Don’t treat a QlikView→Qlik Sense move as a like‑for‑like upgrade. Treat it as a platform modernization that readies your data, governance, and teams for augmented analytics, GenAI, and automation. Use the migration to: rationalize your app estate, land CDC pipelines into a lakehouse/warehouse, harden governance in Qlik Cloud Spaces, enrich metadata in Catalog, and activate AI with Insight Advisor, Qlik AutoML, Qlik Answers, and Application Automation then embed alerts, subscriptions, and actions into business workflows.
Why reframe migration as analytics modernization (not just UI work)
Many enterprises still run mission-critical QlikView apps that encode years of business logic. Simply porting visuals preserves that value but it misses the larger opportunity: moving to Qlik Sense unlocks augmented analytics and automation capabilities that power AI-first use cases. Qlik’s own docs emphasize re-using assets but also treating migration as a chance to re-architect for modern capabilities.
Top IT services firms are actively packaging migration + modernization offerings combining cloud, automation, and AI governance because clients expect transformation outcomes, not just cosmetic dashboard upgrades. Partnering with service leaders helps accelerate platform setup, automation and enterprise adoption.
Why now?
- Qlik Sense (especially Qlik Cloud) concentrates Qlik’s newest AI capabilities natural‑language analytics (Insight Advisor), AutoML, GenAI assistants (Qlik Answers), OpenAI/Azure OpenAI connectors, automation, catalog/lineage, and “Spaces” governance making it the strategic landing zone for modernization.
- Qlik has deepened its data‑to‑AI stack CDC & replication (Replicate), lakehouse integration (Open Lakehouse / Iceberg), data pipelines & “knowledge marts” (vector stores for RAG), and Catalog with data quality so analytics modernization and AI readiness can be executed inside one platform.
- Ecosystem momentum matters: Qlik is recognized in Gartner’s 2025 BI MQ and continues expanding AI partnerships.
Core principles for an “AI-ready” migration program
- Migration = Modernization: Migration work should simultaneously create reusable data products (cleaned datasets, feature stores) and event/streaming feeds where necessary. Reuse code and logic but refactor business rules into maintainable ETL and semantic layers.
- Automate aggressively: Use automated conversion tools, CI/CD, and Qlik Application Automation to accelerate repetitive tasks and reduce manual rebuilds. This reduces human error and frees analysts for higher-value redesign work.
- Design for augmented analytics: Structure apps and data models so Insight Advisor and conversational features can surface meaningful answers (clear field naming, annotated metrics, curated KPI catalogs).
- Embed AI governance: Track datasets, pipelines, model inputs/outputs, and decision flows like any other enterprise asset to control drift, explainability and bias. Data mesh/fabric approaches can be applied to ensure domain-aware, governed data products.
- People + Change: Invest in adoption train citizen analysts to use augmented features, create analytics translators in business teams, and align SLAs for data product owners.
Don’t “lift‑and‑shift” re‑imagine with a six‑wave plan
Wave 0 — Mobilize & business case
- Establish a joint business + data steering group.
- Define value levers: cycle‑time reduction (alerts/automation), cost‑to‑serve (app rationalization), AI adoption (NLQ, AutoML, Qlik Answers).
Wave 1 — Inventory, triage, and rationalize QlikView
- Run QlikView Governance Dashboard to map documents, usage, reloads, dependencies. Use the output to retire, consolidate, or rebuild.
- Use App Analyzer (Sense) and Monitoring Apps to spot heavy expressions, model bloat, and slow loads feed findings into the backlog.
- Apply Unified (Dual‑Use) licensing to keep business running while you transition teams and users.
Wave 2 — Land the data foundation for AI
- Stand up CDC to your target platforms (Snowflake/Databricks/BigQuery/Synapse) using Qlik Data Integration; keep it fresh without batch fragility.
- If you’re adopting an Iceberg lakehouse, consider Qlik Open Lakehouse for optimized ingest/storage and Snowflake Iceberg interoperability.
- Register assets in Qlik Cloud Catalog with tags, classifications; turn curated sets into Data Products with visible quality metrics (Talend integration).
- For GenAI, create Knowledge Marts (vector‑backed stores) to enable RAG on enterprise content.
Wave 3 — Rebuild for Qlik Sense (not “as” QlikView)
- Use the QlikView Converter and/or Qlik Analytics Migration Tool—but plan for intentional redesign; not all objects/macros translate 1‑for‑1.
- Leverage Spaces (Shared for co‑dev, Managed for governed publish) and roles; enforce row‑level security with Section Access.
- Create master measures/dimensions, and add Business Logic vocabularies so Insight Advisor understands your domain (“revenue”, “GM%”, etc.).
- Automate deploys & reloads using Qlik‑CLI / QRS API for repeatable pipelines.
Wave 4 — Activate AI experiences
- Turn on Insight Advisor (NLQ, analysis types) and curate synonyms; this is the fastest route to “search‑driven BI”.
- Use Qlik AutoML for no‑code predictions embedded back into apps; operationalize predictions via reloads/automations.
- Introduce Qlik Answers (GenAI assistant) for explainable Q&A over unstructured + structured content; use Catalog lineage to build trust.
- Where needed, wire OpenAI/Azure OpenAI connectors pre‑load generated features or call models at runtime.
Wave 5 — Close the loop with automation, alerts & reporting
- Build Application Automation flows to orchestrate reloads, notify in Teams/Slack, open tickets, or push actions to SaaS apps (using 3rd‑party connectors).
- Configure alerts (thresholds, outliers) and subscriptions (scheduled email snapshots) to get insights out of the dashboard and into people’s inboxes.
Wave 6 — Operating model, guardrails & adoption
- Governance: publish to Managed Spaces; enforce roles; document lineage; apply Section Access; monitor consumption and capacity.
- Platform Ops: standardize CI/CD with Qlik‑CLI; version assets in Git; automate app import/export and tenant‑to‑tenant moves.
- Skills: graduate developers from QlikView sheet design to Sense scripting, master items, logic vocabularies, and automations.
What top IT services firms are doing (and what to borrow)
Across leading SIs, the playbooks converge: industrialize data & AI platforms, move to agentic/GenAI‑ready architectures, and drive adoption through change and governance.
- Accenture stresses data readiness for AI and a clear path to value use their framing to prioritize data foundations before GenAI.
- Deloitte packages an “AI Factory as a Service” treat your Qlik platform as a factory with advisory→build→operate stages rather than as isolated projects.
- TCS DATOM™ uses maturity baselining across people/process/tech use it to sequence your waves and measure progress.
- Infosys Topaz, Wipro ai360, Cognizant Neuro AI, Capgemini’s GenAI research all emphasize at‑scale AI adoption, agentic automation, and responsible governance mirror that by productizing data, hardening controls, and embedding AI inside workflows.
How ACI Infotech Turned a QlikView Legacy into a Future-Ready Analytics Powerhouse
At ACI Infotech, we don’t treat QlikView → Qlik Sense migration as a checkbox exercise. We treat it as the cornerstone of a broader data-platform modernization, with a structured roadmap to embed AI, automation, and governance.
Our typical approach includes:
- Rationalizing and cleaning up legacy QlikView apps retiring redundant or under-used dashboards.
- Migrating critical assets to Qlik Sense using industry-standard tools (e.g. Qlik Analytics Migration Tool), while re-architecting data flows for scale.
- Building a data foundation aligned with modern cloud architectures enabling seamless integration with warehouses, lakehouses, and downstream AI/ML systems.
- Embedding AI-powered analytics (NLQ, AutoML, conversational chat, dynamic visualizations) so business users get answers instantly no waiting for BI devs.
Clients don’t just see old dashboards migrate, they get a robust, AI-ready analytics backbone that accelerates insights, reduces tech debt, and enables future-proof growth.
Claim Your Competitive Edge, Connect with ACI Infotech Today
Legacy BI is no longer a differentiator, it’s a liability. If you want to leapfrog competitors, democratize data, and infuse AI into decision making, now is the time to act. Contact ACI Infotech to start turning your QlikView estate into a modern, AI-powered analytics platform configured, governed, and delivered for sustained competitive advantage.
Get ahead. Build the analytics backbone of the future. Start your migration with ACI Infotech now.
FAQs
Yes, because Qlik Sense is built on the same associative engine but adds cloud-scale performance, AI/ML capabilities, and governance giving you a platform that evolves rather than ages.
For many cases, yes. The tool transfers scripts, variables, master dimensions/measures, and visualizations; but complex macros or unsupported features may require re-design.
Often within weeks once data models are migrated and cloud foundation is in place, you can enable natural-language search, conversational analytics, dynamic dashboards and begin realizing value immediately.
Absolutely, you can use managed spaces, role-based access, and centralized governance models suitable for enterprise compliance and security.
Yes, with features like conversational analytics, auto-generated insights, chart suggestions and analysis types, even non-technical users can ask questions in natural language and get actionable visualizations.
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