Startup Golden Analytics aims to shine with AI-native BI | TechTarget
Golden Analytics is betting on AI-native BI.
The upstart business intelligence vendor was founded in April by former Tableau and Amplitude product guru Francois Ajenstat, who is also the startup’s CEO. Its AI-powered platform is still in public beta and early access.
But with $21 million to date in venture capital funding, investors including former Tableau CEO Mark Nelson and former Alteryx president George Mathew, and an initial list of close to 20 paying customers that features a Fortune 50 insurance company, general availability is expected in the coming weeks, Ajenstat told TechTarget.
Once its platform gets released, Golden Analytics will be entering a BI market dominated by Microsoft’s Power BI platform and entrenched vendors such as Qlik, SAS, Strategy, Tableau and ThoughtSpot. However, a fully AI-powered platform rather than one built for a different era with added-on AI capabilities could establish Golden Analytics as a viable new option and allow it the time it needs to grow to eventually challenge for significant market share, according to David Menninger, an analyst at ISG.
“That may be enough,” he told TechTarget. “If they can become known as the AI-native analytics company, the next challenge would not be further differentiation but further development of existing capabilities that users have come to expect. That way they would have a larger addressable market.”
Donald Farmer, founder and principal of TreeHive Strategy, similarly noted that there is potential for an AI-native BI vendor to succeed despite the preponderance of established analytics specialists.
“[With] too many incumbents … the AI is little more than a side-panel assistant translating text to basic SQL,” he told TechTarget.
The Golden (Analytics) touch
As the broader market attempts to shift toward AI, Golden Analytics is already there. Being AI-native vs. featuring AI-powered capabilities is akin to the difference between being cloud-based and on premises, according to Ajenstat.
“When your organization was on premises, you could lift and shift that into the cloud and it would still run, but you weren’t taking advantage of all the capabilities of the cloud,” he said.
Platforms such as Power BI, Qlik, Tableau and ThoughtSpot all feature AI capabilities within their platforms whereas the entire Golden Analytics suite is fueled by AI. Large language models power most functions within its platform, and as LLMs get more intelligent, so do the applications it feeds.
“It’s a different way of approaching it,” Ajenstat said.
Vendors that pre-date the AI era could theoretically re-architect their platforms to make them AI-native, putting LLMs behind each of their functions to make AI the underlying engine. But over the course of more than a decade in most instances, and many decades in the cases of providers such as SAS and Strategy, longstanding BI vendors have built sprawling platforms featuring a multitude of individual tools. To re-architect each one and do a complete platform overhaul would be a years-long process.
“It would require a significant amount of work to re-platform the whole thing,” Ajenstat said. “It’s not that it isn’t possible, but we’re talking about years of rethinking, and it is a complete rethink because the way LLMs work is very different. … It would be a massive change. The front end isn’t what changes. It’s everything about how you manage [the platform] that changes.”
However, being AI-native won’t be an advantage forever, according to Menninger.
While Microsoft, Qlik and Tableau — among others — may not be able to completely overhaul their platforms, they will eventually build enough AI-native capabilities that being completely AI-native becomes a negligible advantage, much as being cloud-native stopped being a competitive edge as previously on-premises platforms were augmented with cloud-native capabilities.
“AI-native as competitive advantage will have a half-life of perhaps a couple of years,” Menninger said. “Existing providers are all adding AI capabilities, and like we saw with cloud, at some point it will no longer be a differentiator. The question is how much market share Golden Analytics can grab while it has an advantage and how long will that advantage carry over once there is parity in capabilities.”
Meanwhile, Farmer noted that examples of vendors already adding meaningful AI-native capabilities beyond natural language-based analysis include GoodData and ThoughtSpot, which enable developers to embed AI-powered analytics within applications.
Platform innovation
Beyond delivering an AI-native version of a traditional BI platform, Golden Analytics is attempting to distinguish itself by offering innovative capabilities that widen the potential pool of BI users.
The last generation has made it easier, but still, we haven’t gotten the adoption that we expected. Now, by making data … smarter by default, we’re taking away the mechanics that made data really hard and enabling people to focus on the questions they have that data can answer Francois AjenstatFounder and CEO, Golden Analytics
“The last generation has made it easier, but still, we haven’t gotten the adoption that we expected,” Ajenstat said. “Now, by making data … smarter by default, we’re taking away the mechanics that made data really hard and enabling people to focus on the questions they have that data can answer.”
With Golden Analytics, users can create dashboards with two clicks, according to Ajenstat. The platform’s AI capabilities understand what data is available and build the dashboard on its own. Similarly, users don’t have to search for insights. AI is constantly analyzing data and surfaces meaningful information for users as soon as they log in.
“When people are bolting on AI, sometimes it’s glitchy,” Ajenstat said. “It doesn’t work the way it’s expected. It was the same workflow as before but made a little bit easier. What we’re trying to do is reimagine the entire workflow to eliminate [previous work] and tell you the other things you could do.”
One feature that is designed to differentiate Golden Analytics is its slider of autonomy, which is essentially a control setting — a sliding scale — that enables human users to determine how much work they want AI to perform autonomously and how much they want to do themselves.
The benefit, according to Ajenstat, is a combination of speed fueled by AI and familiarity fostered by human involvement.
“It is telling that [Golden Analytics] calls it the ‘slider of autonomy’ and not the ‘slider of automation,'” Farmer said, noting that the feature reflects insight into how enterprises want to use AI rather than how it’s assumed enterprises want to use AI. “Ajenstat understands deeply what the community is looking for.”
Room for new blood
While the worldwide market for BI has increased steadily in recent years and is expected to continue growing, the vendors with the largest market share have remained relatively static over the past decade with mainstays Power BI, Qlik, SAS and Tableau among the most popular platforms.
Not since ThoughtSpot in 2014 and Domo in 2015 emerged from stealth amid the emergence of new paradigms such as the cloud and self-service analytics provided new opportunities have startup BI specialists made significant inroads in the analytics market.
“There was a lot going on in that period,” Farmer said.
Now, AI’s emergence is providing a similar opening for startups to make their mark, according to Menninger.
“AI is an industry-wide paradigm shift,” Menninger said. “Whenever there is such a fundamental shift, it creates opportunities for new competitors who can move more quickly and more completely to the new paradigm.”
Beyond a rare opening, Ajenstat’s understanding of the data and analytics market and his renown within it may give Golden Analytics a competitive advantage, according to Farmer. In addition, board members and investors such as Nelson and Mathew give the new venture a level of legitimacy.
“Ajenstat’s product leadership history and connection to the community is almost unparalleled,” he said. “Add in those deeply connected colleagues and it’s a powerful counterpoint to the marketing power of Microsoft or Salesforce, who can outspend Golden, but have much less credibility with the community.”
Menninger likewise noted that Ajenstat’s experience and the market knowledge of those backing Golden Analytics could help the startup quickly compete.
“The battle for market share will be critical to their success,” he said. “Celebrity marketing is certainly helpful because it brings immediate awareness. In this case, the celebrities are directly from the industry and have deep knowledge of what has worked and what has been missing. While it doesn’t guarantee success, it sure increases the likelihood.”
Still, with 10% of tech startups surviving 10 years, long-term success could be a struggle for Golden Analytics. However, Menninger noted that Golden Analytics does have the required foundation for success.
“If you look at those that succeed, they have a unique product or service, strong founders and top-notch investors,” he said. “Golden Analytics appears to have all three.”
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
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