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While some vendors fed the hype, Qlik took a pragmatic approach to generative AI.

The business analytics vendor considered its existing capabilities when generative AI surged in popularity following OpenAI's release of ChatGPT in late 2022. It considered its customers' needs. And it considered what it needed to add to meet those customers' needs.

From there, Qlik came up with a realistic strategy with providing trustworthy data at its core, and one that its users believe in.

"I always check on what other vendors are doing," said Angel Monjars, Qlik platform manager at C40 Cities, a network of nearly 100 cities working together to combat climate change. "I have to stay in touch with everything that's out there. I'm confident that Qlik is on the right track."

At this point, generative AI is nothing new. But after the launch of ChatGPT, it suddenly embodied the technology that could finally enable true natural language processing. It was the technology that, when combined with enterprise data, could reduce and eliminate coding and enable anyone within an organization to work with data rather than a small percentage of specialists.

Within months, data management and analytics vendors such as Microsoft, ThoughtSpot, Tableau, Alteryx and Informatica were among the many to unveil plans to augment their platforms with generative AI, introducing tools that would make their platforms smarter and simpler. But the tools were under development rather than nearing general availability.

Some of those tools eventually made their way through the preview process. For example, Microsoft first unveiled its Copilot for Power BI in May 2023, but didn't make it generally available until one year later. Other generative AI systems, however, after more than a year, are still not generally available.

Qlik, conversely, didn't quickly grab attention when generative AI became the rage. It didn't publicize every time it came up with an idea. It didn't introduce tools in development that promised to eliminate the difficulties that have existed for decades that make data management and analytics specialized skills.

It didn't buy into the hype surrounding generative AI.

"Over the last year, there were a whole heck of a lot of people making a lot of noise about [generative AI]. We did a bit of the opposite," said Nick Magnuson, Qlik's head of AI. "We took a step back and asked some key questions about how we wanted to plan an ecosystem."

Qlik might have lost out on some publicity in the process. But according to Susan Dean, director of business technology at heavy equipment manufacturer Takeuchi U.S., the ecosystem Qlik is developing serves customers' needs. And what it is now revealing related to generative AI is accelerating quickly.

"Definitely," she said when asked whether Qlik is proving the necessary tools for generative AI development, in an interview earlier this week during this year's Qlik user conference. "I'm very excited to see what's next. They just keep getting better. The leaps and bounds from last year's Qlik [conference] to this year's is night and day."

In a sense, Qlik has always been pragmatic. It's part of why the vendor is still relevant 31 years after it was founded, while onetime competitors such as Business Objects, Cognos and Information Builders have been swallowed up by other vendors and essentially disappeared.

Based in King of Prussia, Pa., Qlik is a longtime analytics vendor that has evolved as business intelligence has evolved.

When data was kept on premises and analytics was a specialized skill for experts only, Qlik provided a platform to meet the needs of data analysts. When Tableau rose to prominence touting self-service analytics, Qlik adapted and developed self-service tools to meet the needs of business users.

When cloud computing emerged and enterprises migrated their data operations to the various clouds, Qlik complemented its on-premises capabilities with a cloud-based version of its platform. When that was no longer enough, Qlik identified data integration as an opportunity for growth and over the past six years has methodically built up a data integration suite to complement its analytics capabilities.

Now, the vendor is taking that same strategic approach to AI as it creates an environment for customers to develop AI models and applications and apply generative AI to existing data products.

"Despite what everyone else is doing, what matters most is our customer needs," Magnuson said. "That's where we're focused."

To meet customer need for a trusted foundation for generative AI, Qlik started by combining its existing AI and machine learning capabilities in a single environment it calls Staige.

Unveiled in September, Staige includes AutoML, which is a tool that enables users to perform predictive analysis, and Insight Advisor, a natural language interface that lets customers query and analyze structured data and provides natural language responses with accompanying visuals.

In addition, Staige provides automated code generation capabilities, integrations with generative AI capabilities from third-party providers such as OpenAI and Hugging Face, and an advisory council to provide guidance for customers getting started with AI.

While Qlik's existing capabilities combined with third-party integrations was a start, Qlik needed more capabilities to effectively provide a foundation for developing trusted AI models and applications.

One thing missing was support for unstructured data, such as text and audio files, which is estimated to now make up more than 80% of all data.

To add support for unstructured data, Qlik acquired Kyndi in January and on June 4 unveiled Qlik Answers. The tool, scheduled for general availability this summer, uses retrieval-augmented generation to enable customers to query and analyze unstructured data with natural language in the same way Insight Advisor enables natural language interactions with structured data.

Furthermore, Qlik Answers provides data lineage information so that users can trace the data used to inform the tool's responses and ensure that those responses can be trusted.

Also missing was the data management component -- the integration layer that would enable customers to build applications using quality data from the start rather than look back later to see if the data they already used could be trusted.

Therefore, to complement Answers, the vendor on June 4 unveiled Qlik Talend Cloud, which is likewise definitively scheduled for general availability this summer. The suite, which comes a little more than a year after Qlik completed its acquisition of Talend, is a data integration environment that forms the foundation for ensuring the quality of data used to train generative AI models and applications. Included are governance capabilities and tools such as a trust score.

Combined, Qlik Answers and Qlik Talend Cloud succeed at providing quality data for AI models and applications, according to Mike Leone, an analyst at TechTarget's Enterprise Strategy Group.

"Qlik Answers and Qlik Talend Cloud can work together to deliver a trusted data foundation for AI and fuel innovation from AI," he said.

In addition, the acquisition of Kyndi was critical, Leone continued.

"Kyndi is really that enabling factor for Qlik to extend the delivery of predictive AI and generative AI more broadly and at scale," he said. "I like Qlik's focus on unstructured data as it's often overlooked and underutilized."

Given the foundation that's now been formed by addressing practical needs, customers can begin using Qlik as a foundation for developing generative AI capabilities.

"After we saw what Qlik presented, the possibilities [for using generative AI] are open now," Monjars said.

C40 has been using Insight Advisor and other AI and machine learning tools, but had previously been hesitant to add any generative AI capabilities given its strict data security and data compliance requirements, he continued.

"A very real component we saw is the ability to analyze unstructured data, and there's a lot of knowledge there," Monjars said.

By grounding its generative AI plans in reality rather than making promises it might not be able to keep, Qlik is serving the needs of its customers.

But that pragmatic approach might have come with a cost, according to Donald Farmer, founder and principal of TreeHive Strategy.

Data management rivals Databricks and Snowflake have broadcast seemingly every move while creating environments for AI development. Tech giants AWS, Google and Microsoft have similarly maintained a steady presence in the collective mindset. And many of the more specialized vendors have introduced large swaths of capabilities even when they're only just starting to build them.

Qlik's comparatively quiet approach might have resulted in slow customer growth.

Farmer spent nearly 20 years in product development, including a stint at Qlik as vice president of innovation and design. Now, he heads a consulting firm that works with companies to develop analytics and AI strategies. While the evidence is anecdotal, he noted that Qlik's resonance with potential new customers seems to be slowing.

"Qlik still remains a significant vendor, but with one caveat," Farmer said. "There is very little sign of them gaining traction with greenfield customers. The trickle of new logos is slow. Mostly, they are adding more value to existing clients. But to be fair, they are adding significant value."

Qlik Answers could be a means of adding new users, according to Magnuson.

When Qlik added automated machine learning capabilities with its 2021 acquisition of Big Squid and turned that technology into AutoML, it drew in new customers, he said. Once generally available, Qlik Answers, though tightly integrated with the rest of the Qlik ecosystem, will be available as a standalone tool and could likewise be a way to draw new customers.

"We've made a conscious decision as part of a strategy to offer these solutions to a new buying agenda," Magnuson said. "We know a lot of people are generating new budgets to acquire technology. ... Answers potentially gives us a new opportunity to have a conversation with someone where we can open up a net new opportunity."

Regardless of whether Qlik's practical approach to generative AI brings in a significant number of new customers, what Qlik is doing in terms of technological innovation and support for that technology works for the vendor's existing users, according to Dean.

When Dean joined Takeuchi U.S. in 2018, the company had one analyst keeping its data in Excel spreadsheets. Dean subsequently led the company's transition to Qlik, beginning with a single application. Now, Takeuchi U.S. uses Qlik not only in its administrative office, but also with each of its hundreds of dealers.

But while Takeuchi U.S. -- a subsidiary of Japan-based Takeuchi Manufacturing -- is a sizable organization, it does not boast a big roster of data scientists. Dean is part of a team of three BI analysts.

To do more advanced analytics than just developing dashboards and reports, Takeuchi U.S. needs assistance. One of the main reasons the company has remained with Qlik is the relationship Dean and her team have with the vendor and the support they receive.

"My partnership with Qlik is what keeps me," Dean said. "They work with us."

Takeuchi U.S. now uses AutoML. And once a major undertaking to implement an ERP system is finished next spring, the company wants to build new analytics applications to discover insights related to the performance of its excavators, wheel loaders and other products.

"I'll definitely set up demos with [Qlik] to figure out what will suit us when the time is right," Dean said.

While Qlik Answers and Qlik Talend Cloud in some ways complete the foundation for trusted data that Qlik targeted as its role in enabling generative AI development, the vendor nevertheless plans to develop additional capabilities.

Most notably, it aims to enable customers to query and analyze structured and unstructured data together, according to Magnuson. The acquisition of Kyndi led to Qlik Answers, which enables customers to operationalize unstructured data. But that's just a beginning.

"[Qlik Answers] is starting us on this bigger journey to develop strength and muscle around unstructured content that puts us in a position to provide value to customers by integrating both structured and unstructured data in a single analytics experience," Magnuson said.

Monjars likewise noted that Qlik's enablement of access to unstructured data is significant. From a technological standpoint, Qlik is meeting C40's needs. But where he said he'd like to see more investment from Qlik is in another practical area: increasing awareness.

Qlik provides its own data literacy program. But its customer base is not as big as some other platforms such as Power BI, so it is therefore sometimes difficult to find new employees who don't need to be trained to use Qlik, Monjars noted.

"Qlik is doing what we need, but it's a little hard to find people who are Qlik-trained," he said. "A given professional maybe learns Power BI before they learn Qlik, so that affects the availability of people out there. It would be helpful if Qlik were more of a household name and people made it a priority to learn Qlik coming out of school."

Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.

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Qlik meets user needs with realistic approach to generative AI - TechTarget