Click to learn more about author Helena Schwenk.
Your data engineers, analysts, and data scientists are working to find answers to your questions and deliver insights to help you make decisions. They, like most of us, are not particularly fond of seeing the “spinner,” testing their patience as they wait while running queries and algorithms, testing hypotheses, and building dashboards.
To support your data experts in doing their best work, enable them to get into the “flow,” that state of mind where they become fully immersed in their task and can enjoy laser focus and concentration. Remove anything that interrupts or prevents the flow state. The spinner is one of those obstacles.
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Make Your Data Faster
If your most urgent requirement is to simply speed things up because the people working with data are getting frustrated with the performance of the existing system, there is a simple way to do this.
By adding a fast database into your architecture as a thin acceleration layer, you can ensure ultimate performance for all your data loading, transformation, and processing steps. This extends right through to the end user accessing interactive dashboards and reports. Here is an example:
This means you do not need to replace or remove the components of your architecture. You can simply add an analytical database to speed things up.
Not only is this a quick and easy way to address performance issues, it also means that existing business processes are neither impacted nor changed.
How Do You Get Started?
I recommend starting with a process that is particularly slow and painful right now. Whether your data engineers struggle with the time it takes to run ETL processes or your analysts have to wait for their analytics tool to process their queries, identify the challenges you have, their impact on the business, and the expected benefit you could achieve through a better solution.
Once you have clarity on the performance pain points, it’s time to run a test that proves the value of the new solution. You’ll need room to experiment with different parameters, such as data volumes used, number of concurrent users, etc. and show the performance improvements that can be achieved.
You can run such a Proof of Value (POV) phase either on your own, or seek help in assessing the right approach and process.
What Can You Expect?
Once you have your test environment set up, it’s time to really put it to work. Run through your existing business processes and make the system work in the way you expect to use it. Document the results and compare them to the “current state,” those pain points you identified earlier. Then make the system work even harder so you can assess how it would cope with future demand by your analytics department and your end users. This gives you the confidence that the solution doesn’t just work right now, but will also be ready for your future requirements.
A sales engineering team can advise you on what a roadmap for your analytics architecture could look like and how it can grow with your needs.
Getting started and testing how you can accelerate and improve the performance of your various analytics processes and applications is straight-forward and can be done in countless situations.
When it comes down to purely making things go faster, a simple acceleration layer will often address your requirements.
If you need more than just performance and want to address questions around advanced Data Governance, security, and multi-tenant deployments, consider taking a more structured and longer-term approach to finding the right solution.