Top Five Data Platform Trends to Watch in the Coming Year

In 2023, 3.5 quintillion bytes of data were created every day.

It’s almost impossible to imagine. It goes without saying, that number isn’t going down – and no, that’s not my first of the five trends for 2024. As data grows, so too do the challenges of managing it and leveraging it to improve your business in endless ways. Data is the new frontier, which brings innovation, new best practices, methodologies, and real results.

As we approach the end of another year, it’s time to anticipate the trends set to reshape the data platform landscape in the coming year.  

1. Streaming Data

Streaming data can be a challenge, as it often encompasses massive volumes of data from various sources and devices delivered in many different formats. 

The infrastructure and software architecture required to support data streaming can be complex and difficult to manage. Few companies possess the internal resources with the skills and know-how to build and maintain such systems. Business leaders acknowledge the massive benefits of real-time streaming, but it’s often an intimidating process with unpredictable project timelines.

According to CB Insights, the stream processing market is expected to reach a value of $52 billion by 2027. And there’s good reason: it’s become much more manageable with modern data platforms.

Hitachi Solutions Empower Data Platform employs the Lakehouse architecture, providing customers with the best aspects of data warehouses and lakes, supporting structured and unstructured data formats. By leveraging Databricks-managed Apache Spark clusters to scale data ingestion and modeling, Empower automatically manages and deploys powerful compute and orchestration for data streams while managing the infrastructure to remain cost-efficient for your company.

2. Increased Investment in Data Lakes

Data lakes serve as a central repository allowing businesses to store all their data, structured and unstructured, at any scale. They can store raw data and operate as a location to stage data transformation to useful business models. With the power of machine learning and AI, businesses can pull actionable insights from these business models in real-time.

Mordor Intelligence predicts that the data lake market will grow at a compound annual growth rate of nearly 30% through 2026.[MG1]  This significant growth is driven by the massive volumes of data generated by businesses, particularly small and medium-sized businesses. With the increase of IoT devices, social media usage, and digital transactions, businesses now generate petabytes of data that require an affordable and scalable storage solution, which data lakes provide.

The rise of cloud-based solutions has also made data lake technology more accessible. Cloud data lake solutions, built on top of open source Delta Lake technology, allow businesses of all sizes to load and optimize data without any management or infrastructure, offering a flexible and scalable solution for data storage.

Moreover, businesses are not just investing in creating data lakes, but they are also increasing their spending on data lake solutions. A 2022 survey revealed that 21% of respondents plan to increase their data lake investments by 10% or more in the next three years, and 35% plan to increase their spending by up to 9%.

Businesses that can effectively leverage data lakes will be better equipped to handle and gain insights from the vast amounts of data they generate, providing them with a significant competitive advantage.

3. Maturation of Microsoft Fabric and Integration with Databricks

As we move forward into the new year, the maturation of Microsoft Fabric and its integration with Databricks is a trend worth watching in the Data-as-a-Service (DaaS) market. With Microsoft Fabric, Microsoft provides a scalable, feature-rich data management solution that allows businesses to run large-scale analytics without the need for building their own data collection solutions or expensive storage platforms.

In a significant development, Microsoft and Databricks have announced a deep integration between Fabric and Databricks. This integration, which demonstrates the maturation and evolution of these platforms, aims to provide businesses with a unified, comprehensive data platform.

A key highlight of this integration is the Unity Catalog, a unified data security and governance catalog provided by Databricks. The Unity Catalog offers a consolidated view of all data assets, enhancing data discoverability and governance. It is integrated with Fabric, allowing users to access Databricks’ data directly from Fabric and vice versa. This seamless access to data across platforms enables more efficient data workflows and allows businesses to extract more value from their data.

The integration of Unity Catalog with Microsoft’s OneLake, a unified data lake storage system, would be an exciting development. With OneLake, businesses can store vast amounts of structured and unstructured data, while OneLake shortcuts enable them to reference data from other lakehouse solutions within Fabric. OneLake is also built on Delta Lake technology, in the future this could allow businesses to leverage Databricks’ powerful analytical capabilities on data stored within Fabric and vice versa.

This deepening integration between Microsoft and Databricks further signifies the shift away from data warehouse-only towards more integrated, comprehensive data lakehouse focus for businesses. This trend is one to watch, as it will likely shape the future of the DaaS market and influence how businesses approach their big data strategies.

4. DataOps Becoming Mainstream

DataOps, a methodology that applies principles from DevOps to data management, is rapidly gaining traction and is poised to become a standard practice in the coming year. The DataOps methodology emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data scientists, analysts, and other data professionals.

The primary goal of DataOps is to improve the speed, quality, and reliability of data analytics. It accomplishes this by automating many manual processes involved in data preparation, such as data extraction, transformation, and loading (ETL). This allows data scientists and analysts to focus more on generating insights rather than dealing with data preparation.

Moreover, DataOps promotes a culture of collaboration between different roles involved in data management and data analytics. This collaborative approach helps break down silos, leading to more efficient data practices and better alignment between data operations and business goals.

The rise of DataOps is driving significant changes in the design and usage of data platforms. Modern data platforms are being built with features that facilitate DataOps practices, such as automated data pipelines, collaborative tools, and real-time monitoring and alerting capabilities. These features enable rapid data delivery, iterative development, and continuous improvement—key tenets of the DataOps methodology.

The adoption of DataOps can provide businesses with a significant competitive edge. It can help reduce time-to-insight, enabling businesses to react more quickly to changes in the market or customer behavior. It can also enhance the reliability of data analytics, leading to more accurate and trustworthy insights.

Furthermore, DataOps can help businesses achieve regulatory compliance more easily. By automating data workflows and implementing data governance practices, businesses can ensure their data operations are transparent, auditable, and compliant with relevant regulations.

As we move into the coming year, the trend of DataOps becoming mainstream is expected to continue. Businesses that can effectively implement DataOps strategies will be better positioned to harness the power of their data, driving growth and success in an increasingly data-driven world.

5. Increased Investment in AI and Machine Learning Models

A recent survey by Enterprise Strategy Group found revealed that more than 60% of IT leaders say they’re planning to increase spending on artificial intelligence and machine learning in the coming years.

Artificial Intelligence (AI) and Machine Learning Models (LLMs) are becoming increasingly integral to businesses across industries. These technologies are transforming the way businesses operate, providing them with unprecedented insights and capabilities.

As more businesses recognize the potential of AI and LLMs, investments in these areas are set to rise. They can process vast amounts of data far more efficiently than human analysts, identifying patterns and generating insights that can inform strategic decision-making.

Moreover, AI and LLMs are becoming more accessible. Advanced AI platforms and tools are now available as a service, reducing the need for businesses to invest in expensive infrastructure or specialist expertise. This is making AI and LLMs viable even for smaller businesses, broadening their impact.

Another factor driving increased investment in AI and LLMs is the growing emphasis on data-driven decision making. Businesses are increasingly reliant on data to inform their strategies and operations. AI and LLMs are key to unlocking the value in this data, turning raw information into actionable insights.

Yes, LLMs and AI are key in unlocking insights hidden without massive data sources. But as technologies underlying AI and LLMs continue to advance, and their potential applications are expanding. From predictive maintenance in manufacturing to personalized recommendations in retail, AI and LLMs are transforming various industry sectors. Businesses that can effectively harness the power of these technologies will be well-positioned to thrive in an increasingly data-driven world.

As we approach the end of the year, it’s an opportune time to reflect on the progress made in the data platform landscape and anticipate the exciting advancements to come. The trends highlighted here are set to shape the future of data platforms and represent not just technological advancements, but also the evolution of business strategies as they become increasingly data-driven.

It’s clear that the year ahead promises to be an exciting one filled with significant developments in the world of data platforms. By staying ahead of these trends, businesses can ensure they’re ready to leverage the transformative power of their data platforms to drive growth and success in the coming year.

Here’s to a new year filled with innovation, progress, and success. From Hitachi Solutions, wishing you a prosperous and happy 2024.