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MODULE 7
Managing Stakeholder Relationships
Identifying Key Stakeholders
Introduction to Managing Stakeholder Relationships
Roles of Key Stakeholders
Managing Stakeholder Relationships
Objectives
Stakeholders
Individuals or groups who have a stake in the project's / transformations' outcome.
Introduce the importance of stakeholder management in business transformation.
Why is Stakeholder Management important?
Stakeholders' manage expectations, ensure alignment, reduce resistance and enhance a projects success.
Discuss key topics including identifying stakeholders, engagement strategies, and overcoming challenges.
Identifying Key Stakeholders
Discuss transformation scope and techniques for identifying and categorising key stakeholders (organisational / external / functional / group)
Discuss transformation
Define scope using a Project Charter, then utilise stakeholder analysis tools such as stakeholder mapping or a power/ interest grid.
Importance of clarity
Define roles and responsibilities to avoid miscommunication and ensure accountability.
Tools and Techniques for Data Collection
Overview of methods and tools for collecting data
(e.g., manual Data collection , SPC, surveys, sensors, web analytics, AI)
Practical Examples
Pye System Combined with AI
Oil and Gas Industry
FMCG Manufacturing
Take an agile approach and continually review the data required
SLOB (Slow Moving and Obsolete Stock), eg. manual data collection
Pye System for Data Collection
(eg. equipment health, well-bore conditions, and production metrics)
Using Data Insights to Drive Tactical Decision Making
Techniques for Data Analysis
Building a Data-Driven Culture
Challenges in Data-Driven Decision Making
Techniques for Data Analysis
Balanced scorecard
Introduction to various data analysis techniques
Qualitative and quantitative
descriptive, diagnostic, predictive and prescriptive.
Diagnostic
Predictive
Prescriptive
Descriptive
Statistical process Control (SPC) can be applied where it's considered to add value
An airline examines its customer satisfaction survey data to diagnose why there was a drop in satisfaction scores, uncovering that long wait times at check-in counters were a major issue.
A retail company uses descriptive analysis to summarise monthly sales data, generating reports that show sales trends over the past year, such as identifying peak sales periods and average transaction values.
An e-commerce business uses predictive analysis to forecast future sales based on historical purchase patterns, seasonal trends, and customer behaviour, helping to plan inventory and marketing strategies.
A logistics company applies prescriptive analysis to optimise delivery routes and schedules, using data from traffic patterns, delivery times, and vehicle capacities to recommend the most efficient routes for their fleet.
Interactive Question
Which data analysis technique would be most beneficial for your organisation and why?
Using Data Insights to Drive Tactical Decision Making
Data insights empower organisations to make informed tactical decisions by providing real-time analytics that highlight trends, identify inefficiencies, and forecast outcomes, enabling managers to allocate resources more effectively and respond swiftly to market changes.
Building a suite of KPIs drives business performance.
Practical Examples
Prioritisation matrix to rank projects by a standardised set of criteria
A bank uses data from transaction histories and customer behaviour analysis to detect and prevent fraudulent activities.
Food manufacturing business using waste data to repair oven band which was affecting quality and size of product.
A hospital system analyses patient admission data and treatment outcomes to optimise staff scheduling and resource allocation, ensuring that peak times are adequately staffed.
Building a Data-Driven Culture
Key considerations for creating a data-driven culture within an organisation
CI Team Operating Rhythm
Trust in data sets and ownership
C1 Team cycle
Review
& Retrospective
(Last Thursday)
1.5 Hrs
C1 Team cycle
Planning
(1st Tuesday)
1.5 Hrs
Ongoing Cl Team Backlog Refinement
1 Hrs
Importance of data literacy and training programs for employees
Backlog
Refinement
2nd week
Backlog
Refinement
3rd week
Milestone Boards (Daily)
15m
Operating rhythm, where data is reviewed and action determined
Cl Team Huddle (Daily)
15m
Challenges in Data-Driven Decision Making
Confirmation bias
Common challenges in implementing data-driven decision-making processes: organisational resistance, data overload, unrefined data, not owned, not trusted, not understood, not acted upon, not linked effectively to KPIs, data quality issues, privacy concerns.
Neglecting
data quality
Data
illiteracy
Strategies to overcome challenges: create a data-driven culture, simplify data access and visualisation, implement clear KPIs and feedback mechanisms.
Challenges and Misconceptions
Historical data
overreliance
Scattered
data
Interactive Question
What are potential barriers your organisation might face in adopting data-driven decision making?
Poor communication
of data insights
Ethics and Data Governance
The importance of ethical considerations in data collection, analysis, and use is multifaceted, as it ensures compliance with laws and regulations, creates trust with stakeholders, and promotes responsible data practices.
Overview of data governance frameworks
(e.g., GDPR) and their implications.
Case Study Example
How Google ensures user data privacy while leveraging data for ad targeting.
Summary and Key Takeaways
Using data to define KPIs
Owning your data
Data is information!
Review and refine
Data Analysis - are we measuring the right metrics?
Interactive Question
What are your key takeaways from today's session on data-driven decision making?
Implementing Data-Driven Decision Making in Your Organisation
Assessment
Conduct a thorough assessment of current data capabilities and decision-making processes.
Roadmap Development
Develop a strategic roadmap outlining goals, milestones, and timelines for implementing DDDM.
Cultural Integration
Create a data-driven culture by promoting data literacy, training initiatives, and leadership support.
Technology Adoption
Invest in appropriate data analytics tools and technologies to support DDDM initiatives.
Monitoring and Evaluation
Establish metrics and KPIs to measure the effectiveness of DDDM implementation.
Continuous Improvement
Continuously refine and improve DDDM practices based on feedback and insights.