The concept of a modern data stack has promised to fuel a move from clunky monolithic systems to streamlined and efficient data architecture.
It’s easy to see how this concept has captured the imaginations of investors, marketers, and data analysts alike in recent years. But beyond increasing enthusiasm for smoother data handling and faster activation, there is still relatively little understanding of what it actually means.
In this blog, we’ll look at what the term ‘modern data stack’ actually means, and explore a few of the challenges and paradigm shifts that organizations will be up against in making this ideal a reality.
What is the modern data stack?
A data stack refers to a set of technologies and tools that are used to collect, process, and store data. So, for example, your marketing data stack might include a data integration tool, a data warehouse, and a visualization tool.
A modern data stack is a data stack that is cloud-based, highly modularized, and prioritizes data output over input. Since it’s mostly built on top of interoperable cloud platforms, the modern data stack is easier to scale and can handle massive volumes of data with higher throughput and without putting a strain on business resources.
What does that mean?
Two decades ago, companies were using on-premise tech to host and manage data from their own data centers; including an ETL (extract, transform, and load) system, internal database, and visualization tools. Today, the modern data stack is taking on the heavy lifting of big data handling to deliver rapid intelligence that helps answer key business questions.
To pinpoint the real value of the modern data stack, we need to assess what separates it from its predecessors.
1. Processing more data, faster
Ultimately, modern stacks are still collecting information that needs to be processed, transformed, and visualized. The way how the required components are orchestrated hasn't really changed, but the speed and volume of data processing have. And that means that businesses have better, faster access to more accurate insights without having to build massive tech stacks in-house.
2. Prioritizing output over input
The modern data stack prioritizes configuring systems to ensure they generate valuable and genuinely useful output for users. At a practical level, one key starting point is giving greater consideration to user requirements and identifying which components are needed to generate and downstream vital insight to the right recipients; or in other words, determining the terms of a “data contract”, as well as how to meet them.
3. Highly modularized - but not yet vertically integrated
Tools within the modern data stack are often highly specialized and modular. Orchestrating all of these tools into a single source of truth is within the grasp of the modern marketer — but it’s still fairly uncommon to see marketing teams doing this well, and that’s because most data stacks aren’t yet vertically integrated.
Vertical integration allows multiple parts of a data stack to talk to each other much more easily, and break down silos to create a single source of truth. Eventually, the ideal scenario is the automatic execution of unique specifications; with users able to set requirements and intelligent tools immediately retrieving desired insight.
For now, companies need to reduce complications wherever possible by leveraging available tech. For instance, applying automated integration solutions will help create a unified base layer for data infrastructure and a single source of truth. Combined with accessible visualization and a master control console, the result will be comprehensive visibility of reliable insight and the ability to implement big changes swiftly; from adjustments in organizational metrics to pivots in analytical focus as marketing strategy evolves.
What are the challenges to implementing a modern data stack?
Typically, data workflow continues to be divided for most companies. The tendency toward sticking with the same old team structures and data processes means there is room for long-running challenges to cause major issues for accessibility and accuracy.
1. Data silos
General engineering teams often run application activities, while specific data engineers head up data integration and yet more isolated departments lead on building data models and databases. The main difficulty this creates is, of course, data silos. Splitting stacks into several components not only makes for fragmented operations but also enhances the probability systems will fall apart as no single unifying force takes responsibility for keeping the overall pipeline stable.
2. Wasted resources
An equally important issue is wasted resources. When essential data isn’t readily available, specialists such as analysts find themselves needing to hook together, cleanse and sync information into usable order, instead of doing the job they were hired for. With studies revealing that just four in ten (41%) have access to a centralized data store, it’s not surprising analysts name time and energy spent manually wrangling data as their top pain point, closely followed by limited ability to report on multi-channel marketing efforts, due to poor data visibility. Additionally, of those who are struggling with manual wrangling, a whopping 63% have little trust in the data they use, as compared to 15% for analysts who aren’t.
3. Misperception of data maturity level
The fact that 72% remain confident in their own data maturity, and almost as many plan to adopt predictive analytics this year, also suggests a disconnect with reality. Unaware that problems are stemming from flaws in existing data practices, analysts feel they have already achieved data proficiency and think issues can be tackled by layering more tools onto data stacks.
The hype around modern data stacks might make them seem like an instant passport to better results, but there are provisos. Unlocking their power depends on having the right mechanisms to establish a solid grounding of integrated, accurate, free-flowing data. Even then, the constant tide of tech innovation means setups must continually develop in line with fresh opportunities and the data requirements they bring.
The only way to implement future-proof operations is for firms to ensure they prioritize mastering the fundamentals of good data practice and persistently work on refining their systems for long-term success.
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