A Modern Data Stack is a suite of tools used for gathering, storing, transforming, and analyzing data. Each of these layers play a key role in your organization’s goals to get better insights from vast amounts of data and to proactively uncover new opportunities for growth. Unlike legacy technologies, you can usually get started very quickly, enjoy a pay-as-you-go pricing model, and won’t be locked in with a specific vendor for the entire stack, so mix and matching best-of-breed tools for your Modern Data Stack is a core tenant.
When it comes to the analytics layer, the typical tools people usually think about are dashboards for business users monitoring KPIs, SQL query for analysts to dig deeper, and ML modeling for expert data scientists. 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 often times, 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.
With so much data and compute available in the cloud, it should go without saying that all that power should be utilized by automation to simplify and speed analysis and make it easier for technical and non-technical people to get answers from all the data. While manual querying of data will always be an essential tool for analyst teams, automated generation of insights empowers more people with an easier way to obtain important findings and in a much faster way.
Automation solves a problem that many organizations who do not even think they have “big data” actually have. Consider a dataset of just twenty columns or variables. In order to analyze up to four variables at a time in order to find the combinations that are correlated to a target metric, there are more than 6,000 combinations that you would have to visualize or evaluate. A specific example would be for an ecommerce brand to discover that sneakers sales spiked up for a specific brand, in a group of zip codes, in a given color, and for a specific customer age group. Having this type of insight may lead to new targeted campaigns or follow-up actions that would never be possible without understanding deep, granular patterns and relationships in the data.
