Define the scope and boundaries of big data engineering so that responsibility for it is unambiguous.
Big Data Engineering for Digital Transformation
A practical programme in big data engineering for professionals who are accountable for results, not just awareness.
📍 Istanbul🗓️ 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 big data engineering than they can actually act on. Boards are asking for measurable returns from this strand of digital and data-driven work, not demonstrations. The programme converts the wider digital and data-driven work agenda from an area of general awareness into a set of repeatable practices. It is designed for mixed groups, so that big data engineering is examined from more than one functional angle. Improvement in this area of digital and data-driven work stalls when it depends on one capable individual rather than a defined method. Benchmarking exercises repeatedly place this aspect of digital and data-driven work among the areas with the widest performance spread. Work is grounded in real cases drawn from big data engineering, which each participant adapts to conditions in their own organisation. Participants finish able to explain this strand of digital and data-driven work to a non-specialist audience without losing precision. The final module sets out how progress on this area of digital and data-driven work will be evidenced six months later.
Expected Learning Outcomes
Recognise early indicators that big data engineering is drifting away from its intended design.
Design the integration points between big data engineering and the systems already in production.
Design the pilot for big data engineering so its result is decisive rather than merely encouraging.
Quantify the running cost of big data engineering — compute, licensing, and the people who keep it alive.
Build a security and access model for big data engineering appropriate to the sensitivity of the data involved.
Assign clear ownership for each element of big data engineering across the functions involved.
Who Should Attend
Managers with direct responsibility for big data engineering within the transformation programme.
Information security officers reviewing the exposure created by big data engineering.
Chief information officers accountable for the investment in big data engineering.
Operations managers whose processes are changed by big data engineering.
Project and programme managers whose delivery depends on big data engineering.
Risk and compliance staff assessing the controls around big data engineering.
Course Modules
Big data engineering: monitoring, drift and operational ownership
2 sessions · 8 pointsSession 1Where big data engineering touches systems nobody wants to change
- Assign responsibility for keeping documentation of big data engineering current.
- Prepare the summary of big data engineering that senior management will read.
- Assess the regulatory obligations big data engineering triggers in each jurisdiction.
- Identify the data already collected that bears on big data engineering.
Session 2Who owns big data engineering once the project team disbands
- Decide which legacy process big data engineering retires, and set the date.
- Identify every system big data engineering must read from or write to.
- Set the metrics that will show whether big data engineering is drifting from its intended behaviour.
- Distinguish symptoms from causes when big data engineering underperforms.
Big data engineering: from pilot to production
2 sessions · 8 pointsSession 1Sizing big data engineering honestly before committing budget
- Define the service level big data engineering must meet and what happens when it is missed.
- Benchmark the organisation's big data engineering against comparable operations.
- Map the handovers in big data engineering between functions and secure them.
- Name a single owner for each element of big data engineering.
Session 2Escalation and decision rights in big data engineering
- Rehearse the briefing on big data engineering that would follow an incident.
- Define acceptance criteria for big data engineering in advance.
- List the data sources big data engineering consumes and confirm each has a named owner.
- Confirm that contractual obligations around big data engineering are understood.
Big data engineering: measuring benefit and retiring what it replaces
2 sessions · 8 pointsSession 1The paperwork for big data engineering that is actually needed
- Classify the data in big data engineering and apply access controls that match the classification.
- Record the reasoning behind each architectural choice in big data engineering.
- Define the exit route from the supplier supporting big data engineering.
- Specify the fallback path when big data engineering is unavailable.
Session 2Making big data engineering work when resources are constrained
- Build the user briefing that explains what big data engineering does and does not decide.
- Establish what evidence demonstrates big data engineering is under control.
- Plan how big data engineering is versioned and how a bad release is rolled back.
- Confirm the retention and deletion rules applied to data inside big data engineering.
Big data engineering: business case, scope and the data it depends on
2 sessions · 8 pointsSession 1Choosing a supplier for big data engineering without being captured
- Build the internal briefing that explains big data engineering to those affected.
- Agree who is on call for big data engineering outside working hours.
- Arrange the handover of big data engineering so capability survives staff changes.
- Set escalation thresholds for big data engineering that work out of hours.
Session 2What breaks first when big data engineering meets real volume
- Design the pilot for big data engineering so that a negative result is still useful.
- Estimate compute and licensing cost for big data engineering at expected and at peak load.
- Confirm that those complying with big data engineering understand why it exists.
- Compare the cost of big data engineering with the cost of its absence.
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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