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What is a data stack, and why are they important?

Published Oct 31 2022 9 minute read Last updated May 9 2024
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You should know by now that at Funnel, we love data. But what we love best is creating accurate, organized and useful data that's been collected, cleaned and transformed so we can see exactly what we need, exactly when we need it. And that's why we love a data stack.

Back in the early days of data processing, engineers and data scientists had to build data stacks on-site to collect and organize their company data. But that's the old way. Today, cloud storage and modern, remote server technology mean businesses can handle large amounts of data at a lower cost. Analytics and data tools are unlocking more possibilities for businesses of all sizes. These modern data stacks are easier to implement and maintain, and use more widely understood (though still specialized) technologies like SQL.

So much more is possible now, with so much less money and time. And it's all thanks to the modern data stack. Let's dive in and find out more.

What is a data stack?

A "stack" is a term for a group of software products that (when connected) can facilitate different business processes. For example, a marketing stack could include your CMS, website analytics software, CRM platform, social media management software, and more. They are the tools a marketer uses to automate or conduct their daily business tasks more efficiently.

A data stack, meanwhile, is a series of technology products that are specifically focused on raw data collection, storage, manipulation, and movement. For instance, to compile the data from your different digital advertising platforms, you’ll need a software product or two (or sometimes more) to make that happen.

The specific components included may vary depending on whether you have a modular or bundled setup in your modern data stack.

The main features of a modern data stack:

  • Cloud-based: A modern data stack leverages cloud data warehouses and remote servers for scalability, lower costs, and easier implementation.

  • Modular: They employ a "best-of-breed" approach with various tools for specific tasks like data ingestion, transformation, and analytics.

  • User-friendly: The best modern data stack tools are made to be easy to use, often with interfaces that go beyond specialized skills like SQL expertise.

  • Scalable: A modern data stack can handle much larger data volumes compared to legacy stacks.

  • Automated: They feature automated data pipelines (ETL) for efficient data movement and processing through the data stack.

  • Cost-effective: They offer cost efficiencies through cloud-based solutions and avoiding vendor lock-in with a modular approach.

  • Democratized: Good modern data stacks should enable various teams (like marketing or finance) to access and analyze data independently, helping contribute to more data-driven decision-making across the organization.

Flexibility, scalability and speed made modern data stack tools a no-brainer for many businesses. The ability to analyze data and unlock actionable insights is key to moving quickly and efficiently.

Snowflake report

Our partner Snowflake wrote a detailed report about the modern data stack, including information on what companies are in it (in the field of marketing data). 

The main elements of a modern data stack

Building a data stack requires some careful planning to analyze your business needs, use cases and requirements regarding data governance and security.

A modular approach means using a number of specific tools to perform the various tasks in order. Working this way means you can find the best-in-class tools for each task and perform ongoing monitoring and adaption if necessary. There are also tools on the market that can perform multiple tasks within the data stack in one place, which can be more cost-effective and user-friendly.

Data stack flow

While the specific technologies in your data stack will vary, you can expect most tools to fall into one of the following categories below. 

1. Data collection and monitoring

This step involves gathering data from ad platforms, web and mobile analytics, app stores, CRM platforms, payment providers, and many more.

2. Data ingestion or integration

Data ingestion is like bringing raw data from its source into a central storage place, such as a data warehouse or data lake. In a modern data system, extract-transform-load (ETL) tools are usually deployed to handle this task by bringing data from various sources. 

Want to dive deeper? Try our blog: An introduction to marketing data waterhouses.

3. Data transformation

Data transformation is all about cleaning, organizing, and summarizing raw data to make it easier to understand and work with. Transformation can happen during the ETL process or when it reaches the target system (then commonly referred to as extract-load-transform (ELT). 

4. Data storage (data warehouse / lake)

Data storage is the heart of modern data stacks, acting as a historical record for all behavioral and transactional data. These systems are designed for flexibility, speed, cost savings, better data management, and improved developer productivity.

5. Business intelligence (BI) tools

Business intelligence tools are used to analyze data and create reports. Modern BI tools aim to make data accessible to anyone in the organization.

6. Data management, governance, and orchestration

Data management ensures efficient organization of data, orchestration automates data workflows, and governance maintains data quality and security. Together, they create a robust infrastructure for reliable and accessible data.

Modular and bundled data stacks: which one is right for you?

A lot of people ask which is better: modular or bundled data stacks? And there are plenty of blogs out there that will argue for one over the other – but the truth is, you don’t actually have to choose. For some businesses, there will be a clear benefit to having a specialized and tailor-made data stack – the benefits will far outweigh the costs. And for others, a bundled solution will fit their needs perfectly. But for some companies, a hybrid solution will be best.

Let’s quickly look at the pros and cons for modular and bundled setups, and then have a look at how a hybrid approach could work.

Pros and cons of modular and bundled data stacks

1) Modular Data Stack

Pros:

  • Flexibility: You can choose specific tools based on your specific needs and budget. This allows for a highly customized data stack that caters to your unique requirements.
  • Integration with existing tools: If you already have existing tools you're comfortable with, a modular approach allows for easier integration with those tools.
  • Scalability: You can scale your data stack incrementally as your needs evolve. You can add or remove individual tools as needed without having to replace the entire stack.
  • Potentially lower cost: You only pay for the tools you need, which can be more cost-effective compared to a bundled solution with features you might not use.

Cons:

  • Complexity: Managing and integrating multiple tools can be complex and require more technical expertise.
  • Vendor lock-in for individual tools: Even with a modular approach, you might become reliant on specific vendors for certain tools, making it challenging to switch in the future.
  • Learning curve: Each individual tool might have its own interface and learning curve for your team.
  • Security concerns: Managing security across multiple tools can be more challenging compared to a single, unified platform.

2) Bundled Data Stack

Pros:

  • Simplicity: Bundled solutions are generally easier to set up, manage, and maintain. Everything is pre-integrated and configured.
  • Reduced vendor management: You only have one vendor to deal with for the entire data stack, simplifying communication and support.
  • Consistency: Bundled solutions offer a consistent interface and user experience across all the included tools.
  • Streamlined security: Security features are likely already integrated and managed by the vendor, reducing your burden.

Cons:

  • Limited flexibility: Tools in bundles can be limited, but it’s always worth asking the vendor if they can add a tool – some will create custom connectors, for example.
  • Potential for unused features: You might end up paying for features you don't need within the bundled solution if they don’t have tiered options.
  • Potentially higher upfront cost: Some bundled solutions can have a higher upfront cost compared to a modular approach, depending on their pricing tiers and features.

A hybrid approach to modern data stacks

At Funnel, we sometimes discuss if we should talk about data stacks at all. Because isn’t Funnel an all-in-one data stack replacement? Well, yes and no. 

Many Funnel users don’t need anything else, they can collect, store, visualize and share all the data they need. Meanwhile, others find Funnel covers a few bases as part of a larger modern data stack working seamlessly side by side with other tools. It all depends on what your business needs. 

So do you choose what’s right for you? Here’s some of the things to consider when choosing a data stack:

✔️Technical expertise: Does your team have the skills and resources to manage a data stack? 

✔️Scalability needs: How scalable are your data needs? How much flexibility do you predict you’ll need?

✔️Budgetary Constraints: Can you afford the potential costs in time and money of multiple tools over a bundled solution?

✔️Security needs: Do your data stack options have all the bases covered for security and governance?

Funnel's role in modern data stacks

Today's marketers often need to collect data from diverse sources, including e-commerce platforms, landing pages, and social media sites. Funnel makes it easy for you to:

  • Connect with all of your data sources, similar to the data collection component in an MDS

  • Store data in a centralized, secure hub like you would with data storage. However, you can also share your marketing data with a data warehouse based on your organization’s needs. The flexibility is certainly there.  
  • Reformat data as needed with robust data transformation rules

  • Organize your information by source, project, etc.

  • Send data to other team members and analytics software. Yes, you can share your data to any business intelligence tool in your modern data stack or simply share it in a Google sheet - the choice is yours to make. 

In other words, Funnel can serve your needs at the data collection, transformation, storage, and sharing levels of the modern data stack. You don't even need to get your business's data team involved – unless you want to, of course. Instead, marketers retain autonomy over data that drives campaign results. 

Funnel also helps you avoid one of the most persistent challenges marketers face: change! When you start creating or monitoring a campaign, you probably know what types of data you want to collect. By the time a programmer builds data pipelines, though, your needs have probably evolved. 

Funnel's code-free approach to collecting, storing, organizing, and sharing data means marketers can adjust rapidly so you never miss critical information.

Related reading: The better way to work with marketing data

What trends do we see emerging?

Now that we know the history of data stacks and how modern stacks benefit the broader business, we sat down with one of our product managers. And we asked to get their view on where the space is headed next. Together, they identified three key areas:

Emerging trends for the modern data stack

1. No-code platforms, which mean less dependency on data engineers

Remember those marketing teams that we mentioned earlier? They would love to get their hands on some great data management tools. However, advanced coding and data engineering skills are outside their wheelhouse. That’s why we see the emergence of code-free solutions as a potential game changer. More and more dashboards, reporting, and automated analytics tools are emerging for non-technical users.

2. Unbundling the stack

As we covered, data stacks used to be the realm solely of data engineers within the IT department. However, this tightly knit use case is beginning to become (in a sense) unbound. Teams like human resources, finance, product, and marketing  see the value that good data analysis can provide.

3. Focus on real time

The rise of AI and automation in the data stack goes hand-in-hand with real-time processing. The data stack is likely to evolve to handle real-time data streams faster and more accurately. This will help businesses to react and make data-driven decisions much faster, with applications in areas like fraud detection, personalized marketing, and real-time operational adjustments.

4. Democratization of data

Typically, executives can get what they ask for pretty quickly — and understandably so. If they need a report covering the business’s core financial indicators, they can normally expect to receive it from the BI team ASAP. Other teams in the organization can be underserved, though. Marketing teams could be clamoring to understand their performance better, but the many requests to the BI team create a natural bottleneck.

Expect more and more tools to emerge that address this issue, allowing these non-technical teams to service their own data needs easily.

Those tools, like Funnel, can dramatically empower these teams to implement data analysis more deeply into their operations and decision making. Even the famous tech-focused venture capital fund Andreessen Horowitz, has dubbed these newly empowered data-driven employees ‘operational analysts.’ Expect to see this trend continue.

Another emerging trend that our product managers highlighted was the shift away from viewing your data stack as a stack altogether. Instead, they explained, data users will begin to see their many tools as a data mesh.

 

FAQs

 

What do you mean by data stack?

When we use the term "data stack," we're referring to any tool that comes in contact with your data. However, data stacks can vary significantly between organizations. Ideally, you want to find platforms, apps, and other solutions that make data management and analysis as easy as possible.

 

What is the difference between a data platform and a data stack?

When using the term data platform, most people refer to a tool or software related to data. The term data stack is used to describe all of the tools that allow users to work with data. 

The differences might seem small at first. The more data you collect, though, the more obvious the differences become. For example, a new company that only collects information from a few sources might find that a data platform works fine for them. As the company grows – and its data stack grows with it – you will likely need to adopt more specific tools that connect with diverse sources and load data into BI apps.

 

Why is a data stack important?

Companies need data stacks so they can collect, transform, store, and analyze data. A reliable data stack enables businesses to make data-driven decisions that lead to more successful outcomes. 

Without a data stack, it's pretty much impossible to know what strategy to take or whether that strategy reached your goals. You're always flying blind and never even know whether you reach your destination

Sticking with the airplane analogy, a data stack gives you a map, radar, detailed coordinates, and an ETA of when you'll arrive.

 

What does modern data stack mean?

A modern data stack involves cloud-based technologies, such as cloud data warehouses, SaaS apps, and ETL tools. These technologies democratize data, making it easier for people without IT backgrounds to make data-driven decisions.

You can learn more about the modern data stack here.

 

What is an example of a data stack?

A modern data stack might look like this:

  • Pipelines that collect data from sources like e-commerce platforms, social media accounts, and PPC ads to a warehouse.

  • A cloud-based data warehouse – or on-site data warehouse for legacy data stacks – that can adapt to increased or decreased storage needs.

  • Data transformation tools, often in the form of a data pipeline, that reformat and clean information before analyzing it.

  • Analytics tools, such as BI apps, that can find meaningful insights within massive datasets.

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