Impact measurement and management (IMM) provides an excellent opportunity for organizations to understand their social impact. Despite numerous standards and frameworks emerging in the last decade, none provide concrete directions on continuously improving social impact. It is impossible to determine the true impact by simply defining metrics. In addition, metrics-based reports are often nothing more than a marketing exercise that ignores stakeholder-driven outcome management.
Consequently, social-purpose organizations and programs that benefit stakeholders do not focus on outcome-based strategies. Instead, they lower standards with minimum output reporting to investors. The purpose of impact measurement is not a measurement at all, but it is about business or mission success.
You can make continuous product/service improvement decisions based on stakeholder feedback with impact management. This article examines why most organizations fail to implement comprehensive impact management and how impact data pipelines can simplify data analytics and impact management. Let's agree on a comprehensive data strategy to understand the reasons for failure.
Cherry-picking positive programs and stating outputs are examples of impact washing. A trustworthy intentional organization should instead define the overall materiality of all programs and start from the most material social and environmental impact on the minor. In addition, they can achieve success by aligning programs around stakeholders rather than operational approaches. Once you have identified the programs, let’s start with the stakeholder activities, output, and outcome. The challenge is that most organizations focus on activities and outputs.
Dashboards that only focus on limited data do not provide a meaningful approach to improving social impact. Second, they often end up with dashboards with outdated data. These dashboards have limited value as decision-making tools due to their lack of timely (daily, weekly, or monthly) updates. We prescribe a unique and well-tested strategy that helps organizations start small with limited resources and grow. So let’s look at key concepts first.
- What is an impact data pipeline?
- Why “broken impact data pipeline” is a barrier to impact management?
- How do continuous impact data pipelines help improve faster product-market fit for organizations focusing on stakeholder social impact?
What is an impact data pipeline?
Impact data pipelines (IDP) allow disconnected data from partners and connected data internally to ensure continuous decision-making based on business and social impact data. IDP’s unique value is the intelligent integration of stakeholder feedback and further enrichment that provides deep insight for improving program or investment performance.
The following steps describe specific steps and case studies for further understanding.
1. Rethink partner (disconnected) data:
Social sector organizations or corporations working on social impact often work with partners to improve stakeholder outcomes. In some instances, these organizations may also be working directly with stakeholders. Stakeholders are beneficiaries, customers, employees, volunteers, or supply chain, partners. Organizations may have different goals for collecting data, such as product/service adoption, program needs analysis, supplier diversity equity inclusion (DEI), employee satisfaction, etc. Partners outside an organization’s jurisdiction often manage these data.
However, these data may be necessary to understand the aggregate social impact. These data can be considered disconnected data. Most organizations define the metrics and frequency of results collection and use a survey-based approach to aggregate results. Although outside the purview of an organization, these data may have a high value collectively. Instead of gathering data from sources and processing it for aggregation and transformation, we must rethink the disconnected data aggregation approach.
For example, family offices or asset owners that invest in social impact funds have unique challenges for aggregating impact data from funds or companies they finance. Instead of collecting results from primary sources, what if each impact fund or company agreed upon a data format? The data owner can continue to focus on the most critical output and outcome. They can keep it updated and manage data using simple tools like MS Excel or Google Spreadsheets.
This avoids reinterpreting or inputting results into another data collection that may lose context. Instead of spending months agreeing upon metrics, nudging partners to provide results, and aggregating results from multiple sources, partners can keep data at the primary data source. The data pipeline is connected to the dashboard for real-time updates. Organizations can build dashboards that can be shared. Partners can now define the most crucial output and outcome data that matters to stakeholders without relying on arbitrary metrics set by the external organization.
Why “broken impact data pipeline” is a barrier to impact management?
2. Integration Layer:
Organizations may need to gather data from many sources. Typically, this is done on custom spreadsheets or program data applications like Salesforce, Power BI, etc. However, integrating them into the final data source can be time-consuming, error-prone, and create major dependencies with the source data. Often these organizations manually collect data from external partners or export data and transform them for a final visualization. This approach creates a "broken data pipeline” as this process is manual and error-prone as dashboards cannot update if source data changes. Hence, dashboards are often used for marketing purposes and not decision-making. A continuous data pipeline lets you connect to any data source, such as surveys, CRM, case management, marketing, data warehouses, or data lakes. This flexible approach can pull results from the source at a pre-defined frequency and provide data tables to the semantic layer for data analytics.
3. Semantic Layer:
Every organization has a unique understanding of social impact. The semantic layer enriches data for analytics. A semantic layer allows for proper calculation and alignment with various frameworks personalized to each organization. Some examples are:
- Due diligence score, for example, scoring for 2x challenge for financing for women
- Monitoring outcome or product traction through stakeholder data alignment with five dimensions of impact from impact management project (IMP)
- Comparison and benchmarking through external data comparison, statistics, and calculations
- Unification of data from multiple sources to understand stakeholder product adoption and social impact goals.
4. Dashboard Layer:
While there are 100s of dashboard platforms, including popular ones like Tableau, Power BI, Sisense, and many others, each can have substantial user-based subscription fees. More importantly, as described above, you still have to hire costly programmers and data scientists to create continuous data pipelines. Most social sector organizations lack the budget or skills to execute such a project.
The broad range of impact data pipeline approaches allows for continuous data integration and modern data visualization and storytelling. Effective dashboards must start with a transparent storytelling approach; the dashboard must document the theory of change, evaluation strategy, key impact management goals, and hypothesis. Finally, the effective dashboard must be comprehensive enough to focus on the most critical materiality and demonstrate impact based on “five dimensions of impact” based on impact management project (IMP) or other publicly agreed upon evaluation approaches.
5. Track Outcome/Progress Through Impact Experiments:
An impact data pipeline provides a dynamic framework for analyzing impacts based on impact management and ESG frameworks, but stakeholders still need to participate in continuous learning. Impact Experiments are short and frequent experiments designed to collect feedback from stakeholders.
The idea of impact measurement and management scares many organizations. Many teams lack skills in data design and architecture. Often, they hire external consultants who have either impact management or data management and data science skills but not both. On top of that, they may not be familiar with the capabilities of modern technology architectures that can streamline the process in the first place.
The best advice we can give you is to avoid analysis paralysis, whether you are your enterprise, funders, or corporate. You can start small and grow iteratively by partnering with an impact management provider with modern technology and IMM advisory. This approach will boost long-term success and reduce costs significantly.