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What data-driven decision making methods can you use to improve your program management?

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Data-driven decision making (DDDM) is the process of using facts, metrics, and evidence to guide your actions and choices as a program manager. DDDM can help you improve your program performance, align your stakeholders, and optimize your resources. But how can you apply DDDM methods to your program management practice? Here are some tips and tools to help you.

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Rahid Alekberli, Ph.D., MPhil, MSc

Researcher, Technology Business Leader, Technology adviser-ADA University, IEEE member, ACM Professional Member
Ausra Gustainiene

Helping C-Leaders Deliver Digital Transformation Journeys || 20+ Years of Experience in Global SAP Program Management…

1 Define your program objectives and metrics

The first step in DDDM is to clearly define your program objectives and the metrics that will measure your progress and success. Your program objectives should be SMART: specific, measurable, achievable, relevant, and time-bound. Your metrics should be aligned with your objectives and reflect the outcomes and impacts of your program. You can use a logic model or a theory of change to map out your program logic and identify your inputs, activities, outputs, outcomes, and impacts.

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Rahid Alekberli, Ph.D., MPhil, MSc

Researcher, Technology Business Leader, Technology adviser-ADA University, IEEE member, ACM Professional Member
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Key Drivers of Aligning Data Governance with Corporate Strategy: - Understanding Organizational Needs and Maturity - Balancing Offensive and Defensive Activities - Aligning with Organizational Strategy - Integration with Primary Transformation Themes - Prioritizing Data Assets and Focusing Data Leadership - People, Policies, Processes, and Technology - Promoting a Data Culture Mindset - Connecting Data Across the IT Landscape

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Akshay Patil

Senior Program Manager
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Embracing data-driven decision-making is essential for effectiveness and success. This involves closely tracking Key Performance Indicators (KPIs) that align with your program's goals, enabling you to measure progress and pinpoint improvement areas. Analyzing customer feedback data is important aspect as well, it provides valuable insights into customer needs and experiences, helping tailor your program more effectively. Using predictive analytics can forecast potential challenges or opportunities, fostering a proactive management approach.

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2 Collect and analyze your program data

The next step in DDDM is to collect and analyze your program data. You need to decide what data sources, methods, and tools you will use to gather and store your data. You also need to ensure that your data is accurate, reliable, and valid. You can use qualitative and quantitative methods to collect data, such as surveys, interviews, observations, experiments, or administrative records. You can use data analysis techniques, such as descriptive statistics, inferential statistics, or data visualization, to explore, summarize, and communicate your data.

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Ausra Gustainiene

Helping C-Leaders Deliver Digital Transformation Journeys || 20+ Years of Experience in Global SAP Program Management || Advisor & Consultant || Published Author & Speaker
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We use numbers, dashboards and reports in managing risks and making decisions. But trusting data is a RISK itself. Data will never be 100% reliable, accurate and valid. On the top - an interpretation of that data will be subject to different biases. Suggestion: use data in dashboards and reports not as a source of single truth, but rather as vehicle to start conversations. And use visualizations not as a means "to represent the data", but rather a way for showing a conflict between different programme tensions (eg. scope change vs impact to budget vs impact to schedule) and a way to force different stakeholders to have conversation, to discuss, understand and find a compromise.

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3 Interpret and communicate your data insights

The final step in DDDM is to interpret and communicate your data insights. You need to use your data analysis results to answer your program questions and inform your program decisions. You also need to share your data insights with your stakeholders, such as your sponsors, clients, team members, or beneficiaries. You can use data storytelling techniques, such as narratives, charts, dashboards, or infographics, to convey your data insights in a clear, compelling, and actionable way.

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Rahid Alekberli, Ph.D., MPhil, MSc

Researcher, Technology Business Leader, Technology adviser-ADA University, IEEE member, ACM Professional Member
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Interpreting and communicating data insights involves several steps. Here's a brief overview: - Data Analysis - Data Interpretation - Visual Representation - Narrative Construction - Delivery - Actionable Recommendations The goal of interpreting and communicating data insights is to transform raw data into clear, understandable, and actionable information that can drive decision-making and strategy.

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4 Implement and monitor your data-driven decisions

Once you have made your data-driven decisions, you need to implement and monitor them. You need to plan and execute your actions based on your data insights and evaluate their effectiveness and efficiency. You also need to track and report your performance and outcomes using your metrics and indicators. You can use feedback loops, adaptive management, or agile methods to adjust and improve your actions based on your data.

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5 Learn and improve from your data

The last step in DDDM is to learn and improve from your data. You need to use your data to identify your program strengths, weaknesses, opportunities, and threats. You also need to use your data to generate lessons learned, best practices, and recommendations for your program improvement and sustainability. You can use learning reviews, after-action reviews, or retrospectives to capture and share your learning from your data.

DDDM is not a one-time event, but a continuous cycle of collecting, analyzing, interpreting, communicating, implementing, monitoring, learning, and improving from your data. By applying DDDM methods to your program management practice, you can enhance your program quality, impact, and value.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What data-driven decision making methods can you use to improve your program management?

1

2

3

4

5

6

1 Define your program objectives and metrics

2 Collect and analyze your program data

3 Interpret and communicate your data insights

4 Implement and monitor your data-driven decisions

5 Learn and improve from your data

6 Here’s what else to consider

Program Management

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What data-driven decision making methods can you use to improve your program management?

1

2

3

4

5

6

1 Define your program objectives and metrics

2 Collect and analyze your program data

3 Interpret and communicate your data insights

4 Implement and monitor your data-driven decisions

5 Learn and improve from your data

6 Here’s what else to consider

Program Management

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