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decisions made through data findings are most likely to be Good and sound. they help us to avoid the effect of our own biases. it however requires an open mind as it is not easy to do. there are some biases that subconsciously affect our decisions without even realizing it. data based decisions have concrete evidence that is unbiased. it does not leave a room for feelings and interests, rather it gives the actual reflection of the program. it helps in analysis and evaluation, as findings can be clearly displayed, conclusions easily drawn and recommendations easily made and actions planed,

This is interesting staring with findings; mostly we are blind to the effectiveness of a program by attendance.

Us ing this too its very vital in decision making to avoid errors based on bias instead on facts

This is a very nice step to observe before making a decision.. do to the findings and conclude and take an action.
Because if once missed your deciosn will affect your NGO in very negative impact.

collect data
Analyze the data
the decide based on the analysis

One should be objective when going through the four stages of making decisions with data.

Merci beaucoup

decision making using viable data is the best strategy every organization must adopt

the decisions driven by data is important because it helps us to make the right decisions because it comes out of the actual actions that we have done in the field.

These Four steps makes a lot of sense in decision making, it helps an organization to make wise and effective decision which is free of any form of bias, just like that of Dora.
This shows that datas are very paramount to the M&E team and the organization at large.

In after 6 month, Mentorship programmed 8% of participate receive new job, So this program is most cost effective and bias.

As mentioned in the previous class, one has to be careful to avoid bias when making a decision. The only way through this is effectively implementing the following steps;

1. Making proper and accurate findings
2. Draw evidence-based conclusion or decision
3. Make Recommendations on how to effectively implement the decision
4. Create a system and timeline that allows for effective delivery of the expected outcome of the decision.

In decision making process both qualitative and quatitative data should be analyzed and interpreted. Considering the fact the quality of data is directly affects the decision making outcome. Poor data brings poor decisions.

when you finalize to collect your data and after you analyzed you have to keep your funding's because it used in next session.

when you finalize to collect your data and after you analyzed you have to keep your funding's because it used in next session.

Know your vision. Before you can make informed decisions, you need to understand your company's vision for the future and collect the data and have to know and methods used in that sources Once you've identified the goal you're working towards, you can start collecting data, Organize your data, Perform data analysis, Draw conclusions.

Try to verified the data

You need to verified the data

decision making is always tricky considering we humans have emotional attachments, but in every project we do we're ought to listen to what our data findings are telling us and then make evidence based decision

Dora’s example clearly shows how data-driven decision-making prevented the effects of her confirmation bias. The four steps to decision-making are so simple to follow through.

I think shutting down the organization will lead to jobless but what I was thinking Dora should try the sampling random methods to eliminate some people from the group

it is important to make decision based on data analysis and findings as to not let our biases get in the way

Without data analyses, bias will be the most used tool in decision making

like in the case of dora she had a bias towards mentorship but after working with data she found out that mentorship was actually lagging

data driven decision making is the key.

1. Draw conclusions
2. Make reccomendations

DORA SHOULD MAKE DECISION BASE ON THE AVAILABLE DATA

In short, to make a good decision is necessary to make cost and benefits analyzes according to the project data.

You should always use data inorder to make a good decision.
Following your intuition or instinct might be dangerous.

I think that Dora decision-making method was correct and based on a scientific method, as the criterion is the market need and cost reduction due to the lack of support provided.

Process for finding data will matter's on her goals, organization,tool she used
Step 1 she has to find out the problem
Step 2 come up with conclusion of step 1
Step 3 she has to come up with final recommendations on her findings and conclusion s

new good

First collect the data, analyse then make recommendations

No Matter the amount of data collected if not been put to use is still a useless data, unless it is been organized and put in action.

This example shows that is very important to take a decision based on gathering and analyzing data and information.

It is easier to make decisions with the influence of confirmation bias, the bandwagon effect, or the in-group bias but making decisions from findings from the information collected is most effective.

when making decisions, it is always important that we have adequate information. ensure that you have additional information to back up your organisation data available.
at times you can find that when you take a deep dive into additional information or conduct a case study to try and validate your org data, some of the things you might be thinking are best can have some other detrimental effects.

Good Finding gives you insight on what conclusions and recommendations you should make.

These are 4 great steps in making data-driven decisions.

To make decisions with Data it is necessary to base on the following steps:

1. Start with findings (collecting data).
2. Draw conclusions (Data-driven decision-making).
3. Make recommendations (Basing on the Conclusion).
4. Schedule actions (Acting).

Data-driven decision making requires an open mind and analytical skills and should be based on data and results evaluation.

This implies that making decisions with data is in relation with the organization's goal , therefore a certain creteria should be taken which consists of starting with information finds, drawing conclusions, makinging recommendations, and finally scheduling actions.
With starting with findings simply means that a project manager you should pull from your data.
with drawing conclusions simply means that you should interpret these findings.
Therefore, you come up with recommendations and finally scheduling actions.

as said one should start with findings, do surveys, do interviews, etc. be knowledgeable of what you are going to decide about. then draw conclusions from those findings, is A better than B? if it is then how?
from there make recommendations then schedule actions from your conclusions. these action becomes a decision

Define the problem or decision to be made. Clearly articulate what you are trying to solve or accomplish. This step helps to ensure that everyone involved is on the same page.

Gather information. Collect relevant data and information to help inform the decision. This may involve conducting research, analyzing data, or seeking input from others.

Analyze the information. Use critical thinking and analysis skills to interpret the data and draw conclusions. Look for patterns, identify key factors, and consider different perspectives.

Develop options. Brainstorm potential solutions or courses of action based on the information and conclusions. Evaluate the pros and cons of each option and consider how they align with the goals and values of the organization.

Make a decision. Choose the best option based on the analysis and evaluation. Consider any potential risks or consequences and make a plan for implementation.

Take action. Put the decision into action by implementing the plan. Communicate the decision and the reasoning behind it to relevant stakeholders.

Evaluate the results. Monitor the outcomes of the decision and evaluate its effectiveness. Make any necessary adjustments or modifications to the plan based on feedback and results.

When using data to inform your decision, it's essential to start with the approach because it's simple to become overwhelmed by the amount of information available. What do you hope to achieve on behalf of the organization? What commercial areas desire to improve?

Mrs Dora has make the right recommendation why b/se the mentorship program is more expensive and less efficiency than the others program. in these scenario, we better make a good choice that will feasible for the project to achieved their goals by selecting the best indicators for the project to exist.

decision first you have data collection and than you will come like 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.

decision first you have data collection and than you will come like 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.

The data can helps in decision making with difference steps.
-Make finding and represent it clearly
-Draw the conclusion
-Make recommendation
-Make time for action

The week before she makes her decision, Dora gathers her M&E team. What do the data say about her organization's programs? Which programs are the most impactful and cost-efficient? Which programs are the least impactful and cost-efficient?

Her team pulls some data out of the database. They are careful to include only data about individuals who participated in only one of the programs: they don’t want to confuse their analysis by showing people who participated in more than one program.

They present a few findings:

Your process for making decisions will depend on your goals, organization and tools. However, broadly speaking, it should follow the steps that Dora just followed:

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.

Data-driven decision making is the process of collecting data based on your company’s key performance indicators (KPIs) and transforming that data into actionable insights. This process is a crucial element of modern business strategy. In this regard Dora had made a good decision based on the data she founded.

Pour prendre des decisions sur la base des données collectées il faut:
Faire des constations c'est a dire sortir les données collectées sur le projet
conclure c'est a dire analyser les informations et en tirer les résultats
Prendre les décisions sur la bases des résultats
Planifier les actions prises.
Ces etapes sont primordiales pour prendre les meilleures décisions pour l'organisation sans peur de biais

This is a nice learning for me

decision making also needs to follow the scientific steps, as in the case of Dora's project. this procedure helps eradicate all possible cognitive bias choices. the following steps are professionally recommended:

1. begins with findings from data gathered and analyzed
2. draw conclusion and interpret findings according to the result.
3. make recommendation based on the conclusion and
4. schedule actions to be taken.

According to calculations you decide to shutdown a project or a program,so you cannot shut it down immediately you need time to close the topics you start .

It's important to note that making decisions with data is an iterative process that requires continuous learning, adaptation, and improvement. Therefore, it's essential to be open-minded, willing to adjust your approach, and continuously learn and incorporate feedback.

Taking the necessary steps in making decisions for project is very vital especially when you're Inna difficult situation to make certain decisions. However, if accurate data is available, and that data has been analysed, it is easy to drive at a right decision just like Dora and her team have done.
It will be less risky to make decisions based on data than using cognitive ability without any data.

She made a good decision based on the data

I totally support Dora's decision to shut down the mentorship program. The decision was based on facts and that is a better way to take decisions.

Starting from Findings, Conclusions, Recommendation and Actions are very logical that build on each to help find gain a better insight from our data and help us make decision.

It is important to be aware of cognitive biases while making important and difficult decisions

It was good that Dora chose to involve the M&E team for decision making. This is one area that most companies or organizations over-looks when it comes to making decisions that may affect a lot people if wrongly or intuitionally done.

every data has to make a decision because it is important to find what type of information we take and how we pull a form then interpret the findings for the step that our organization don for this data and how we implement it

The steps taken by Dora for decision making is data driven evidences.

Interpretation of data is very important

Verify your data

check what the data of each program is telling then draw a conclusion, after that she developed a set of actions that can be taken then she decided on one of the options and scheduled the next steps.

Some important and well-organized steps.

We have seen that according to the findings and the conclusion. A data based decision, is that Dora should cut off the mentorship program. Because it is less effective and there is no need to continue sponsoring the less effective programs.

The best way to decide which program to shutdown is to use findings provided by data regarding the three programs because decision made based on our intuition, most of the time lead to a great failure of our organizations.

First of all, we collect information regarding to the problem, this information is based on facts. After analyzing data, conclusions are made. Later on, recommendations are provided on the basis of conclusions. Then, recommendations are translated into actions.

Dara's steps for making decisions was clear and real based on the impacts the program has on people as it doesn't make sense to continue after it only few find jobs. And that i made from research are more successful

The decision to close mentorship base on impact and cost effectiveness is the right decision

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.