The AI Platformization of Storage – The Data Intelligence Platform

The IT industry uses the word “platform” all the time. Often, I find myself shifting between the many jargons circling “platform”, loosely. I am pretty sure many others are doing so as well.

I finally found the word “platformization” giving right vibes in a meaningful way in February last year, when Palo Alto Networks pivoted to platformization. Their stock tumbled that day. Despite the ambiguous definition “platformization” when Palo Alto Networks (PANW) mentioned it, I understood their strategy.

Defence-in-Depth in cybersecurity wasn’t exactly working for many organizations. Cybersecurity point solutions peppered the landscape. There were so many leaks and gaps. Platformization, from the PANW‘s point-of-view, is the reverse C&C (command & control), if you know the cybersecurity speak. PANW wants to take charge all the way for all things cybersecurity, and it made sense to me from a data perspective.

Paradigm shift for Data. 

For the longest time, networked storage technology has been about data sharing, be it blocks, files or objects. The data from these protocols is delivered over the network, mostly over Fibre Channel and/or Ethernet (although I remembered implementing NFS over Asynchronous Transfer Mode at Sarawak Shell in East Malaysia), in a client-server fashion.

By late 2000s onwards, unified storage or multi-protocol storage (where the storage array is able to served all 3 SAN, NAS and S3 services) was all the rage. All the prominent enterprise storage vendors had a solution or two in their solutions portfolio. I started viewing networked storage as a Data Services Platform which I started explaining it in 2017. Within the data services platform, various features revolve around my A.P.P.A.R.M.S.C. framework (I crafted the initial framework in 2000, thanks to Jon Toigo‘s book – The Holy Grail of Data Management). This framework and the approach I used for my consulting and analyst work worked well and is still relevant, even after 25 years.

But AI is changing the data landscape. AI is changing the way data is consumed and processed through the networks between the compute layer and the storage layer. It is indeed, for me, a paradigm shift of data, and the storage layer, better known as AI Data Infrastructure now, is shifting as well. And this shift will accelerate the exponential growth in innovations, with AI and super-charged data leading the way.

DDN Infinia Data Intelligence Platform (screencapture from DDN Beyond Artificial webinar)

Continue reading

AI and the Data Factory

When I first heard of the word “AI Factory”, the world was blaring Jensen Huang‘s keynote at NVIDIA GTC24. I thought those were cool words, since he mentioned about the raw material of water going into the factory to produce electricity. The analogy was spot on for the AI we are building.

As I engage with many DDN partners and end users in the region, week in, week out, the “AI Factory” word keeps popping into conversations. Yet, many still do not know how to go about building this “AI Factory”. They only know they need to buy GPUs, lots of them. These companies’ AI ambitions are unabated. And IDC predicts that worldwide spending on AI will double by 2028, and yet, the ROI (returns on investment) remains elusive.

At the ground level, based on many conversations so far, the common theme is, the steps to begin building the AI Factory are ambiguous and fuzzy to most. I like to share my views from a data storage point of view. Hence, my take on the Data Factory for AI.

Are you AI-ready?

We have to have a plan but before we take the first step, we must look at where we are standing at the present moment. We know that to train AI, the proverbial step is, we need lots of data. Deep Learning (DL) works with Large Language Models (LLMs), and Generative AI (GenAI), needs tons of data.

If the company knows where they are, they will know which phase is next. So, in the AI Maturity Model (I simplified the diagram below), where is your company now? Are you AI-ready?

Simplified AI Maturity Model

Get the Data Strategy Right

In his interview with CRN, MinIO’s CEO AB Periasamy quoted “For generative AI, they realized that buying more GPUs without a coherent data strategy meant GPUs are going to idle out”. I was struck by his wisdom about having a coherent data strategy because that is absolutely true. This is my starting point. Having the Right Data Strategy.

In the AI world, from a data storage guy, data is the fuel. Data is the raw material that Jensen alluded to, if it was obvious. We have heard this anecdotal quote many times before, even before the AI phenomenon took over. AI is data-driven. Data is vital for the ROI of AI projects. And thus, we must look from the point of the data to make the AI Factory successful.

Continue reading