DATA FOUNDATIONS FOR AI
Qlik data products package a dataset with the things AI actually needs: a contract, a named owner, enforced quality thresholds, a freshness commitment and lineage back to source.
Artha designs and builds them on Qlik Talend Cloud, one business decision at a time, so the model teams stop spending eighty percent of their week re-verifying inputs.
AI OVERVIEW
Artha Solutions builds AI-ready data foundations using Qlik data products in Qlik Talend Cloud: governed, discoverable datasets with contracts, named owners, enforced quality thresholds, freshness commitments and lineage, designed to serve AI models, assistants and analytics reliably.
The model was never the problem. AI initiatives stall because every project starts by rediscovering the same questions: where is the customer data, which copy is right, who owns it, how fresh is it, and may we legally use it. Answer those once, per dataset, and publish the answer. That is all a data product is.
None of this needs a new platform if you already run Qlik Talend Cloud. It needs the discipline of treating data as something you publish deliberately, with a name on it.
Six properties separate a data product from a dataset with a nice name. Remove any one of them and the AI team is back to re-verifying inputs by hand.
The schema, semantics and permitted uses are stated and versioned. A consuming model knows what each field means and what changes will be announced rather than discovered. When the contract changes, consumers hear about it before their pipelines break, not from them breaking.
One accountable person answers for the product: its meaning, its quality thresholds and its roadmap. Disputes about what counts as an active customer end at a desk rather than circulating between teams. Ownerless data products decay into exactly the swamp they were built to replace.
The rules from your governance layer run inside the product's pipeline, and a batch that fails its thresholds does not get published. Consumers see the quality status with the data. This is what makes the difference between a certified input and a hopeful one.
Stated as a number tied to the decision the product serves: minutes for a fraud signal, hours for operational reporting, daily for finance. The commitment is monitored, and a missed window is an incident with an owner rather than a silent staleness nobody notices until quarter end.
Every field traces to the systems and transformations that produced it, recorded by the platform rather than by a diagram from 2023. When a model's output is challenged, the provenance of its inputs is an answer, not an investigation.
Who may consume the product, for what purpose, with which fields masked, is enforced at the product boundary. AI teams get self-service access to what they are entitled to, and the compliance conversation happens once at design time instead of per request.
Operational systems · SAP and ERP · SaaS applications · Events and history
Change data capture · Batch · Streaming
Contracts and owners · Quality gates · Freshness commitments · Qlik Talend Cloud
ML models and Qlik Predict · Qlik Answers · Analytics and reporting · Open Lakehouse workloads
Start from one decision or model that matters and is currently starved: churn scoring, demand forecasting, an assistant that needs governed context. The decision defines the product's scope, not the other way round.
Agree schema, semantics, quality thresholds, freshness and permitted uses with the owner and the consuming team in the same room. Most of the value of a data product is created in this conversation, before any pipeline exists.
Implement the pipelines, quality gates and transformations in Qlik Talend Cloud, drawing on ingestion patterns that already exist in your estate rather than inventing parallel ones.
Run the product against its own contract: thresholds met on production data, lineage complete, access policy tested with real roles, freshness observed over a full cycle including month-end. Certification is evidence, not a meeting.
Make the product discoverable, onboard the first consumer, and measure usage. A data product with one committed consumer beats a marketplace of forty that nobody trusts, and the second consumer is where the reuse economics start paying.
CUSTOMER EVIDENCE
Selected from Artha’s existing published case-study system. Customer anonymization is preserved.
Challenge: A healthcare analytics provider needed to ingest and govern more than 100 TB of diverse data while improving access and processing performance.
Artha solution: Artha deployed Talend with its Dynamic Ingestion Framework across AWS and Snowflake, adding automated validation and scalable processing.
Talend, AWS, Snowflake, Tableau
Challenge: Clinic staffing decisions ran on inconsistent history and patient volumes that swing daily, weekly and seasonally, so sites were routinely over- or under-staffed.
Artha solution: Artha implemented AI/ML demand forecasting tuned to each site, including cold-start handling for locations with no history.
Talend, Data Integration
Challenge: A healthcare organization wanted to adopt AI/ML but had no validated prototype, governance model or clear starting point.
Artha solution: Artha stood up a Quick-Start AI/ML Lab: a working forecasting prototype on the organization's own data, plus adoption, retraining and governance guidelines.
Azure
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FREQUENTLY ASKED QUESTIONS
Concise answers based on current Qlik product information and Artha’s consulting approach.
A governed, packaged dataset built to serve a specific business outcome: it carries a contract describing schema and semantics, a named owner, enforced quality thresholds, a freshness commitment, lineage and access policy. Qlik Talend Cloud provides the pipelines, quality functions and metadata that make the package enforceable rather than aspirational.
A table is a location; a data product is a commitment. The difference is operational: versioned schema changes announced to consumers, quality gates that stop a bad batch from publishing, a freshness target that is monitored, and one person accountable when any of that slips. Consumers build on it the way they build on an API, because it behaves like one.
Because models are consumers that cannot ask clarifying questions. A human analyst who spots a suspicious figure investigates; a model trains on it. Reproducibility, auditability and quality gating have to be properties of the input data, and a data product is the unit that carries those properties. It is also what lets a second and third AI use case start in days rather than re-fighting the same data battles.
Data products are an operating pattern, not a storage decision. They can be published into a warehouse, an Apache Iceberg lakehouse or both, and the right home follows the workload: high-volume history and multi-engine ML access favour the lakehouse, while governed BI serving often stays in the warehouse. The contract, owner and quality gate travel with the product either way.
Qlik Answers needs governed, current content it can cite; Qlik Predict needs training data whose lineage and quality are known. Data products supply both: the assistant draws on certified structured data, and the model trains on inputs that can be reproduced exactly. Without that layer, both tools work, but their outputs inherit whatever the ungoverned inputs happened to contain.
One, chosen because a decision that matters is starved without it. The first product proves the operating pattern: contract, owner, gates, publication. The second and third reuse the ingestion and quality patterns and land much faster. Programmes that announce forty products up front spend their budget on cataloguing intent rather than serving consumers.
The business owns meaning, thresholds and the roadmap, because the product exists to serve a business outcome. The platform team owns the pipelines and enforcement mechanics. Artha sets this split up explicitly with names attached, because a product owned by everybody is owned by nobody, and it shows within a quarter.
The operating model and the first products: contract workshops with owners and consumers, pipeline and quality-gate implementation in Qlik Talend Cloud, certification evidence, publication and consumer onboarding, and the runbook for operating products in production. Where the foundation needs work first, we say so; the Data Quality & Governance page describes that layer.
NEXT STEP
Pick the one decision that is currently starved of trustworthy data, and ship the product that feeds it.
