Quantify the running cost of data quality measurement frameworks — compute, licensing, and the people who keep it alive.
Measuring Data Quality and Designing Automated Validation Frameworks
A structured, applied course in data quality measurement frameworks — designed to be used the week you return.
📍 Istanbul🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
The constraint on data quality measurement frameworks is rarely the model or the platform — it is the data and the operating discipline behind it. Technology choices around measurement frameworks are easy to reverse on paper and expensive to reverse in practice. The course sets out a working method for the digital and data-driven work capability that participants can apply the week they return. The most reliable predictor of sound data quality measurement frameworks is whether anyone reviews it when nothing has gone wrong. Participants apply this area of digital and data-driven work to their own transformation programme throughout, so the output is directly usable. It is appropriate for those preparing to take on wider responsibility for measurement frameworks. They gain the ability to sequence improvements to data quality measurement frameworks in an order their organisation can absorb. What blocks progress on measurement frameworks is usually unclear ownership rather than unclear intent. The final module sets out how progress on the digital and data-driven work capability will be evidenced six months later.
Expected Learning Outcomes
Build the internal skills to operate measurement frameworks without permanent vendor dependency.
Communicate the purpose of data quality measurement frameworks to those who have to comply with it.
Structure records of measurement frameworks so that they answer the questions an auditor will actually ask.
Identify the failure points in data quality measurement frameworks most likely to cause loss, and control them first.
Document the decision record for measurement frameworks so successors understand why it is built this way.
Evaluate the bias, fairness and explainability obligations attaching to data quality measurement frameworks.
Who Should Attend
Chief information officers accountable for the investment in data quality measurement frameworks.
Product owners prioritising the roadmap for measurement frameworks.
Vendor and contract managers overseeing suppliers involved in data quality measurement frameworks.
Managers with direct responsibility for measurement frameworks within the transformation programme.
Experienced practitioners formalising an approach to data quality measurement frameworks that has grown up informally.
Solution architects designing how measurement frameworks fits the existing estate.
Course Modules
Data quality measurement frameworks: monitoring, drift and operational ownership
2 sessions · 8 pointsSession 1Moving data quality measurement frameworks from approval to execution
- Define acceptance criteria for data quality measurement frameworks in advance.
- Distinguish symptoms from causes when measurement frameworks underperforms.
- Plan how data quality measurement frameworks is versioned and how a bad release is rolled back.
- Set escalation thresholds for measurement frameworks that work out of hours.
Session 2Making measurement frameworks secure without making it unusable
- Test data quality measurement frameworks against edge cases drawn from real historical records.
- Identify every system measurement frameworks must read from or write to.
- Design the pilot for data quality measurement frameworks so that a negative result is still useful.
- Map the handovers in measurement frameworks between functions and secure them.
Measurement frameworks: from pilot to production
2 sessions · 8 pointsSession 1Comparing measurement frameworks with recognised practice
- Define the exit route from the supplier supporting data quality measurement frameworks.
- Confirm the retention and deletion rules applied to data inside measurement frameworks.
- Estimate compute and licensing cost for data quality measurement frameworks at expected and at peak load.
- Measure the current quality of the data feeding measurement frameworks before assuming it is usable.
Session 2Who owns measurement frameworks once the project team disbands
- Define the trigger that would require data quality measurement frameworks to be redesigned.
- Verify that measurement frameworks still performs when input volume doubles unexpectedly.
- List the data sources data quality measurement frameworks consumes and confirm each has a named owner.
- Define the service level measurement frameworks must meet and what happens when it is missed.
Measurement frameworks: cost, licensing and total running expense
2 sessions · 8 pointsSession 1Explaining measurement frameworks to people whose jobs it changes
- Record what was learned when data quality measurement frameworks did not go as planned.
- Prepare the response for the most likely failure in measurement frameworks.
- Assess the regulatory obligations data quality measurement frameworks triggers in each jurisdiction.
- Decide which legacy process measurement frameworks retires, and set the date.
Session 2Choosing a supplier for data quality measurement frameworks without being captured
- Set out how exceptions to data quality measurement frameworks are requested and approved.
- Set the metrics that will show whether measurement frameworks is drifting from its intended behaviour.
- Identify where judgement in data quality measurement frameworks is legitimate and where it is not.
- Close out actions on measurement frameworks rather than leaving them open indefinitely.
Measurement frameworks: people, skills and the change that follows
2 sessions · 8 pointsSession 1Where measurement frameworks touches systems nobody wants to change
- Anticipate the objections data quality measurement frameworks will raise and prepare the answers.
- Agree the indicators that will show whether measurement frameworks is improving.
- Identify single points of dependency in data quality measurement frameworks and reduce them.
- Reduce the variation in how measurement frameworks is carried out between teams.
Session 2Closing out measurement frameworks and capturing what was learned
- Classify the data in data quality measurement frameworks and apply access controls that match the classification.
- Build the competence framework that supports measurement frameworks.
- Agree who is on call for data quality measurement frameworks outside working hours.
- Agree the smallest change to measurement frameworks that would be visibly useful.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Complete your registration
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