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Data and analytics

Keep the data handoff connected to the checks and decisions behind the result.

Data reaches the warehouse through exports, scheduled jobs and transformations, while review and publication often happen elsewhere. Nexus connects those stages for the operational pipelines and analytical tasks covered here. Source systems and the warehouse retain their records; the workflow preserves the scope, checks, owner decisions and destination outcomes behind the delivered dataset or answer.

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Processes in this department
8Processes in this department
Systems they run across
9Systems they run across
Customer stories
3Customer stories
On your infrastructure
Self-hostedOn your infrastructure

The work

What data and analytics runs on Nexus

  1. ERP and operational data delivery

    Move the approved source scope into the designated analytical structures. Preserve source keys, period boundaries and the load references needed to review the handoff.

  2. Commerce and cross-system analytics

    Assemble selected channel, CRM, ERP or cloud data for the agreed analytical dataset. Apply the defined transformations and compare the required control totals before release.

  3. Data-quality review

    Route validation differences and model-assisted record investigations to the responsible data owner. Apply authoritative corrections only through the approved source or warehouse process.

  4. Questions and planning outputs

    Connect permitted operational questions and load forecasts to their input scope, validation and review. Retain the query or forecast version and the owner's decision alongside the published result.

Consolidation

One layer, not one integration at a time

A completed extraction job does not by itself establish that the resulting dataset passed its checks or was approved for release. Nexus connects extraction, transformation, reconciliation and publication through one execution record, with versioned rules and explicit exception handling. The data team can follow the selected source scope to the destination response and reviewer decision rather than assembling that history from job logs, spreadsheets and separate approval messages.

Ownership

Where a person decides

The named data owner approves new scopes, metric definitions and authoritative corrections, while the planner approves the forecast version and any override. Changes affecting financial documents or access require their respective authorities. Routine data processing follows the agreed rules, and the workflow pauses wherever the release or correction policy calls for a decision.

AI for operational data and analytics Where a model is worth the call in this department, and where a rule already settles it.

8 processes

Data and analytics use cases

Explore operational data pipelines, cross-system analytics, quality review and the focused question-and-forecast workflows built on those datasets.

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3 customer stories

Customer stories

See customer stories in energy load forecasting, logistics fleet analytics and manufacturing production forecasting.

Systems in scope

What data and analytics processes connect

Counted across the processes above. Anything outside a pre-built action runs through an API, protocol or database call, or as Rules + Code.

Talk to a solutions engineer

Bring one data and analytics process you want to consolidate

We will map the handoffs it crosses today, where the approval belongs, which steps stay deterministic and what the run record has to show afterwards.

  • Runs on your infrastructure, including air-gapped.
  • Your systems keep their own records.
  • Existing automation can be invoked rather than rewritten.

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Goes to a solutions engineer who will run the session against the capability you select, in a shared environment.

FAQ

Data and analytics: common questions

Does Nexus replace our warehouse or business-intelligence tools?
No. It coordinates the source-to-result process while those systems retain their datasets, analytical definitions and specialist interfaces.
Can we coordinate existing data jobs rather than rewrite every transformation?
Where a job exposes a supported interface, the workflow can invoke it and record the returned result alongside the surrounding validation, review and publication steps.
How does a reviewer trace a published result?
The run records the selected source scope, applicable rule versions, system requests and responses, and any required approval, connecting the delivered result to its processing history.
Where can we explore model-assisted analytics?
See AI for operational data and analytics at /solutions/ai-for-data-analytics for questions over approved data, load forecasting and interpretation of quality exceptions.

Architecture session

Map your data and analytics processes onto one execution layer

We will go through what you run today, which parts already work, and what consolidating the execution path would actually change.

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