Data analytics services Hong Kong: from Qlik implementation to data warehousing and AI
What data analytics consulting actually involves
Data analytics services Hong Kong organisations buy cover the role that translates a business problem into a data solution and stays with it all the way to a working system in users’ hands — tool selection, data pipelines, model design, dashboard delivery and ongoing operations.
The difference from pure software resale is concrete: a reseller’s job ends when the licence is delivered, while a consultant is accountable for whether anyone actually uses the thing. Most failed analytics projects fail in that middle stretch, not in the software.
We are set up across two cities. ATPEP INTERNATIONAL CO., LIMITED in Hong Kong owns the client relationship, solution design and on-site delivery. Our affiliated company, Shenzhen Zhuoyue Fangda Technology Co., Ltd., has focused on data analytics since 2007 and brings 19 years of engineering experience, supplying the development and technical support bench. The practical benefit of that structure: the people you talk to are next door, and behind them is a team that has worked together for years.
For company background and team details, see about ATPEP.
Data analytics services Hong Kong: our four service lines
Qlik implementation and migration
As a Qlik Elite Channel Partner we handle licensing, environment deployment, data source connections, dashboard design, version upgrades and migration, plus day-to-day operational support.
iModel data science platform
iModel is developed in-house by our affiliated company on the open source KNIME core. It handles data preparation, statistical analysis and machine learning through visual workflows with no coding, and can carry an analytics engagement or be deployed on its own.
Data warehousing and platform build
From source inventory and model design through ETL pipelines to governance standards. The aim is that analysts stop exporting to Excel by hand, and data refreshes on schedule against a single agreed definition.
AI application development
From use case assessment and data preparation through to build. Covers predictive models, document question answering and knowledge retrieval, process automation, and embedding AI into the systems you already run rather than standing something up beside them.
Data analytics services Hong Kong: how a project actually runs
Figure 1 — The five project phases, from diagnosis to handover
Stage three is the watershed. If the pilot produces nothing, none of what follows is worth discussing — and we would rather say that in the first meeting than six months in, when it is awkward for everyone. For migration work specifically, see upgrades and migration.
Typical timelines and deliverables
| Service | Typical duration | Main deliverables |
|---|---|---|
| Qlik deployment | 4 – 12 weeks | Installation documentation, data model, first set of dashboards, permission configuration, user training |
| Qlik upgrade and migration | 6 – 16 weeks | Application inventory, migration plan with rollback, rebuilt applications, cutover acceptance report |
| Automated report distribution | 2 – 4 weeks | Report templates, distribution rules, recipient and permission configuration |
| iModel platform rollout | 4 – 10 weeks | Platform deployment, analytical workflows, process documentation, modelling training |
| Data warehouse build | 3 – 6 months | Source inventory, warehouse model design, ETL pipelines, definitions and governance standards |
| AI application development | 4 – 8 week pilot first | Use case assessment, data preparation plan, pilot application, results review and rollout recommendation |
Official information on the Qlik product range is on the Qlik website; details of the open source core behind iModel are on the KNIME website. If you have already settled on Qlik, go straight to what we deliver around Qlik.