficient and effective, improving their ability to deliver reliable additions to the cloud data warehouse very quickly.
The fourth justification for using data warehouse automation is the mandatory requirement for reliable, up-to-date documentation.Because data warehouse projects are part of a program, documentation becomes critical to the overall maintenance and sustainability of the environment.
Developers, data modelers, architects come and go in projects but understanding why they did what they did must remain.
We all know that documentation and impact analysis capabilities help lower risk for future changes to the data warehouse environment. Unfortunately, documentation is the last thing most team members want to do – it is difficult, not very interesting to do, and gets out of sync very quickly. Removing the drudgery of creating documentation is certainly welcomed. That is the beauty of automation – documentation is automatically created and maintained, leading to better sustainability and maintainability of the future data warehouse configurations.
The final justification must be the cost factor. Any technology that increases the productivity and efficiency of the implementation team results in reduced costs and lower risk for the overall implementation.
Data warehouse automation results in a team that can turn on a dime and be far more innovative. The ability to fast-track migration and new cloud-based data infrastructure projects not only reduce implementation costs and risk, it ensures that companies are in a better position to reap the ongoing benefits the cloud provides sooner.