The Modern Data Stack is a great innovation with a bright future, but the benefits shouldn’t just be about the underlying data plumbing. Associations ought to likewise look at how the modern data stack can offer another experience that takes knowledge-driven associations to a higher level, settling on it simpler to pursue basic decisions quicker and with more certainty than any time in recent memory.
As someone who knows how to code, but does not consider myself a programmer, I know there is a time and place for applying code in analyzing data. But I much rather prefer the modern experience of a search interface where I can ask questions to get the information I need, instead of needing to query in SQL or Python. That’s where natural language plays a part in the modern analytics layer. With a search interface that supports a natural language, users can ask questions, like you would if you were speaking to one of your smart home devices, to get answers and visualize data that not only helps you understand what is happening in the business, but also identify the reasons why metrics change, and how to improve business performance through granular recommendations found in data.
These techniques have been with us for decades and reinforce the traditional analytics process where businesses wait on data teams to work through their backlog in order to answer the important, and oftentimes, new business questions. If organizations are going to take a fresh, modern approach to their data stack, they should also update the analytics experience for their users as well. At Tellius, we call this new approach Decision Intelligence or Augmented Analytics 3.0, which combines business intelligence with AI and machine learning to get faster insights from their modern data stack. Let’s dive deeper into the four essential pieces for modernizing the analytics layer of the Modern Data Stack.
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