The High Cost of Modern Data Stacks: Breaking Down Ownership Expenses

The rise of data-driven decision-making has necessitated the use of modern data stacks (MDS) in businesses of all sizes. While the benefits of these stacks are profound, the associated costs often remain hidden beneath the surface. Today, we dive deep into these often overlooked costs to shed light on the true cost of ownership of modern data stacks.

What is the Modern Data Stack (MDS)?

To appreciate the costs, it's essential to understand what we're dealing with. The modern data stack is a collection of point solutions businesses assemble to aggregate, manage, and analyze data. This collection can include a data warehouse to store the data, ETL tools to import data into the warehouse, data transformation tools, and ultimately a BI tool to create dashboards and reports.

The MDS can be a powerful tool in the hands of a competent data analyst, but it is not without its pitfalls. The most glaring of these is the high cost and often low ROI, particularly when businesses take the DIY approach to building their MDS.

The True Cost of Ownership

A common mistake businesses make when budgeting for an MDS is underestimating the total cost of ownership. While the upfront costs may appear manageable, the true costs extend far beyond the initial purchase of the software.

Initial Investment

Embarking on the path of purchasing a data warehouse and hiring a data analyst to create cross-departmental reporting and analytics, you can expect to invest an additional $50k minimum in the tools they need to do their job. This is before your new hires invest an additional 4-6 months cobbling together these tools and wrangling your data just to make it presentable.

Yearly Expenses

The recurring costs of maintaining an MDS can be significant. From data storage and data processing to ETL and analytics, the costs can quickly add up. Here's a quick breakdown:

As you can see, the actual cost in the first year alone can exceed the projected cost by nearly 60%. And these costs continue to rise dramatically in the following years.

Why the Cost Variance?

The unpredictability of the actual costs stems from the difficulty in predicting how much compute and data processing will be needed in a given year. This unpredictability shows up in multiple places in the modern data stack: Compute

Based on the data I found, the cost of setting up a modern data stack can be substantial. The total cost involves both the cost of technology and the personnel required to build, manage, and maintain the various components of your data stack. The technology costs include data storage, data processing, ETL (Extract, Transform, Load), reverse ETL, analytics, and observability/catalog. The personnel typically required are data engineers, BI engineers, analytics managers, data analysts, data scientists, and data leaders.

Here's a breakdown of the projected and actual costs over three years, according to one source:

Year 1 (projected):

• Technology Cost: $53,000
• Personnel Cost: $200,000
• Total Cost: $253,000

Year 1 (actual):

• Technology Cost: $84,000
• Personnel Cost: $250,000
• Total Cost: $334,000

Year 2:

• Technology Cost: $166,000
• Personnel Cost: $600,000
• Total Cost: $766,000

Year 3:

• Technology Cost: $335,000
• Personnel Cost: $2,000,000
• Total Cost: $2,335,000

It is worth noting that the actual costs can often be higher than the projected costs because it is difficult to predict the exact amount of compute and data processing needs in a given year. This unpredictability can show up in various places in the modern data stack, including compute costs on top of your data warehouse, ETL processing costs (monthly active rows), and reverse ETL distribution costs (number of destination fields).

As for the specific costs of implementing a data stack at your company, it would depend on a variety of factors, such as the specific tools and platforms you choose, the size and complexity of your data, the skill set of your team, and the specific needs of your business.

As a final note, beyond the direct costs, there can be significant opportunity costs associated with setting up a modern data stack. The time and energy spent on learning new tools, switching between tools, and managing multiple vendor relationships can detract from time and energy that would otherwise be spent running your business.

What does the "high cost" in modern data stacks refer to?

The "high cost" in modern data stacks refers to the financial, time, and resource investments associated with the construction, management, and maintenance of modern data architecture. These costs include, but are not limited to, licensing or subscription fees for commercial software, costs of hiring skilled personnel, expenses related to data storage and processing, data security, infrastructure and operational costs, costs for software and hardware upgrades, and time required for learning and adapting to new technologies and methods.

Why are modern data stacks considered expensive?

Modern data stacks are considered expensive due to several reasons. Firstly, the breadth and complexity of modern data ecosystems involve a multitude of technologies, each requiring its own set of expertise and resources. The need to integrate these technologies further complicates and increases the costs. Secondly, the rising data volumes demand significant storage and computing resources, contributing to increased expenses. Lastly, ensuring data privacy and security is a costly but essential undertaking, with potential legal and reputational ramifications if not executed properly.

What is meant by "ownership expenses"?

"Ownership expenses" refer to all the costs incurred during the entire lifecycle of a data stack – from its inception and installation to its operation and maintenance. This includes initial setup costs, licensing fees, operational costs, expenses related to upgrades and expansions, costs for maintenance and support, and eventual decommissioning costs.

How do the costs of on-premises and cloud data stacks compare?

On-premise data stacks often have higher upfront costs because of the need to invest in physical hardware, infrastructure, and software licenses. On the other hand, cloud-based solutions typically operate on a subscription or pay-as-you-go model, resulting in lower initial costs but potentially higher ongoing expenses depending on the usage. However, cloud solutions offer additional benefits such as scalability, flexibility, and lower maintenance, which can offset some costs in the long term.

What role does data security play in the cost of modern data stacks?

Data security is a major component of the cost of modern data stacks. Organizations need to invest in secure infrastructure, encryption technologies, and tools for monitoring and prevention of data breaches. Additionally, there can be significant costs associated with compliance to data protection regulations. In the event of a data breach, there can also be substantial financial penalties, remediation costs, and damage to reputation.

How does the cost of skilled personnel factor into the overall expenses of a modern data stack?

Skilled personnel are crucial for managing and maintaining modern data stacks, and the cost of hiring and training these professionals can be substantial. This includes data engineers, data analysts, data scientists, and database administrators, among others. The more complex the technology stack, the higher the level of expertise needed, and therefore potentially higher salary demands.

Can open-source tools help in reducing the cost of a data stack?

Yes, open-source tools can help in reducing costs, but they also come with their own challenges. While they can eliminate the need for expensive software licenses, the costs for implementation, customization, support, and maintenance can still be significant. Also, using open-source tools might require additional technical expertise, and in the absence of official support, troubleshooting issues can be time-consuming.

What strategies can businesses adopt to manage the high costs of modern data stacks?

Businesses can manage high costs by strategically choosing technologies that provide the most value for their specific needs, consolidating tools where possible, and effectively utilizing cloud and open-source solutions. They can also invest in training their staff to manage multiple parts of the stack, reducing the need for highly specialized roles. Businesses can also consider data

lifecycle management practices, such as archiving and deleting unneeded data, to reduce storage costs.

How does the cost of data migration factor into the overall expenses?

Data migration is the process of moving data from one system or storage environment to another. It is a critical but often overlooked cost in the overall expenses of a modern data stack. The process involves not only the movement of data, but also its cleaning, transformation, and validation, which can be time-consuming and costly, especially if not planned well.

Why is it important to consider total cost of ownership (TCO) for a modern data stack?

The total cost of ownership (TCO) provides a comprehensive understanding of the financial impact of a data stack by accounting for all direct and indirect costs. A clear understanding of TCO allows organizations to make informed decisions about investments in technology, helps them in planning their budget, and enables a fair comparison of different technology options. It can also reveal hidden or overlooked costs that can significantly impact the return on investment (ROI).

‍