Data stacks are an integral part of a modern marketer's tool box. Learn what they are and how they can help you perform at your best.
"Data rules everything around me. DREAM get the info."
- Method Man (Universe 54B23, where Clifford Smith became a renowned data scientist instead of a rapper)
We love data at Funnel. It's what we do, and we do it well.
Of course, not everyone can have our level of expertise, so we often find ourselves explaining terms like "data stack." Recently, we decided to make it easier on everyone by writing a blog post that gets into the nitty-gritty of modern data stacks, tools that make them successful, and how anyone – including you – can benefit from them.
What is a data stack?
The term "data stack" refers to all the technologies you use to capture, store, manipulate, and analyze data. If you have a product that does something – anything, really – with data, it's part of your data stack.
The specific components included in your data stack may vary depending on whether you have a modular or bundled setup in your modern data stack (MDS). Before discussing this further, let's take a look at the primary elements of a typical data stack.
The main elements of a modern data stack
While the specific technologies in your data stack will vary, you can expect most tools to fall into one of the following categories below.
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.
Data ingestion
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.
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).
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.
Business intelligence (BI) tools
BI tools are used to analyze data and create reports. Modern BI tools aim to make data accessible to anyone in the organization.
Reverse ETL
This process moves data from storage to other systems for operational decision-making, reversing the data ingestion process.
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.
The choice between modular and bundled
At first glance at the infographic of the modular data stack (as shown above), it might seem like the only choice is to buy a software license from a company in each bucket. And you can. The benefit of doing this is that you can theoretically attain a “best-in-breed” setup with handpicked vendors from each category. The drawback of this approach includes:
- It’s a costly approach: Depending on the size of your business and the scope of your use case, each software license can cost anywhere from a few thousand to hundreds of thousands of dollars annually.
- Interdependence: With each tool you add, you create a dependency that the tool integrates well with the other software you have in your stack.
- Maintenance costs: The more software you have in your stack, the more time and money you have to budget for your revenue operations, data engineering, or IT team to maintain it.
- Competence: The more complex your stack, the more you have to rely on the competencies of your team. Things might be just fine as long as the same team is in place, but without a contingency plan for when key personnel leave, it can hinder a data stack very quickly.
Due to these drawbacks, a fully or close to fully modular data stack is typically only pursued by large enterprises, and even then, the same challenges still remain. Therefore, most organizations choose to consolidate and bundle a few of the tasks into the same tool, which brings down both complexity and costs.
When these consolidation decisions are made, it’s crucial to consider the teams most impacted, both in maintenance and the end-users. If too many corners are cut, and you consolidate tasks into a tool that is not built to carry out the necessary functions, the trade-off might no longer be worth it, and the process needs to start over again.
Funnel's role in 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
- Reformat data as needed with robust data transformation rules
- 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.
- Organize your information by source, project, etc.
- Send data to other team members and analytics software. Yes, you can share your data to any BI tool in your MDS 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
FAQ
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.
