Imagine Canada

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Are You Leaving Valuable Knowledge on the Table?

Tuesday, June 27, 2017
Guest Writers
Sector Research
Heather Krause

The value of data can’t be underestimated.

Data is the key to understanding the impact of your work. It provides insight and aids in decision-making. It can help you determine where to dedicate resources, when to scale, and how to move forward.

Without data to facilitate impact measurement, it’s impossible to know for sure where and how your efforts are making a difference. Are you changing the world, or do you just hope you are? Large or small, startup or established organization, data that enables you to measure your impact and articulate it to the world is absolutely critical.

Unfortunately, data is also often prohibitively expensive and time-consuming to collect.

So what can you do?

Combining data across projects can provide a better understanding of the unique impact of your investment in a more efficient, effective way. A combined lens enables us to understand the cumulative impact of many projects simultaneously.

Shifting to Collective Data Use

Over the past decade, I’ve been exposed to a multitude of datasets — many used once and then never again. I’ve watched as organizations collected data similar to that of their partners or peers, analyzed it, and then locked it away in a database. I was struck by the virtual waste — so much valuable knowledge was being left on the table.

I’d like to highlight a few successful examples of cross-project data combination: at national and provincial levels, but also on a smaller scale. The common theme here is data-sharing across multiple teams, groups, or organizations — you don’t have to have massive infrastructure or a big budget to leverage the power of data-sharing.

NPC’s Justice Data Lab

New Philanthropy Capital (NPC) began as a charity in 2002 and has since grown into a widely recognized consultancy and think tank operating at the intersection between nonprofits and funders. Their Justice Data Lab project was developed to provide impact measurement data to organizations working with offenders.

By providing these groups with central reoffending data, JDL enabled them to better understand which rehabilitation programs were working and effectively measure the impact of their efforts. The broader picture painted by this information would have been inaccessible to these organizations without the cross-project data provided by JDL.

Alberta’s Child and Youth Data Lab

In Western Canada, officials in Alberta recognized that combining their data across projects could improve their ability to understand and measure the impact of programs and policies on children and youth in the province — both their youngest and most vulnerable citizens and their future.

The province’s Child and Youth Data Lab (CYDL) combines data from the provincial ministries of human services, aboriginal relations, education, health, and justice to gain a more comprehensive understanding of how individual programs and policies are changing the lives of young Albertans. Without cross-ministry collaboration and communication, each unit would only see a small piece of the puzzle.

CARE Efforts in Asia & Africa

Multiple teams working under the banner of humanitarian aid agency CARE recognized that the siloed data each team was gathering could provide valuable insight into local and national trends if combined with the data of their fellow aid workers.

Data from three separate initiatives (Strengthening the Dairy Value Chain, Pathways to Secure Livelihood, and Link Up) was combined and analyzed, resulting in the identification of an unexpected impact. CARE’s efforts, while focused primarily on improving the livelihood and financial security of those in rural African and Asian communities, were also making significant progress towards empowering women in those areas — a huge step forward for the projects, the communities, and the women involved.

Data Sharing Pros and Cons

Combining data across projects allows for data reuse. Datasets that would traditionally be used once and then archived can be accessed again and again to provide ongoing insight.

But what about privacy? And consistency?

There are ways to protect privacy and ensure consistency while combining data available to analysts today, which we will outline below in our step-by-step process. Careful data cleaning will keep sensitive details secure, and the reality is that the benefits of combining cross-project data far outweigh the concerns it may raise. Advantages include:

  • Larger sample sizes
  • Less biased results
  • More culturally sensitive data
  • The ability to distinguish local from global trends

Make Your Data Clean and Green

When I suggest your data should be green, I’m not just drawing parallels to the environmental virtues of reusing resources. Sharing data also extends its useful life. Rather than withering in storage after a single use, your shared data can be evergreen — valuable season after season.

Data gathered by single organizations or partner efforts can be used collectively. A little bit of privacy protection and some statistical standardization can go a long way to making data combinable, comparable, and reusable — so its value lasts and lasts.

The steps are simple:

  1. Clean each dataset carefully, ensuring you understand what the indicators are measuring. (Not sure how well you know your data? Create a data biography.)
  2. Standardize the outcome datasets. Make sure all scales are going in the same direction. (i.e., higher numbers mean positive results across all datasets)
  3. Remove individual identifying information from each project. (This includes names, birthdates, etc.) Retain unique IDs instead.
  4. Combine the datasets.
  5. Analyze to assess all the data while controlling for individual projects. (Multiple regression is ideal for this, but other types of analysis can also be applied.)

Regardless of the size, scope, or focus of your project, the odds are good that you could benefit from data collected by other organizations — or they could benefit from yours — to provide more context or deeper insight into your efforts. So share your data and keep it green.

About the Author

Heather KrauseHeather Krause is the founder and principal data scientist at Datassist, a Canadian company that provides impact evaluation, business performance measurement, and social return on investment metrics to nonprofits and social sector organizations. Heather is passionate about supporting data literacy and advanced data analysis in the nonprofit, public policy and data journalism sectors.

Guest contributions represent the personal opinions and insights of the authors and may not reflect the views or opinions of Imagine Canada.

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