My Communities

Data collection is the process of gathering and measuring information on variables of interest, in an established systematic fashion that enables one to answer stated research questions, test hypotheses, and evaluate outcomes.

systematic assessment of the relevance, adequacy, progress, efficiency, effectiveness,

RDPC:\Users\felix\RDP6|

For data holders, the main benefit of sharing their data is to gain efficiency savings, develop new or improve existing products, create new or better ..

what tools of data collection are we using
who will collect the data
who will use the data and how will it be used
who will analyse the data or how will the data be analysed

No data management questions. Again, I do not see the question prompt.

Data management is crucial for data analysis. Especially if a program collects lots of data, the data need to be tracked and managed so that when it comes time to conduct analyses, we can track what data was taken and how that data was obtained. The metadata is important for retracing steps so that we can understand where and how the data were collected for accuracy during the analysis phase. It's also important to maintain a strong data management system so that when we get to the report writing phase, we can paint a more complete picture about how the data were obtained and what the strengths and weaknesses or gaps in the data are.

Its good to set roles and responsibilities and the frequency of data collection in the data flow map. This is most commonly dismissed and data management is left to be intuitive when it should be a foundational skill that needs continuous development to improve

Difficulty posting again.

Data collection tools are essential instruments used to gather information and data for monitoring, evaluation, research, and decision-making purposes. Designing effective data collection tools is a critical step in ensuring the accuracy, reliability, and relevance of the data collected.

I think of the data management as machine parts. Once they are well thought out in the planning stages and everything fits properly in terms of roles and responsibilities involved in the data management process, the "machine" will run smoothly

I would like to know if Monitoring and evaluation team can work in isolation without data management. I have seen an organisation where the M&E team are collecting data for the organisation and there is a data management team collecting other data which is not contributing to the organisations performance.

Thank you for this learning

I have learnt a lot

This is 100% practical

This is powerful

I am now a trained M and E Officer

Thank you so much

Data needs to be collected and managed at different stages of the process. Data is collected my the field officers and others on the field. It can be collated and analysed by the M&E team as well as other professionals. Data will then be organised into a report or findings that can be disseminated to the donors as well as within the organisation internally and with the public or civil society at large.

This module is helpful particularly during data evaluation. The part itself contains the plan how to organize survey from the starting to the end of the process. The skills and Knowledge will help to plan and conduct a survey successfully. Responsibilities and roles are the key to every achievement.

I learned something new: the data flow map. it's an interesting exercise

When considering data management, the biggest risk is to not collect data that is eventually needed at later stages (e.g. for impact evaluations). Often retrospective data collection is not possible (for instance about perceptions at the inception of a programme), which hinders effective M&E processes.

When considering data management, the biggest risk is to not collect data that is eventually needed at later stages (e.g. for impact evaluations). Often retrospective data collection is not possible (for instance about perceptions at the inception of a programme), which hinders effective M&E processes.

How will you store and access the data over the short- and long-term?

I'm very grateful

I'm very grateful with this program

Effective data management is crucial for organizations as

• it ensures accuracy, accessibility, and security of their information assets
• leads to informed decision-making and improved operational efficiency

Challenge in M&E Data Management: A common challenge faced in Monitoring and Evaluation (M&E) data management is the lack of standardization across data sources and collection methodologies. This often results in fragmented and inconsistent data, making it difficult to compare, aggregate, and analyze information accurately. Addressing this challenge requires establishing clear data collection protocols, providing training to data collectors, and implementing robust data validation procedures to ensure the reliability and coherence of M&E data.

To minimize individual biases in data collection, particularly when dealing with qualitative data and interview guides, several mechanisms can be adopted. First, designing structured interview guides with well-defined questions helps ensure consistency and reduces the potential for interviewers to introduce personal biases. Second, providing thorough training to interviewers on the purpose of the study, interview techniques, and the importance of neutrality can enhance their ability to remain impartial. Third, employing multiple interviewers and having regular debriefing sessions can help identify and address any emerging biases. Lastly, using techniques like member checking, where participants review and validate the collected data, can further enhance the credibility and objectivity of the qualitative data.

Data management include a range of important considerations in today's information-driven landscape. One fundamental aspect is data governance, which involves establishing policies, roles, and responsibilities for data quality, security, and compliance. Effective data governance ensures that data is accurate, reliable, and used appropriately.
Data security and privacy are equally crucial. Protecting sensitive information from unauthorized access and ensuring compliance with data protection regulations are top priorities. Encryption, access controls, and regular audits contribute to a secure data environment.
Efficient data storage is also paramount. Organizations must choose suitable storage solutions that accommodate their data volume and access requirements. This might involve a mix of on-premises and cloud storage options.
Data quality is foundational for insightful decision-making. Maintaining clean, consistent, and relevant data enhances the reliability of analysis and reporting. Data cleansing, validation, and integration processes play a vital role here.
Data lifecycle management means managing data from creation to deletion. It involves deciding when data should be archived, retained, or deleted, reducing clutter and compliance risks.
Scalability is key, given the ever-expanding volume of data. Implementing scalable solutions ensures seamless growth and prevents data infrastructure bottlenecks.

There are several common mistakes that can occur during data management that can negatively impact an organization's efficiency, accuracy, and overall success. Some of these mistakes include:

1. Lack of Data Governance: Failure to establish clear data governance policies, roles, and responsibilities can lead to inconsistent data quality, security breaches, and compliance issues.
2. Ignoring Data Security: Neglecting data security measures can result in data breaches, unauthorized access, and compromised sensitive information.
3. Poor Data Quality: Neglecting data cleansing, validation, and integration can lead to inaccurate, incomplete, and unreliable data, undermining decision-making.
4. Inadequate Backup and Recovery: Not implementing proper backup and recovery strategies can result in data loss during system failures or disasters.
5. Overlooking Data Privacy: Ignoring data privacy regulations can lead to legal liabilities and damage to the organization's reputation.

Data flow maps help the whole team see how their role is important to the overall process.

Data management questions could be questions that are about the data management process. How data will be collected? Who will ensure proper quality data is being entered by the enumerators?
These questions when answered correctly, they then can help us develop the task that each team member will do and eventually a data flow map.

Through the process of Data Management, in the video a critical point was made about data, that it represents people and Data needs to be treated with respect. How do you get your staff on the ground to really follow through on this, as this is critical.

Data management is both simple and complex process that requires care, attention to details and meaningful use. Based on this facts how do we translate the qualitative data or information into useful analysis for feedback?

Data management is both simple and complex process that requires care, attention to details and meaningful use. Based on this facts how do we translate the qualitative data or information into useful analysis for feedback?

1. Data Collection and Acquisition:
   • Data management starts with the collection and acquisition of data from various sources, such as sensors, databases, APIs, user interactions, and more.
   • It's important to define what data needs to be collected, in what format, and at what frequency.
2. Data Storage:
   • Data needs to be stored in a reliable and scalable manner. This might involve databases, data lakes, data warehouses, or cloud storage solutions.
   • Choosing the right storage solution depends on factors like data volume, velocity, variety, and accessibility requirements.
3. Data Quality:
   • Ensuring data quality is crucial for accurate analysis and decision-making. Data quality involves aspects like accuracy, completeness, consistency, and reliability.
   • Data cleansing and validation processes are often employed to improve data quality.
4. Data Governance:
   • Data governance involves establishing policies, procedures, and responsibilities for managing and using data.
   • It includes defining data ownership, access controls, data classification, and compliance with regulations like GDPR or HIPAA.
5. Data Integration:
   • Data often comes from various sources in different formats. Data integration involves combining and transforming this diverse data into a unified format.
   • ETL (Extract, Transform, Load) processes are commonly used for data integration.
6. Data Security and Privacy:
   • Protecting sensitive data from unauthorized access and ensuring privacy is a critical aspect of data management.
   • Encryption, access controls, and regular security audits are common practices.
7. Data Analysis and Visualization:
   • Once data is properly managed, it can be analyzed to extract insights and make informed decisions.
   • Tools like data analytics platforms and visualization tools help in understanding trends and patterns within the data.
8. Master Data Management (MDM):
   • MDM focuses on creating a single, authoritative source of truth for key business data like customer, product, or location information.
   • It helps eliminate data inconsistencies across different systems.
9. Data Lifecycle Management:
   • Data goes through a lifecycle from creation to archiving or deletion. Effective management throughout this lifecycle optimizes storage and access.
   • Some data might have legal or regulatory retention requirements.
10. Data Ethics:
    • As data management involves handling personal and sensitive information, ethical considerations are important.
    • Organizations must ensure that data is collected, stored, and used in a way that respects individuals' rights and privacy.

Hi, i would like to ask about data management in the step 3 which comprises of data analysis, verification, and storage. How does data quality ensure for M&E ? is there any standard for this?.
Thanks.

Data management involves the processes, strategies, and technologies used to collect, store, organize, secure, and utilize data effectively and efficiently. It's a crucial aspect of modern business operations and research endeavors. Here are some key aspects of data management:

1. Data Collection and Acquisition: This is the process of gathering data from various sources, which can include manual data entry, automated data collection systems, sensors, surveys, and more.

2. Data Storage: Data needs to be stored in a structured manner for easy retrieval and analysis. This could involve using databases, data warehouses, cloud storage, and file systems.

3. Data Organization and Classification: Data should be organized in a way that makes sense for the specific use case. This involves creating a data taxonomy, categorizing data, and assigning relevant metadata.

4. Data Quality: Ensuring data accuracy and reliability is crucial. This involves data cleaning, validation, and addressing inconsistencies to prevent errors and misinformation.

5. Data Security and Privacy: Protecting sensitive data from unauthorized access, breaches, and ensuring compliance with privacy regulations (like GDPR or HIPAA) is a top priority. Encryption, access controls, and regular security audits are common practices.

6. Data Governance: This refers to the framework and policies that oversee data management within an organization. It includes defining roles, responsibilities, and procedures for data handling.

7. Data Integration: Combining data from various sources to provide a unified view is important for informed decision-making. Integration can involve ETL (Extract, Transform, Load) processes.

8. Data Analysis: Once data is collected and organized, it's analyzed to extract insights, patterns, and trends. This can involve statistical analysis, machine learning, and data visualization.

9. Data Archiving and Retention: Not all data is actively used, but it might still need to be retained for compliance or historical purposes. Proper archiving strategies ensure data remains accessible when needed.

10. Data Lifecycle Management: This encompasses all stages of data, from creation to deletion. It includes considerations for data usage, storage costs, and data disposal.

11. Master Data Management (MDM): MDM involves maintaining a single, consistent source of truth for important data entities across an organization, such as customers, products, or locations.

12. Data Ethics: With the increasing use of data, ethical considerations are vital. Organizations must handle data responsibly, ensuring that biases are minimized and individual rights are respected.

Effective data management requires a combination of technology, processes, and people. It's an ongoing effort to ensure that data remains accurate, accessible, secure, and aligned with business goals.

Hello Everyone,

This topic is relevant start point of using data for decision making, that is why is very important to make sure that people that will collect data are trained in data collection methodology, if this step is perfectly done the data management will be very easy, you will note spend a long time cleaning data and analyses will be very eligible for decision making.

Data management is an important aspect of a data flow chart. It is important as it brings the the aspect of data collection in a project using agreed data collection methods and tools.
It also brings data entry and collation- this takes care of the data organization and collation will help in making data analysis simpler. Data analysis will make use of data to answer questions and reach conclusions. It also help in removing the individuals bias as they interpret the data. Data use will result in creating reports, communicating outcomes to the community and helping in designing future projects.

Data management is crucial and essential for any project or an organization. The data management include:
Data Collection
Data Entry and collation
Data analysis, verification and storage
Data use
There are some important questions regarding the data management are keeping in the mind
who will be responsible for data collection? who will responsible for ensuring data quality ?
who will enter data? who will collate data? who will analyses data ? who will generate reports ? who will send reports? who will archive useful data once the project is over?

data management questions are very omportant.

très bon module et bien détaillé

Within M&E, data management refers to the systematic storage, management and sharing of raw data – the facts
and opinions generated and recorded through an M&E system. Clearly, a data management system needs to be
designed according to the needs, size and complexity of a project or programme. However, many features are
common. Data is routinely used by different stakeholders, in different locations, for different purposes. This
means it needs to be properly stored, processed and shared, either physically or electronically. The goal of
a knowledge management system is to generate and share usable knowledge based on this data. Data and
knowledge management systems are often supported through information technology (IT) solutions.

data management, I would say is the heart of the project. It will help to distribute the information to the whole organization.

Data Management and Data Flow Map are like a compass guide, it guides anyone who come across the monitoring plan. It describes what the data are intended for, and where they are being processed to. It is easy to understand and follow through.

The steps for data management process are: data collection; entry and collation, data quality checks, data analysis, data storage and use

To monitor every stage of projects

Data management is crucial for data analysis. Especially if a program collects lots of data, the data need to be tracked and managed so that when it comes time to conduct analyses, we can track what data was taken and how that data was obtained. The metadata is important for retracing steps so that we can understand where and how the data were collected for accuracy during the analysis phase. It's also important to maintain a strong data management system so that when we get to the report writing phase, we can paint a more complete picture about how the data were obtained and what the strengths and weaknesses or gaps in the data are.

The topic was very interesting and helping for data collection and their uses.
From the skills aquired will help us to to help my organization. I will optimize the knowledge of this short course because it is very important my organization M&E

Data management is very necessary to see how data provide information to the tested situation

good to know

Data management refers to the process of collecting, storing, organizing, and maintaining data in a structured and secure manner to ensure its accuracy, accessibility, and usefulness.

Maintaining accurate and high-quality data is crucial. Inaccurate or incomplete data can lead to erroneous decision-making and poor analytics results.

it is very important to come up with effective tools of data collection in order to correct accurate data that can be measured with realistically set indicators and targets

Firstly its important to know who will collect data?
Who will input the data?
Who will do the collating?
Who will verify and analyze the data?
Who will send the reports?
Who will archive the data?

Firstly its important to know who will collect data?
Who will input the data?
Who will do the collating?
Who will verify and analyze the data?
Who will send the reports?
Who will archive the data?

Data management in a project is the cornerstone of effective decision-making and impact assessment. As we engage in this discussion today, let's recognize that data isn't just numbers and spreadsheets; it's the stories, experiences, and outcomes of the individuals we serve. It's our responsibility to ensure that this data is collected ethically, securely, and with a clear purpose. How can we improve data collection processes? What tools and systems should we implement to streamline data entry and analysis? And most importantly, how can we translate data into actionable insights that drive positive change in our project and ultimately, in the lives of those we aim to support?

While I have gotten a good grasp of data management I wish more was discussed about data analysis.

Collecting data is very important as we know ,but is it okey for me to share my detail data to the Funding aid upon requested? And what are the questions that should have consider if provide them those dat?

Data management questions are very important and necessary to see how data provided and tracked information to test and analyze current situation. questions are necessary to probe the solution from the audience.

The first part of any M&E Plan is the Introduction Document. This document explains the goal of your M&E plan and provides some background information on your organization and your project.

Your first assignment is to complete an Introduction Document for your M&E plan. Before you start, gather any project design or project planning documents that you already have. You may find that you have already completed pieces of the Introduction Document in these documents. You may also want to work with other members of your organization to complete this assignment.

The prompts below will ask you to provide the information typically included in an Introduction Document. Complete each prompt and press the "Submit" button at the bottom of this page.Active+Listening+Peer+Assessment.pdf Active+Listening+Peer+Assessment.pdf

Data management is an important task in M&E. Roles and responsibilities need to be defined for the smooth functioning of data management.

I think that Effective M&E and data management rely on clear roles and responsibilities. Various roles, including data collectors, data entry personnel, quality control teams, analysts, and decision-makers, play vital parts in this process. They collectively ensure that data is collected accurately, processed efficiently, and used to inform decisions. Clarity in these roles is essential for effective data utilization and project success.

When you are managing online data especially on ERP system, what would be the best was to avoid double admission especially with trainees from the first year.

Congratulations for the achievement

i learned and applied and still go back to read the old notes to perfect my work

what software do you use for the analysis?

sometimes its unavoidable especially when the collection tools are not automated

what software do you use to store and achieve your data?

Data Management is a vital part of M&E activity.

Data Management is a vital part of M&E activity. With the data collected by field officers, MEL and Project management team work together to analyze and process the data and finalize reports to submit to the donors.

To store data a cloud drive folder with differently assigned subfolders is my go to option for long and short term.

This section impressed on me how important it is to assign responsibilities, if there isn't a clear data flow, the odds are that at the end of the project there won't be the data products that are so vital to donors and the organization's learning.

La gestion des données est un ensemble d'opération qui consiste à collecter , à stocker , à organiser, à analyser et à utiliser les données

Here are some data management questions that MEH could consider as they develop their data flow map:

What data security measures will be in place to protect the confidentiality of project data?
How will data be backed up to prevent loss?
How will data be shared with external stakeholders, such as donors and government agencies?
How will data be used to improve project implementation and achieve project goals?
How will data be used to learn from the project and inform future programming?
MEH can use these questions to guide their thinking about how to design a data management system that is secure, reliable, and informative.

Here are some specific questions that MEH could consider for each of the roles in their data flow map:

Data entry clerk:

How will data entry be verified to ensure accuracy?
How will data be entered into the digital database in a way that is consistent and easy to use?
How will data entry be monitored to identify and address any problems?
Data analyst:

What tools and resources will be needed to analyze the data?
How will the data be cleaned and prepared for analysis?
How will the data be analyzed to generate meaningful insights?
How will the findings of the data analysis be communicated to stakeholders?
Project manager:

How will the data be used to inform project decision-making?
How will the data be used to track project progress and identify areas for improvement?
How will the data be used to report on project outcomes and impact?
Director:

How will the data be used to make strategic decisions about the organization's programming?
How will the data be used to advocate for the organization's work?
How will the data be used to ensure that the organization is accountable to its stakeholders?
By considering these questions, MEH can develop a data management plan that is tailored to their specific needs and goals.

Data management needs to involve all participants. Assign the roles to the team according to capacity.

In the digital era, proficient data management is the cornerstone of innovation and informed decision-making. As data sources diversify, ensuring integrity, consistency, and security becomes paramount. The interconnectedness of modern systems demands seamless data integration and agility. Yet, with great data power comes the ethical responsibility of privacy and transparent usage. Ultimately, effective data management bridges the gap between raw data and meaningful insights, driving progress across sectors.

Data management is critical to getting the desired results of the project. Therefore, when planning for data collection and management, it is important to answer the questions: what type of data are we collecting?, how are we collecting the data, what tools are we going to use, how will data be stored, how will data be verified, how will data be used, and who is responsible for what role at each stage?
When all these questions are answered, it makes it easier for data analysis and decision making.

During the course process, i discovered that data management is key in project life circle and success. however, there should be a provision for full module on data analysis to help us understand and put into perspective what we have learnt here. Thanks

This is very clear and well pointed out

To promote a culture of data respect among our field team, it's imperative to adopt a comprehensive approach. Initiate thorough training programs to convey the importance of ethical data handling. Lead by example, setting clear standards that emphasize the non-negotiable nature of data ethics. To ensure compliance with ethical data practices, implement regular audits and accountability measures. Encourage team involvement in shaping data policies to instill a sense of ownership. Foster open communication, offer incentives, and acknowledge ethical behavior to reinforce this desired mindset. Ultimately, it is through our collective commitment to data ethics that we can integrate the practice of treating data with respect into the very fabric of our organization.

I have learned many things in this module, and i hope i am ready to go on.

Data management questions are questions that help to understand how data is managed within an organization. These questions typically ask about the type of data collected, the methods used to store and access the data, the security measures in place, and the processes used to analyze the data. Data management questions can also address how the data is used to inform decision making. By understanding how data is managed, organizations can ensure that their data is used responsibly and effectively.

It is important that everyone including the donors, the Government, the Project Managers, the data entry operators and the Project officers know the importance of the role of Data Management. This process is concerned with data that has great value and leading to impacting the project and making decisions that will benefit the project. Hence, on that understanding, questions are developed respectively and accordingly to collect specific data that can translate to reports, data that is communicated as outcome to the community, data that is considered to make good project management decisions and data that can also help to design future projects and otherwise enhances or improves the current project.

Managing data is essential for the m&e project manager as this ensures adequate information control and flow.

Data management is a critical aspect of any organization's operations, as it involves the collection, storage, organization, and utilization of data. Here are some common questions and topics related to data management:

-Data Collection:

How do you collect and ingest data into your organization's systems?
What types of data sources do you use?
Are there regulatory or ethical considerations in your data collection processes?
Data Storage:

Where and how is your data stored? On-premises, in the cloud, or in a hybrid environment?
What storage technologies or databases do you use?
How do you ensure data security and data redundancy in your storage solution?
-Data Quality:

How do you ensure the quality and accuracy of your data?
What data cleaning and validation processes do you have in place?
Do you have data quality standards and metrics?
Data Governance:

Who is responsible for data within your organization?
What policies and procedures do you have for data management?
How do you ensure compliance with data regulations such as GDPR or HIPAA?
-Data Integration:

How do you integrate data from different sources and systems?
What tools or platforms do you use for data integration?
How do you handle data transformation and normalization?
-Data Security:

What security measures are in place to protect your data from breaches and unauthorized access?
How often do you conduct security audits and assessments?
Do you have a disaster recovery plan for data?
-Data Privacy:

How do you handle customer and user data privacy?
What procedures are in place to obtain consent and protect personal data?
Are there protocols for data anonymization and pseudonymization?
-Data Retention:

How long do you retain different types of data?
What are your policies for data archiving and deletion?
Are there legal or regulatory requirements that affect data retention?
-Data Analytics and Reporting:

How do you utilize data for analytics and reporting?
What tools or platforms do you use for data analysis?
How do you ensure that data is available for reporting in a timely manner?
Data Accessibility and Sharing:

Who has access to the data, and how is it controlled?
How do you enable data sharing within and outside the organization?
Do you have a data access request process in place?

Hello everyone

What if you don't have a complex structure

Wow just like a conveyer belt in a factory, data management processes must flow step by step to ensure ensure that the end result is meaningful. Knowing what to do and who to take which role is also vital to ensure that data flows from person 1 to the next person. No sleeping on the job and no jumping the queue

AUCUNE QUESTION, CAR LE MODULE EST BIEN COMPRIS

Data
Management

Home Data management strategies
Tech Accelerator
DEFINITION
What is data management and why is it important?
Craig Stedman, Industry Editor
Jack Vaughan
Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. Effective data management is a crucial piece of deploying the IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning by corporate executives, business managers and other end users.

The data management process includes a combination of different functions that collectively aim to make sure the data in corporate systems is accurate, available and accessible. Most of the required work is done by IT and data management teams, but business users typically also participate in some parts of the process to ensure that the data meets their needs and to get them on board with policies governing its use.

This comprehensive guide to data management further explains what it is and provides insight on the individual disciplines it includes, best practices for managing data, challenges that organizations face and the business benefits of a successful data management strategy. You'll also find an overview of data management tools and techniques. Click through the hyperlinks on the page to read more articles about data management trends and get expert advice on managing corporate data.

Importance of data management
Data increasingly is seen as a corporate asset that can be used to make better-informed business decisions, improve marketing campaigns, optimize business operations and reduce costs, all with the goal of increasing revenue and profits. But a lack of proper data management can saddle organizations with incompatible data silos, inconsistent data sets and data quality problems that limit their ability to run business intelligence (BI) and analytics applications -- or, worse, lead to faulty findings.

Data management has also grown in importance as businesses are subjected to an increasing number of regulatory compliance requirements, including data privacy and protection laws such as GDPR and the California Consumer Privacy Act (CCPA). In addition, companies are capturing ever-larger volumes of data and a wider variety of data types -- both hallmarks of the big data systems many have deployed. Without good data management, such environments can become unwieldy and hard to navigate.

Types of data management functions
The separate disciplines that are part of the overall data management process cover a series of steps, from data processing and storage to governance of how data is formatted and used in operational and analytical systems. Developing a data architecture is often the first step, particularly in large organizations with lots of data to manage. A data architecture provides a blueprint for managing data and deploying databases and other data platforms, including specific technologies to fit individual applications.

Databases are the most common platform used to hold corporate data. They contain a collection of data that's organized so it can be accessed, updated and managed. They're used in both transaction processing systems that create operational data, such as customer records and sales orders, and data warehouses, which store consolidated data sets from business systems for BI and analytics.

That makes database administration a core data management function. Once databases have been set up, performance monitoring and tuning must be done to maintain acceptable response times on database queries that users run to get information from the data stored in them. Other administrative tasks include database design, configuration, installation and updates; data security; database backup and recovery; and application of software upgrades and security patches.

Core data management functionsData management involves a variety of interrelated functions.
The primary technology used to deploy and administer databases is a database management system (DBMS), which is software that acts as an interface between the databases it controls and the database administrators (DBAs), end users and applications that access them. Alternative data platforms to databases include file systems and cloud object storage services, which store data in less structured ways than mainstream databases do, offering more flexibility on the types of data that can be stored and how the data is formatted. As a result, though, they aren't a good fit for transactional applications.

Other fundamental data management disciplines include the following:

data modeling, which diagrams the relationships between data elements and how data flows through systems;
data integration, which combines data from different data sources for operational and analytical uses;
data governance, which sets policies and procedures to ensure data is consistent throughout an organization;
data quality management, which aims to fix data errors and inconsistencies; and
master data management (MDM), which creates a common set of reference data on things like customers and products.
Data management tools and techniques
A wide range of technologies, tools and techniques can be employed as part of the data management process. The following options are available for different aspects of managing data.

Database management systems. The most prevalent type of DBMS is the relational database management system. Relational databases organize data into tables with rows and columns that contain database records. Related records in different tables can be connected through the use of primary and foreign keys, avoiding the need to create duplicate data entries. Relational databases are built around the SQL programming language and a rigid data model best suited to structured transaction data. That and their support for the ACID transaction properties -- atomicity, consistency, isolation and durability -- have made them the top database choice for transaction processing applications.

However, other types of DBMS technologies have emerged as viable options for different kinds of data workloads. Most are categorized as NoSQL databases, which don't impose rigid requirements on data models and database schemas. As a result, they can store unstructured and semistructured data, such as sensor data, internet clickstream records and network, server and application logs.

There are four main types of NoSQL systems:

document databases that store data elements in document-like structures;
key-value databases that pair unique keys and associated values;
wide-column stores with tables that have a large number of columns; and
graph databases that connect related data elements in a graph format.
The NoSQL name has become something of a misnomer, though -- while NoSQL databases don't rely on SQL, many now support elements of it and offer some level of ACID compliance.

Additional database and DBMS options include in-memory databases that store data in a server's memory instead of on disk to accelerate I/O performance and columnar databases that are geared to analytics applications. Hierarchical databases that run on mainframes and predate the development of relational and NoSQL systems are also still available for use. Users can deploy databases in on-premises or cloud-based systems. In addition, various database vendors offer managed cloud database services, in which they handle database deployment, configuration and administration for users.

Big data management. NoSQL databases are often used in big data deployments because of their ability to store and manage various data types. Big data environments are also commonly built around open source technologies such as Hadoop, a distributed processing framework with a file system that runs across clusters of commodity servers; its associated HBase database; the Spark processing engine; and the Kafka, Flink and Storm stream processing platforms. Increasingly, big data systems are being deployed in the cloud, using object storage such as Amazon Simple Storage Service (S3).

drivers, inspectors, loaders, data collectors, data processors, data analyst, evaluation manager,

One important tool for the data management is the Data flow map. It allows us to define the responsibility of everyone in the team management.

Data management includes four standard steps which at least every organization considers

1. Data collection
2. Data entry and collation
3. Data analysis, verification, and storage
4. Data use
   When coming up with a data flow map, one needs to consider the data collection methods indicated on the the log frame of the project as the first step of the data flow map, followed by roles and responsibilities, receipts of the reports and actions

All steps in the Data Management process are important since each has its role. Skipping any of the Data Management tasks in the chain may have a negative impact on the overall result of what was being measured. Proper Data Management is a sure way of ensuring organizations continuously improve the programs they offer to the communities. and people they serve.

"Data Management Questions" refer to inquiries or concerns related to the handling, organization, storage, retrieval, and overall management of data. These questions may cover various aspects of data management, including:

1. Data Collection: How is data collected, and what methods are used to ensure accuracy and reliability?
2. Data Storage: Where and how is data stored to ensure security and accessibility?
3. Data Organization: How is data categorized, labeled, and structured for easy retrieval and analysis?
4. Data Cleaning and Validation: What procedures are in place to check and clean data for errors, inconsistencies, or missing values?
5. Data Privacy and Security: What measures are in place to protect sensitive or confidential information?
   6.Data Sharing and Access: Who has access to the data, and under what conditions? How is data shared with collaborators or stakeholders?
6. Data Backup and Recovery: What strategies are in place to back up data and recover it in case of loss or corruption?
7. Data Retention and Disposal: How long is data retained, and what protocols are followed for securely disposing of data when it's no longer needed?
8. Compliance with Regulations: How does the data management process comply with legal and regulatory requirements, such as privacy laws or industry-specific standards?
9. Data Documentation and Metadata: Are there records or documentation describing the characteristics and context of the data?
10. Data Quality Assurance: What steps are taken to ensure the quality, accuracy, and completeness of the data?
11. Data Governance: What policies, roles, and responsibilities are in place for overseeing and managing data within an organization or project?

These questions are crucial for ensuring that data is handled responsibly, ethically, and effectively throughout its lifecycle. Proper data management practices are essential for reliable research, decision-making, and organizational efficiency.

A data management plan developed effectively in M&E may ensure that the data collected is of high quality, contributes to evidence-based decision-making, and eventually leads to program improvements.

A data management plan developed effectively in M&E may ensure that the data collected is of high quality, contributes to evidence-based decision-making, and eventually leads to program improvements.

A data management plan developed effectively in M&E may ensure that the data collected is of high quality, contributes to evidence-based decision-making, and eventually leads to program improvements.

It is very important to develop a data flow chart as it helps in data management