Once you have determined the type of SDG outcomes you're trying to achieve, it's a common mistake to jump right into metrics, asking what data can we collect? Instead, we recommend first making sure your enterprise team has the right mindset in place for impact measurement and management. Specifically, a data for decision-making mindset. We recommend aligning your team on five guiding principles to establish the data for decision-making mindset. Principle 1, we will align our impact measurement processes with actionable decisions. One of the core purposes of the SDG impacts standards is to generate trusted, credible, and actionable impact information that informs decision-making to optimize contributions to the SDGs. In other words, impact measurement is a means to improve decision-making. The data you collect should support this purpose. This may seem obvious, and enterprise shouldn't spend valuable resources collecting data that doesn't have a purpose. Yet if you've already been collecting data, you may indeed find, you've done just that. You've been tracking data that is not particularly relevant to the decisions you need to make about your business, and the impact it has on the SDGs. Many enterprises find themselves in this situation. Unfortunately, it's not uncommon for funders or investors to request enterprises collect data that isn't relevant to decision-making. As a first-principle, ensure you have a culture where all team members ask, what are the most important decisions we need to make to improve on our SDG outcomes, and which data will be most useful to assess options and make those short and long-term decisions. Here's an example of an enterprise called DBL group in Bangladesh that articulated their most important decision-making questions along the value chain. Starting with a decision-making questions, help them prioritize the indicators that would inform those decisions. Here's another example. From 60 decibels remote survey toolkit. It shows the kinds of data you might need to make different decisions related to customer experience. For example, if you're trying to decide whether to amend your product, you'd ask customers questions about the value they get from the current product and their satisfaction level. Principle 2, we will align our data quality to decision importance. In addition to determining what data to collect, you need to determine the appropriate quality level of that data. The care and quality that you are sure and collection should be aligned to the importance of a decision you can make with it. Here's one example of comparing data quality with decision-making from maximize your impact, a guide for social entrepreneurs. As you can see, the type of decision you make with the data, the consequences if the decision is wrong, and the frequency of decision, all have implications for the data quality and assurance required to make the decision. Some decisions like modifications to products and services, should be made whenever possible. There's no need to take time for independent assurance of these kinds of choices. Other decisions, like changing a major strategy, have higher stakes, and may well require higher levels of data quality and assurance. In the book measuring social change, Alnoor Ebrahim of Tufts University posits that the rigor of your impact management approach should depend on the complexity of your theory of change. If you're working toward a complex theory, such as through a systems change model, you may have lower direct control over outcomes and more uncertainty about the link between your actions and your outcomes, and therefore, you need a more rigorous data approach. The point is, you have decisions to make about data quality and those decisions will likely reveal the need to make trade-offs between the quality and the resources necessary to gather and assure that data. That leads us to Principle 3. We will consider trade-offs to get good enough data. When you spend time collecting one set of data, there is an opportunity cost for not collecting something else. Your mandate is to get to the data that is good enough to support the decisions you want to make. The maximize your impact guide lists four attributes to consider in terms of what makes good enough data: quality, credibility, timeliness, and consequences. Quality covers how accurate, complete material, and consistent the data is. Particularly related to the outcomes your stakeholders say matter most to them. Credibility speaks to whether the people making decisions with the data, understand the data itself, how it was collected, and to have trust in it's quality. Timeliness relates to matching frequency of data collection with decision time-frames, as we've already discussed. Consequences speaks to matching data rigor with the consequence on stakeholders of being wrong. The bigger the consequences of a wrong decision, the more rigorous the data needed, and perhaps more time taken over the decision. You can download the full report for more detail on using these four attributes to make trade-offs in your impact management plan. Another good resource is this guide to the assessment of social environmental impact produced by Insper Metricis in Brazil. It includes this chart which shows how you can analyze the usefulness of your potential metrics by the factors most important to you. This principle encourages you to acknowledge the priorities and trade-offs you're making in data collection, and the reasons for them, and to consider the way this calculus could change in the future. Principle 4, we will start with imperfection and get better. As the previous discussion of trade-offs reveals, perfectionism in data-driven decision-making is impossible. You will always be lacking in some data you realize later is important. Focus now on getting some data and using it to draw insights into how you can increase impact. At the same time, explore the risks related to the data you have. For example, in quality, and you don't have, for example, in data gaps. Evaluate how your stakeholders could experience unexpected negative outcomes and how you will know about them. These considerations are all part of the process of using data for decision-making. You want to create a process that allows and encourages iteration. One specific suggestion for iteration leads us to principle 5. We will strive to collect data across more of the five dimensions over time to improve our analysis. Adding data across the five dimensions to your measurement and management system, gives you greater ability to pinpoint the most important actions you can take to improve the impact. Here's a tangible example. Grace's target SDG outcomes include reducing infant mortality. Let's say Grace's team gathered this initial data on infant mortality over the last year at two of her maternity clinics. Which clinic is having greater success? The team wants mortality to be as low as possible. If this, what outcome data is the only dimension of data collected, it looks like clinic A is more successful. Let's add data from another of the five dimensions. With some WHO segmentation added, you can see that these two clinics are actually serving populations with different characteristics. How should we compare the data now? Let's add data on baseline outcomes to analyze how much change Grace's clinics have achieved in the different settings. Based on this additional data, which clinic is more successful? Clinic A is actually underperforming compared to the baseline for it's population, and clinic B is performing better than the baseline benchmark. Adding additional data across the five dimensions of impact dramatically improved the analysis. Because Grace's team is using data for decision-making mindset, they will focus on gathering additional decision relevant data to determine what actions to take to improve the results in clinic A. The team may develop hypotheses about ways they could improve care to improve outcomes, and then determine the data they'll need to gather to test those hypotheses. For each intervention, they'll need to decide on the data quality level that will enable them to make the best decisions. They've already seen the benefit of gathering additional data across the five dimensions of impact to improve their analysis. In the next step, we'll introduce the specific impact data categories underneath the five dimensions that you can use to deepen your own analysis. Making it real. Align on the five principles for a data for decision-making mindset, so your team asks the right questions, and puts limited valuable resources in the right place. You'll end this lesson with a fundamental understanding of how to apply the principles of a data for decision-making mindset to your impact management practice.
