Business Intelligence is a business-oriented aspect of data analytics wherein the data acquired by the business is converted into actionable insights through which business owners can make useful decisions so that the business can become more profitable.
It is an increasingly important tool in the social sector. As social organizations are looking to measure their impact and communicate their results to stakeholders, data visualization is becoming an essential part of their toolkit

Data visualization can be used in a variety of ways in the social sector. For example, organizations can use visualizations to:

1. Show the impact of their programs and services: By using data visualization, social organizations can show the impact of their programs and services in a more compelling way. This can help them to attract more donors and supporters, and can also help them to improve their programs and services.

2. Identify trends and patterns: By visualizing data, social organizations can identify trends and patterns that may be difficult to see in raw data. This can help them to better understand the needs of their clients and to develop more effective programs and services.

3. Track progress over time: By creating visualizations of data over time, social organizations can track their progress and identify areas where they need to improve. This can help them to make better decisions and to allocate resources more effectively.

4. Advocate for policy change: Data visualization can be a powerful tool for advocating for policy change. By creating visualizations that demonstrate the need for policy change, social organizations can help to raise awareness and build support for their cause.

The social sector, which includes non-profit organizations, government agencies, and social enterprises, faces a number of issues related to data. Here are some of the most significant ones:

Data quality: Social sector organizations often rely on data to make important decisions and to measure the impact of their programs. However, the quality of the data they collect can be unreliable due to factors such as inadequate data collection methods, insufficient data cleaning and management, and lack of standardization. This can lead to inaccurate or incomplete information, which can compromise decision-making and program effectiveness.

Data privacy and security: Social sector organizations often collect and store sensitive personal information about their clients or program participants. Ensuring the privacy and security of this data is critical, but can be challenging, particularly for smaller organizations with limited resources. Organizations must be vigilant about protecting this data from cyberattacks, data breaches, and unauthorized access.

Data access and sharing: Collaboration and data sharing are important for social sector organizations to address complex social challenges. However, organizations often face challenges in accessing and sharing data due to legal and ethical constraints, differing data formats and standards, and concerns about data ownership and control.

Data analysis and interpretation: Social sector organizations often have limited capacity to analyze and interpret data. They may lack the resources, skills, or tools needed to make sense of large volumes of data or to derive meaningful insights from it. This can limit the organization’s ability to learn from its programs and improve their impact.

Some of the BI tools are Power BI, Tableau, Google Data Studio etc. They all have different functionality, and pricing but their main focus is to provide users with the information they need to make informed decisions about their businesses. By presenting data in a clear and actionable way, BI dashboards can help businesses improve their performance.
Before directly jumping on creating a dashboard, first, we need to understand certain points that will help us in understanding the perspective of the BI dashboard.

These are the certain points that we should first focus on:

1) Data Source – This is the most important part as the quality and relevance of the data source significantly impact the accuracy, reliability, and effectiveness of the insights and models derived from it.
First, we need to go through the datasets to see that do we have the data fields that are required in creating indicators and do we have the dataset in the required format that is needed in creating some specific charts or if we have to do some kind of data transformation.

2) Problem Statement – A problem statement in data visualization is a concise description of the issue that needs to be addressed or the question that needs to be answered through data analysis and visualization. It is a critical step in the data visualization process, as it provides a clear focus for the analysis and ensures that the visualization is relevant and useful to the audience.
The problem statement should clearly define the research problem, the purpose of the data analysis, and the audience for the visualization. It should also identify the key variables and data sources that will be used in the analysis. This helps to ensure that the visualization is based on reliable and relevant data.

3) Dashboard design sheet – This sheet is an essential tool in the dashboard design process, helping to organize and structure the various elements of the dashboard, ensure consistency, facilitate collaboration, and provide documentation. By using a well-designed dashboard design sheet, we can create effective and user-friendly dashboards that provide valuable insights and support informed decision-making. In this, we can mention the calculation logic, visualization type, and any other indicator-specific information that is used while creating the indicator.

4) Thresholds/Reference/Target – Thresholds, references, and targets are all important concepts in data analysis and reporting. They help to provide context and meaning to the data and allow users to compare the data to some standard or expectation.
Thresholds can be used to identify when the data is outside an acceptable range and can trigger alerts or actions to address the issue
References are values or benchmarks that provide a point of comparison for the data. References can be used to determine whether the data is good or bad and whether the data is improving or declining over time.
Targets are predefined goals or objectives that the data is expected to meet or exceed. Targets can be used to motivate and guide performance and can provide a basis for evaluating the success or failure of a project or initiative

5) Format of Data Collection- The format of collecting data is indeed an essential aspect of data analysis, and it can have a significant impact on the accuracy and reliability of the resulting visualizations. Without collecting data in a proper format, it may be challenging to create meaningful visualizations that can be used to draw insights and make informed decisions.
Once data is collected in a suitable format, data visualization can be used to communicate the data effectively. Data visualization can help to highlight patterns, trends, and relationships within the data, making it easier to understand and communicate with others. However, it is important to note that data visualization should not be used as a substitute for a well-designed data collection process.

Overall, data visualization has the potential to be a powerful tool for social organizations in the 21st century, as it can help to communicate complex data and create a shared understanding of the challenges they are working to address. By using data visualization effectively, social organizations can build support for their programs and services, and ultimately achieve better outcomes for the communities they serve.

This blog is part of our #TechTuesdays Series curated for our CTO For Non-Profits Community

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