Exploring the Latest Trends in the Big Data Market

The big data market is evolving rapidly, presenting both opportunities and challenges for enterprises of all sizes. Remarkably, larger corporations with more than 5,000 employees are adopting cutting-edge technologies like Hadoop at an accelerated pace compared to their smaller counterparts. This phenomenon challenges the conventional notion that smaller and younger companies are inherently more agile and responsive to new technologies.

Oil and gas companies, pharmaceutical manufacturers, and other large industries possess vast amounts of data. Despite this, our analysis reveals that these sectors are not rapidly adopting Hadoop. In contrast, financial services companies are leading the charge, even though this sector is often known for its slower adoption of new technologies. This shift could be attributed to early adopters in the financial sector, such as American Express, which have set a precedent. Alternatively, some companies might be bypassing traditional technologies and making a direct transition to Hadoop, bypassing generations of tech.

Key Players in the Market:
Paxata and Syncsort are startups that have emerged to assist businesses in making this transition. Their tools simplify the process of leveraging Hadoop without the need for significant investments in proprietary hardware and software.

Big Data as an Evergreen Technology

Big data remains a resilient and foundational technology, with a robust future ahead. Our recent exploration into the latest trends in big data for 2018 has uncovered several significant developments. Here are seven key trends that are shaping the big data landscape:

1. Accessibility and Simplification

The biggest and most fundamental trend in big data is the increasing accessibility and simplification of data processing and analysis. For a long time, big data required substantial IT infrastructure and specialized programming. Custom code often utilized MPI on supercomputers or clusters for complex computations. However, advancements in hardware and cloud solutions have made big data projects more accessible to a broader audience.

Today, hardware is both faster and cheaper, allowing for a cloud-based model that requires minimal investment in custom hardware or software. As a result, more people can perform big data projects without requiring extensive optimization or a dedicated computer room. This shift towards commodity hardware means that the class of problems requiring more than a data scientist with an AWS account has significantly narrowed.

2. Enhanced User Interaction

A significant trend in the big data market is the emphasis on improving the user experience. Previously, data handling was often a behind-the-scenes process, with a focus on back-end infrastructure. Now, the emphasis has shifted to creating more interactive and engaging user interfaces.

In the past, a skilled computer scientist might spend days or weeks to achieve a certain result. Today, users expect instant gratification with just a simple gesture. This trend is evident in the move of partial indexes to client-side rendering, which enhances real-time data interaction.

3. Valuation of User Experience

The underlying shift in the big data market is the increased investment in user experience. It used to be common to invest millions in back-end infrastructure to produce tables and basic visualizations. Today, this investment is increasingly directed towards enhancing the end-user experience.

This shift is reflected in the valuation of different forms of intellectual property and investments. As this trend continues, we expect it to become more formalized, perhaps even part of MBA curricula.

Conclusion

The big data market is in a period of transformation, driven by technological advancements and changing user expectations. Larger enterprises are leading the charge in adopting innovative technologies, while smaller companies are finding new ways to leverage cloud and commodity solutions. The focus on user experience is becoming a critical component of big data strategy, signaling a significant change in how we approach and utilize data.