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You could be facing a mountain of barriers in building an effective data culture, including:
Barrier #1 – fear of losing control
Barrier #2 – fear of losing funding
Barrier #3 – fear of no staff time and training
Barrier #4 – fear of losing privacy
Barrier #5 – fear of lack of resources
Barrier #6 – lack of a program toward data literacy

So, what are you doing about it? Could you use some help?

Data Culture Part 1: Overcome the fear of losing control of your time and data,
and become a more effective nonprofit.

Health Workers Coordinate Mobile Data Collection Campaigns | by UN Foundation

Health Workers Coordinate Mobile Data Collection Campaigns | by UN Foundation

Collecting meaningful data is an essential part of being an effective nonprofit. If your organization isn’t data-friendly, you’ll only get some of the information you need to make good long-term decisions. And you’ll probably feel the pain when the conditions change again.

  • Get rid of faulty assumptions – You could be losing out on donations if you don’t have data to make sure that what you think is actually what is true.
  • Shed specific light on your impact – With data, you can mold vague summary statements into a specific shape, like changing from “helped farm families improve incomes” to “improved household income by 20% for 21,000 farm households”.
  • Justify your time – when you know how each touch point contributes to your mission, you can allocate staff and resources accordingly, knowing that no person-hours are going to waste.
    Become an expert BS detector – Data-informed nonprofits know which tactics are truly worthy of their hard-earned dollars.
  • Show your true colors – you may feel there’s a disconnect between hard data and helping, but your data can be a lifeline between your cause and your supporters.
  • Appeal to every part of your audience’s brain – A recent study found that the brain can extract information after just 13 milliseconds of exposure to visuals
  • Wear the crown of authority – The more you give reliable and evidence-based information, the more you’ll become a sought-after resource in your sector, which will boost public awareness of your cause.

Data Culture Part 2: Overcome the fear of losing funding;
incorporate data in your reporting and compete more effectively for funding.

With everything on your plate, getting the numbers together for reporting can seem like an unscalable mountain. The solutions to the two primary issues are that, if you think:

  1. You don’t have time
    Make data an ongoing part of your culture. Datassist shows you how – keep reading.
  2. You fear losing control over your data
    Keep in mind that shared data provides impartiality and a sense of “truth” and expertise. Donors require performance-based accountability for the use of the funds they provide, giving them a clear picture of your impact. So shared data is an imperative for more funding.

Data Culture Part 3: Overcome the fear of no staff time and training
by carefully selecting your go to data expert.

The person you choose should have:

  • A data management plan
  • A strategy for how data will drive your mission
  • A willingness to experiment
  • An ability to adjust tactics and strategy over time
  • The ability to discern what data is relevant, and what data needs more context to tell the whole story
  • The ability to know what the relevant causal relationships are in the data
  • A skill and thirst for finding facts to support evidence-based decision-making, trends, and real meaning, rather than hunting for “truth”, which can be a slippery fish.

Data Culture Part 4: Overcome the fear of losing privacy
by securing your data.

You can find a healthy middle ground for secured data by conducting a simple privacy audit, creating a data security policy, and asking key questions to keep sensitive data safe.
Overall, a good rule of thumb is simply to have a good reason for the data that you are collecting. If you can’t “match” your data with a purpose, than you don’t need the data.
Datassist’s work on the Gender and Work Database is a good example of how you can provide privacy and data protection, while accomplishing an in-depth analysis of the individual data.

Some common data-scrubbing tactics that protect individual data include:

  • Data masking, such as removing names and addresses from an Excel sheet.
  • Pseudonymisation: identifying information with a code.
  • Reducing information precision: instead of an exact age, use an age group.
  • Aggregation: add people as numbers to a group.
  • Avoiding disseminating your data through cross-tabulations.

Data Culture Part 5: Overcome the fear of lack of resources
by starting down the data culture path.

Start small, get out into the wilderness, and keep communicating with your pack.

squirrel nutsThe Squirrel approach – To avoid losing your information and “how to’s” in the jungle of file storage, start small and develop a squirrel-like stockpile of resources over time, including:

or any other online collaborative tool to help you track your resources while keeping them easily available.

The Primate approach – Data is definitely not a competition; when you share like other primates, you find out what other organizations with similar missions have learned, which will give you a better idea of where you stand.

The Sniffer Dog approach – Try channeling your inner Basset Hound to sniff out the best free and affordable data tools you can, including Tableau Public, Excel hacks, and how to extract data from PDFs.

Here are some other sniff-worthy articles:

The Dolphin approach – Get into a data pod like a dolphin by connecting with data services organizations, see what they offer, get the skinny on their solutions, sign up for newsletters, read their blogs.

  • Blackbaud has an incredible wealth of how-tos, surveys and white papers. You can search its Knowledgebase on just about anything non-profit related.
  • NTEN has scads of resources on data, from webinars to workbooks.
  • And of course, Datassist is always here to help!

Data Culture Part 6: Overcome lack of a program toward data literacy

You are now ready for data school: How to Become Data Literate in 7 Easy Steps

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