How we acquire impact data goes a long way towards how much we are able to leverage such data for the benefit of our beneficiaries and other stakeholders. Our approaches need to be catered toward the motivations, capacities, and context of those providing us with data. We need to be cognizant as well of our organizational resources and the limits of our know-how. In this blog we will learn about how acumen lean data methodology improves organization efficiency and faster feedback for product and services.
Acumen’s Lean Data framework offers us a way to address these needs, harnessing technology to collect data and leverage it to better serve our beneficiaries. And yet, there isn’t a universal model for the implementation of such a framework. Because each social enterprise is as unique as the people and the context for which it exists.
To understand better why a complementary solution is needed to execute and analyze impact strategies on an internal (organizational) level, we need to dive deeper into what Lean Data offers.
What is Lean Data and Where it Falls Short?
The Acumen Lean Data approach aims to improve social performance by harnessing technology for data collection and for making informed strategic decisions based on insights from those data. It attempts to solve the problem of costly data collection or of being trapped within standardized methods that don’t fit a project or program.
As presented in this SSIR article, the Lean Data framework creates a pathway towards efficiency in our impact measurement and management journeys. In the Execution and Learning phases, however, we need complementary tools to manage the data that we receive.
For example, how should data be stored? Analyzed? How can data be sent to investors for independent analysis? Do social enterprises have the complementary tools and expertise for all this? These “back-end” realities are part of the frustration-building inefficiencies that still exist for enterprises attempting to implement a Lean Data approach.
Read More: Impact Measurement
Challenges of Social Sector Data Analysis:
Depending on the type of organization and internal or external reporting requirements, intelligently designed tools are needed to simplify tasks for a wide array of users upstream and downstream, from field managers to project managers, and asset managers to non-profit executive directors or asset owners. Let’s take a look at a few of these possible users and the data challenges they face:
Non Profit ED & Social Enterprise Managers
Those who get funding from foundations or government sources are often required to commit outcome and output numbers, which usually requires them to communicate results from unique beneficiaries across different programs.
Often nonprofits end up hiring M&E managers/analysts whose job it is to collect data from different departments, slice and dice each of the results by quarter, etc. and manage reporting separately. Unfortunately, this often creates data islands and dilutes the ability to leverage the power of data or understand where there may be gaps to fill.
University / Business School Program Directors
Similar to the above example, but with added pressure to demonstrate program effectiveness. They are also interested in gaining insights from unique participants across multiple programs, analyzing student survey history and easily filtering the source of data (where data came from originally) to determine its relevance across different filters.
CDFI / Impact Asset Managers / Owners
These users often zero in on tracking the regional effectiveness of their portfolios. Being able to filter across broader categories like country all the way down to zip code or country enables asset managers to visualize areas of high performance or areas in need of improvement.
Their data management needs frequently include aligning and tracking social & financial metrics defined between upstream and downstream players: Asset Owners, Asset Managers, and Assets (social enterprises). Easy communication and data sharing in their ecosystems is a huge hurdle, and it is essential to overcome it.
Public Sector (Health, Employment & Youth Agencies)
These agencies are required to report outcomes at a cohort and individual level to prove outcome improvement. They must track various factors such as social behavior, employability, job readiness, education, etc. over time, often dealing with the data nightmare that comes with longitudinal outcome schemes.
A Lean Data strategy can bring good things to the impact space and to the players listed above, but such a strategy doesn’t solve all these needs. So we need something that would.
Finding a needle in the haystack: Bringing Impact Insights to Lean Data
Behind every innovative framework is a need for tools of execution. The Sopact Impact Cloud is that tool for the impact sector in general, and especially for those on a Lean Data journey.
The ImpactCloud is a data management platform which houses your impact data and allows you to do anything you might want to do with it. Before you even conduct your research you can search thousands of metrics (IRIS, GIIN, RobinHood, BOND, GuideStar, etc.) to design a Lean Data plan fit for your needs, those of your beneficiaries, and the rest of your stakeholders (e.g. investors, staff, volunteers, etc.)
Data collected and stored on the Impact Cloud can be thoroughly analyzed, with graph- and report-generating capabilities, and utilizes impact insight technology intelligently designed to streamline much of the impact analysis workflow. In other words, you and your teams don’t need to have extensive and expensive M&E expertise to be able to understand and leverage your own impact data.
- Intelligent impact insight: Empowers users of at any level of expertise to manage and analyze data. The Impact Cloud makes both analysis and data sharing across teams a much more manageable process.
- Finally, ease of use paired with powerful tech: Most users at Asset (Social Enterprise) level are not tech-savvy. Hence, it is important to provide radically simple but powerful approaches for analysis and visualization. The ImpactCloud achieves this because it was built by tech savvy people and tested by those who are not.
- Data analysis like Excel: Tools like Excel and Tableau are powerful, but not without their issues and inherent learning curves. The Impact Cloud intelligently guides users with the creation of automatic Filters and Facets, based on field and data types, enabling users to choose the most important dynamic filters for their analysis and reporting needs.
- Easy filtering for better visualization: Users can dive into their data across project or program in the ways that are most relevant to them, such as reporting period or demographics. Results can be viewed in batch form or individually as well.
- Index both qualitative and quantitative results: Users gain deep insights when they can compare and contrast qualitative results with quantitative. The Impact Cloud uses impact learning technology to discover such insights, zeroing in on causality and correlation trends in user data.
- The speed of results: Relational data stores are perhaps not the best approach when an organization wants to build reports from millions of records. The use of the latest technology provides answers that the shrewd data scientist looks for but perhaps couldn't find with traditional tools.
The goal of the ImpactCloud system is to empower practitioners to identify that needle in the haystack – that particular impact insight that will transform an intervention or the life of a beneficiary.
The Impact Cloud ultimately bridges the data islands of your impact ecosystem so that leveraging that data for good isn’t just something you say you do, it can be something you ACTUALLY do.