The concept of devops has taken root in the world of business intelligence and analytics.
The overall concept of devops has been around for a while in traditional IT departments as they sought to expand and refine the way that they implemented software and applications. The core of devops in the world of analytics is called DWA (data warehouse automation), which links together the design and implementation of analytical environments into repeatable processes and should lead to increased data warehouse and data mart quality, as well as decreased time to implement those environments.
Unfortunately, for several reasons the concept of data warehouse automation is not a silver bullet when it comes to the implementation of analytical environments.
One reason is that you really shouldn't automate concepts before you fully understand them. As the saying goes, don't put your problems on roller skates. Automating a broken process only means that you make mistakes faster. Now, while I often advocate the concept of failing faster to find the best solution to an analytical problem, I don't really agree with the concept of provisioning flawed database structures very quickly only to rebuild them later.
Another issue with applying devops to analytical practices is that the software development community has a 10-15 year head start on the analytical community when it comes to productizing elements of their craft.
Software developers have spent years learning how to best encapsulate their designs into object-oriented design, package that knowledge, and put it in libraries for use by other parts of the organization, or even by other organizations. Unfortunately, the design, architecture, and implementation of analytical components, such as data models, dashboard design, and database administration, are viewed as an art and still experience cultural resistance to the concept that a process can repeat the artistry of a data model or a dashboard design.
Finally, there is the myth that data warehouse automation or any devops practice can replace the true thought processes that go into the design of an analytical environment.
With the right processes and cultural buy-in, DWA will provide an organization with the ability to leverage their technical teams and improve the implementation time of changes in analytical environments. However, without that level of discipline to standardize the right components and embrace artistry on the tricky bits, organizations will take the concept of data warehouse automation and fail miserably in their efforts to automate.
The following is good advice for any DWA practice.
- Use the right design process and engage the analytical implementation teams. Without this level of forethought and cultural buy-in, the process becomes more of an issue than it does a benefit and actually takes longer to implement than a traditional approach.
- Find the right technologies to use. There are DWA platforms available to use, but there are also toolsets such as scripting and development environments that can provide much of the implementation value of a data warehouse automation solution. The right environment for your team's skills and budget will go a long way to either validating a DWA practice or showing its limitations.
- Iterate and improve. Just as DWA is designed to iterate the development of analytical environments, data warehouse automation practices should have the same level of iteration. Start small. Perfect the implementation. Expand the scope. Repeat.
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