What is Qlik Sense? Modules, integrations and alternatives
Qlik Sense is an analytics platform built around an associative data engine that lets users explore relationships across data without a fixed query path. Manufacturers use it for operational and supply chain analytics. It is available as a cloud service and as a client-managed deployment.
Also known as: Qlik Cloud Analytics, QlikView successor, Qlik Sense Enterprise
Qlik Sense loads data into an in-memory associative engine. When a user selects a value, the engine highlights related and unrelated values across all fields, which makes it easy to spot what is connected, and what is missing, in the data. Apps combine a data model, load script, sheets and visualisations.
In manufacturing it is used for inventory and supply analysis, production performance, quality trending, finance and sales reporting. Qlik also has a long history with ERP analytics, and there are data integration tools in the wider Qlik portfolio for moving ERP data into analytics stores.
It can be used on its own, loading data directly into apps, or on top of a warehouse. Because apps are driven by a load script and a modelled data set, good data modelling and incremental loads matter for performance and maintainability.
Modules and capabilities
Associative engine
In-memory engine that tracks relationships across all loaded data and updates every chart on each selection.
Qlik Cloud Analytics
Vendor-hosted service for building, sharing and managing analytics apps.
Client-managed Qlik Sense
Self-hosted deployment for organisations that must run analytics in their own environment.
Data load scripting
Script-based extraction and transformation to build the app data model from many sources.
Visualisation and dashboards
Sheets, charts, filters and storytelling features for building interactive apps.
Reporting and alerting
Scheduled reports, subscriptions and data-driven alerts for operations teams.
Data integration tools
Related Qlik products for replicating and transforming source data into analytics targets.
Embedded analytics
APIs and mashup tooling for placing analytics inside portals and applications.
Integrations and data access
Databases and warehouses
Connectors for common relational databases, Snowflake, Databricks and other warehouses.
ERP data
Load scripts or replication tools bring ERP tables into analytics apps; many teams stage ERP data in a warehouse first.
File formats
Reads Excel, CSV, JSON, XML and QVD, Qlik's own optimised file format.
REST connectivity
REST connectors read APIs, and platform APIs support automation, app management and embedding.
Identity
Supports enterprise single sign-on and section-access style data security rules inside apps.
Data replication
Change-data-capture style tools from the wider Qlik portfolio can keep analytics targets in step with source systems.
Strengths
- Associative exploration reveals relationships and gaps that query-driven tools can hide.
- Mature in-memory performance for well-modelled apps.
- Both cloud and client-managed options.
- Strong history in enterprise and ERP-related analytics.
- Data integration and analytics available from the same vendor.
Limitations
- Load scripting and data modelling skills are needed to get the best from it.
- Smaller community than the largest BI tools, so hiring may take longer in some regions.
- Large in-memory apps need sensible sizing and incremental loading.
- Product naming and editions have changed over time, which can confuse buyers.
- Feature parity between cloud and client-managed editions should be checked.
Where AI helps Qlik Sense users
- - Natural language questions over curated Qlik apps, with selections shown so users can verify the answer.
- - Drafting load script fragments and expressions from a description, reviewed by a developer.
- - Explaining an existing legacy QlikView or Qlik Sense estate and finding duplicate or unused apps.
- - Surfacing unusual values or missing associations in supply and inventory data.
- - Joining analytics answers with ERP documents and records in a private on-prem assistant.
Evaluating Qlik Sense? Check these
- Decide between Qlik Cloud and client-managed early, and compare features for your use cases.
- Plan data modelling and incremental load strategy before building many apps.
- Check how ERP security roles will translate into section access or equivalent rules.
- If you have QlikView apps, scope the migration effort honestly.
- Ask which data integration tools you need in addition to analytics.
- Pilot on one real operations question to judge fit with your analysts' skills.
Common alternatives: Microsoft Power BI, Tableau, Looker, Sisense, ThoughtSpot, Domo.
Frequently asked questions
What makes Qlik Sense different from other BI tools?
Its associative engine keeps track of how every value relates to every other loaded value. When a user makes a selection, related data is highlighted and unrelated data is shown as excluded, which helps people spot gaps and unexpected links. Most other tools follow a more query-driven path.
Is Qlik Sense the same as QlikView?
No. QlikView is Qlik's earlier product, while Qlik Sense is the newer platform with a more self-service interface. They share the associative engine idea and some file formats. Many organisations run both and migrate gradually, so check migration tooling and effort for your estate.
Can Qlik Sense run on-premises?
Yes, through the client-managed edition that you install and operate yourself. There is also the Qlik Cloud service hosted by the vendor. Features differ between them, so compare the current feature lists against your requirements, especially if you need air-gapped or restricted environments.
How does Qlik Sense connect to an ERP?
Typically through database connectors, load scripts or replication tools that bring ERP tables into an analytics store or app. Many manufacturers stage ERP data in a warehouse first, then model it once. Always respect the ERP's security model when exposing financial or HR data.
Can AI be used with Qlik Sense data?
Qlik offers its own AI-assisted features, and you can also build a separate assistant over the underlying data. Quality depends on clean definitions and governance. For private plant data, an on-prem assistant that shows its queries and honours roles is simpler to audit.
Running Qlik Sense?
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