Thursday, September 21, 2023

Software Tools for monitoring and evaluation professionals

 



Software Tools for monitoring and evaluation professionals

February 6, 2021

Monitoring and evaluation is the process of data collection and analysis to track project progress and support decision making. As the adage goes, what gets measured gets managed. M and E is central to the success of every project as it involves the gathering of data which brings out insights into what is working and what is not. This helps project teams find ways to improve upon project implementation to bring about planned results.

M and E personnel are tasked with the responsibility to produce M and E plans, develop tools for data collection, collect data, develop and manage databases, analyze data, produce meaningful visualizations, write reports and communicate results. For this to happen, M and E personnel must possess a wide range of skills and utilize a number of software tools.

In this article, I will round up the most important tools that every M and E professional working in social impact organizations (and just about anyone responsible for the tasks listed above) must be skilled in.

Data collection software

One of the most fundamental tasks every M and E professional is responsible for is the development of tools for data collection. For this, there are desktop, we-based and mobile-based data collection solutions you can use.

Many international development organizations are migrating from paper-based data collection to mobile data collection due to the efficiency, ease-of-use, and ability to collect different kinds of data among many reasons. Hence it is advisable that you invest into learning one or more tools for developing and deploying mobile data collection projects.

The following are the top data collection software available:

Paid

Free

Database software

For organizations that collect large volumes of data, using powerful relational databases is a must. Relational databases allow for the aggregation of large volumes of data generated from multiple systems. This data can then be used to gain a 360 degree view of the organization as a whole from projects and operations to administration.

Here are the top database software being used in international projects today:

Paid

Free

Data visualization software

Data visualization is the visual representation of data through the use of charts, graphs and other diagrams. Rather than trying to get meaning from a bunch of numbers, visualizations make it easy to detect trends and patterns in data by presenting them visually. Our brains process visual information much quickly; easily detecting changes in size, shape, position, quantity and color. Data visualization taps into this ability to make getting and communicating insights easy.

Every M and E professional is tasked with communicating results with a wide range of project stakeholders – most of who may not have a good grounding in understanding advanced statistics. Using data visualizations such as line charts, bar charts, pie charts, maps, Gantt charts, word clouds and others ensures that your presentation is easy to understand, is visually appealing and engaging to all stakeholders.

Some of the most popular data visualization software are listed below:

Paid

Free

Statistical data analysis software

Data professionals rely on statistical methods of data analysis to unearth deeper insights from quantitative data. Statistics allow us to summarize data into meaningful metrics as well as infer and predict phenomena from a small set of data to a broader population.

As a Monitoring and Evaluation professional, you are responsible for running statistical analyses on data. Therefore, it is key that apart from understanding statistical methods of data analysis, you should also be well vested in the use of software for statistical analyses.

 The following is a list of the most popular statistical analysis software:

Paid

Free

Qualitative data analysis software

In many cases, data will come in the form of descriptions, stories, videos, audios and photos among other formats. This is known as qualitative data.

As an M and E professional, you will need to utilize qualitative data to collect broader insights that might otherwise not be captured quantitatively using numbers. Such scenarios may include capturing significant change stories, interviewing key informants and doing participant observations among others.

The most popular qualitative analysis software include the following:

Paid

Free

GIS software

A Geographic Information system (GIS) is a computer system used to capture, store, organize, manipulate, analyze and present geographically referenced data.

Adding geographical information to your reports enriches the insights you are trying to communicate by highlighting patterns and trends identifiable spatially.

Examples of the application of GIS in M and E include using heatmaps to show the concentration of cholera cases, mapping project beneficiaries and their household characteristics, using remote sensing to monitor forests, among others.

The following are the most popular GIS software

Paid

Free

Sector-specific data management software

There exist software specifically developed to collect, manage, analyze and report data for specific sectors.

For example, if you are working in the health sector, you may need to be familiar with District Health Information System (DHIS2). DHIS2 is a powerful platform for collecting, managing, analyzing, and presenting health data. It is used in many countries in the world to manage their national health reporting. Applications include patient health monitoring, disease surveillance, mapping of disease outbreaks.

M and E professionals in the Water, Sanitation and Hygiene (WASH) sector, may have to be vested in mWater  – a data management platform for the WASH sector. The platform includes a mobile data collection app and a portal for aggregating and analyzing data.

Document and Presentation software

One of the areas where M and E professionals spend much of their time is putting together reports and presentations of their findings and recommendations. To do this, they have to use tools for writing documents and presentations.

The most popular tools used are:

Paid

Free

Communication software

As an M and E professional, you typically work in a team of other project staff such as field staff, project managers and coordinators among others. You also have to communicate with other stakeholders who may include donors and organization leadership. This calls for familiarity with communication software such as the following:

Paid

Free

Most of the paid options offer free plans of their software

Cloud storage software

Cloud storage software provide a more secure file storage solution which in most cases includes the ability to collaborate with other users.

Knowledge of how to use cloud storage platforms and collaborate with others online is very crucial to the work of M and E professionals, who have to work with other members in a team and ensure the availability and security of data and reports.

The most popular cloud storage platforms include:

Paid

Free

Most of the paid options offer plans of their software

Summary

Monitoring and Evaluation professionals have many responsibilities that include planning for M and E activities, data collection, analysis, visualization, reporting and communication.

There exist many software tools that can be used to accomplish these tasks as discussed in this article. Many of these tools are being updated very frequently and more tools and standards are being introduced.

The successful M and E professional is versatile enough to work with a number tools across the categories listed.


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Monday, September 18, 2023

How To Analyze Data in 7 Simple Steps: A Definitive Guide

Company leaders can examine various types of data to make smart business decisions. The process of data analysis involves transforming numerical values into accessible insights about different business areas. By learning how to gather and assess data properly, you can improve your critical thinking, strategic thinking and decision-making skills in the workplace.

In this article, we explain how to analyze data, discuss the types of assessments you can conduct and describe the benefits of this process.



What is data analysis?

Data analysis is the process of collecting and examining statistical information

and drawing conclusions from it. When conducting this process, professionals evaluate large amounts of information from various sources to find patterns and trends. This activity transforms data, such as customer reviews and feedback, into insights that managers can use to make decisions and develop strategic and tactical business plans. 

To gather facts and make smart decisions, businesses collect information from audiences of customers. A large coffee chain, for example, may seek to understand winter sales patterns across several states to develop a new marketing plan for the coming year. With customer data from past years, company leaders can determine which seasonal coffees were in high demand in specific states and whether they could reintroduce them successfully.

Related: Ultimate Guide to Strategic Planning

How to analyze data

Follow these steps to analyze data properly:

1. Establish a goal

First, determine the purpose and key objectives of your data analysis. Think about the questions or concerns you have and the goal you want to achieve by conducting this analysis. For example, your goal may be to increase your customer base.

Related: How To Conduct Exploratory Data Analysis in 6 Steps

2. Determine the type of data analytics to use

Identify the type of data that can answer your questions. If a simple overview is all you want, a descriptive analysis could be useful. You may already have access to data from a previous project. If these numbers are still relevant, you may want to consider using this information.

Related: Guide to OKRs

3. Determine a plan to produce the data

It can take varying amounts of time, effort and resources to collect data, depending on the type of information you're trying to gather. Before investing in this type of project, consider factors like budgetary and schedule needs. Planning these aspects in advance can ensure you have the capacity to complete the data analysis project.

Related: 12 Data Modeling Tools for Data Analysis

4. Collect the data

Set up the necessary data collection processes and assemble a team of people who can accumulate and manage the information. To save time, you may choose to buy access to a data set such as demographic information for a specific segment of consumers. You can also use data analytics software to implement, streamline and track processes with ease.

Related: 6 Methods of Data Collection (With Types and Examples)

5. Clean the data

Once you've built a large data set, the next step is to clean or process the data to ensure it's accurate and free of errors. Check for duplicates and other issues and standardize the data to make it easier to assess later. You can also confirm there are no typographical or format problems.

Read more: Data Cleaning: Definition, Importance and How To Do It

6. Evaluate the data

Investigate the data you've gathered carefully. Use various mathematical interventions to look for connections between points. Interpret what the data could mean and why it could be important for the organization.

Related: What Is Customer Data?

7. Visualize the data

Once you have collected, cleaned and analyzed the data, you can use a method like data visualization to summarize the information and ensure it's meaningful. Visualization tools can help you understand the data and present it to others in an engaging manner.

Colorful charts and graphs can represent hundreds of points that demonstrate a trend or a powerful narrative. A sales data run tied to a geographic region, for example, may help you decide to reallocate marketing funds.

Related: Color Choices in Data Visualizations: Importance and How-To

Types of data analysis

The following are four common types of data analysis:

Descriptive analysis

Descriptive analysis turns raw numbers into information that's easy to interpret and understand. It rearranges, orders and manipulates data to generate simple summaries that show what happened within a study. This method of data analysis shows you the distribution of your data.

It also helps you detect outliers, inconsistencies and errors, allowing analysts to identify how these elements relate to each other. Facts from descriptive analysis can help businesses understand what processes are working well and what areas might require further analysis.

Read more: Descriptive Analytics: A Definitive Guide

Diagnostic analysis

While descriptive analysis tells you what happened, diagnostic analysis determines why it happened. If you want to use data to learn how to repeat a successful project or to reassess a process that may benefit from some improvement, diagnostic analysis can provide you with this information. A diagnostic analysis enables you to discover the reasons a particular marketing campaign was successful, for example, or how you could enhance it in the future.

Related: What Are the 4 Types of Data Analytics? (With Tips)

Predictive data analysis

In predictive data analysis, you can use information from descriptive and diagnostic frameworks to anticipate the future outcomes of a decision or process. Predictive analysis reviews past data to identify potential trends. For example, a department store can use predictive research to determine whether it could be a good idea to hire additional employees during the busy holiday season.

Prescriptive analysis

Prescriptive analysis combines data and helps organizations form a decision on how to improve their work processes. Because the information you gain from this process is on a large scale, prescriptive analysis requires organizational commitment.

Artificial Intelligence (AI) can be a tool to conduct prescriptive analysis. AI consumes a large amount of data and learns continuously, using this information to make recommendations and even implement updates automatically.

Related: Descriptive vs. Predictive vs. Prescriptive Analytics

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Benefits of analyzing data

Analyzing data can further an organization's mission, vision and goals. This information can help you suggest continuous quality improvements, product expansions and workplace innovations. It can also help develop:

  • Upgraded software to streamline services

  • Improved inventory control for faster delivery

  • Enhanced leadership training to empower employees

  • New strategic alliances or mergers to stay competitive in the market

  • Added volunteer training to increase outreach efforts

  • Expanded use of machine learning

  • Increased understanding of variables that affect customer satisfaction

By conducting data analysis, you can interpret data that applies to your project. Data insights can support decisions like what new products customers might like, whether to expand into a new region or country or where to reduce specific costs.

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