Emerging Data Stack Trends In 2024

A well-optimized data stack is crucial to derive valuable insights from your data.
May 21, 2024

A well-optimized data stack is crucial to derive valuable insights from your data. But as the data landscape gets more fragmented, so do the questions surrounding it.

  • How do data teams adapt to changing data perspectives?
  • What's the impact of AI on data use?
  • What's ahead for emerging data categories and tools?
  • Is the modern data stack still relevant?
  • How does the economy affect business data strategies?
  • What's the future of today's data ecosystem?


We took a stab at these pressing questions in our recent webinar. The session featured data experts Benn Stancil, Ben Rogojan, and Tarush Aggarwal. While you can watch the full webinar on-demand on our YouTube, here's a concise overview of the data stack trends discussed.

TL;DR

Company objectives drive data team structure: Companies vary in how they view and use data, influencing their data team structures. Adaptation is crucial as perspectives on data evolve.


AI's role in data: Data's value is enhanced through AI for personalization and customer engagement. The adoption of AI tools varies, with some businesses creating custom solutions and others opting for off-the-shelf tools.


Uncertainty in data categories: Emerging data categories like observability and governance face uncertainty as standalone billion-dollar businesses.


Relevance of "Modern Data Stack": The term "modern data stack" may no longer fit the needs of some companies, especially larger enterprises, as their requirements exceed the original use cases.


Economic factors influence adoption: Economic conditions impact how businesses perceive and invest in data technologies, affecting data stack strategies.Tech giants' influence: Established tech giants like Microsoft and Salesforce have the power to shape the future of data tools and infrastructure, potentially leading to vendor consolidation.


Focus on actioning insights: Simplifying data infrastructure setup allows data engineering teams to focus on actionable insights, not building infrastructure.

Company objectives influence data team structure

Companies view data differently; some see it as a strategic asset, while others mainly use it for reporting and business intelligence (BI). This has led to the rise of new roles like analytics engineers and distinctions between data engineers and data scientists. Data teams should adjust their structure and responsibilities to match each company's unique needs as perspectives on data change.
Here’s what it means for different companies 👇

Using data as a strategic asset

Tech giants like Google consider data a strategic asset. They collect and analyze vast amounts of user data to improve their search algorithms, enhance user experience, and tailor advertising. This strategic use of data allows them to maintain their competitive edge in the tech industry.

For leveraging data as a strategic asset, the organization needs specialized roles such as data scientists and machine learning engineers who innovate using advanced analytics and insights from vast user data. 

Using data for reporting and BI

A legacy manufacturing company utilizes data primarily for reporting and business intelligence. They collect production data to generate regular reports on operational efficiency, which helps them streamline production processes and reduce costs.

For utilizing data primarily in reporting and BI, the data team includes BI analysts, data analysts, and operations managers. They focus on creating reports and improving operational efficiency with production data.

Unlocking insights from data through AI

Today, data is as valuable as oil but needs refining for insights. Companies use it to personalize products and engage customers. AI plays a pivotal role in this. AI has advanced from merely predicting trends to foreseeing future changes, telling us what to do to get the best results. This helps companies adapt to disruptions by suggesting novel solutions. However, the AI tools adoption within the data stack can vary among companies. 

While some businesses create their own AI solutions for precise customization, data control, and innovation, most opt for off-the-shelf AI tools. These tools provide cost-effective, reliable, and scalable solutions, eliminating the need for extensive in-house development. Ultimately, organizing and using the data for a competitive edge remains the key to growth.

The future of oversold data categories seems dull

There is a growing number of categories, such as observability, governance, and diversity. While real problems exist in these areas, some categories might be overhyped, and their sustainability as standalone billion-dollar businesses is still uncertain. 

Moreover, the market for specific data vendors can be speculative, especially in emerging categories. Assessing the sustained spending and the number of companies that can thrive in these spaces remains a challenge. As a result, data warehouses may evolve as more comprehensive data solutions.

Is the term “modern data stack” still relevant? Not really!

The modern data stack is a complete set of tools for today's data analytics and management. It empowers businesses to collect, process, store, and analyze data from various sources, enabling data-driven decision-making and insights. 

However, the term "modern data stack" may no longer be as relevant as it once was since it doesn't fit the needs of some companies today, especially larger enterprises. Their requirements exceed the original use cases that modern data stack solves for. 

Economic factors influence data vendor adoption

Economic factors play a pivotal role in the context of the data stack. The global financial situation and market dynamics can impact how businesses perceive and adopt data technologies, influencing the overall data stack strategy.

In a good economy, businesses focus on innovation and invest in data tools with confidence. In contrast, they tend to be more cautious during economic downturns and reduce spending on data-related initiatives.

Tech giants can shape the future of data stack

Established tech giants like Microsoft, Salesforce, and Oracle can significantly shape the future of data tools and infrastructure. Their financial power and ability to acquire or integrate with emerging technologies may influence the direction of the industry. This might signify the beginning of vendor consolidation. 

Amid these changes, 5X emerges as the only fully managed data platform that combines various data tools into one comprehensive solution tailored to your unique needs. 

Simplifying the complex data ecosystem is the way ahead

Today’s data ecosystem is complicated, with various tools, pipelines, and processes. Simplifying this could be a focus for the industry. Moreover, within this competitive landscape, the market remains far from settled, offering ample opportunities for innovation and specialization.

Creating a data platform doesn’t have to be hectic, though. Spending over four months and 20% dev time just to set up your data platform is ridiculous. Make 5X your data partner with faster setups, lower upfront costs, and 0% dev time to manage. Let your data engineering team focus on actioning insights, not building infrastructure ;)

Remove the frustration of setting up a data platform!

Building a data platform doesn’t have to be hectic. Spending over four months and 20% dev time just to set up your data platform is ridiculous. Make 5X your data partner with faster setups, lower upfront costs, and 0% dev time. Let your data engineering team focus on actioning insights, not building infrastructure ;)

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