Clean, standardise, reconcile
Cleaning, standardisation, normalisation, enrichment, transformation, mapping, validation and reconciliation across every connected source.
Product · Data Hub
The enterprise data hub.
The enterprise data hub.
Connect every source. Trust every number.
Your data is scattered across SAP, cloud apps, legacy systems and spreadsheets — and every team walks into the room with a different number. SCIKIQ Data Hub unifies them into one governed layer without moving or replacing a single source system, so analytics and AI run on data everyone can trust — live in 60–90 days.
60–90days to deploy
80%cost reduction
GenAIready by design
01How it works
From raw data to decisions in four steps
No complex pipelines. No ripping out existing systems. Just a clean path from your scattered data to reliable intelligence.
01
Connect your sources
Point SCIKIQ at any data source — on-prem databases, cloud warehouses, SaaS tools, APIs, flat files. No-code connectors get you live in minutes, not months.
02
Unify & govern automatically
SCIKIQ maps relationships, enriches metadata and enforces data quality rules automatically. Your data arrives structured, labelled and trustworthy.
03
Access via a single intelligent layer
Every team — finance, ops, sales, engineering — queries one consistent, governed dataset. Cross-departmental visibility with zero duplication.
04
Power AI, dashboards & decisions
Feed GenAI agents, ML models and BI tools with clean, semantically rich data. Your intelligence layer is finally ready for the AI era.
02The platform
One governed pipeline, four modules
Connect every source, curate it into trusted data and consume it any way your teams work — all on one lakehouse foundation. Explore each module below.
Enterprise-grade integration, out of the box
SCIKIQ unifies fragmented data from Excel, business apps, SQL Server, Oracle, Teradata, Db2, Snowflake, SAP and more — into one intelligent, governed layer. Real-time, multi-cloud, multi-vendor. No manual exports, no broken pipelines, no replication.
15 of 187+ connectors shown · connect on-prem and cloud sources without replication — bring compute to your data, not the other way around.
85%Faster data integration
90%Reduction in IT integration cost
MultiCloud & hybrid connectivity
Real-timeBatch & log-based ingestion
Curate and transform, without the manual grind
Connecting data is only half the job. The Data Hub curates it too — cleaning, standardising, enriching and reconciling every source, then modelling it logically and semantically so business and technical teams work from the same trusted definitions. ML-driven optimisation and an orchestrator keep curation running in real time.
Logical & business modelling
Describe entities, attributes, keys and relationships, then define and edit metadata — models built once in Data Prep Studio and reused everywhere.
ML-driven curation
ML-driven optimisation for real-time data curation, enabling faster access, proactive crisis management and predictive decision-making.
Orchestrator & schedules
Automated listing, scheduling and tracking of the tasks, jobs and emails that keep your curated data fresh — with no manual babysitting.
70%Reduction in data prep costs
120+ hrsSaved per month per data scientist
$15M+Data quality losses prevented
60%Lower total cost of ownership
Consume it any way your teams work
A BI semantics layer separates users from the complexity of the underlying databases, so governed data reaches everyone in the shape they need it — dashboards, APIs, ready-to-use models or packaged data products. Six consumption models, one governed source.
Reporting & dashboards
Intuitive, integrated reporting with self-service analytics on top of a BI semantics layer — no database expertise required.
Data-as-a-Service & API hub
Enterprise APIs for scalable data-as-a-product, with a centralised hub offering full read/write access across the organisation.
Model-as-a-Service
Deploy ready-to-use ML models for real-time intelligence, embedded straight into the applications and workflows that need them.
Data products & exchange
Convert raw data into AI-ready, monetisable products and share them securely across partners for innovation and new revenue.
70%Faster business insights with harmonized reporting
5xFaster time-to-market for data products
90%Reduction in ML deployment time
$2M+Annual API revenue potential
One platform: lake, warehouse and hub
The Data Hub unifies integration, curation, governance and visualisation in a single lakehouse — with Auto ML and GenAI built in for real-time summaries, profiling and insights from any source. Scalable, cost-effective and flexible enough for data scientists, analysts and researchers alike.
80%Cost reduction with a better solution
No-codeDeeply automated, easy to use
$42.6BLakehouse market by 2030 (from $7.05B in 2023)
03Who it is for
One trusted layer answers every team's hardest question
Whether you are closing the books or training ML models, every team queries the same governed data — and finally stops arguing about whose number is right.
Finance & reporting
Reconcile data across ERP, banking systems and spreadsheets automatically. Close the books faster with a single source of truth your CFO can trust.
Operations & supply chain
Live visibility across procurement, inventory and logistics. Spot bottlenecks before they become problems.
Customer 360
Merge CRM, support, billing and behaviour data into a unified profile — without building a warehouse from scratch.
Compliance & risk
Full data lineage, access control logs and audit trails across every source system. Audit-ready without separate tooling.
AI & ML development
Feed models clean, labelled, semantically enriched data from day one. Eliminate the 80% of ML project time lost to data preparation.
Executive intelligence
A live, cross-functional view of the business. One governance layer, zero conflicting numbers in the boardroom.
04AI readiness
GenAI that answers from your data, not around it
Most data platforms were built before GenAI existed. Every layer here — ingestion, governance, storage, delivery — is designed to feed models what they need: answers from your actual data, not hallucinations.
- Natural language queries across all connected data sources
- Automated data lineage so AI outputs are explainable and auditable
- Semantic enrichment that helps models understand business context
- Pre-built connectors to leading LLM and ML platforms
- Role-based access that keeps sensitive data out of AI prompts
05Why the Hub
Why teams choose the Hub over the alternatives
The old answer to fragmented data is a hand-built pipeline stack or a lift-and-shift onto a single cloud — both take quarters, and both start with ripping something out. The Hub activates the data you already have.
How the approaches compare
Hand-built pipelines & mega-platforms
SCIKIQ Data Hub governed & non-invasive
Time to first trusted dataset
6–18 months
60–90 days
Touch to your source systems
Rip-and-replace or full migration
None — sources stay put
Governance, lineage & quality
Bolted on later, if at all
Built in from ingestion
Vendor lock-in
High — one cloud, one stack
Vendor-neutral, multi-cloud
GenAI-ready semantics
Do-it-yourself
Native to every layer
What you get, either way
Integrates with your existing stack — no rip-and-replace
Structured for GenAI, ML and analytics
Built-in data quality, lineage and access control
Deploy in weeks, not months
Real-time business 360°
From one use case to enterprise-wide rollout
Have a data or AI challenge on your mind?
We would love to think through it with you — no pitch, no form maze.
06The pattern
What an enterprise data hub actually is
A definition worth having before a shortlist: a data hub is defined by what it guarantees, not by where it stores bytes.
An enterprise data hub is a governed point of mediation between the systems that produce data and the people and applications that consume it. It guarantees three things: that an entity — a customer, a supplier, a part, an employee — resolves to one identity across every system, that every metric has one agreed definition, and that any figure can be traced back to the source record it came from.
That is a different promise from the storage patterns it gets compared with. A data warehouse conforms copied data to a model designed in advance, which is excellent for the questions you knew you would ask and slow for the ones you did not. A data lake stores everything cheaply and leaves meaning for later. A data lakehouse brings warehouse behaviour to lake storage — a genuinely good answer, but still an answer about storage: it gives you an excellent place to put the CRM customer ID and the billing account number, and no opinion on whether they are the same company. A data fabric is the broader property of a distributed estate behaving uniformly, which is usually built on top of the shared meaning a hub establishes.
The practical consequence of the hub pattern is that data does not have to move. Source systems stay authoritative; the hub supplies the meaning they never agreed on. That is why there is no consolidation milestone standing between kick-off and a working view — and why the timeline is 60–90 days rather than the 6–18 months in the table above.
What that looks like in production
EFS, a facilities management group, ran 11 disconnected systems. The hub unified 6.5 million records across more than 6,100 attributes, with over 110,000 transactions flowing daily through the governed layer — and phase two was run in-house by EFS’s own team. Their CIO described the change as “from siloed to connected, from reactive to proactive”.
- Data hub vs data fabric vs data lakehouse — the full comparison, including data mesh
- Data hub implementation guide — the six stages, and what goes wrong in each
- Cloudera Data Hub alternative — why the two products share a name and solve opposite problems
- SAP data integration — no-code, non-invasive extraction from S/4HANA, BW/BW4HANA and ECC
Frequently asked questions
What is an enterprise data hub?
An enterprise data hub is a governed point of mediation between the systems that produce data and the people and applications that consume it. It guarantees that an entity — a customer, supplier, part or employee — resolves to one identity across every system, that every metric has one agreed definition, and that any figure can be traced back to the source record it came from. Unlike a warehouse, lake or lakehouse, it is defined by what it guarantees rather than by where it stores data, so the data does not have to move.
What is SCIKIQ Data Hub?
Data Hub connects, unifies and governs data from SAP, cloud apps, legacy systems, data lakes and warehouses into a single governed intelligence layer.
How many data sources can SCIKIQ connect to?
SCIKIQ ships with 187+ connectors for enterprise integration out of the box.
How long does Data Hub take to go live?
Data Hub is typically live in 60–90 days.
Do I have to move all my data into SCIKIQ?
No. Data Hub unifies and governs data across your existing sources without a rip-and-replace migration.