Set retention, lineage and deletion rules for the data flowing through data lifecycle management.
Managing the Data Lifecycle from Collection to Archiving and Secure Deletion
An applied course in data lifecycle management built around the decisions practitioners actually face.
📍 Cairo🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
Most organisations now hold more data about data lifecycle management than they can actually act on. Vendors sell this part of digital and data-driven work as a product; it behaves in practice as a change programme. The outcome is a practitioner who can hold a position on this strand of digital and data-driven work and revise it on evidence. The content is relevant to those who own data lifecycle management and to those who are held accountable for its results. It establishes a shared vocabulary for the practice within digital and data-driven work so that teams can disagree productively about it. Where this area of digital and data-driven work is measured, it improves; where it is only discussed, it drifts. A common pattern is strong design of data lifecycle management paired with weak follow-through. Cases are chosen to expose the trade-offs in this aspect of digital and data-driven work rather than to illustrate ideal conditions. The programme ends where implementation begins, with the practice within digital and data-driven work broken into steps someone can start on Monday.
Expected Learning Outcomes
Quantify the running cost of data lifecycle management — compute, licensing, and the people who keep it alive.
Handle the trade-offs in data lifecycle management between speed, cost and assurance explicitly rather than implicitly.
Assign clear ownership for each element of data lifecycle management across the functions involved.
Define escalation and fallback for data lifecycle management when the automated path fails.
Design the pilot for data lifecycle management so its result is decisive rather than merely encouraging.
Structure records of data lifecycle management so that they answer the questions an auditor will actually ask.
Who Should Attend
Quality staff verifying that data lifecycle management performs as designed.
Technology and digital transformation managers leading data lifecycle management.
Public sector digital leads applying data lifecycle management under procurement and privacy rules.
Compliance and governance staff whose remit includes data lifecycle management.
Operations managers whose processes are changed by data lifecycle management.
Business analysts translating requirements for data lifecycle management.
Course Modules
Data lifecycle management: security, privacy and regulatory obligation
2 sessions · 8 pointsSession 1Choosing a supplier for data lifecycle management without being captured
- Assess the regulatory obligations data lifecycle management triggers in each jurisdiction.
- Build the competence framework that supports data lifecycle management.
- Confirm that contractual obligations around data lifecycle management are understood.
- Plan how data lifecycle management is versioned and how a bad release is rolled back.
Session 2Comparing data lifecycle management with recognised practice
- List the data sources data lifecycle management consumes and confirm each has a named owner.
- Write down the assumptions underpinning the approach to data lifecycle management.
- Close out actions on data lifecycle management rather than leaving them open indefinitely.
- Set the review interval for data lifecycle management and who attends.
Data lifecycle management: business case, scope and the data it depends on
2 sessions · 8 pointsSession 1Who owns data lifecycle management once the project team disbands
- Set the metrics that will show whether data lifecycle management is drifting from its intended behaviour.
- Set out how exceptions to data lifecycle management are requested and approved.
- Assign responsibility for keeping documentation of data lifecycle management current.
- Estimate compute and licensing cost for data lifecycle management at expected and at peak load.
Session 2Making data lifecycle management work when resources are constrained
- Define the service level data lifecycle management must meet and what happens when it is missed.
- Identify the skills the team lacks to operate data lifecycle management independently.
- Verify that data lifecycle management still performs when input volume doubles unexpectedly.
- Test data lifecycle management against edge cases drawn from real historical records.
Data lifecycle management: governance, ethics and explainability
2 sessions · 8 pointsSession 1Proving data lifecycle management paid for itself
- Specify the fallback path when data lifecycle management is unavailable.
- Record what was learned when data lifecycle management did not go as planned.
- Name a single owner for each element of data lifecycle management.
- Arrange the handover of data lifecycle management so capability survives staff changes.
Session 2What to measure in data lifecycle management and what to ignore
- Decide which legacy process data lifecycle management retires, and set the date.
- Compare the cost of data lifecycle management with the cost of its absence.
- Confirm the retention and deletion rules applied to data inside data lifecycle management.
- Design the pilot for data lifecycle management so that a negative result is still useful.
Data lifecycle management: monitoring, drift and operational ownership
2 sessions · 8 pointsSession 1The governance data lifecycle management needs and the governance it does not
- Review whether data lifecycle management is aligned with the objectives of the transformation programme.
- Identify every system data lifecycle management must read from or write to.
- Identify the data already collected that bears on data lifecycle management.
- Define the exit route from the supplier supporting data lifecycle management.
Session 2Reading the true cost of running data lifecycle management
- Establish what evidence demonstrates data lifecycle management is under control.
- Classify the data in data lifecycle management and apply access controls that match the classification.
- Verify six months later that changes to data lifecycle management have held.
- Decide what will be stopped to create capacity for data lifecycle management.
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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