on April 05, 2023 · 2 mins read
Over the last months you’ve heard a lot about the Modern Data Stack, be it from our series of posts on the matter, LinkedIn, or the web itself. We’ve gone over the game-changing benefits of implementing a Modern Data Stack and hopefully established that it is a necessary foundation for companies to effectively scale and eventually build ML and AI capabilities upon to gain a competitive advantage.
That said, instead of doing all the talking ourselves we thought you might want to hear from three leading industry experts with years of experience in Data Science, Engineering, Product Development, and Modern Data Stacks. So we set up a Webinar on Modern Tech Stacks with our friends at Product Minds and MODO. If you weren’t able to attend, we’ve summarized the event in three main takeaways below.
Prefer to watch the whole video and avoid spoilers? You can do so here! Already seen the webinar? Jump straight to our whitepapers!
Modern Data Stacks don’t force users into tooling components that “come in the box”. They allow users to swap out components without affecting the proper functioning of the rest of the stack. With a Modern Data Stack, each company handpicks the best tools for each component, considering goals, constraints, and scaling needs as they appear.
In the past, creating a data platform required hiring a single vendor with a one-size-fits-all solution. This meant a larger investment, a longer and riskier procurement process, longer commitments, and less opportunity for trial and error.
It may be complex in the sense that one has to make many decisions, but the business is in charge of making those decisions, not the vendor. Discover more differences between Legacy Data Stacks and Modern Data Stacks on our FAQ post.
Everything starts with the data. The first step will always be having the data necessary to enable data-driven decisions. Modern Data Stacks are not an exception. Is my data exploitable? How is it generated? Where is it stored? How and where can it be accessed? These questions are simplistic examples, but emphasize the importance of a comprehensive discovery process.
Businesses should take the time to understand and analyze their goals, the main KPIs they want to measure, the issues they’re looking to solve with their stack, and the impact they’re looking to produce. Read more about the difference between symptoms and underlying issues and the importance of a thorough discovery process in our insights post.
We cover just some of the essential best practices to account for before working on your Modern Data Stack. But there are definitely points to look out for:
Missed the webinar? Don’t fret, just head down to our Youtube channel and watch the entire content when convenient!
We hope we’ve peaked your interest in the Modern Data Stack, if you’re interested in learning more you can check out our resources:
Ready to tackle your own tech stack? Get in touch with an expert today!
