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Cost-Efficiency:

Dora's team analyzed the cost per participant for each program. This is crucial information, especially when facing budget constraints. It helps ensure that resources are allocated efficiently.
Effectiveness:

The effectiveness of each program was assessed by looking at the percentage of participants who found a new job after six months. This is a key performance indicator directly tied to the organization's mission. It allows for an objective evaluation of program impact.
Participant Ratings:

Dora considered the average participant ratings for each program. While participant satisfaction is important, Dora wisely didn't solely rely on this metric. The combination of satisfaction ratings with other quantitative data provides a more comprehensive picture.
Balancing Likability and Effectiveness:

Dora faced the challenge of balancing the likability of the mentorship program with its cost and effectiveness. This showcases the importance of making decisions based on a holistic view of program performance rather than relying on a single aspect.
Data-Driven Decision-Making:

Dora's decision to shut down the mentorship program was based on the data-driven conclusion that it was less effective and more expensive. This demonstrates the power of using data to inform decisions rather than relying solely on intuition or personal preferences.
Continuous Improvement:

By scheduling actions for shutting down the mentorship program, Dora ensures a structured and thoughtful approach to implementing the decision. This includes developing a plan for project closeout, which is essential for transparency and accountability.
Consideration of Stakeholders:

Throughout the process, Dora considered the impact on program participants, acknowledging the value participants found in the mentorship program. This ethical consideration is crucial when making decisions that affect individuals and communities.
Opportunity for Feedback:

Dora's decision-making process opens the door for feedback and suggestions from her team. This collaborative approach ensures that multiple perspectives are considered before finalizing the decision.

Discussion:

The discussion phase is essential for reflecting on the decision-making process. It allows stakeholders to share their perspectives, raise concerns, and provide insights that might have been overlooked. It promotes transparency and inclusivity in decision-making.

this is really an interesting topic and i hope most of the implementation plans can be productive or not if this step is not considered

It is always logical to use evidence and fact to make decision in our day to day activities. Use of data to make decision enables us to take rational decision. It prevents us to be bias on our decision. Sometime intuition based decision are taken but it could lead to false direction. Data analysis by using appropriate method by maintaining ethics has to be done for data based decision. Data should be analyze from different perspectives and and the findings has to be critically checked before making any decisions. Based on the analysis of data findings should be revealed and conclusion should be drawn. After than appropriate recommendation should be made to take action for the future.

Making decisions with data involves a systematic process to ensure informed and effective choices. Here's a guide on how to make decisions with data:

1. Define Your Objective:

   • Clearly articulate the decision you need to make.
   • Specify the desired outcome and any constraints.

2. Identify Relevant Data:

   • Determine what data is needed to inform the decision.
   • Consider both quantitative and qualitative data sources.

3. Collect Data:

   • Gather relevant information from reliable sources.
   • Ensure data quality, addressing issues like accuracy and completeness.

4. Organize and Clean Data:

   • Structure the data for analysis.
   • Address any inconsistencies or errors in the dataset.

5. Data Analysis:

   • Apply appropriate analytical methods.
   • Utilize statistical tools or visualization techniques.

6. Interpret Results:

   • Draw meaningful insights from the analyzed data.
   • Consider the implications for the decision at hand.

7. Consider Context:

   • Take into account the broader context of the decision.
   • Consider external factors that might impact the outcome.

8. Involve Stakeholders:

   • Engage relevant stakeholders in the decision-making process.
   • Gather diverse perspectives and insights.

9. Risk Assessment:

   • Evaluate potential risks associated with each decision.
   • Consider uncertainties and their impact.

10. Make the Decision:

    • Synthesize information and make a well-informed decision.
    • Clearly communicate the decision to stakeholders.

11. Implement and Monitor:

    • Execute the decision and monitor its implementation.
    • Track outcomes and adjust strategies if necessary.

12. Learn and Iterate:

    • Reflect on the decision-making process.
    • Learn from both successes and failures.
    • Use insights to improve future decision-making.

13. Ethical Considerations:

    • Ensure that decisions align with ethical standards.
    • Address potential biases in data interpretation.

14. Document the Process:

    • Maintain a record of the entire decision-making process.
    • Document data sources, analyses, and the rationale behind the decision.

15. Continuous Improvement:

    • Foster a culture of continuous improvement.
    • Seek feedback and refine decision-making processes over time.

By following a structured approach and integrating data-driven insights with contextual understanding, organizations can enhance their decision-making processes for better outcomes.

Step 1 - Identify business objectives: This step will require an understanding of your organization’s executive and downstream goals. This could be as specific as increasing sales numbers and website traffic or as ambiguous as increasing brand awareness. This will help you later in the process to choose key performance indicators (KPIs) and metrics that influence decisions made from data—and these will help you determine which data to analyze and what questions to ask so your analysis supports key business objectives. For instance, if a marketing campaign focuses on driving website traffic, a KPI could be tied to the amount of contact submissions captured so sales can follow-up with leads.

Step 2 - Survey business teams for key sources of data: To ensure success, it is crucial to get inputs from people across the organization to understand short and long term goals. These inputs help inform the questions that people ask in their analysis and how you prioritize certified data sources.

Step 3 - Collect and prepare the data you need: Accessing quality, trusted data can be a big hurdle if your business information sits in many disconnected sources. Once you have an idea of the breadth of data sources across your organization, you can start data preparation.
Step 4 - View and explore data: Visualizing your data is crucial to DDDM. Representing your insights in a visually impactful way means you’ll have a better chance of influencing the decisions of senior leadership and other staff.
Step 5 - Develop insights: Critical thinking with data means finding insights and communicating them in a useful, engaging way. Visual analytics is an intuitive approach to ask and answer questions of your data. Discover opportunities or risks that impact success or problem-solving.
Step 6 - Act on and share your insights: Once you discover an insight, you need to take action or share it with others for collaboration. One way to do this is by sharing dashboards. Highlighting key insights by using informative text and interactive visualizations can impact your audience’s decisions and help them take more-informed actions.

Making data-driven decisions with M&E data is beneficial
Set goals: Know what you want to achieve and what questions the data should answer.
Get relevant data: Quantitative (e.g., surveys) and qualitative (e.g., interviews) data, etc.
Analyze and interpret: Don't just collect numbers, make sense of them through statistics and thematic analysis.
Consider context: Analyze data in its social, economic, and political context.
Be ethical: Protect privacy, avoid bias, and be transparent about limitations.
Make informed decisions: Weigh data with other factors like feasibility and cost.
Monitor and adapt: Keep collecting data and adjust your approach based on new findings.
Remember, data is a tool, not a dictator. Combine it with critical thinking and ethical considerations for truly impactful decisions!

Making decisions with data in Monitoring and Evaluation (M&E) involves a systematic and analytical approach to interpreting information collected during the monitoring and evaluation processes. Here are steps to guide the decision-making process in M&E:

Define Clear Objectives:
Establish Key Performance Indicators (KPIs):
Use Reliable Data Collection Methods:
Collect Quality Data:
Analyze Data Effectively:
Contextualize Findings:
Engage Stakeholders:
Generate Actionable Recommendations:

Start with findings. Findings are facts that you pull from the data. One of Dora’s findings was that only 8% of mentorship program participants found new jobs after six months.
Draw conclusions. Interpret the findings. For this step, you might rely on the analysis skills that you learned in the last module. One of Dora’s conclusions was that the mentorship program was less effective than other programs. You may find, at this step, that you do not actually have enough information to draw a strong conclusion. In this case, you should decide whether it is feasible to gather more information from other sources.
Make recommendations. Based on your conclusions, what should your organization do? This is where the decision actually gets made. Dora made the difficult recommendation to shut down the mentorship program.
Schedule actions. Once you have made a recommendation, implement that decision by proposing actions. Dora asked her program manager to propose a schedule and project closeout plan.

Confirmation bias: We prefer ideas that confirm what we already believe. In Dora’s case, she may believe that the mentorship program is the most valuable program because it confirms her own personal experience.
The bandwagon effect: We tend to adopt the most popular opinion. For example, if everyone in your office believes that a program is inefficient, you are more likely to believe this as well.
The in-group bias: We tend to believe people that belong to the same group as ourselves. For example, you might be quicker to believe a story if it is told by someone with the same education, ethnicity or gender as yourself.

COST

Program Name Cost per year for each participant
Mentorship $45
Technology skills $23
Writing skills $31
RATING

Program Name Average participant rating 1-10
Mentorship 8.4
Technology skills 7.3
Writing skills 6.9
EFFECTIVENESS

Program Name % of participants who find a new job after 6 months
Mentorship 8%
Technology skills 15%
Writing skills 13%
Looking at the findings, Dora draws a few conclusions. It’s clear that the mentorship program is liked by most participants. However, it is also more expensive and less effective than the other programs.

So, while she would like to keep the mentorship program, she decides to make a difficult recommendation: the program should be slowly shut down over the next three months. She asks her program manager to create a plan for shutting down the mentorship program.

We have been taught that every decision we make should be backed by data than our biasness. Basing on our biasness we could recommend that mentorship program should continue on the expense of other programs. With the data provided it has proved us wrong we need to drop mentorship program though it was favored program.
However the exit should be systematic so that the participants should not feel dumped

Confirmation bias: We prefer ideas that confirm what we already believe. In Dora’s case, she may believe that the mentorship program is the most valuable program because it confirms her own personal experience.
The bandwagon effect: We tend to adopt the most popular opinion. For example, if everyone in your office believes that a program is inefficient, you are more likely to believe this as well.
The in-group bias: We tend to believe people that belong to the same group as ourselves. For example, you might be quicker to believe a story if it is told by someone with the same education, ethnicity or gender as yourself.

Start with findings. Findings are facts that you pull from the data. One of Dora’s findings was that only 8% of mentorship program participants found new jobs after six months.
Draw conclusions. Interpret the findings. For this step, you might rely on the analysis skills that you learned in the last module. One of Dora’s conclusions was that the mentorship program was less effective than other programs. You may find, at this step, that you do not actually have enough information to draw a strong conclusion. In this case, you should decide whether it is feasible to gather more information from other sources.
Make recommendations. Based on your conclusions, what should your organization do? This is where the decision actually gets made. Dora made the difficult recommendation to shut down the mentorship program.
Schedule actions. Once you have made a recommendation, implement that decision by proposing actions. Dora asked her program manager to propose a schedule and project closeout plan.

It's critical to halt any program where you don't feel the impact as a Program/Project Manager through your executives. Reason is, no matter how unfeasible a project is, few people may have benefitted from it. Our goal is always to maximize profit, not at a minimum.
Considering the basic stages a project/program has to undergo before closure, we need be keen. This is only done with our data. Findings from the data give us concrete information about whether we performed or underperformed during a particular project.

Based on our findings, we then conclude that amongst many, a project(s) are unproductive and must be halted. Then we recommend to senior management and implement the actions from our recommendations.

Step 1: Define the problem. Every data project is really a business project. ...
Step 2: Collect relevant data. ...
Step 3: Analyze the data. ...
Step 4: Develop and implement a plan. ...
Step 5: Evaluate the results.

It is very important to always make logically fact based decision. Doing so will help you avoid errors

Com base nos dados apresentados pela equipe de M&A, podemos fazer algumas análises para ajudar Dora e sua equipe a tomar uma decisão informada:

Impacto dos Programas:

O programa de habilidades tecnológicas parece ser o mais impactante em termos de eficácia na colocação no emprego, com uma taxa de 15% dos participantes conseguindo um novo emprego após 6 meses. Isso sugere que as habilidades tecnológicas estão em alta demanda no mercado de trabalho.
Em seguida, o programa de habilidades de escrita tem uma taxa de colocação no emprego de 13%, enquanto o programa de mentoria tem uma taxa de apenas 8%. Isso indica que, apesar de serem bem avaliados pelos participantes, os programas de habilidades de escrita e tecnológicas têm um impacto maior na obtenção de emprego.
Avaliação dos Participantes:

Embora o programa de mentoria seja altamente avaliado pelos participantes, com uma avaliação média de 8.4, os programas de habilidades tecnológicas e de escrita também têm avaliações decentes, com médias de 7.3 e 6.9, respectivamente. Isso sugere que todos os programas são percebidos como úteis e valiosos pelos participantes.
Custo dos Programas:

Em termos de custo, o programa de mentoria é o mais caro, custando US$ 45 por participante por ano. Os programas de habilidades tecnológicas e de escrita são mais econômicos, custando US$ 23 e US$ 31 por participante por ano, respectivamente.
Com base nessas análises, Dora e sua equipe podem considerar várias opções:

Manter o programa de habilidades tecnológicas devido ao seu alto impacto na colocação no emprego e custo mais baixo em comparação com a mentoria.
Reduzir os custos do programa de mentoria, talvez buscando formas de otimizar seus recursos ou buscar financiamento adicional para mantê-lo.
Reavaliar o programa de habilidades de escrita para determinar se há maneiras de aumentar sua eficácia na colocação no emprego ou reduzir seus custos.
Essa análise baseada em dados permite que Dora e sua equipe tomem uma decisão mais objetiva e fundamentada sobre qual programa cortar, levando em consideração não apenas o custo, mas também o impacto e a eficácia de cada programa na vida das mulheres atendidas pela organização

Making decisions with data involves a systematic approach that incorporates data collection, analysis, interpretation, and action. Here's a step-by-step guide to effectively use data in decision-making:

Identify the Decision to Be Made: Clearly define the decision that needs to be made and the problem or opportunity it addresses. This ensures alignment between the decision-making process and organizational goals.

Define Objectives and Criteria: Establish clear objectives and criteria for evaluating potential options. What outcomes are desired, and what factors will influence the decision? Criteria could include cost, impact, feasibility, and alignment with organizational values.

Gather Relevant Data: Collect relevant data to inform the decision-making process. This may involve quantitative data (such as metrics, performance indicators, and financial reports) and qualitative data (such as surveys, interviews, and customer feedback).

Validate Data Quality: Ensure that the data collected is accurate, reliable, and up-to-date. Validate data sources and methods of data collection to minimize errors and biases.

Analyze Data: Analyze the data using appropriate analytical techniques and tools. Identify patterns, trends, and insights that are relevant to the decision at hand. This may involve statistical analysis, data visualization, and other data analysis methods.

Consider Alternatives: Generate and evaluate potential alternatives or options based on the analysis of the data. Compare the alternatives against the established criteria and objectives to determine their suitability.

Make Informed Decisions: Use the insights gained from the data analysis to make informed decisions. Select the option that best meets the established criteria and objectives, considering both quantitative and qualitative factors.

Monitor and Evaluate: Implement the decision and monitor its implementation and outcomes over time. Continuously evaluate the effectiveness of the decision based on key performance indicators and feedback loops. Adjust the decision as needed based on new information and changing circumstances.

Communicate Decision and Rationale: Communicate the decision and the rationale behind it to relevant stakeholders. Provide transparency and clarity about the decision-making process, including the data used and the factors considered.

Learn and Iterate: Use the outcomes of the decision-making process as learning opportunities to improve future decisions. Reflect on what worked well and what could be improved, and iterate the decision-making process accordingly.

When making a decision on the mentorship program, based on the analysis of collected data, the organization should consider making hard, but correct decision. This suggestion stems from the higher cost per participant, relatively low effectiveness of their job placement and slightly high ratings in participants' survey. To ensure the seamless implementation of this decision, Dora has instructed the program manager to make a comprehensive schedule. Moreover, she will need to come up with a project closeout plan. Such plan will provide for the change process to be undertaken while minimizing any negative effects associated with the person’s program, thus the organization will have more resource to dole out to other programs contributing more impacts on the community. Through a sound evidence-based approach, the organization will enhance its capacity to make a real difference in the number of females coming from the disadvantaged areas by giving them an opportunity to get professional training and jobs.

Identify the Problem and Goals:

Start by clearly defining the issue you're trying to solve. What decision needs to be made? What are your organization's goals in this situation? This helps frame the questions your data analysis will answer.

Gather Relevant Data:
Collect data that pertains to the problem and your goals. This could involve internal data (e.g., program completion rates, client feedback) or external data (e.g., industry trends, job market statistics). The more relevant the data, the clearer the insights.

Clean and Organize Data:
Raw data often needs cleaning and organization for effective analysis. This might involve removing inconsistencies, formatting data for analysis tools, and checking for errors. Messy data leads to messy conclusions.

Choose the Right Analysis Method:
Depending on your data and goals, different analysis methods are best suited. For comparing programs, calculating success rates might be useful. Identifying trends might involve using charts and graphs. Choose the tool that best reveals the data's story.

Analyze and Interpret the Data:
Run your chosen analysis and interpret the results. Look for patterns, trends, or correlations that inform your decision. Don't just focus on what confirms your initial thoughts; explore all the data reveals.

Communicate Findings Clearly:
Present your data analysis in a way that's easy to understand, even for non-data experts. Visualizations like charts and graphs can be powerful tools. Clearly explain how the data supports your decision.

Make a Data-Driven Decision:
Based on the data analysis, make your decision. Data should be a major factor, but it might not be the only one. Consider other factors like feasibility, cost, and alignment with overall strategy.

Monitor and Refine:
Decisions based on data are rarely set in stone. Keep track of the outcomes of your decision. If new data emerges, be prepared to re-evaluate and refine your approach as needed. Data is a continuous loop for improvement.

Quality data will bring more effective decision making.

Data helps to make valid decision to avoid biasness. For example in this Dora's case, she prefers to keep the mentorship program because it was effective at some point in her life than any other program hence she preferred the mentorship program. But now depending on the Data analysis it shows that the mentorship program was expensive and also less effective that the or other programs.

Data driven decision are the best and brings good recommendation because they come with valid evidence based and are effective. Decision have to be made with what the available data defines after the analysis to help unbiasing one side because of personal beliefs or preferences

To make decisions effectively using data, follow these steps:

Define the Problem:
Clearly outline the issue or decision you need to address.
Collect Relevant Data:
Gather data that is pertinent to the problem at hand.
Analyze the Data:
Utilize tools and methods to analyze the collected data, extracting insights and patterns.
Identify Business Objectives:
Determine the goals you aim to achieve with the decision.
Survey Business Teams for Key Data Sources:
Collaborate with relevant teams to identify essential data sources.
Develop a Strategy:
Establish a plan based on the analyzed data to address the problem effectively.
By following these steps, you can ensure that your decisions are informed by data, leading to more effective and efficient outcomes.