According to HG Data, 1.5 million companies are slated to spend $133.6 billion on modern data infrastructure in 2023.[1]

It’s no surprise that the volume of data in our hands has grown exponentially over the past few years. To keep up with it, the data infrastructure had to evolve at a similar pace. This meant things had to be managed from a cloud-based control plane. 

Gartner predicted that close to 35% of data center infrastructure would be managed from the cloud by 2027.[2]

If you feel that’s a good sign, we’ll be going over some of the major data infrastructure trends that are shaping up this year. Let’s dive in!

Data Democratization

Back in 2021, this is what the modern data stack looked like:

Data Infrastructure Trends: State of Modern Data Stack
Image Source

It has only grown to include more tools in 2023. 

With multiple tools and databases on board, that might not fully integrate, you end up with data silos.

With the rapid advent of the modern data stack, companies face a dilemma. 

They could stick with their current vendors and risk falling behind competitors using newer, more capable tools.

 Or they could adopt new technologies which only ends up creating more disjointed data silos.

As real-time demands increase, companies might face difficulties in sharing data among tools with diverse protocols and functions.

One way to fix these data silos is through data democratization. It allows everyone in a company to work on the same data, irrespective of their technical knowledge.

By improving the accessibility of your data, you can make better use of the expertise and knowledge of the entire workforce.

Data democratization would also free up your data team’s bandwidth to take on more advanced work.

Given that data democratization provides more people in the team access to the right data, it would ultimately lead to better data-driven decisions. With more cross-functional alignment, you could focus more of your time on generating more ROI from your data stack as compared to defining it.

As data cases continue to boom in 2023, so does the search for better interoperability of systems. So, we’d probably see a steady rise in the adoption of data democratization across data stacks this year and in the years to follow. 

Infrastructure Cost Optimization in the Face of Looming Recession

Public Cloud usage has picked up pace but a lot of implementations are still poorly implemented. In the face of a looming recession this year, a lot of companies have been reducing IT infrastructure, slashing costs, etc. When asked to do ‘more with less’, a great way to optimize costs would be to improve the previously built infrastructure to make it more efficient and resilient.

In an attempt to modernize your analytics and data efforts, you could uproot the current system and go for a complete overhaul. But, this effort could become quite costly, if you don’t have a complete understanding of why every component of your previous data stack was there. (Also known as the Chesterton Fence principle)

This means we’re back to optimizing the infrastructure through non-disruptive ways. This would include the following: 

  • Building business resilience instead of service-level redundancy.
  • Getting rid of overbuilt, redundant, or unused cloud infrastructure.
  • Using cloud infrastructure to reduce supply chain disruptions.

Increased Adoption of the Data Mesh

Over the last few years, people have realized that the data is comparatively more distributed than centralized for more organizations (existing in data silos in a lot of cases, as we discussed previously)

Data Mesh has been on the rise since 2021 alongside data fabric as a key data architectural approach to better access and manage the distributed data.

You can use a Data Mesh to delegate data responsibility at the domain level and provide high-quality transformed data as a product — while ensuring data governance and quality.

A core pillar of data mesh is the federation and automation of data governance across the team. It aims to provide a single structure and interoperability to the ecosystem of disjointed data products.

Data fabric is a single framework that can be used to connect all the sources used by a company, irrespective of their physical locations.

Making data more accessible might raise concerns about security and privacy. That’s why, for data fabrics, you get more data governance guardrails in place, specifically for access control. This would ensure that specific data is only available to certain roles.

Data fabric architectures also let security and technical teams execute encryption and data masking around proprietary and sensitive data. This would help reduce the risks around system breaches and data sharing.

Edge Computing and Real-Time Integration

Sending all device-generated data from IoT devices to either the cloud or a central data center may cause latency and bandwidth issues.

Edge computing lets you bring enterprise applications closer to data sources such as local edge servers or IoT devices.

Given this proximity to source data, edge computing can help reduce data latency. Edge computing also creates the opportunity for extracting deeper insights, improved customer experiences, and faster response times.

With the rapid transformation of IoT (Internet of Things) devices, edge computing has become more crucial for the healthcare, manufacturing, and logistics industries.

The rise in edge computing also means that there needs to be a way for edge and cloud computing to work together. A key challenge here would be ensuring edge-to-cloud interoperability.

Interoperability is important here because, without it, you might find it a tad challenging to manage data across both cloud and edge environments.

Plus, without interoperability, you’ll just be making way for more data silos.

In the years to come, we’ll start seeing edge-to-cloud interoperability become a pivotal trend; which would ensure a seamless flow of data between environments.

Convergence of Reverse ETL and CDP

Reverse ETL is described as the process of taking data out of the data warehouse and putting it back into SaaS applications among others. CDP (Customer Data Platforms) refers to products that collect customer data from various sources. This can then be segmented into a virtually endless number of ways to create more personalized campaigns.

Reverse ETL tools started to become more like CDPs given that both provide direct customer data analytics without relying on other tools for it. This similarity was borne out of the realization that simply being a data pipeline on top of a data warehouse won’t cut it. 

They needed to provide more value around customer data, which is why various Reverse ETL vendors started positioning themselves as a CDP from a marketing perspective. [3]

CDPs started to become more like Reverse ETL tools, both integrating more closely with data warehouses. This similarity came out of the realization that being another repository where customers had to copy huge amounts of data pitted them against more well-known centralized data repositories like data warehouses or lakehouses.

Based on the trajectory, we might see these categories merging, or at least an amalgamation. 

Not only that, the complexity of the Modern Data Stack has also paved the way for players that package “various products” as one fully-managed platform. 

The overarching benefit of these platforms is that they can abstract away the business complexity of managing those vendors individually along with the technical complexity of stitching together various solutions. Some examples of these platforms are FruitionData, Mozart Data, Keboola, and Adverity to name a few. 

The Emergence of Quantum Data Infrastructure

Quantum data infrastructure is still in its infancy, but its potential to transform encryption and data processing is promising. With quantum computers becoming more accessible, it’ll only be a matter of time before businesses harness them to take care of complex data analysis tasks and enhance data security.

What quantum cryptography does is that they substitute the math problems that are easy for quantum problems to solve with math problems that are difficult to solve for both quantum and classical computers. Ergo, more robust data security.

Rise of Hybrid and Multi-Cloud Architectures

Data Infrastructure Trends: Types of Architectures
Image Source

More companies will adopt multi-cloud and hybrid architectures in the coming years. People have slowly started realizing the importance of diversifying their cloud strategies by piggybacking the strengths of multiple cloud providers. This proves beneficial by providing companies with improved redundancy and scalability. It also helps them get rid of vendor lock-in.

Multi-cloud infrastructure provides you with the flexibility to run workloads on any cloud as needed by the business. When executed properly, multi-cloud infrastructure allows companies to carry out speedy migrations affordably, while reducing the risk across a distributed IT landscape.

With a hybrid cloud architecture, you could extend operations and infrastructure consistently to provide a single operating model that’ll manage application workloads across both environments. Various organizations have adopted the hybrid approach to reduce risk, support cloud migration without having to refactor it, reduce overall cloud and IT costs, data center consolidation, and meet ad-hoc requests for storage and compute resources. [4]

Rise of LLMOps

Late 2022 saw ChatGPT rising through the ranks in the tech domain for its ability to generate well-structured and generally accurate responses. This opened up opportunities for LLMs (Large Language Models) to transform various aspects of enterprise operations such as data analysis and customer support.

As more businesses realize the value of integrating large language models into their workflows, the demand for LLMOps solutions is expected to grow. This would allow organizations to overcome common challenges that one might stumble upon while managing and deploying ML models like model monitoring, data versioning, continuous integration/deployment, and drift detection.

Also, LLMOps would improve transparency and response time for regulatory requests, particularly as LLMs are often under regulatory scrutiny. This will ensure better adherence to industry or organizational policies, mitigating potential challenges and enhancing risk management.

With LLMOps, you can also ensure access to suitable hardware resources like GPUs for efficient fine-tuning while optimizing and monitoring resource usage.

AI-Integrated Data Infrastructure

Artificial Intelligence can play a pivotal role in improving data infrastructure by automating tasks such as data categorization, data cleansing, and anomaly detection, which would lead to reduced manual efforts and improved data accuracy.

However, when AI decides the result, we don’t have a way to suppress the inherent bias in the algorithm. Therefore, we need a regulatory framework to ensure data governance and privacy, accountability, auditability, and algorithmic transparency.

As more and more companies use AI for data infrastructure, ethical AI is bound to become more important in the coming years.


Data Infrastructure trends in 2023 will focus on cost optimization, decentralization, increased usage of AI, and stitching up the security gaps that might be left wide open while moving in this direction.

Innovators that can satisfy all these needs would end up as the frontrunners in the race for tech stack dominance.

Businesses that embrace these data trends will be able to build a solid foundation to handle the vast amounts of data generated in the digital era, allowing them to make more informed decisions and stay a step ahead of the competition.

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