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Data with Integrity

MFG Archive, Opinion

Returning to Markets For Good, Emily S. Lin announces an exciting new investigation into how to keep your data honest by nFocus.


The goal of the Community Analytics and Learning team at nFocus Solutions is to support learning that creates positive social change in the youth and community-based sectors. Today, as a way of contributing to the larger conversation around how best to use data and research in creating social impact, we are pleased to announce to the Markets For Good audience, our Data With Integrity blog series.


With this series, we seek to challenge the “we needed better data so we got it and then everything was better” narrative. That story is too easy, and it’s not that helpful for people who are in the weeds of trying to create data cultures in their organizations. Instead, we will be sharing stories that detail the guts and messiness of real, on-the-ground implementation and evaluation, and small wins on the side of actually making progress toward having data with integrity. In addition, we also hope to highlight research-based and conceptual pieces that propose perspectives on what it might mean to have data with integrity in the social sector.


We have three overall goals for this series:


1. Establish a meaning and rationale for the idea of “data with integrity” through first-hand and research-based stories.

2. Suggest and discuss shared, measurable goals for tracking data integrity among human services organizations.

3. Create public conversation, momentum, and commitment among relevant stakeholders toward achieving those goals.

We have several lively and thought-provoking posts from clients and colleagues from around the sector that we are eager to share with you in the coming weeks, and we invite you to subscribe to our blog to stay tuned! In the meantime, read on to learn more about where the idea for “data with integrity” came from, and why we think it’s important.


“Data with integrity” as a vision of success


One of the most common complaints we hear from clients is about data entry – it takes too long, it’s too complicated, and therefore the data are either too delayed to be useful for short-term decision-making, or so dirty as to be meaningless. On top of this, though, is the fact that the “easy” data to gather often don’t represent the true value of the work, as seen by front-line staff, especially.


Separately, these ideas – of improving data quality and accuracy, of being intentional about how data fit into the larger picture, of focusing on the questions and informational needs of front-line staff – are not new, and great work is being done to make progress on all these fronts and more. The now-classic SSIR piece by A.C. Snibbe, “Drowning in Data,” has framed many of the practice-oriented conversations about these issues, and fantastic, ethnographically rich and theoretically robust research on research and data use in education has come out of work sponsored by the Spencer Foundation, the W.T. Grant Foundation, and the Institute of Education Sciences.


This kind of work is much-needed and highly valuable, but the processes that people like our clients experience as they engage with these challenges are non-linear, complex, and often difficult to map to this body of knowledge, leading to a feeling of going in circles.


The idea of data with integrity, however, could potentially serve as a unifying vision of what it looks like to do “data work” successfully, regardless of the particular path each organization takes to get there.


Why “data with integrity?”


Last December, we hosted a get-together for a number of evaluators and social scientists to discuss their experiences partnering with direct service organizations and non-profits. A powerful theme that emerged was around the need for “data integrity,” with the tagline, “When you have good data, no matter what it tells you, it will be useful.”


Indeed, as many funders, thought leaders, and policymakers have attested, the lack of accurate, complete, interoperable data is indeed a huge barrier to efforts to implementing processes like continuous learning, data-driven decision-making, and evidence-based practice in the social sector. Typically, the challenges to collecting these high-quality data are seen as technical (such as a need for open data standards or robust longitudinal student information systems), informational (such as a lack of data literacy) or financial (such as a lack of paid time to focus on data collection). For examples of how challenges like these are being successfully tackled in education at the state level, check out the great work the Data Quality Campaign is doing.


While these above conceptions of data integrity are valuable, they are not complete. The beauty of the idea of data with integrity is that the word “integrity” has a deeper, more complex meaning than just “quality.” It’s true that one dimension of integrity references things like accuracy, honesty, trustworthiness. But the other dimension of integrity references wholeness, a solidity and internal consistency.


Integrity, as a descriptor, is most powerful when both trustworthiness and wholeness are simultaneously evoked. The character of Atticus Finch, for example, resonates so deeply in American literature not only because of his integrity as a lawyer – that is, his moral uprightness – but also because of integrity as a person – that is, his consistency and wholeness in being “the same in his house as he is on the public streets” (with apologies to middle schoolers everywhere for stealing your thesis).


What might “data with integrity” be?


What, then, would data with integrity of both kinds look like in the social sector?


Marshall Ganz, a mentor of mine, often mentions that while Cesar Chavez is remembered as a great organizer of the people, the first thing he and the farmworkers did when they started organizing was fairly mundane – conducting a door-to-door census. As many people know, conducting a census is one of the most tedious forms of data-gathering there is. But when it’s clear what the greater purpose and potential of the data are to the gatherers and the contributors (in this case, establishing the potential constituency for organizing farmworkers), the task becomes integrated into a larger values-driven and strategic narrative. This integration then turns data work from being an externally mandated task that must be endured into a meaningful contribution toward achieving a shared goal.


Data with integrity, then, are data that are accurate and complete and trustworthy, but also data worth gathering. Data whose purposes are known and valued by all stakeholders, and whose collection, analysis, reporting, and consequences are integrated into a larger strategic and mission-driven whole. Data that are information, connected to worthwhile knowledge, informed by the individual and collective wisdom of everyone involved.


Moving forward


As we explore these and other ideas and stories about data with integrity in the coming weeks, we invite you to participate, as readers and as contributors! If you are interested in submitting to the Data with Integrity series, please contact us at Feel free also to use the #dataintegrity hashtag out in the parts of the world where hashtags are used.


We look forward to engaging with the many voices out there about what it might mean to have data with integrity. In the meantime, stay tuned for our first post of the series, coming next week.

Many thanks to Emily S. Lin for sharing her announcement with us here at Markets For Good. We too believe in the importance of data integrity and look forward to sharing their results in a future blog piece. To keep up to date with nFocus’ investigation, you can read their blog here, where you can also follow their future articles in this series.


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