THE MODERN DATA STACK IS BROKEN.

In its most basic form, a modern data stack will include:

• Cloud-based data storage
• Data ingestion tools
• Data transformation and modeling tools
• Business intelligence and visualization tools 

However, the tools in modern data stack can be expanded to cover virtually any data and analytics use case or workflow:

NEW DATA STACK. SAME OLD DATA PROBLEMS.

The modern data stack promises to provide a smarter, faster, and more flexible way to build data solutions by leveraging the latest tools and technologies available on the market.

While the modern data stack is a significant improvement over the traditional method of coding data pipelines by hand with legacy tools, it has also faced criticism for failing to live up to its promises in many ways.

Not only does the modern data stack fail to overcome the challenges of the Data Divide, it creates additional obstacles that must be addressed if you choose this fragmented approach:

• Tool Sprawl: One of the main criticisms of the modern data stack is the sheer number of tools and technologies available, which can be overwhelming for organizations and make it difficult to choose the right combination of tools to fit their specific needs. Recent research from the Connectivity Benchmark report found that organizations are using 976 individual applications, on average. Yet only 28% of these applications are integrated. Using disconnected tools and technologies across multiple teams can result in data silos, inefficiencies, poor data quality, and security risks due to overlapping functionality and poor integration.

• Procurement and Billing Headaches: With so many tools and technologies to choose from, it can be challenging to navigate the procurement process, which includes negotiating contracts with multiple vendors, managing licenses and subscriptions, and keeping track of multiple billing cycles. This can result in wasted time, budget overruns, and administrative headaches.
• High Cost of Ownership: Implementing and maintaining a highly-complex stack of tools requires a team of specialized experts, which can be very expensive. Additionally, the cost of licensing fees, infrastructure costs, training costs, support and maintenance costs, and other operating expenses can quickly add up, especially if your organization has limited resources or budget constraints.
• Lengthy Setup, Integration, and Maintenance: The modern data stack has yet to fulfill its promise of providing "plug-and-play" modularity. Setting up, integrating, and maintaining a complex stack of tools is still a time-consuming and resource-intensive process. With so many different tools and technologies to manage, it can be difficult for organizations to keep up with updates, troubleshoot issues, and ensure that all components of the stack are working well together. This can lead to delays and increased costs, as well as reduced agility and the inability to respond quickly to changing business needs. Additionally, support is fragmented across multiple vendors, leading to confusion and delays when issues arise.

• Manual Coding: While many tools in the modern data stack promise low-code, user-friendly interfaces with drag-and-drop functionality, the reality is that manual coding is still often necessary for many tasks such as custom data transformations, integrations, and machine learning. This can add another time-consuming layer of complexity to an already fragmented stack, and requires specialized expertise that can be difficult to find and afford.

• Disjointed User Experience: Due to the highly-fragmented nature of the modern data stack, each tool has its own user interface, workflow, and documentation, making it difficult for users to navigate and learn the entire stack. Users often have to switch between multiple tools and interfaces to complete a single task. This disjointed user experience can be frustrating and time-consuming, leading to reduced productivity and burnout.

• Knowledge Silos: This fragmented approach can also lead to knowledge silos, where different teams or individuals become experts in specific tools or technologies and don't share their knowledge or collaborate effectively with others. This can create a lack of cross-functional understanding and communication, which can result in missed opportunities and suboptimal data solutions. Additionally, if a key team member with specialized knowledge leaves the organization, it can create a knowledge gap that is difficult to fill and may impact the overall effectiveness of your data solution.
  

• Staffing Challenges: The highly specialized nature of many of the tools in the modern data stack means that organizations may need to hire or train staff with specific skill sets in order to use them effectively. This can be a challenge in a highly competitive job market, and can further increase the costs associated with building and maintaining a data solution using this fragmented approach.

• Lack of Consistent Data Modeling: With different tools and systems being used across teams and departments, it can be difficult to ensure that everyone is working with the same data definitions, schema, and semantics (a "single version of truth"). This lack of consistent data modeling can undermine the reliability of your data, make it difficult to perform accurate analysis and reporting, and lead to misinformed decision-making.

• Lack of Holistic Data Governance: In the modern data stack, control is dispersed across multiple tools, systems, and users. With this fragmented approach, it’s extremely challenging to enforce policies that ensure data is being collected, analyzed, stored, shared, and used in a consistent, secure, and compliant manner.

• Lack of Observability: As the number of tools and systems grows, it becomes increasingly difficult to document and monitor data as it flows through the various stages of the pipeline. Lacking a unified view of your entire data infrastructure (a "single pane of glass") significantly reduces your ability to catalog available data assets, track data lineage, monitor data quality, ensure data is flowing correctly, and quickly debug any issues that may arise.

• Increased Security Risks: With data spread across so many tools and systems, it can be difficult to identify where data is stored and who has access to it, which increases the risk of misuse by internal users and unauthorized access by malicious actors. Additionally, if the infrastructure is not regularly maintained, patched, and monitored for anomalies, there is an increased risk of malicious actors exploiting vulnerabilities in the system.

• Lack of End-to-End Orchestration: With the modern data stack, end-to-end orchestration can be a challenge due to the multiple tools and systems involved, each with its own workflow and interface. This lack of orchestration can result in delays, errors, and inconsistencies throughout the data pipeline, making it difficult to ensure that data is flowing smoothly and efficiently.
  

• Limited Deployment Options: One of the biggest limitations of the modern data stack is the lack of support for on-premises or hybrid approaches. Many organizations still prefer to keep some or all of their data infrastructure on-premises or in a hybrid environment due to security or compliance concerns, as well as cost considerations. However, most tools in the modern data stack are designed to be cloud-native, which means they are optimized for use in a cloud environment and do not support on-prem or hybrid setups.

• Vendor Lock-In: Over time, you may become dissatisfied with a particular vendor’s service or support, or they may suddenly raise their prices to an unacceptable level. Many vendors may make it difficult or expensive to migrate data out of their system, which can create significant challenges if your organization decides to switch to a different solution. Vendor lock-in can limit flexibility and innovation and make it difficult for your organization to adapt to changing business needs.

These obstacles have caused the modern data stack to fail at delivering on its most basic promise: to help companies build smarter, faster, more flexible data solutions in a timely, cost-effective manner.

Unfortunately, most organizations are spending significant amounts of time and money implementing this "modern" data stack, but they aren't getting any closer to turning their data into actual business value.

Questions?