Photo by fauxels:

Is Generative AI a Game Changer for Data Organizations?

Gary Cheung
Published in
4 min readApr 11, 2024


I’ve been in the data world for a bit more than a decade, starting my career when “Big Data” was the buzzword everyone was talking about. Just like now with generative AI, there were voices claiming it was all just a bubble, while others saw its potential. To get a grip on what the future holds for generative AI in our data landscapes, it’s enlightening to revisit the journey of big data and its evolution.

What happened to the big data hype?

The majority of the technology I supported in my early career — Hadoop, YARN, Hive, to name a few — have now morphed into the backbone of numerous cloud data services. They’ve essentially turned into what’s best described as digital utilities, neatly packaged by cloud platforms. We’ve transitioned from the hardware-heavy days of Hadoop to the new world of serverless computing. Technologies like Hive and Impala have given way to modern solutions like Snowflake and Athena. The monumental task of querying structured and unstructured datasets has evolved from groundbreaking projects to standard functionality on Athena or Snowflake.

Did Big Data Revolutionize the Business Landscape as We Hoped?

In many ways, Yes! As hardware costs plummeted and internet speeds surged, the volume of data stored and transferred expanded dramatically. Big data, in many respects, has simply become “data.” Challenges that once seemed insurmountable, due to the sheer size and storage requirements, are now easily managed thanks to advancements in database technology. Projects that required massive teams and were considered digital transformations are now baseline expectations for organizations and startups alike.

This brings us to generative AI. If history repeats itself, we can expect a similar trajectory. The hurdles we’re facing today in generative AI — like context window limitations, building RAG systems, and prompt optimization — are likely to be resolved within the next decade. Generative AI is poised to become another tool in our arsenal, seamlessly integrating into our data and business workflows, much like how big data technologies have evolved. For data leaders, this isn’t just a trend to watch; it’s a pivotal shift we should be preparing for.

Revolutionizing Data Ecosystems with Generative AI: Opportunities Ahead

Let’s explore how this transformative technology can elevate our data practices:

  • Optimizing Data Operations: Utilize Gen AI to improve existing data pipelines and analytical queries
  • Simplifying Data Access with Chat Interface: Integrate chat functionalities to make data requests straightforward, enhancing user experience and accessibility.
  • Creating New Datasets: Extracting unstructured data from existing datasets
  • Automating Insight Generation: Employ AI to sift through your data, identifying trends, anomalies, and strategic insights that can inform decision-making.
  • Crafting Comprehensive Data Strategies: Utilize generative AI to develop and implement powerful data strategies, ensuring your organization stays ahead of the curve.

Industry-Specific Innovations:

  • Healthcare: Transform patient care by generating new datasets from doctor’s notes, offering deeper insights into patient health.
  • E-commerce: Enhance customer satisfaction by automating the classification and analysis of feedback and complaints, ensuring every voice is heard.
  • Marketing: Create personalized customer communications based on purchasing behaviors, elevating the customer experience.
  • Fraud Detection: Improve your security measures using Generative AI to detect fraudulent activity through pattern recognition and anomaly detection.

The Future of Work with Generative AI:

While some fear the potential for generative AI to replace existing jobs, the reality is that data teams are already stretched thin. There are always strategic projects delayed by organizations struggling to fund the budget. Generative AI promises to be a powerful ally, augmenting teams by:

  • Filling Skill Gaps: Automate time-consuming tasks, allowing your team to focus on strategic initiatives. An engineers time should be spent on strategic project or new features, not simply building boiler plate code.
  • Enabling New Capabilities: Produce novel data assets and products that were previously unimaginable, broadening your organization’s capabilities.
  • Improving Your Existing Systems: Generative AI can act as a architect for your team, providing new insights to solve higher level problems. It can improve your existing data ecosystem design, scope new projects, and resolving operational data challenges efficiently.

As we stand at the cusp of a new era, reminiscent of the early days of Big Data and Web 2.0, the potential for Generative AI in your data ecosystem is immense. Now is the time to explore, innovate, and lead the charge in harnessing AI’s full potential.

Let’s Connect:

If you’re navigating the integration of generative AI into your data ecosystem, or if you’re curious about its possibilities, I’m here to discuss and exchange ideas.

Feel free to reach out at

Together, let’s pioneer the future of AI in our data ecosystems. I look forward to connecting with fellow visionaries on this exciting journey.



Gary Cheung

Big Data focused architect & strategist. Life Sciences and Genomics enthusiast