The modern data stack has been increasing in popularity as companies of all sizes are attempting to leverage their data. By utilizing a modern data stack, organizations are able to lower the technical barrier to access their data, spend less time managing their data, and better analyze and understand their data, resulting in faster time to insight. Before we get into the importance of a modern data stack, let’s first establish what exactly a modern data stack is.
What Is a Modern Data Stack?
At the core, the modern data stack (MDS) is a new approach to integrating data that leverages a suite of tools designed to tackle each of the major processes in the data stack in a microserviced manner. These tools include solutions for ETL pipelines like Fivetran, all the way to robust business intelligence tools like Sisense. By picking and choosing the solution for each process in a modern data stack, organizations have the freedom to design their data stack to meet their specific needs, all while preventing long-term lock-in on any solution they select. While traditional or legacy data stacks can provide value if built correctly and maintained accordingly, modern data stacks remove the burden of past data management tools and allow for the organization to be proactive in leveraging their MDS to uncover new opportunities and launch decision making to a new level.
Now that we’ve covered what MDS is, we will discuss why one is important. While there are numerous benefits to using a modern data stack, there are three primary advantages that elevate a modern data stack above legacy counterparts: management, modularity, and barrier to entry.
Management Is More Straightforward
Traditionally, data stacks have been built and developed by the teams using them. While there is nothing inherently wrong with this approach, these data stacks often have a lot of customization and tend to be brittle. Without the support of data engineers and other technical staff, these data stacks can quickly cause a major maintenance headache and become problematic. By using a modern data stack, the same solutions can be implemented using tools designed specifically for each use case.
For example, imagine an eCommerce company wants to obtain data from their eCommerce platform (Shopify) and their advertising channels (Google Ads and Facebook Ads), to better understand how advertising may be impacting overall sales. By using a modern data stack ETL tool like Fivetran, through simple configuration, this organization can sync data from source to destination in minutes. This alleviates the need for large data teams to spend time building and maintaining custom data pipelines. By using a tool like Fivetran, the organization is able to pass the burden of maintenance and management to a platform with the sole purpose of providing and maintaining robust data pipelines.
Additionally, when considering maintenance and management of traditional data stacks, scalability often rears its head. Scalability can be a serious challenge when restricted to the design of a traditional data stack. By using a modern data stack, scalability is achievable immediately and is not confined to certain tools. MDS technologies are designed to handle as much or as little traffic and processing that are sent their way. An example of this is a company that is experiencing performance issues with their data warehouse. This could be resolved simply by increasing the size of the warehouse through simple user interface configuration, and scale immediately.
Modularity & Flexibility
The beauty of the modern data stack is that each tool is designed to resemble a microservice. This allows for the robust development of tools that solve a particular process of the modern data stack. Additionally, by designing tools this way, it allows for all processes of the modern data stack to be loosely coupled together, creating flexibility of choice when it comes to interchanging processes of the stack.
Because the tools in the modern data stack are created to resemble microservices, it is not uncommon for several providers to offer a solution for the same process of the MDS. Take ETL for example. Fivetran, Stitch Data, Hevo, and open source alternative Airbyte are all focused on solving the exact same problem: moving data from source to destination. Because the ETL process has been modularized, each of these providers can solve this problem in a similar manner. This allows organizations to use any of these tools and obtain the same data from their sources, regardless of the tool used.
Why is this important? Aside from having the flexibility of choice and the power of negotiation derived from the number of options available, modern data stack tools designed as modules enable organizations to mitigate the risk of provider lock-in. Because tools in the modern data stack are designed as microservices, or modules, they are effectively solving the exact problem, albeit with minor variations. Additionally, because these tools are not dependent upon the tools around them, they are loosely coupled together, allowing for easy interchangeability.
This means an organization using an ETL tool like Stitch can easily move to a tool like Fivetran with minimal impact on the data stack. Because the same data is obtained from both tools, if an organization can strike a better deal with Fivetran, or needs certain functionality of Fivetran over that of Stitch, the organization is not locked in to using Stitch and has the freedom and flexibility to easily make the change with little impact to the data stack.
Low Technical Barrier to Entry
An often overlooked value proposition of the modern data stack is the low technical barrier to entry needed to effectively utilize the data stack. The benefits of this are twofold. First, organizations do not need massive data teams involved in building and maintaining data stacks. Secondly, the time that data teams may have traditionally spent building and managing data stacks can now be reallocated to utilizing and understanding the data, allowing faster time to insight and an agile data team that can increase the capacity of data requests.
As noted, by leveraging a modern data stack, it is no longer necessary to hire an expensive team of data engineers. Additionally, other roles on the data team may become more obsolete as utilizing an MDS decreases the involvement of those roles. We can recognize the value from this monetarily. According to Indeed, the average salary of a data engineer is $121,605 per year. If a data team consists of four data engineers, the salary expense of those engineers is almost half a million dollars.
Consider an organization that decides to implement a modern data stack. By leveraging a tool like Fivetran, they can ingest 1000 monthly active rows (MAR) of data for anywhere between $1 – $2. For organizations spending $1 per 1000 MAR, that organization can ingest 500 million records for the same price as their four data engineers. But the benefits don’t stop there. Not only is that data ingested without the need of data engineers, but by using a tool like Fivetran, all of the maintenance and management is passed off to Fivetran, completely alleviating the responsibility of data pipeline management from the organization, to the provider. This allows organizations with little to no technical expertise to gain the same level of insight from their data as the largest organizations in the world.
Now consider an organization that employs several data engineers and decides to implement an ETL tool like Fivetran which effectively takes over the majority of the data engineers’ jobs. Instead of firing the engineers, these individuals can now be reallocated within the data team to spend more time understanding the data, and less time managing and building data processes. This means that the data team is now a completely focused unit that deeply understands the organization’s data and has an increased capacity to serve insights back to decision makers. The value of deferring low value work such as maintaining data pipelines to a MDS tool, and reallocating a data team to focus on problem solving and deep insight is unquantifiable.
Conclusion
Many organizations who are currently leveraging their data suffer from brittle data stacks and inefficient processes. This makes continuing to build and scale the data stack difficult and slows the insight needed to make critical data driven decisions. Other organizations desire to leverage their data, but don’t have the resources or technical understanding to hire and manage a data team. In both of these cases, by utilizing a modern data stack, both organizations benefit massively. From highly modular components of the data stack that make gaining insight highly accessible, to lowering the technical barrier providing enormous value for companies with little to no technical expertise, the modern data stack covers it all.
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Why A Modern Data Stack Is Important
If a modern data stack sounds like the piece that your organization has been missing, reach out to us. We rapidly deploy modern data stack in days, as opposed to the months it traditionally takes to get a data stack operational. This means that your time to insight decreases dramatically, and you can start making data driven decisions quickly.
