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At the beginning of last year, who knew that Generative AI (Gen AI) and ChatGPT would seize the moment? A year ago, we forecast that data, analytics, and AI providers would finally get around to simplifying and rethinking the Modern Data Stack, a topic that's been near and dear to us for a while. There was also much discussion and angst over data mesh as the answer to data governance in a distributed enterprise. We also forecast the rise of data lakehouses. For the record, last year’s predictions are here and here. Turns out, many of them came true, but one thing we didn’t predict was the emergence of Gen AI.

It’s been barely six months since IBM unveiled the new watsonx family of products targeting enterprise clients, AI builders, data scientists, and data professionals. And since May, IBM has generally released all three pillars of the new AI lifecycle stool: watsonx.ai for AI builders; watsonx.data, as the data lake house for data professionals; and just now, the last major piece: watsonx.governance for overseeing bias, ethics, risk, and compliance issues over the lifecycle. And to boot, we saw the logo slide showing over three dozen clients and partners that have already signed on to watsonx. An ecosystem is building, and customers are buying into watsonx, just months out of the gate.

What a difference a year makes. At the beginning of the year, if you asked anyone outside the AI research community about Generative AI, you would have gotten a blank stare. Our first quarter briefings with data and analytics vendors barely made notice of Large Language Models (LLMs) or vector storage.

Analytics has long been highly silo’ed, from the days where the dashboard from desktop BI tools, monthly reports, and SAS data mining addressed different stakeholders on different platforms. Those silo’ed deepened when the ability to analyze “Big Data” became real in the early 2010s, as business analysts wouldn’t dare stepping into the world of the mysterious zoo animals, while data scientists decided that the traditional walled garden data warehouse environment was too limiting.

It's a big day. Am jazzed to announce that Big on Data, the series co-authored by Andrew J. Brust & myself, is moving from ZDnet to VentureBeat under a new nameplate: The Data Pipeline. We're gratified that founder Matt Marshall shares our vision and wants to build VB into a destination for all things data.

Looking back on the past six years, the headlines may have pivoted to cloud, AI, and the continuing saga of open source. But peer under the covers, and this shift in spotlight has not been away from data, but because of it.

In some ways it seems like Groundhog Day. If last year we wrote that 2020 was the year we preferred to forget, 2021 was the year we were glad to survive. It’s not surprising that in these years of displacement that adoption of the cloud continued to climb.

The past 18 months have been a slog, to put it quite mildly. For those of us that could work virtually, we’ve gotten used to meeting on Zooms and comparing the layouts of different people’s dens.

In the data world, few other topics have taken over the conversation during the past year than Data Mesh. there are fewer topics that are drawing more discussion than data mesh. Just look at Google Trends data for the past 90 days: searches for Data Mesh far outnumber those for Data Lakehouse; just about the only topic that comes close in search activity is data fabric.

The spring cleaning of dormant Hadoop projects touched a nerve. It’s been fashionable to say that “Hadoop is dead” for some time – at least since Gartner published studies showing declining use as of 2015. ZDnet colleague Andrew Brust’s post on the project purge went positively viral.

It’s safe to say that 2020 is a year that we would probably all want to regret. It was a year where survival through adaptation became the rule. At the outset of 2020, we forecasted that generational change in back office systems and growing demand for taking advantage of AI services would drive the next wave of cloud adoption. Looking back, countless Zoom meetings later, the pandemic accelerated enterprise adoption of cloud services as reflected in the very healthy double digit growth rates of each of the major clouds. Hold that thought.

There’s little question that when it comes to cloud, for most organizations, multi-cloud is already reality. According to Flexera’s latest 2020 State of the Cloud report, 93% of enterprises respondents reported having multi-cloud strategies.

As we’ve noted in our posts over the past year, hybrid cloud has been a frequent subtheme of our research. Prior to the onset of the pandemic, we were already sensing the cloud to be taking a front and center role with enterprises shaping their future strategy.

A sleeper trend that we identified in our annual look ahead was, not simply the growing embrace of cloud computing, but the drivers behind it. In our conversations with enterprise IT executives and practitioners, we are increasingly finding that the default option for deploying or re-deploying IT systems was changing, with the cloud starting to replace on-premise as the base case. But, of course, this is not a blind march to the public cloud – instead, the trigger is gaining the operational simplicity and flexibility of the cloud control plane, regardless of where the systems were going to get deployed. Enter the era of the Hybrid Default.

Numerous indicators out there prove that there is a definite move toward the cloud. We've seen this in our own discussions with enterprises. Back when AWS introduced modern cloud computing back in 2006 (well, maybe we should give Salesforce credit for devising modern SaaS back in 1999), developers embraced it as a quick way to hustle up machines to conduct DevTest without having to get IT to sign off or go through the hassle of ordering and installing machines that would otherwise sit idle much of the time.