Data-driven decisions
Establish ownership, classification and purpose, then connect Arabic and English business records to governed memory, analysis and source-attributed reporting.
Aliph Data establishes the shared foundation for information used in AI, governance and security. Begin with ownership, classification and permitted purpose. Prepare Arabic and English records, privacy transformations and business definitions, then connect the resulting organisational memory to analysis, executive insight and reporting that can be traced to its sources.
For data leaders, operational decision-makers, reporting teams and governance functions.
Agree the meaning, calculation and reporting period of each priority measure. Identify who can change the definition and how the change is communicated. Resolve differences such as whether a count includes reopened work, cancelled records or items awaiting approval.
Map source records through discovery, transformation and use. Confirm purpose, ownership and classification, including Arabic and English personal data, national identifier and Iqama fields where relevant. Evaluate the selected detection and transformation rules before information reaches a model or analytical destination.
Connect documents, decisions and business records to source-attributed organisational memory. Present metrics with the period, scope and definitions needed to interpret them. Where model improvement is included, use an evaluated, owner-approved cycle with release and rollback inside the agreed environment.
An operations lead needs a consistent view of outstanding improvement actions across two teams. One team counts items awaiting validation as closed; the other keeps them open. The first step is to agree the status definitions, map each source to them and identify records that need an owner or due date.
An illustrative analytical view then separates open work from work awaiting validation. Reviewers check sample totals against source records, inspect exceptions and confirm that the view supports a decision about which actions need attention.
Choose a recurring decision and identify its source records, owners, classification and permitted purpose. Review privacy transformation needs and metric definitions before connecting models or reporting.
Identify the meeting, process or operational decision the work will support. Record the questions people ask and the shortcomings of today’s information.
Confirm measures, source ownership, reporting periods and access. Profile representative data and resolve the quality issues that affect the intended use.
Implement the agreed flows and views. Reconcile sample results with source records, test refresh behaviour and make exceptions understandable to reviewers.
Document quality checks, support responsibilities and how changes to definitions or sources are reviewed. Agree how users report a result they cannot explain.
These inputs help turn an initial discussion into a focused scope.
Check data quality, consistency of metric definitions and whether the result gives decision-makers the context they need.
Explore the relevant product capabilities, then confirm the integrations, configuration and delivery scope for your organisation.
Bring ERP, CRM, policies and organisational memory into one conversation. AliphChat helps your teams understand what is happening, check the information behind the answer and decide what to do next across the business.
Bring risk appetite, control performance and assurance into one connected view. Aliph Risk & Compliance helps teams explain the exposure, follow the evidence and put the next action in the right hands across the institution.
Bring the workflow, people and implementation together.
Explore Aliph Data servicesThat depends on the sources, required scale and operating needs. Start with the decision and the existing environment. The discovery work should identify what can be reused and where a pipeline, analytical view or platform change is justified.
A focused scope can work with understood limitations. Agree which quality issues prevent the intended decision and which can be made visible. Avoid presenting an incomplete measure as comprehensive, and give unresolved issues an accountable owner.
The same ownership, context, freshness and access decisions help prepare sources for AI-assisted work. Whether a source is suitable for retrieval or another model-assisted task should be evaluated against that specific use case.
Check that people interpret the measures consistently, that sample results reconcile to supporting records and that freshness is appropriate for the decision. Ask users to act on a representative result and explain what additional context they need.
Tell us about your current process, intended users and requirements. We’ll help define a first implementation and a practical way to evaluate it.
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