Social Impact Data: Unlock Actionable Insights

Discover the Power of Social Impact Data Analysis with Sopact

Social impact data refers to information about the effects of an organization's actions on society. It includes metrics such as the number of jobs created, community engagement, and the overall impact on the environment.

The benefits of understanding social impact data are clear. It can help organizations make informed decisions, improve their reputation, and drive positive change. However, collecting and analyzing this data can be challenging. Sopact's social impact data analysis software makes it easy to unlock actionable insights and make informed decisions.

Before digging deeper into measuring and managing the impact, let’s look at what measuring impact means. In very simple terms, the impact is the effect of activities of an organization on the people (and the planet) they serve through its products and services. Going by this definition, it is clear that organizations need to be in touch with their people and continue to measure the impact of their activities on them using social impact data. There is no magic here. Want to understand the outcomes of your activities? You MUST be close to the people (stakeholders) and talk to them and continuously improve your methods to talk to them.

In this article, you will find an approach for social enterprises, nonprofits, impact investors, and accelerators that can help them to use the social impact data better.

So, this brings us to the question. What does it take to measure and manage the impact? 

Key to good social impact data

• A simple (not simplistic) strategy helps an organization focus on the most important outcome they want to learn about from their stakeholders about their activities.
• Data pipelines help organizations execute the strategy above, which ultimately leads to impact learnings that can be acted upon.

The whole article is summarized below, representing what we call Impact Experiment. It is essentially smaller but continuous cycles of learning about the most important outcomes at any given time.

Having worked with many organizations, we can confidently tell that IMM doesn’t happen, even if one of the ingredients is missing. 

With that, let's get started.

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What is a simple (and not simplistic) strategy?

The definition of “strategy” is beyond the scope of this article. If you are interested in understanding our perspective on the role of a sound strategy in IMM, download Actionable Impact Management. It will do you a ton of good if you can read up on “Understanding Michael Porter” to better grasp what a sound business strategy looks like.

Let's start with the first ingredient. In general, a simple strategy permits organizations to put something into action and follow it until they see it working (or not), as long as it can be executed. Organizations that dwell upon their theory of change, consisting of 30 outcomes and 80 metrics, for 6 long months are on the wrong foot because it is nearly impossible to execute that kind of thought process. 

Strategy is as much about knowing what NOT to do as it is about knowing what to do. 

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A simple strategy for IMM is to start with

• One or two most important outcomes an organization wants to learn about from their stakeholders (Outcomes must be directly related to the activities of the organization)
• Next, think about what data you may already have that helps you answer the question from step 1
• Figure a way out to collect additional data that may be needed to fill the data gap from the perspective of learning about the outcomes. 

Step 3 is where an organization thinks about additional stakeholder surveys (or interviews) that might be needed to understand what outcomes stakeholders are experiencing. 

The three steps above constitute the first part of the diagram. Sounds easy in this article but very treacherous when you try to follow it through. Again the trick here is to start small. The more complex this step, the harder it gets to execute. And the faster you go from strategy to it being just a wish list.

The work doesn’t end with putting all the thought processes together; it begins there. Executing the thought process is where technology comes into the picture, especially the ability to create a data pipeline. This is much like water pipelines that get water from different sources till we get the water in our taps. 

Onto the second ingredient, data pipelines….

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Data pipelines help better decisions

What’s the big deal about data pipelines, and why do we say it's hard and tricky? The biggest reason is that impact data is not just data we collect in the survey; it comes from every corner.

Example

One of the organizations we are working with helps fishers to improve their income through access to a marketplace where they can sell their catch at better prices, ultimately helping them improve the quality of their lives. To accomplish this, the organization has developed an accounting application where the fishers log their fishing trips and their catches and get connected to the marketplace.

To help the fishers learn how to use the application, the organization has developed an LMS (learning management system) that allows the fishers go through accounting courses, and there is a test at the end of each class.

In addition to the above, the organization is doing a baseline survey on the fishers to understand their pressing needs and socioeconomic condition.

As you can imagine, there are several data sources, and they are all related to the fishers. The diagram below shows what it looks like.

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Now let’s take one of the obvious questions the organization may want to answer.

Are fishers more likely to use the accounting application due to passing the course in our LMS? In short, what is the impact of the LMS course on the fishers?

To answer this question, data from two systems need to come together

• The LMS system where fishers are taking the course and where we get to know their general ability to read and understand accounting practices.
• The data from the accounting app that fishers use to log their catch.

With a systematic data pipeline, this question can be answered continuously. And with understanding this, the organization can gain the ability to learn quickly and constantly.

This is where technology, especially around data pipelines, comes into the picture. With technology,  It has gotten a lot easier, but it is still complex. Not easy. The image below depicts what needs to happen for the organization to develop this core learning ability. 

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Data pipeline gives faster learning

• Connect to the existing data source, the APP usage database, and the LMS database.
• We need to bring appropriate data from each source to help us answer that question.
• Finally, combining the two data sources lets us analyze the data and answer the question.

On top of that, this pipeline should work continuously, allowing the organization to learn if things have changed. And when the need for analyzing some other information arises, the data pipeline should be flexible enough to accommodate such changes. 

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While the data pipeline above helped answer the organization’s question, the pipeline shouldn’t be rigid or restricted to answering only that question. The ability to integrate additional data sources and ask varied questions allows the organization to run these impact experiments continuously.

This helps the organization measure and manage the impact, as it can make data-driven decisions. However, this is the complex and tricky part. If the system to connect and fetch data from different sources is not flexible and easy enough to implement, organizations would find it hard to even bring this data together in a form that lets them ask questions and get answers. 

The final puzzle piece is the dashboard that lets organizations learn from the data. Even here, we emphasize learning as opposed to proving. 

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Social Impact Data: Continuous Learning 

I hope this article helps explain what it takes to measure and manage the impact continuously. It is complex and tricky, but all is preserved, and we can say this with the experience of working with many organizations and helping them learn about their impact through data pipelines.