ANALYTICS & AI
Qlik Cloud Analytics implemented around the decisions people actually make: governed self-service, associative exploration, Qlik Predict for what happens next and Qlik Automate for what happens automatically.
Artha modernizes QlikView and client-managed Qlik Sense estates to Qlik Cloud, and builds new analytics capability on data foundations that were designed to be trusted.
AI OVERVIEW
Artha Solutions implements Qlik Cloud Analytics: dashboards and associative exploration, governed self-service, QlikView and Qlik Sense modernization to Qlik Cloud, Qlik Predict for predictive AI, Qlik Automate for orchestration and alerting, reporting and embedded analytics.
Every analytics platform can draw a bar chart. The questions that decide whether the investment pays are different: does the person making a decision trust the number, can they interrogate it when it surprises them, and does anything happen automatically when it crosses a line.
The platform provides all of this. Whether it changes any decisions depends on implementation choices: which data is certified, who may publish, and what happens when a number crosses a threshold. That is the part Artha does.
Six lines that cover the path from a legacy estate to analytics people act on.
Qlik Cloud Analytics stood up properly: tenant and space architecture, certified datasets wired to your data foundation, app design around the decisions each audience makes, and performance tuned so exploration stays interactive at real data volumes. Dashboards are designed with the person who will use them, not presented to them.
QlikView and client-managed Qlik Sense estates moved to Qlik Cloud with an inventory-first method: which apps are used, which duplicate each other, which should be redesigned rather than lifted. Section access and governance translate as part of the move, and the apps nobody has opened since 2023 get retired instead of migrated.
The operating model that lets analysts build without creating chaos: managed and shared spaces, certified data with visible quality status, publication gates, naming standards and usage monitoring. The goal is a hundred people exploring one version of the truth, not one team defending a bottleneck.
Predictive use cases delivered end to end: framing the target variable honestly, preparing training data from governed products, building and evaluating models in Qlik Predict, and putting predictions in front of the people who act on them with the confidence context they need. No model ships without a baseline it has to beat.
Orchestration and action wired to the analytics layer: data-driven alerts with sensible thresholds, automations that open tickets or update systems when conditions fire, and reload orchestration that respects upstream data arrival instead of guessing at schedules.
Qlik visualizations and insight embedded into portals, products and operational applications, with security that maps your application's users to data entitlements correctly. The analytics goes where the work is, instead of asking everyone to visit one more tool.
Wire the analytics layer to governed data: certified datasets, semantic consistency and reload orchestration that respects when data actually arrives. Analytics built on ungoverned extracts undermines itself within a quarter.
Per audience, start from the decision and its cadence, then design the app around it. An executive scanning for exceptions and an operations lead working a queue need different surfaces, not the same dashboard with different filters.
Ship the first apps inside the self-service operating model: spaces, certification, publication gates and usage monitoring from day one, because retrofitting governance onto a sprawl is far more expensive than starting with it.
Add Qlik Predict models and Qlik Automate actions where a decision is made often enough to justify them. Prediction without an action path is trivia; the design work is deciding what fires when the model speaks.
Track who uses what, retire what nobody opens, and iterate with the audiences that engage. Adoption is the metric that matters: an unused analytics estate is a cost centre with good typography.
CUSTOMER EVIDENCE
Selected from Artha’s existing published case-study system. Customer anonymization is preserved.
Challenge: Inventory and logistics events took more than 24 hours to reach executive reporting, limiting hot-order visibility and increasing operational intervention.
Artha solution: Artha implemented Qlik Replicate change data capture, Qlik Compose modeling and a governed cloud analytics pipeline.
Qlik Cloud, Qlik Replicate, Qlik Compose, Snowflake, Microsoft Azure, Azure, Qlik
Challenge: Talent acquisition data sat fragmented across HR systems and third-party recruiting platforms, with formats too inconsistent for reliable hiring analytics.
Artha solution: Artha built a unified talent analytics platform on Azure using Talend, standardizing the feeds and the definitions behind recruiting KPIs.
Talend, Azure
Challenge: An automotive pricing platform needed purchase prices, buyer activity and dealer listings consolidated and kept transparent for consumers, dealers and manufacturers.
Artha solution: Artha built the integrated capture, storage and analytics pipeline behind the pricing and lead-generation services.
Talend
Read the case studyRELATED RESOURCES
ANALYST CONNECTION Sponsored by: Qlik and Artha Solutions AI and Data Modernization: Enterprise Readiness and Value Realization December 2025 Questions posed by: Qlik and Artha Solutions Answers by: Stewart Bond.
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Explore articleCONNECTED QLIK MICROSITE
FREQUENTLY ASKED QUESTIONS
Concise answers based on current Qlik product information and Artha’s consulting approach.
Qlik Cloud Analytics is Qlik's cloud analytics service: visualizations and dashboards, associative exploration, AI assistance, reporting, automation and embedded analytics delivered as SaaS. Qlik Sense remains the client-managed product for organizations that must run analytics on their own infrastructure. Most new capability, including Qlik Predict and Qlik Answers integration, lands in Qlik Cloud first, which is why modernization conversations usually point there.
Yes, inventory first: which apps are actually used, which duplicate each other, and which encode logic that should be redesigned rather than copied. QlikView scripts and section access translate to Qlik Cloud patterns with known techniques, but a lift of every app as-is migrates the sprawl along with the value. Retirement is a legitimate outcome for a meaningful share of most estates.
Qlik Predict is Qlik's predictive AI capability in Qlik Cloud: it builds, evaluates and deploys machine-learning models for questions like churn, demand and risk, working from the governed data already in the platform. Its value depends on the training data being trustworthy and the prediction reaching someone who can act, which are implementation concerns rather than product features, and they are where an engagement spends its effort.
Qlik Automate is Qlik's automation and orchestration capability: no-code workflows that connect analytics events to actions, such as raising a ticket when a threshold breaches, updating a downstream system, or orchestrating reloads when upstream data arrives. It is how a dashboard stops being something people check and starts being something that acts.
Qlik Answers answers natural-language questions with cited sources, which suits knowledge lookup; dashboards and associative exploration suit investigation and monitoring; Qlik Predict addresses what happens next. They share the same governed data foundation, and the design question is which surface each audience and decision deserves. Qlik Answers has its own implementation page covering source governance and evaluation.
With an operating model, not with prohibition: certified datasets that carry visible quality status, managed spaces with clear publication gates, naming standards, and monitoring of what is actually used. Analysts get freedom inside guardrails, and the estate keeps one version of the truth. The alternative, locking everything down, just moves the chaos into spreadsheets where nobody can see it.
It needs enough of it: certified sources for the first decisions you want to serve, not a completed enterprise programme. Analytics on ungoverned extracts erodes its own credibility with every discrepancy someone spots. The practical sequence is to govern the data behind the first two or three apps properly, ship them, and let that pattern pull the rest of the foundation along.
A first governed app serving a real decision typically lands within weeks once the data source is accessible, because the method starts narrow: one audience, one decision, certified data behind it. Estate migrations are sized from the inventory, not guessed; the app count matters less than how much duplicated and dead weight the inventory finds.
NEXT STEP
Start from one decision that matters, put certified data behind it, and ship an app people actually use.
