Qlik this week unveiled improved integrations aimed at enabling customers running on the AWS Cloud Infrastructure to quickly and easily integrate data to prepare it for real-time data analysis and AI development.
The vendor introduced the integrations with Databricks, SAP and Snowflake Tuesday during AWS re:Invent 2024, a user conference hosted by the tech giant.
In addition to improving connectivity to key data sources, Qlik on Thursday highlighted recent cloud-based innovations such as pixel perfect reporting and generative AI-powered query capabilities to spur customers with on-premises deployment to migrate to the cloud. Once deployed in the cloud, Qlik users can take advantage of the vendor's recent additions, including generative AI (GenAI) capabilities, and develop AI tools of their own.
Together, the integrations and features that simplify analysis in the cloud are useful, according to Donald Farmer, founder and principal of TreeHive Strategy. However, they are not revolutionary advances that will influence the broader analytics and data management markets.
These developments are significant but evolutionary. The new work addresses meaningful pain points for customers who have already invested in platforms like SAP, Databricks and Snowflake. But there's nothing radical.
Donald FarmerFounder and principal, TreeHive Strategy
"These developments are significant but evolutionary," Farmer said. "The new work addresses meaningful pain points for customers who have already invested in platforms like SAP, Databricks and Snowflake. But there's nothing radical."
Qlik is a longtime analytics vendor based in King of Prussia, Pa., that, in recent years, built up a full-featured data integration platform. Now, with interest in AI development on the rise, Qlik is also taking steps to provide customers wanting to develop AI models and applications with the requisite tools, including the trusted data needed to feed and train AI.
New integrations
Enterprises have rapidly increased investments in developing AI models and applications in the two years since OpenAI launched ChatGPT and greatly improved efficiencies and GenAI capabilities.
By combining their proprietary data with generative AI models such as ChatGPT and Google Gemini, organizations can develop applications that enable employees to interact with data using natural language. They can then expand the use of analytics beyond only those with coding skills to improve the quality of decisions.
In addition, by training models with proprietary data to understand their operations, organizations can automate repetitive tasks such as documentation that previously had to be done manually.
However, AI tools won't be effective without the right data to train models and applications -- which includes both the relevancy of the data to a use case and the quality of the data. As a result, access to data and ensuring that data is high-quality and can be trusted are key.
Qlik's new integrations with Databricks, SAP and Snowflake are therefore significant, according to Mike Leone, an analyst at Informa TechTarget's Enterprise Strategy Group.
"SAP, Snowflake and Databricks are behemoths in the data space, and Qlik has valuable relationships with each of them," he said. "The announcements expand those partnerships to enable customers to improve decision-making on relevant data faster and more confidently than ever."
Nick Magnuson, Qlik's head of AI, similarly noted that the rising interest in AI development necessitates smooth, consistent connectivity between data sources and the applications they inform.
AI tools are rapidly evolving, moving from question-and-answer applications to agents that can act autonomously. That means development needs to similarly move quickly. Fast, easy access to data enables the speed required by enterprises to be competitive. Qlik's integrations aim to enable that requisite speed.
"Businesses are under immense pressure to modernize their data ecosystems to remain competitive in an AI-driven world," Magnuson said. "The rapid evolution of AI models, particularly generative and agentic AI, has heightened the need for seamless data flow, real-time insights and infrastructure that supports scalable, adaptive AI deployment."
SAP is an ERP vendor that produces a wealth of data for many organizations, including information about customers, employees, finance and sales, inventories and supply chains. Connectivity to that data enables analysis by feeding analytics tools such as reports and dashboards, as well as AI models and applications key to myriad decisions.
Qlik's new integration is with SAP Rise and SAP Cloud. It enables easier data extraction so that data can be operationalized to inform analytics and AI tools.
Databricks and Snowflake, meanwhile, are data management and AI development vendors that provide cloud-based data storage platforms. Connectivity to their data warehouses, lakes and lakehouses enables Qlik customers to easily access data -- including unstructured data such as text and images that now represent more than 80% of all data -- to inform analytics and AI tools.
Qlik's new integration with Databricks enables joint customers to link real-time data ingested into Databricks with AI models, which improves time-to-insight. Similarly, Qlik's new integration with Snowflake improves access to real-time data so that joint customers can make AI-driven decisions as events take place.
"Organizations need to make the best decision based on the latest information," he said. "Whether it be the key partners in SAP, Snowflake and Databricks -- or Kafka -- fast data ingestion and integration will empower organizations to unlock insights that feed real-time events and the needs of a dynamic business."
Farmer, meanwhile, highlighted the integration with SAP, noting the importance of data generated in SAP.
Qlik and SAP already have a close relationship that helps Qlik stand out from its competitors, he continued. Qlik is building on that close connection.
"What is important here is the improved SAP support," he said. "SAP themselves are pushing cloud migrations very strongly. As SAP is a critical data source for Qlik, it's important that Qlik also supports this [cloud migration]. For some time, Qlik's connectivity to SAP has been excellent -- a significant differentiator -- so this is important for Qlik. But it's incremental."
Regarding the impetus for the new integrations, customer feedback and Qlik's observations on current business needs each played a role, according to Magnuson.
Enterprises require links to data sources. Otherwise, they have to build them on their own, which is a costly, labor-intensive process that increases the complexity of their data systems rather than simplifies them. Meanwhile, the integrations not only enable Qlik customers to access real-time data to inform current AI models and applications but also are intended to help them as AI evolves and new models and applications emerge.
"Modern enterprises operate in complex, hybrid environments where seamless data integration and real-time analytics are non-negotiable," Magnuson said." Customers are demanding … solutions that avoid vendor lock-in and integrate across diverse ecosystems. Additionally, these enhancements set the stage for the next generation of AI applications."
Enterprises might receive these seven benefits when using generative AI.
Encouraging migration
Qlik's new integrations aim to help organizations remain competitive by developing AI models and applications. They are only useful, however, if an organization has a cloud-based data system, given that generative AI development is largely a cloud-based process.
As a result, the vendor has developed tools that encourage customers with on-premises deployments to move to the cloud. There, they can benefit from features such as Qlik Answers, an AI-driven tool that enables users to query and analyze unstructured data using natural language.
In addition, features including anonymous access to analyze data securely and Qlik AutoML to develop predictive analytics tools are available exclusively to cloud-based customers. Meanwhile, pixel-perfect reporting, which had been available to only on-premises users and was a reason some customers chose not to move their data operations to the cloud, is now available to cloud-based users as well.
Providing cloud-based tools that make it attractive to transition from on-premises deployments is helpful, according to Farmer. However, adding tools that simplify the migration itself would also be beneficial.
"The improvements here are useful," Farmer said. "The cloud transition remains a complex journey. Some enterprises need more hands-on guidance, change management support and financial incentives to make the move. Qlik could strengthen its positioning by providing migration accelerators, cost-offset programs or deeper partnerships with systems integrators to remove organizational barriers."
From a competitive standpoint, the integrations with key partners and cloud-based capabilities that encourage on-premises users to migrate to the cloud continue Qlik's evolution, Farmer continued.
Qlik now provides an end-to-end data platform that includes integration and analytics capabilities. In addition, it provides features that enable customers to develop AI models and applications. However it remains to be seen whether the vendor's evolution can attract new customers in a competitive market in which data platform vendors such as Databricks and Snowflake, as well as tech giants AWS, Google Cloud and Microsoft have more recognition.
"I really hope these new capabilities can help them find new customers looking for end-to-end solutions rather than just going deeper into existing customers," Farmer said. "To this end, what [Qlik] could do next [is add] more AutoML, predictive analytics and integrations with major cloud AI platforms to enhance its value for advanced users."
Leone likewise noted that Qlik's challenge is attracting new customers. The vendor now provides the needed analytics and AI-driven decision-making capabilities, but other vendors currently attract more attention.
"Qlik continues to be a hidden gem," Leone said. "They have everything needed to support an organization's ongoing data and AI strategy. And they've executed well on their data and AI promises of enabling customers to build and maintain trusted data foundations that feed advanced analytics and AI at scale."
Coming up
As Qlik plans its product development, AI plays a significant role, according to Magnuson.
One initiative is to continue improving capabilities that enable customers to develop business-ready AI tools. Data is the foundation of any AI model or application, so integrated, governed and real-time data are key. Qlik plans to refine integration capabilities such as Talend Cloud and data integrity tools such as the AI Trust Score to address the need for trusted data as a base for AI development.
Another initiative is to improve its conversational AI features and no-code interfaces to enable more people within organizations to access and analyze data to inform decisions. Finally, with interest rising in agentic AI, Qlik plans to introduce tools enabling customers to configure agents built on structured and unstructured data that can autonomously analyze data and take corresponding actions.
"These strategic themes reflect Qlik's commitment to not only addressing today's challenges but also equipping enterprises to lead in a rapidly evolving AI landscape," Magnuson said.
Farmer, meanwhile, stressed that Qlik would be wise to do more than merely encourage customers to migrate to the cloud with cloud-based capabilities. It also needs to provide tools that actually aid the complicated cloud migration process.
"Many customers need more than technology," he said. "They need roadmaps and support to transition from on-premises to the cloud. Qlik could invest more in packaged migration services, detailed frameworks and deeper strategic partnerships with consulting firms."
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.
