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Without bias and not without analysis .

Here are some examples of how data can be used to make decisions:

A company can use data to identify its target market and develop products and services that meet the needs of that market.
A government can use data to track crime rates and allocate resources to areas where crime is most prevalent.
A doctor can use data to diagnose diseases and recommend treatments.
A teacher can use data to track student progress and identify students who need additional help.

there is objectivity in decision making based on data analysis.

Which programs are the most impactful and cost-efficient? Which programs are the least impactful and cost-efficient?
Data driven decision helps us to avoid the effects of our biases.
The decision should be make after the strong data analysis, analyzing program cost, rating and effectiveness of the program leading to outcome as well as impact on the lives of the beneficiaries.

It is important to follow steps, start with data findings, draw a strong conclusion as a result of the findings, make recommendations ( What should organization do? )and Schedule actions by implementing that decision by proposing action.

Which programs are the most impactful and cost-efficient? Which programs are the least impactful and cost-efficient?
Data driven decision helps us to avoid the effects of our biases.
The decision should be make after the strong data analysis, analyzing program cost, rating and effectiveness of the program leading to outcome as well as impact on the lives of the beneficiaries.

It is important to follow steps, start with data findings, draw a strong conclusion as a result of the findings, make recommendations ( What should organization do? )and Schedule actions by implementing that decision by proposing action.

To make decisions you must use data that tell you the facts.

should the program be shut down over the next three months. She should ask her program manager to create a plan for shutting down the mentorship program

To ensure there is no bias in decision making try to follow the guided four step decision making tool.
i. Start with the findings- what is data telling us about the project in question, get facts from the data
ii. Draw Conclusions- when data is presented, you should be able to interpret the data and come up with actual findings for recommendations.
iii. Make recommendations- Arising from the conclusion drawn, what should be the course of action by the organization?
iv. Schedule actions- Once the recommendation has been made, there is need to implement the decision by proposing the actions to be undertaken, e.g. Schedule a plan for close out.

We ars subconsiously biased

Making project decisions based/using the project DATA is vital and KEY to successful ACTION being taken rather than drawing conclusions based on cognitive bias.

Dora's decision to do a research was very wise because it helped her make a wise but also a challenging decision but also for the best. She was able to know that this what will be needed and be of help a lot. Her findings in short led her to a final decision making.

Data analysis is very important, it helps one to make tangible decisions without assuming things .Rather, have an idea of what is on the table and see the way forward.

I think to make the best choice, Dora should follow a structured decision-making process, including defining clear objectives, gathering relevant program data, analyzing the data objectively, engaging stakeholders, considering long-term impact, and ultimately making an informed decision that aligns with the organization's mission and goals.

Data-driven decision-making in Monitoring and Evaluation (M&E) is crucial for organizations. It ensures that choices are based on facts and evidence, leading to cost-effective and impactful programs. Data enables transparency, accountability, and adaptive management, promoting continuous program improvement. It also helps in optimizing resource allocation and efficiently implementing decisions. Moreover, data can support external advocacy efforts by providing concrete evidence of program effectiveness.

Quantitative analysis functions are mathematical and precise. There is only one correct answer to the question, “What is the mean age of the participants?” A dozen different data analysts could answer this question and, assuming that they were looking at the same data, arrive at exactly the same answer.

Qualitative data analysis is different. There is no objective, mathematically precise way to analyze a 500-word interview with a participant. Instead, you will need to be flexible about your approach. According to the National Science Foundation, qualitative analysis is:

This are important guides in decision making processes.

Agreed all decisions should be based actual data findings. It is possible to make error if we base our decisions on our passions, past experiences or preferences. We should be open to surprised or discouraged by our findings.

Making decisions with data involves several steps to ensure a well-informed choice:

Define Your Objective: Clearly outline the decision you need to make. Be specific about what you want to achieve.

Collect Relevant Data: Gather data that is pertinent to your decision-making process. Ensure the data is accurate, reliable, and up-to-date.

Clean and Prepare the Data: Data can be messy. Cleanse it by removing errors and outliers. Transform the data into a usable format, making sure it's ready for analysis.

Choose the Right Analysis Method: Depending on your objective, choose appropriate statistical or analytical techniques. Common methods include descriptive statistics, regression analysis, or machine learning algorithms.

Visualize the Data: Use charts, graphs, and other visualization tools to represent your data. Visualization often makes complex data more understandable.

Interpret the Results: Analyze the outcomes of your data analysis. What patterns or insights does the data reveal? Understand the implications of these results in the context of your decision.

Consider External Factors: Think about external factors that might influence your decision. Data doesn't exist in a vacuum; it's essential to consider the broader context.

Make the Decision: Based on your analysis and considering all relevant factors, make your decision. Be confident, but also be open to revising your decision if new data or insights emerge.

Implement and Monitor: Put your decision into action and monitor the results. If possible, establish key performance indicators (KPIs) to track the impact of your decision over time.

Learn and Iterate: Regardless of the outcome, learn from the process. Understand what worked well and what didn't. Use this knowledge to refine your decision-making process for the future.

Before you make any decision, have your facts right!

The purpose of any M&E process is, ultimately, to learn something and to use what you learn to improve performance. How effective are your programs? Who are your beneficiaries, and which of them are being helped?

Data are simply pieces of raw information. To understand what data mean requires special tools and processes.

Before starting this module, consult the stakeholder map that you created in Module 2. What are the questions that your stakeholders would like answered? These questions should be the starting point for your data analysis.

Organization programs should be community driven and not imposed by the organization staff. Sometimes what we strongly believe in is just that; it may not be beneficial to others. Collecting facts and figures helps us gather the popular opinion and it is one of the factors/exercises that ensure the longevity of some programs run by organizations.

We should make decisions based on cost and effectiveness

Cost-Efficiency Analysis:

Dora's team assessed the cost per participant for each program. This is crucial for optimizing resources, especially when facing budget constraints.
Mentorship was identified as the most expensive program, prompting a deeper evaluation.
Effectiveness Metrics:

The team evaluated program effectiveness by analyzing the percentage of participants finding new jobs after six months.
Mentorship, with an 8% success rate, was deemed less effective compared to the other programs.
Participant Ratings:

Dora considered participant ratings, recognizing the positive perception of the mentorship program.
However, she made a strategic decision by not solely relying on participant likability but balancing it with cost and effectiveness.
Balancing Stakeholder Preferences:

Dora faced the challenge of aligning organizational goals with participant preferences. This balancing act is common in decision-making.
Difficult Decision-Making:

Dora's decision to shut down the mentorship program reflects the courage to make tough choices for the greater good of the organization.
Such decisions require a careful balance between emotional considerations and rational analysis.
Data-Driven Conclusions:

Dora drew conclusions based on the data, emphasizing the importance of grounding decisions in factual findings.
The analysis highlighted a misalignment between program costs and outcomes.
Feasibility of Data Gathering:

Dora acknowledged the possibility of needing more information. This recognition of the limits of available data and the feasibility of gathering additional insights showcases a realistic approach to decision-making.
Clear Communication and Action Plan:

Dora's decision-making process includes clear communication with the program manager to ensure a smooth transition.
The request for a schedule and project closeout plan demonstrates a proactive approach to implementing the decision.
In summary, Dora's decision-making process exemplifies the integration of data, cost-effectiveness considerations, and strategic thinking. It emphasizes the importance of aligning organizational priorities with available resources, even if it means making challenging decisions.

Discussion:

Cost and Efficiency:

Consider the cost per participant and the cost-effectiveness of each program. How do these financial metrics influence the decision-making process?
Discuss the trade-offs between program cost, participant ratings, and effectiveness.
Effectiveness Metrics:

Analyze the effectiveness metrics, such as the percentage of participants finding a new job after six months. How do these metrics contribute to the overall evaluation of program impact?
Discuss the importance of balancing participant satisfaction with tangible outcomes.
Participant Ratings:

Explore the significance of participant ratings. How do participant ratings reflect the perceived value and quality of each program?
Discuss whether participant ratings alone should be a decisive factor in program continuation.
Balancing Likability and Effectiveness:

Reflect on the dilemma of balancing program likability (as indicated by high ratings) with program effectiveness. How can organizations strike a balance between popularity and impact?
Discuss scenarios where a program might be well-liked but less effective, and vice versa.
Data-Driven Decision-Making:

Discuss the importance of data-driven decision-making in organizations. How can a systematic analysis of data lead to more informed and objective decisions?
Explore challenges and benefits associated with relying on data for decision-making.
Communication and Transparency:

Consider the communication strategy when implementing decisions based on data. How should Dora communicate the decision to shut down the mentorship program to stakeholders?
Discuss the importance of transparency in decision-making processes.
Sustainability and Adaptability:

Explore strategies for ensuring the sustainability and adaptability of programs. How can organizations continuously assess and adjust their programs based on data and evolving needs?
Discuss the role of monitoring and evaluation in maintaining program relevance.
Ethical Considerations:

Consider any ethical considerations associated with program closure. How can organizations ensure a fair and respectful process for participants and staff affected by program changes?
Discuss the ethical responsibility of organizations toward participants and the community.

Discussion:

Cost and Efficiency:

Consider the cost per participant and the cost-effectiveness of each program. How do these financial metrics influence the decision-making process?
Discuss the trade-offs between program cost, participant ratings, and effectiveness.
Effectiveness Metrics:

Analyze the effectiveness metrics, such as the percentage of participants finding a new job after six months. How do these metrics contribute to the overall evaluation of program impact?
Discuss the importance of balancing participant satisfaction with tangible outcomes.
Participant Ratings:

Explore the significance of participant ratings. How do participant ratings reflect the perceived value and quality of each program?
Discuss whether participant ratings alone should be a decisive factor in program continuation.
Balancing Likability and Effectiveness:

Reflect on the dilemma of balancing program likability (as indicated by high ratings) with program effectiveness. How can organizations strike a balance between popularity and impact?
Discuss scenarios where a program might be well-liked but less effective, and vice versa.
Data-Driven Decision-Making:

Discuss the importance of data-driven decision-making in organizations. How can a systematic analysis of data lead to more informed and objective decisions?
Explore challenges and benefits associated with relying on data for decision-making.
Communication and Transparency:

Consider the communication strategy when implementing decisions based on data. How should Dora communicate the decision to shut down the mentorship program to stakeholders?
Discuss the importance of transparency in decision-making processes.
Sustainability and Adaptability:

Explore strategies for ensuring the sustainability and adaptability of programs. How can organizations continuously assess and adjust their programs based on data and evolving needs?
Discuss the role of monitoring and evaluation in maintaining program relevance.
Ethical Considerations:

Consider any ethical considerations associated with program closure. How can organizations ensure a fair and respectful process for participants and staff affected by program changes?
Discuss the ethical responsibility of organizations toward participants and the community.

Discussion:

Dora's decision-making process demonstrates the importance of leveraging data to inform organizational decisions. Let's discuss the key aspects of this scenario:

Cost Analysis:

The cost per participant for each program is a crucial factor. Mentorship is the most expensive, followed by writing skills and technology skills. Discuss the implications of these costs on the organization's budget and resource allocation.
Participant Ratings:

Participant satisfaction, as indicated by average ratings, is considered. The mentorship program has the highest rating, suggesting that participants value and appreciate it. Explore the significance of participant satisfaction in the decision-making process.
Effectiveness Metrics:

The percentage of participants finding a new job after six months provides insights into program effectiveness. Mentorship has the lowest effectiveness in terms of job placement. Discuss the trade-off between program popularity and effectiveness.
Balancing Popularity and Effectiveness:

Dora faces a dilemma where the popular mentorship program is less cost-efficient and effective. Engage in a discussion on the challenges of balancing program popularity with financial sustainability and impact.
Data-Driven Decision-Making:

Analyze the importance of data-driven decision-making in this scenario. Dora's decision to recommend shutting down the mentorship program is based on the data's objective evaluation rather than personal preferences.
Interpretation and Conclusions:

Discuss the process of drawing conclusions from the data. Dora interpreted the findings to conclude that the mentorship program is less effective. Explore the challenges of interpreting data accurately and avoiding misinterpretations.
Recommendation and Implementation:

Evaluate the difficulty of making recommendations based on data-driven conclusions. Dora's recommendation to shut down the mentorship program involves a strategic decision for the organization's sustainability. Discuss the potential challenges and benefits of implementing such recommendations.
Scheduling Actions:

Consider the importance of a well-planned schedule and project closeout plan. Dora's approach ensures a thoughtful and organized process for phasing out the mentorship program. Discuss the elements that should be included in the closure plan.
Continuous Improvement:

Explore the concept of continuous improvement in program management. Discuss how organizations can learn from data, make informed decisions, and adapt their strategies to enhance overall effectiveness.

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.