Source system and metric inventory
Source system and metric inventory is considered against your goals, existing systems, timeline and agreed deliverables.
TESTING & QA
Data Quality Assurance.
Newly Connects provides data quality assurance to help your business address specific technical needs within a clear, agreed scope. Understand release readiness before customers encounter problems. Match test coverage to business risk and combine repeatable automation with focused investigation.
Discuss your requirements ↗☎ Call 223 369 5352THE CONNECTED APPROACH
Data Quality Assurance
Your peopleYour processesYour systems
Understand release readiness before customers encounter problems.
A PRACTICAL START
Understand release readiness before customers encounter problems.
Start with the outcome you need from data quality assurance. We work with your team to identify the current constraints, the users affected, and the information required to make a sound implementation decision.
The scope can cover a new initiative, an improvement to an existing environment, or specialist support within a wider program. Deliverables, responsibilities, acceptance criteria, and support expectations are agreed before implementation.
Explore our delivery process →DESIGN & ENGINEERING
Data Quality Assurance in a connected delivery model.
Every engagement starts with the real workflows, users and systems involved. We translate the requirements into practical design decisions, an implementation roadmap, clear acceptance criteria and a maintainable handover.
- Discover current workflows and business rules
- Prototype the experience and information architecture
- Integrate data and connected systems
- Test, launch and improve in reviewable stages
WHAT THIS SERVICE INCLUDES
Data Quality Assurance offerings
Explore what a data quality assurance engagement can include, from the initial business problem and user needs to the implementation plan, testing and a supportable release. The scope below is illustrative and tailored to each project.
Data ingestion and validation
Data ingestion and validation is considered against your goals, existing systems, timeline and agreed deliverables.
Reporting model and KPI definitions
Reporting model and KPI definitions is considered against your goals, existing systems, timeline and agreed deliverables.
Interactive dashboards and alerts
Interactive dashboards and alerts is considered against your goals, existing systems, timeline and agreed deliverables.
Governance and refresh monitoring
Governance and refresh monitoring is considered against your goals, existing systems, timeline and agreed deliverables.
EXAMPLE PRODUCT EXPERIENCE
See how data quality assurance can be structured.
This locally hosted interface illustration is a concept, not a claim about an actual customer project. The final screens, workflows and metrics depend on the project brief.
- Defined users, roles and permissions
- Clear workflows and accountable handoffs
- Reviewable progress and quality checks
- Measurable, maintainable deliverables
SERVICE-SPECIFIC ROADMAP
Our data quality assurance process
A practical five-stage workflow adjusted to your objectives, team and delivery model.
Define reporting questions
Identify the users, success criteria, constraints and decision makers.
Audit sources and quality
Confirm the smallest useful scope and its dependencies.
Build data transformations
Review tangible designs and decisions with stakeholders.
Design dashboard experience
Verify quality, accessibility, integration and acceptance criteria.
Validate and operationalize
Document the handover, measure results and agree next priorities.
WHAT WE CAN SCOPE
Capabilities for your requirements.
Choose the elements that fit your project. The final scope is defined during discovery.
01
Data Quality Assurance requirements workshop
02
Data Quality Assurance implementation roadmap
03
Existing system and dependency review
04
Access and data handling requirements
05
Acceptance criteria and validation
06
Documentation and handover
FROM DISCOVERY TO HANDOVER
A clear route to delivery.
01
Risk-based test strategy
Define the objectives, constraints, and evidence needed to move forward.
02
Test cases and environments
Translate agreed priorities into specific deliverables and acceptance criteria.
03
Manual and automated execution
Review work in stages, record decisions, and validate against the agreed scope.
04
Defect evidence and release reporting
Provide the agreed documentation, operating guidance, and next-step recommendations.
YOUR QUESTIONS
Before we begin.
What do you need to start a data quality assurance project?
A short description of your goal, the systems involved, any known constraints, and your preferred timeline. Existing documentation or sample workflows help us define the next step.
Can you work with our existing systems?
We begin by reviewing the current architecture, interfaces, permissions, and data requirements. Integration feasibility and any platform limitations are addressed in the proposed scope.
How are cost and timing determined?
The estimate depends on complexity, integrations, data preparation, review needs, and delivery responsibilities. You receive a proposed scope and engagement model before work begins.
What happens after delivery?
Documentation, training, warranty terms, maintenance, and operational support are defined in the engagement. We can discuss a separate support arrangement where needed.