1. And Big Awkward Data Madeleine Thornton: madeleinet@buttleuk.org

2. Where we started 2 ½ years ago Me: Background in social research Little bit of experience with public / open data Zero experience with administrative data Handy with a spreadsheet Buttle UK: General idea that we are sitting on valuable data but Little idea how to analyse and use it.

3. Reports from our database

4. Geographic data Started with the basics; a list of the number of grants made in every LA.

5. Geographic data Wanted a more nuanced understanding of geographic spread of grants. Are they going to areas of greatest need? Are there places we are not reaching? Turn to the Indices of Multiple Deprivation; the National Statistics Postcode Lookup and Google maps (plus trusty Excel).

6. Getting there. We know reaching areas of high need. But still don’t know where we’re missing…

7. And now we can see where we want to target our outreach

8. Geographic data Steps were: Download data from our database with a postcode for every grant. Match postcodes to Datazones using the National Statistics postcode lookup available from ONS Match Datazones to rank in Index of Multiple Deprivation (available from Scottish Government) Map it using Google Fusion tables.

9. Client experiences Each case is coded with up to four categories indicating “reasons” for giving a grant. Knew what they were and which were top of the list, but how do they interact? Started messing around in Excel Reached the end of my skills Turned to Datakind for help!

10. Quick detour to say THANK YOU to Datakind UK & volunteers

11. Started here

12. Datakind UK got us here

14. The hardest one. Text data Like many charities, vast amounts of our data is in text format. ____ with her two sons fled her abusive husband and came to our refuge in November. She has engaged with her support plan here very well became more independant. She is a wonderful mother. Her boys are doing very well. Yesterday she has been offered a property by our council and will be moving to it on the 9th ____. When she left her husband she had to leave everything behind. She is still awaiting for a decission for ____ benefit to be paid to her and not to her ex husband. So her finances are limited. There’s only so far you can get with traditional qualitative analysis when you have hundreds of thousands of these…

15. Got started with Datakind: Keyword counts cross-tabulated with financial info We had no idea this relationship existed… What else is there to know?

16. Now working on it ourselves What are we looking for? Not sure yet, just exploring! Tools I am using (or trying out): – Python – Lots and lots of online tutorials and forums

17. A glimpse (still early days) This is me trying out Python’s Natural Language Processing Toolkit (NLTK) to explore some qualitative text survey data I have. We ask grant recipients: “Can you describe one change in your family life since getting your grant from Buttle UK?”

18. What words commonly appear together? I type: Python tells me:

19. In what context are people using the word “stress?” I type: Python tells me:

20. What next? So many more places to go with Natural Language Processing. Want to explore other types of data mining. But also need to focus on producing usable tools (like with our geographic data).

21. What for in future? Fundraising: We support a really broad range of families but many funders have more niche interests. Can explore our data quickly and systematically to see if we are likely to be a good fit by looking for certain problems/experiences etc. Targeting: What if we could work out what people might need before they even know to ask for it?