Next-Level On-Premises Insight and Analytics From Your Data

Qlik Cloud Analytics brings all the power of Qlik analytics solutions in a cloud-based SaaS deployment. Qlik Sense is our on-premises solution for highly regulated industries and customers that prefer a client-managed offering.

Qlik makes it easy to connect and combine data from hundreds of data sources – from apps and databases to cloud services, files and more. Explore common data sources and connection options for Qlik Sense.

Yes, Qlik Sense has AI technology built in at a foundational level, combined with our unique associative engine. Insight Advisor suggests analyses and insights, automates analytics creation and data preparation processes, and supports natural language interaction for search-based and conversational analytics. Qlik AutoML provides powerful key-driver analysis, predictive analytics, and what-if scenarios directly for analytics users and teams. And with advanced analytics integration, you can easily include predictive calculations from data science platforms and AutoML directly in Qlik Sense dashboards, supporting real-time business-user exploration.

For Qlik Sense client-managed pricing, please contact us. Qlik Cloud Analytics pricing can be found on the pricing page.

Unlike many popular data analytics tools that only offer focused point solutions, Qlik Sense provides the most complete data analytics capability set in the industry. It supports the full range of analytics use cases – from self-service visualization and associative exploration to dashboards, embedded analytics, reporting, mobile, augmented analytics (AI), and more. Our cloud-native data analytics platform delivers high performance scalability, flexible open and standard APIs, powerful data integration and connectivity, and centralized management and governance to meet the demands of any enterprise.

On-premises analytics is analytics software deployed and run on an organization's own servers and infrastructure, inside its own data centers, rather than hosted by a vendor in the cloud. The organization manages the hardware, security, and maintenance itself, which gives it full control over its data and environment. It's often chosen by highly regulated industries or organizations with strict data-residency, security, or compliance requirements. 

On-premises analytics runs on infrastructure the organization owns and manages, offering maximum control over data and security but requiring in-house maintenance and upfront investment. Cloud analytics is hosted and managed by a vendor and accessed over the internet, providing faster setup, elastic scaling, and lower maintenance, usually on a subscription. Many organizations adopt a hybrid mix, and the choice generally comes down to control, compliance, cost model, and scalability needs. 

An analytics platform is an integrated software environment that provides the tools to connect to data, prepare and model it, analyze it, and present insights through visualizations, dashboards, and reports. Unlike a single-purpose tool, a platform supports the full analytics workflow and often adds capabilities like self-service exploration, augmented analytics, governance, and embedding. It gives an organization a unified foundation for turning data into decisions across many users and use cases. 

Query-based analytics, the traditional approach used by most tools, returns only the data explicitly requested by a query, which can obscure related data and limit exploration to predefined paths. Associative analytics indexes and links all the data so users can freely select any value and immediately see which data is related and which is unrelated across every field, exploring in any direction. The associative approach makes open-ended discovery easier, while query-based analytics is well suited to answering specific, known questions.

Guided analytics is an approach in which analytics applications are designed to lead users along a curated path — with predefined dashboards, workflows, and interactions — toward relevant insights. It sits between fixed static reports and fully open self-service, giving users freedom to explore within guardrails set by analytics designers. This helps less technical users reach reliable answers without getting lost, while keeping analysis aligned to business context. 

Mobile analytics is the delivery of analytics — dashboards, reports, and data exploration — on mobile devices like phones and tablets, so people can access insights anywhere. Well-designed mobile analytics uses responsive layouts and touch interaction, and often supports offline access and push notifications for alerts. It extends data-driven decision-making beyond the desk to the point where work and decisions actually happen. 

Collaborative analytics is an approach that lets people work together on data analysis — sharing dashboards, adding notes and narrative, and discussing findings directly within the analytics environment. By capturing context and conversation alongside the data, it helps teams align on what the numbers mean and decide together. This turns analytics from a solo activity into a shared process that strengthens collective decision-making. 

Data-driven decision-making is the practice of basing decisions on data and analysis rather than intuition or guesswork alone. It involves gathering relevant data, analyzing it for insight, using those findings to guide choices, and then measuring the results. Organizations that build this into their culture — supported by accessible data and analytics tools — tend to make more consistent, defensible, and effective decisions. 

Traditional business intelligence is typically IT-led and centered on static, predefined reports that business users request and wait for, which can be slow and inflexible. Modern BI emphasizes self-service, interactive visualization, and faster, more agile analysis, letting business users explore data and answer their own questions with less reliance on IT. The shift reflects a move from centralized reporting toward broad, governed access to data across the organization.

Enterprise analytics is the practice of deploying analytics at organization-wide scale, so many users across departments can access consistent, governed data and insights. It goes beyond isolated tools or team-level dashboards to provide shared standards, security, and a common data foundation for the whole enterprise. The goal is to make trustworthy, well-managed analytics broadly available while maintaining governance, performance, and scalability.