A modern data stack is only as good as your ability to use it, and data stacks today often miss the mark when it comes to usability.
Organizations are still overspending on cloud. They’re saddled with tool sprawl. They’re buying software licenses that sit unused. They’re not getting value from data even as it rapidly grows. And in an economic climate when everyone is under more pressure to show ROI, they’re struggling to deliver outcomes.
Yet at the same time, the underlying technologies to handle data integration have never been better! We saw this with the rise of the cloud-based data warehouse and data lakehouse. We’re seeing this right now in AI, as generative AI and LLMs become mainstream.
The challenge is empowering your workforce to take advantage of those technologies. And many data analytics stacks don’t do a good job of it.
We need to make way for a new vision of a data analytics stack that better serves today’s varied roles and use cases. A truly modern data analytics stack should empower different personas to leverage the powerful cloud-based and AI technologies available today.
Here are some best practices for designing a stack that will deliver value:
No one has their entire data stack figured out all at once, and no one sticks to that same stack forever. Don’t put pressure on yourself to choose an architecture and declare it your data stack ‘til the end of time.
One of the benefits of a modern analytics stack is that it’s flexible and modular. You should be able to iterate and adjust as your business evolves, without breaking the entire thing at once. So get the basic pieces in place before you start over engineering and over-tooling. Like starting a puzzle by building the outside frame, you can assemble the essentials first: data storage, ETL, ELT, analysis and reporting.
A modern data stack can be both powerful and simple. Get things up and running and you’ll be surprised how much value you can start showing right off the bat.
Choose technologies that support multiple deployment scenarios
The benefits of cloud-based technologies are well documented — but the cloud isn’t the only place businesses store, access and analyze data. In fact, by 2025, 85% of organizations expect to maintain some level of on-premises analytics.
Yet many versions of the modern data stack frequently over-index on cloud, without acknowledging that organizations deserve to manage data pipelines across any environment: on-premises, hybrid cloud, multi-cloud, private cloud, public cloud.
When choosing your analytics platform, choose the platform that’s flexible and ecosystem-compatible. See if it enables the kind of deployment you need, from SaaS-deployed, to hybrid.
Think about where you need to execute your analytics workflows – ideally right where the data platform lives, to minimize costs and data movement. Can you transform your data in the data warehouse environment of your choice, such as Snowflake or Databricks? Can you build a data workflow in one place, and choose to execute that workflow in another?
Solutions that enable pushdown processing give you the ability to complete a data transformation directly in your data warehouse or native processing engine. Better yet – you can choose an analytics solution that enables business users to execute pushdown without needing to code or know how to operate a data warehouse.
Accommodate different preferences for working with data
By 2025, Gartner projects that 70% of new applications will use low-code functionality or no-code technology. Why? Because we’re recognizing the need for collaboration, and low-code apps make it possible for the workers with functional domain knowledge to contribute to analytics. You shouldn’t need to be a data scientist or data engineer in order for people to get value from your modern data analytics stack.
Code is great too. Code and low-code are not mutually exclusive approaches. For instance, that awesome pushdown processing capability we just talked about? It can be done with SQL, but it can also be done without code (if you have the right analytics platform). The more people from varied backgrounds who can collaborate on a problem, the more you’ll solve.
Your business shouldn’t need to choose between either using spreadsheets, or limiting data tasks to the highly technical employees. There are multiple ways to solve data problems.
Make it realistic for the organization to use your technology investments
Business outcomes don’t come from traditional tech roles. They come from all workers. And many modern data tools weren’t built for all workers; they were designed for very specific roles. You can’t expect your line-of-business analysts to know exactly how to go about getting to the data in the first place, let alone analyzing it.
This has left organizations with modern data source infrastructure, like a cloud-based data warehouse or data lake, that isn’t used to its full potential because it’s inaccessible to employees who don’t have specific skills. Which might be why most companies fall below 30 percent on cloud resource utilization—and, in some cases, below 10 percent.
With its deep technology integrations, Alteryx is the easy-to-use, accessible interface that allows any employee to better utilize best-in-class technologies like AWS, Databricks, Snowflake, Azure and more. Anyone in the organization who needs to work with data can still benefit from the powerful storage and compute resources your business has invested in. No extra training or expertise needed.
Alteryx is designed to give you flexible options for both where you build and run your analytics workflows, and also who can do the building.
The modern data analytics stack that wins today is the one that meets people where they’re at. With a simple, accessible interface, your brightest business minds can work their magic on the data and solve more analytical problems.
That’s how you design a data stack that people can actually use. And it can be done seamlessly with Alteryx and its technology partner ecosystem.