Modern Data Stack: What are the Challenges?

Originally published on

, the following is a revised edition.

Introduction

The modern data stack has gained a lot of popularity within enterprises that have data-driven ambitions. And that’s not even a surprise, given that the stack itself is driven by cloud-native tools designed to support Artificial Intelligence (AI), Machine Learning, and advanced analytics. The stack comes with a promise of scalability, modularity, and speed.

Machine Learning, AI and Data Paradigm — The MAD (ML, AI, & Data) Landscape | Source: First Mark

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The need to manage data through a stack arises due to the vast volume of data being generated worldwide. Statista forecasts that the global data creation volume will breach the 394 zettabytes mark by 2028, further highlighting the need for an advanced stack with a high operations threshold.

Everything looks nicely sorted, but only in theory. As enterprises adopt this data stack, things change, and teams often go through multiple pipelines and platforms. While the intent was to streamline processes, the outcome led to the creation of new silos, such as increased complexity and fragmentation.

This is because teams in the same organisation use many tools for different data functions. While every tool has overlapping features, the interoperability attributes are far less than expected.

The result?

Redundant data pipelines, siloed workflows, and increased integration overheads, with significant implications in cost.

Maintenance and integration require constant resources and effort.
Escalating infrastructure and tooling costs.
Steep learning curves and specialised skills make bringing in new talent or democratising data usage tough.

Repetitive financial overheads in the modern data stack. — Challenges of the Modern Data Stack | Source: Evolution of the Data Stack

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Intended to facilitate quicker insight generation, the modern data stack risks becoming a bottleneck because of some glaring trade-offs. For organisations looking to scale up their data and AI ambitions, having a clear understanding of the challenges of this data stack is crucial so that the stack becomes an ally and not a hindrance.

Modern Data Stack Challenges

The data stack has been constantly evolving, but as mentioned above, some significant challenges keep it from reaching its full potential.

1. Tool Fragmentation

Tool fragmentation is among the most pressing challenges in modern data stacks today. A typical data stack consists of tools for ingestion, transformation, storage, orchestration, BI, machine learning, and reverse ETL, among others, with each tool having its own capabilities. This approach, however, creates an inflated ecosystem of multiple tools that are not even as tightly integrated as they should be.

This lack of interoperability between tools increases the overall complexity, and teams spend a lot of time integrating these tools properly rather than tackling actual business pain points.

An inflated ecosystem of multiple tools in a data stack without proper integration. — Tool Fragmentation in the MAD Landscape | Source: Evolution of the Data Landscape

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Redundant workflows through tools with overlapping features create a lot of confusion in proper decision-making between teams. As a result, it becomes tough to manage configuration consistency, lineage, and access permissions.

Data evangelists on the need for a unified structure than a fragmented approach. — The need to have a unified structure instead of fragmented tooling of Modern Data Stack | Source: LinkedIn Data Communities

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2. Operational Complexity

Fragmentation leads to elevated operational complexity. How? Each tool requires its own set of monitoring, expertise, and configurations. This stretches data teams to their maximum limit, as they must maintain infrastructure, look after incidents, tune performance, and maintain uptime across the entire data stack.

One of the most significant problems with this complexity is its impact on overheads, which increases drastically. More tools create more pipelines needing debugging, increased integrations that need to be monitored, and the delegation of more tasks across different teams. A modular architecture becomes a tangled mess of excess responsibilities, slowing down things and putting everything at risk.

3. Data Quality and Trust Gaps

Enhanced data quality is a significant objective for any data stack. However, inconsistency in standard validation, ambiguity in data ownership, and pipeline failures lead to a loss of trust in the data. Without testing and observability, teams are always reactive to quality issues, looking at them only when they have influenced decision-making in a not-very-smart way.

Inconsistency, ambiguity, and technical failures are the biggest causes for loss of trust in data. — Traditional Data Quality Lifecycle | Source: Data Quality is a Cultural Device

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Aspects like quality monitoring and data contracts are still in their emergent stage and are not integrated tightly into workflows. The result? Users are left questioning the timeliness, completeness, and accuracy of data. Without absolute trust to back things up, consequences are effort duplication, projects on hold, and dependency on manual spreadsheets. The value of the stack diminishes as a whole.

A lot of questions in the users’ minds about data accuracy, timeliness, and completeness. — Challenges around Data Quality on Traditional Data Stacks | Source: Data Quality, a Cultural Device in the Age of AI-Driven Adoption

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4. Metadata Debt

Metadata management is one of the most under-tapped aspects of the modern data stack. As new tools enter the data ecosystem, the metadata often bears the brunt, becoming dated or fragmented.

Metadata, in layman’s terms, is the context around data or the meaning and relevance behind it. The story of the data. What does the data mean? Where did it come from? How frequently does it arrive? Where does it sit, and who uses it? What can it be used for, and how frequently? And so much more…

In short, data has no value without metadata and falls into chaos. Not surprisingly, most organisations are sitting on loads of data that has no use because it is disjointed from the core semantic model. In popular lingo, this is called dark data. It is not so much the cost of storage, but the cost of money left on the table because of not leveraging rich, valuable data.

The three rules of metadata:

Partial metadata unlocks partial value for the data.
Metadata streams that do not talk to each other do not generate new, valuable metadata.
Metadata is most meaningful when extracted from the entire journey instead from within limited boundaries or components.

Consequently, the metadata collection process itself impacts the potential of the metadata. Collecting metadata is not enough, collecting it right is the point of priority.

Below is a comparative overview of two collection methods.

Assembled and unified systems both follow different approaches for metadata management. — Metadata Management on Assembled Systems vs Unified Systems | Source: Animesh Kumar, LinkedIn

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Assembled Systems or Metadata on Modern Data Stacks

Metadata is partially injected from disparate components that are externally integrated together. There's not much leeway for these disparate components to continuously interface or interact and thus generate rich metadata from dense networks.

This situation leads to the creation of metadata debt and is one of the biggest challenges for the modern data stack. It is the cost of unclear data definitions, lack of context, and poor discoverability levels, as data analysts spend considerable time locating and validating the data. Also, engineers must work around pipelines because the existing assets lack the required visibility.

Unified Systems

The unified architecture is composed of loosely coupled and tightly integrated components that densely interoperate/network with each other and, in the process, generate and capture dense metadata that is looped back into the components on a unified plane.

5. Lack of Clear Ownership

The whole premise of a modern data stack revolves around increasing flexibility through tools. However, it has led to a lot of confusion when it comes to defining clear ownership across data teams.

Different tools for ingestion, transformation, orchestration, and other related functions lead to responsibility diffusion across different teams and roles. In the context of the end-to-end data lifecycle, there is a lack of accountability for each function. The fragmented architecture creates a lot of confusion, diluting accountability and cutting down the pace of issue resolution.

Effective data governance also suffers, as enforcing policies and data standards often trickles down through the cracks of team boundaries. The right data ownership needs more than just assigning names to datasets or dashboards to truly become an enabler.

6. Gaps in Compliance, Security, and Access Control

As the volume of data increases, the associated risks witness a proportionate rise too. A report from Cybersecurity Insiders states that 91% of cybersecurity professionals felt that their systems were not ready to handle zero-day breaches or respond to newly discovered vulnerabilities. This shows that existing compliance practices are behind when it comes to progressive data stacks.

Yes, the tools in use have their own access controls, but without a hybrid governance framework, the chinks in the armour show up pretty soon. Issues such as inconsistent role access, weak audit links, non-compliance with standards such as HIPAA, and insufficient encryption creep up and weaken flows and pipelines over time.

The assimilation model suffers from issues such as non-compliance, insufficient encryption, and inconsistent role access. — Governance Bottlenecks in an Assimilation Model | Source: Solve Governance Debt with Data Products

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7. Silos and Shadow Flows

It’s an ironic paradox that the data stack for unifying data ends up recreating those silos that were intended to be eliminated with this stack in the first place. This is because different teams have their own set of tools, pipelines, and processes, which leads to redundant workflows and inconsistent data access.

When data governance is weak, it leads to shadow workflows, where unauthorised datasets, undefined pipelines, and siloed dashboards are everyday challenges that go over defined governance controls, giving rise to compliance risks, duplicated logic, as well as reporting inconsistencies among others.

The Impact of Modern Data Stack Challenges on ROI

The modern data stack looked like a winning opportunity by bringing scalability, agility, and data democratisation to the fore. However, once organisations started adopting a wide range of tools, each offering narrow functionality in the process, the overall complexity put a healthy return on investment into question.

While speed and agility were crucial points in focus, the inclusion of too many disconnected tools led to disjointed integrations, new silos, and a dramatic increase in operational overhead.

Too many fragmented tools, disjointed integrations, and newer silos increase the operational overhead drastically. — High Operational Complexity in the MDS. As we progress the complexity exceeds the flexibility that the MDS structure conceptually entails | Source: What’s Modern in the Modern Data Stack

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The biggest challenge here is that it is not just the data teams that get affected, but the organisation as a whole. Users face delays in getting the right kind of insights, the trust in data gets diluted, and data governance becomes a reaction, instead of a proactive function. Yes, every tool does add a little benefit with its entry into the scheme of things, but costs pile up with monitoring, orchestration, and compliance.

The stack becomes ‘modern’, but the efficiency takes a hit along with the ROI. The time to get actionable insights increases as teams need to spend a lot of time bringing fragmented pipelines together rather than working diligently on securing positive strategic outcomes. To get the right value, there is a need for organisations to line up their data strategy in sync with product thinking principles. This is essential to create the right kind of business impact.

The right business impact misses the mark as the overall returns from data value is put up in question without a unified structure. — The Vicious Cycle of Data ROI | Source: KDNuggets

Future of the Modern Data Stack: A Data-first Approach

As organisations work around the complexity of the modern data stack, a version is being born where data takes priority over the different tools and architectural influences. This is the approach of a data-first stack, where the entire data ecosystem is crafted around the data lifecycle, accessibility, and value of the data, rather than just unifying it through different technologies.

The Data Developer Platform (DDP) is a self-serve infrastructure standard that is a pivotal element of this shift, as a framework that empowers teams to create, govern, and scale data products with a lot of efficiency. DDP is deeply rooted in self-serve principles, where each domain team can take up ownership without the need for specified infrastructure knowledge. The self-serve aspect is the one that transforms a modern data stack from a fragmented tool collection to a well-oiled machine.

The self-serve feature plays a key role in transforming the modern data stack fragments into a cohesive unit. — The Data Developer Platform Standard for building Unified Infrastructures | Source: datadeveloperplatform.org

Essential Factors of a Data-first Stack

There are quite a few important factors that come into play with a data-first stack:

Easy Management: DDPs make it possible to keep operational simplicity a built-in function, offering centralised monitoring, policy enforcement, and lineage tracking across the complete data lifecycle.
Unified Architecture: With DDP’s modular Lego blocks, the tech stack becomes a set of loosely coupled and tightly integrated components instead of hard-coded integration of tools, making ingestion, transformation, access control, and storage seamless throughout the organisation.
Governance by Design: The data-first approach ensures that governance is deeply embedded into every layer, right from access control to metadata, to ensure compliance, security, and trust.
Rapid Value Realisation: When combined with DDP capabilities, a data-first approach can drive meaningful results in just a few weeks, and not months. The principles of data mesh include decentralised ownership with centralised standards for seamless delivery.

Solution, not a Conclusion

The ‘modern’ in modern data stack is not just an adjective but a highlight that bends towards a self-serve platform that assists enterprises in data solutions at speed, becoming a necessity for the data mesh approach.

With this data stack, enterprises can leverage all their services and tools to their potential with the aid of standardised integration, access, resource optimisation, and other low-priority complexities. All of this is made possible through the Data Developer Platform (DDP).

It allows development teams to build and deploy applications with ease through a set of tools and services so that data can be managed and analysed in a better manner. The unifying capability of a DDP is one of its biggest strengths, offering a single point for complete management.

The message is clear: the challenges with the modern data stack are significant, but a thought process ingrained in data-first philosophy can be integral in solving them.

Stay tuned for the next article in the series!

Conclusion

2025 is brimming with new opportunities—greater specialization in AI for industries, deeper integration of autonomous systems, and a surge in demand for real-time, privacy-conscious solutions. This year isn’t just about smarter AI—it’s about AI that acts, adapts, and delivers tangible value across every domain.

2025 will surely see some awesome updates in data engineering, with new tech updations knocking on our door almost daily, mergers, acquisitions, and funds in the space hint towards a brighter future.

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