Chapter 11

Appendix: Understanding Data Advocacy

Data is all around us

Look at this Apple.apple

 

 

 

 

Your first thought might be, ‘yummy apple.’ But if you are encountering the apple in a store, you might be interested in data about the apple. Such as:

Type: Cox Location Picked: Kent Size: 50 grams
Date picked: 10/09/2013 Price: 50 pence

You might also be interested in nutritional data, such as:

Amount Per 100 grams
Calories 52 0%
Total Fat 0.2 g 0%
Saturated fat 0 g 0%
Polyunsaturated fat 0.1 g
Monounsaturated fat 0 g
Cholesterol 0 mg 0%
Sodium 1 mg 0%
Potassium 107 mg 3%
Total Carbohydrate 14 g 4%
Dietary fiber 2.4 g 9%
Sugar 10 g
Protein 0.3 g 0%
Vitamin A 1% Vitamin C 7%
Calcium 0% Iron 0%
Vitamin D 0% Vitamin B-6 0%
Vitamin B-12 0% Magnesium

Now take a bushel of apples:

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The data we first want to know might be similar to when we go to buy an apple:

Type: Braeburn Location picked: Kent Date: 10/09/2013

However if I’m a wholesale buyer and want to know what sort of profit I can get. I’ll want to know the following:

Price per kilo from farmer: 1 pound 25 pence Price per kilo at market: 2 pounds

If I’m a farm labourer, there is different data about the bushel that I would want to know:

Price paid per bushel picked: 75 pence Average time taken to pick a bushel: 8.5 minutes

Relevant data varies from the consumer, the merchant to the farm labourer. The relevant data to advocates change dramatically depending on the issue. Perhaps they are advocating for better eating habits among children, or against price fixing in a store or better pay for farm labourers.

The key to effective data advocacy is to understand the difference between data and evidence. Data is discrete pieces of information, such as prices, measurements, dates, names of places and people, and addresses. Evidence is when data is used to establish facts or expose truth. For activists and advocates, the ability to take relevant data and turn it into evidence is the key to creating change.

Here are the basic steps that advocates go through when using data:

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Common challenges that transparency and accountability organisations face when using data in advocacy:

  1. Organisations often collect information and data without knowing what is relevant or what they could do with the data. They don’t always know the potential of the data, particularly in regards to turning it into evidence.
  2. Often an institutional structure to hold the data and package it effectively does not exist. Organisations often don’t fully understand what it will take to effectively use data before they undertake a data project.
  3. It can be very difficult in translating technical data on policy issues like budgets into terms that make the connection to real people’s lives.
  4. Organisations often don’t understand that the power of data is not just something that is in a spreadsheet – but it is also in people’s stories, images, audio recordings, etc.
  5. The value in getting data directly from citizens or stakeholders that are impacted by the issue

A Data Advocacy Checklist

Since data is not enough to convince decision makers, you need to organise it into evidence and connect it to a larger advocacy strategy. To get past these challenges answer the following questions:

  • Do you have clear advocacy goals?
  • Have you identified who will be connecting to the data?
  • Do you know the work that the data needs to do? Are you using it to engage allies? Educate neutral parties? Or counter opponents?
  • Do you have the capacity to aggregate and analyse the data?
  • Do you know creative ways to package data that will help stakeholders connect and engage?
  • Can you provide your stakeholders with a way to GET INVOLVED with the data?
  • Do you have capacity to crowd-source data from stakeholders?
  • Do you need to work with an external consultant/developer?
    • You don’t need a developer when:
      • you can use a spread-sheet to work with your data
      • you can use tools to manipulate data that are already made without needing to write code to use them
    • You do need a developer if:
      • you need to combine data in a more complex way
      • you need to get data through a web service in an automatic way
      • you are working with a big amount of data
      • you need to discover patterns in a data set
      • you need to visualize real time data

Gabriela Rodriguez’s handy list of tools that anyone can use to work with data:

Finding Data

Getting Data

Cleaning Data

Presenting Data

Forums and Community in Internet

What exists above is a grouping of resources on using data in advocacy that was generated from a variety of TABridge events, including a session led by Jed Miller at the Glen Cove,
NY meeting in November, 2012 and a webinar on October 23rd, 2013 with Lucy Chambers, Gabriela Rodriguez and Dirk Slater.