5X named Leader in DataOps!
5X named Leader in DataOps!
Discriminative models have dominated the AI landscape for long. The rise of generative models, like Gen AI, marked a shift, enabling a broader audience to leverage AI tools.The idea of the modern data stack was to make things more specialized, but it ended up getting too complicated with too many small specializations. This made it hard to put everything together smoothly and keep track of it all. We need to have common standards for metadata and observability.
Data fabric simplifies data management by bringing together different data components into one system, reducing the complexities associated with modern data stacks.
They blend AI seamlessly into existing systems, changing the way how data is organized. By separating storage and computation layers and adding AI to the mix, organizations can use advanced analytics and AI without needing a complete overhaul of their systems.
Generative AI, like ChatGPT, makes AI more accessible by letting people talk to data in a simple language. This could change lots of industries by combining structured and unstructured data analytics.
Identifying AI use cases that offer significant business value and return on investment is paramount, as organizations navigate through experimentation and strive for production-level deployment of AI applications.
Despite the dynamic landscape, established players in the data and AI space have ample opportunities to innovate and regain leadership positions by leveraging advancements in AI technologies and addressing evolving business needs.
“What I've noticed is that a specialization is good, but we have micro specialization now. In each category, we've got literally dozens of products. Businesses don't have that time to look and understand each product and bring it all together. The glue that connects all these products is the metadata, but there are no common metadata standards “
Sanjeev Mohan
“GPU is becoming like an accelerator to do your analytics faster. So all this time we've been focused on how do you have a better analytical compute engine, how do you store your data in a more normal way that speeds up indexing and compression. But now we are also starting to look at performance and improving latency at the hardware level”
Sanjeev Mohan
“Don't sell data fabric, don't sell data mesh, don't sell anything. You only have one thing to sell, which is what is the problem that you're trying to solve and how efficiently can you solve that problem? Businesses don't give a damn whether you call it a data fabric or not”
Sanjeev Mohan
Curious about the backbone of modern data analytics? From Barcelona's emerging tech scene to the challenges facing data vendors in a competitive market, they explore key insights shaping the industry. Alessandro shares invaluable advice for newcomers and sheds light on emerging trends like analytics engineering and the pivotal role of semantic layers in data governance.
