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Making decisions based on data eliminate bias and personal inclination that might intrude on the decision making process

Making decision with data Is an important tools in decision making
Mostly when the following step are taking
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.

We can make good decisions with the data because this reflects what's on the ground.

Here, you need to follow the four steps in making decision with data that is start with findings, draw conclusion, make recommendation and scheduled action.

When you're about to make decision using data, there are few steps to follow.
Start with finding.
Use the finding to draw conclusion
Recommend from the conclusion
Then finally schedule an action.

by following steps that include finding facts that you pullout from your data.
make conclusions
recommendations
actions

Using M&E data is critical for unbiased decision making. Although difficult, it helps draw lessons for what works and why. It also helps in making choices among competing alternatives based on the efficiency, effectiveness, relevance, appropriateness, sustainability and economy. Lastly, arriving at conclusions and making recommendations for ways forward should be backed by concreate implementable actions to move ahead.

Alright, so these are the steps you must use to avoid biased decision.

Yes, your right

Start with findings.
Draw conclusions.
Make recommendations.

After looking at the results of the analyzes, it is difficult to close a program directly, so the idea of gradual closure gives opportunities to review the results again and make a decision after several months

it is important to let data do the talking in this way we will be effective

How can I recognize cognitive biased?

Having sufficient information during a decision making process is so crucial in creating an effective action to take.

It is not appropriate to make decisions basing on personal experience for example Dora was going to cut the more effective program and maintain the less effective.
the best decisions should be made based on data analysis.

Wow! So it's possible for a project to be massively liked but very ineffective! In the end, it's the effectiveness we should go for, not mere ratings and emotional likes!

my recommendation on Dora previous discussion where totally confirmed. use the M&E and make decision based on the findings from the data to discard our biased, now Dora is safe.

Wow! So it's possible for a project to be massively liked but very ineffective! In the end, it's the effectiveness we should go for, not mere ratings and emotional likes!

Data driven is very important for decision making process in any project implementation
Right data makes right decision in any project.

Data driven is very important for decision making process in any project implementation
Right data makes right decision in any project.

Shutting down the mentorship programme is not the best

to be sure that the decision made is free off Baias, bad wagon, in group bias, clear and fare we need to carry out findings first from our data. from the findings we should be able to make conclusions. after the conclusions make recommendations to adjust or effect a change be sure to take actions and steps to make sure the decision made is backed by actions. draw an action plan on how to go about it. this is a very critical stage as most people always fail to track the actions taken in responds to a decision made.

Evaluation helps to clarify things and must be done for good decision making.

These 4 steps- start with findings, draw conclusions, make recommendations and schedule actions are very important and should be taken sequentially. For example, putting the step 'drawing conclusions' before 'start with findings' are one way that biases are allowed to thrive. Hence, the importance of following these steps sequentially.
It is also important that the decision made is actually implemented with a clear plan.

Few steps for making decisions will depend on your goals, organization, and tools are start with findings, draw conclusion, make recommendation, and make schedule action.

when making decisions with data it is important to start with findings, draw conclusion, make recommendations and schedule actions

In making decisions with data, it involves facts pulled from the data, interpretation of the findings, making conclusions and implementing decisions.

Data-Driven Decision is very vital because it enabled Dora to make smarter decision after evaluation of what could be the priority among all her programs.

Never trust what we think, always take our decision based on data proof. only that can justify better our decision

they need to look at the findings before conclusion either its benefiting or not

One thing Dora did which I held high was backing the shutting down of mentorship program decision by strategic action

Action has to be strategic so that it may not leave a scar on the participants emotion.

Data is very important in making decisions to avoid biasness.
It is through data that we avoid biasness like the confirmation bias, the bandwagon effect and the in-group bias.
Data gives you the facts about something!

Evidence-based decision making should be at the heart of M&E system

It was good of Dora to put aside her bias and decided to use data to make the decision. Because data is neutral helps managers and decision make reliable and unbias program decision. It is important to use the facts; and base your conclusion the facts, make the decision based on your conclusion and put in place a plan to execute the plan.

Facts we pull from data help our organization to draw dedicated conclusions for making recommendations based on conclusions while scheduling actions. so i call upon projects take these steps important.

The project close out plan should be made based on the analysis made from the data.

Dora's program is to help better the lives of women, and research as shown that lerning technology and writing sklls helps them get jobs at a higher percentage in a stupilated time compare to mentorship. So the data showing the percentage of success from the other skills are enough reason to close out mentorship and focus on the skills that are more productive, more effective and less expensive.

choose a data collection tool that best fits your organization needs.

Data driven decisions ensures that one avoid making cognitive biases such as confirmation biases, the group in bias and this ensures that conclusions made are supported by evidence hence one can make recommendations that's are sound, though not comfortable but for the greater good of all.

Dora's steps was well taught out and correct however I don't think that she got the correct or all the information from her team e.g. "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" so before she makes an holistic decision she needs to go back to her team.

Starting to make decisions will be determined by our aims, structure, and tools. However, in general, it should follow the below steps  just took:
Begin with our findings. Findings are facts extracted from data.
Draw our own conclusions. Interpret the results. You might use your analyzing skills for this phase.
Make suggestions. What should our organization do based on our findings, and so on?
Plan your activities. Once you've made a recommendation, put it into action by suggesting actions.
This is what I've learnt from this topic.

1. Know your mission. A well-rounded data analyst knows the business well and possess sharp organizational acumen. Ask yourself what the problems are in your given industry and competitive market.
2. Identify data sources. Put together the sources from which you’ll be extracting your data. You might be coordinating information from different databases, web-driven feedback forms, and even social media.
3. Clean and organize data. Surprisingly, 80 percent of a data analyst’s time is devoted to cleaning and organizing data, and only 20 percent is spent actually performing analysis.
4. Perform statistical analysis. Once you’ve thoroughly cleaned the data, you can begin to analyze the information using statistical models.
5. Draw conclusions. The last step in data-driven decision making is coming to a conclusion. Ask yourself, “What new information did you learn from the collection of statistics?”

Dora should use the most listed and cost effective mode from the gathering of her data

Like in the case of my CBO, it is always fine to start with findings the facts or gather data about the program the organization is implementing. The data gathered will assist in drawing the conclusions after analyzing it. Then make recommendations that organization should adopt for the better changes in future and plan when to put the recommendations into practices.

After collecting the data you should now be clear on what informed decisions should be done and recommended by the data collected and make step by step planning to address this issues

As a monitoring and Evaluation team we should always be data driven . Our decisions should be based from. data analysis.

This scenario shows us that any decision must be guided by a good analysis of data and not guided by emotions, experiences or biases

Using data to make decisions is very crucial in a project. There is need for one to indeed be open minded and flexible to adjust or adapt to the findings being made in the project. Personal experiences are subject to biases; and cannot be generalised to other beneficiaries in the project. If the monitoring system in a project is vibrant and strong, it can provide reliable data to inform decision makers.

Now that Dora has find facts from the data pulled, that mentorship is less effective than other programme.
The data showed Technology skills is more effective.
however, before she make a decision she need to follow the 4 process of making decisions

1. findings = facts that she pulled from the data
2. Conclusions = interpret the findings
3. Recommendations = Based on her conclusion, what should her organization do?
4. Actions = Implement the decision by proposing actions.

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.