The Modern Data Stack: Beyond the Hype

What is the Modern Data Stack?

The Modern Data Stack is essentially a host of platforms, each addressing a specific data-related challenge. Here are the components that largely make up the stack (though more could be included):

• Cloud Data Warehouse

• Data Replication

• Data Transformation

• Data Validation

• Business Intelligence, Analytics, Machine Learning, and Applications

• Governance

• Data Activation

A modern data stack aims to analyze your business’s data to uncover new areas of opportunity and improve efficiency proactively. The modern data stack is hosted in the cloud and should require little (but often a bit of) technical configuration by the user. These characteristics ideally promote end-user accessibility and scalability to quickly meet the growing data needs of an enterprise without the costly, lengthy downtime associated with scaling local server instances.

Is the Modern Data Stack Just Marketing Hype or Is It Relevant for Enterprise Customers?

The term "modern data stack" may sound like a buzzword–though it will probably sit next to “Big Data” in the buzzword hall-of-fame.

But it is more than just a fleeting trend.

In 2023, it represents the direction in which organizations are moving their data strategies, aiming to become more data-centric, data-driven, and data-informed. The modern data stack is an ecosystem of multiple vendors, providing a suite of plug-and-play technologies that streamline and expedite the acquisition and utilization of data. It's not just about having a stack of tools; it's about integrating these tools in a way that they work seamlessly together to deliver valuable insights quickly and efficiently.

Data warehouses are at the heart of this ecosystem–Snowflake has emerged as a key player, having figured out how to scale faster than others. Lots of companies make it the central component of their data stack.

For previous and current owners of data warehouses, you will generally find that the pace of change is faster, and governance is looser–so containing cloud costs is critical from Day One.

But features of the modern data stack include:

1. Specific: Each technology in the modern data stack is focused on solving a specific challenge with data at the highest levels. Most technologies like to call themselves “Best-In-Breed” because they are focused on one part of the stack.

2. Interchangeable: The technologies are designed to be easy for data to flow in and out of. Making it easy to swap technologies faster.

3. Scalability: The modern data stack can dynamically adjust to handle increasing or decreasing data loads, ensuring that organizations can manage their data effectively regardless of volume or complexity.

4. Robust data governance principles: These principles are integral to the modern data stack, ensuring that data is managed in a way that is consistent, reliable, and meets all necessary standards and regulations.

5. Observability, compliance, and security: The modern data stack provides transparency into data operations, ensures adherence to regulatory requirements, and protects sensitive data from unauthorized access or breaches.

6. Software Development Life Cycle (SDLC): The modern data stack incorporates strong SDLC practices, including version tagging, which allows for better management, tracking, and control of changes made to the data stack.

7. Promotes data democratization: The modern data stack aims to make data accessible and understandable to a wider range of users within the organization, promoting a culture of data literacy and enabling more informed decision-making.

8. Business value: Above all, the modern data stack is driven by the need to deliver business value. Every component and process within the stack contributes to the overarching goal of driving business growth and success by turning data into actionable insights.

Benefits of the Modern Data Stack

The modern data stack can have some serious benefits, which include the following:

1. Unified Data Source: One of the key strengths of the modern data stack is its ability to consolidate data from multiple sources into a single, reliable source of truth–your Data Warehouse. This unification ensures consistent metrics across the organization, leading to improved data quality and more reliable insights.

2. Enhanced Scalability: The modern data stack is built with flexibility in mind. Its cloud-based nature and compatibility with a wide range of tools allow it to adapt and grow with your business needs. Unlike traditional systems, the modern data stack can handle increases in data volume and velocity without the need for costly hardware upgrades or extended downtime.

3. Reduced Costs and Overhead: The modern data stack leverages the declining costs of cloud computing and storage, providing a more cost-effective solution for data management. Plus, the pay-as-you-go model of many cloud services means you only pay for what you use, reducing unnecessary expenditure. While this is a benefit, buyers need to track what is happening in their environments, as consumption of an unregulated platform can get out of hand quickly.

4. Accessibility and User-Centric Design: The modern data stack is designed with the end user in mind. Many of its components, particularly those related to reporting and analytics, require minimal coding knowledge. This user-friendly approach reduces the technical barriers to data access, empowering business users (such as those in marketing and sales) to routinely consult data and derive valuable insights.

5. Risk of Vendor Lock-In: Over time, dissatisfaction with a vendor's service, support, or pricing can arise. And while migrating data out of their system can be difficult or expensive, there are so many technologies serving the same purpose that you should be able to get out easily.

Drawbacks of the Modern Data Stack

While offering numerous advantages, the modern data stack also presents several challenges that can hinder its effective implementation and use. Here are some of the critical drawbacks:

1. High Cost of Ownership: The modern data stack, while powerful, still requires a team of specialized experts for implementation and maintenance. Additionally, if you don’t watch your data consumption closely, your costs can quickly add up.

2. Challenges in Procurement and Billing: With multiple tools comes the complexity of managing procurement processes. This includes negotiating contracts with various vendors, handling different licenses and subscriptions, and keeping track of multiple billing cycles.

3. Limited Deployment Options: The modern data stack predominantly supports cloud-based solutions, limiting options for on-premises or hybrid deployments. This can be a concern for organizations with specific security or compliance considerations.

4. Complexity of Tools: The modern data stack comprises a wide array of tools and technologies. This diversity, while offering flexibility, can also lead to confusion and inefficiencies. The overlapping functionalities can result in data silos, compromised data quality, and potential security risks.

5. Time-Consuming Setup and Maintenance: With the fragmentation of the data stack, each technology needs to be onboarded, integrated, and maintained through separate workstreams. While the technologies are typically easy to work with, delays can occur if a clear plan is not in place.

6. Fragmented User Experience: Each tool in the modern data stack has its unique user interface, workflow, and documentation. This can make it challenging for users to navigate and learn the entire stack, leading to a disjointed user experience.

7. Knowledge Silos: The use of different tools can result in knowledge silos, where expertise is confined to specific individuals or teams. This can hinder effective collaboration and knowledge sharing.

8. Challenges in Data Governance: The modern data stack disperses control across multiple tools, systems, and users. This can make it challenging to enforce policies for consistent, secure, and compliant data handling.

9. Limited Observability: As the number of tools and systems increases, documenting and monitoring data as it flows through the pipeline stages becomes increasingly difficult.

10. Difficulties in End-to-End Orchestration: Orchestrating end-to-end processes can be challenging due to the multiple tools and systems involved, each with its workflow and interface.

Alternatives to the Modern Data Stack

While the modern data stack has its advantages, it’s not the only solution for managing and analyzing data. There are several alternative approaches that organizations can consider:

1. Major Cloud Providers: So you want to go to the cloud. You also are intimidated by taking on so many technologies. Your alternative is major cloud providers like Amazon Web Services, Google Cloud Platform, or Microsoft Azure. These platforms have similar technologies on their platform to the modern data stack. The trade-off is often both quality and cost. But with everything on one platform, there is consistency and single location to monitor billing.

2. On-Premises Solutions: Some organizations may prefer to keep their data infrastructure on-premises due to security, compliance, or cost considerations. This approach can offer a high level of control, but it may not provide the same level of scalability and flexibility as cloud-based solutions.

3. Hybrid Solutions: A hybrid approach involves using a combination of on-premises and cloud-based solutions. This can balance control and flexibility, allowing organizations to take advantage of the benefits of both approaches.

4. Open-Source Solutions: Many open-source tools and technologies can be used to build a data stack. This approach can provide high flexibility and customization, but it may require more technical expertise to implement and maintain.

The Real Modern Data Stack Goes Beyond Technology

The modern data stack is not just about the tools and technologies that make up your data infrastructure. It's also about the people who use these tools and the processes that govern how they are used.

As the data landscape becomes increasingly complex, it's crucial to invest in upskilling your team and refining your processes. This involves providing training and support to help your team adapt to new tools and technologies, as well as implementing robust data governance processes to ensure data quality and compliance.

Many of the tools in the modern data stack are designed to be user-friendly and accessible, making it easier to upskill your team. However, the proliferation of tools can also be overwhelming, making it all the more important to have clear processes in place to guide their use. By investing in people and processes as well as technology, you can ensure your organization can fully leverage the benefits of the modern data stack.

Wrap Up

The modern data stack is more than just a set of tools–it's a comprehensive approach to data management that requires a strategic investment in technology, people, and processes.

When done correctly, it offers scalability, cost savings, and improved data quality.

However, it's not without its challenges, including tool sprawl, high cost of ownership, and the need for specialized expertise. While there are alternatives to consider, the modern data stack's flexibility and scalability are hard to match.

But to truly harness its power, organizations must invest not just in technology but also in the people who use it and the processes that govern its use. By fostering a culture of data curiosity, choosing the right tools, and investing in user enablement, organizations can make the most of their data and drive their business forward.