Lessons Learned/Best Practices:
- Using data to answer a question doesn’t often lead to an answer. It just leads to more questions and more data.
- Our work with data needs to remain agile and flexible depending on where these questions lead.
- Often the most useful data is qualitative.
- Sentiment analysis remains important
- Even the wealthiest of post-codes have pockets of extreme deprivation.
- Finding a categorisation system for grants can work.
- Adam’s foundation has had some success with categorising grantees by effectiveness.
- Those grantees with A’s will be given grants with greater flexibility in how they are spent.
- A resource library build for and by people using data in community foundations will be helpful if resources are shared with the names of poeple that use them, what they use them for and how are they useful.
- The big challenge for the workshop on February 1st is going to be making sure we have enough time.
Agenda:
- Go round: Why did you come to the discussion today? Is there a particular challenge in getting data from other people you want to overcome?
- Small Group Breakouts - Groups of three - what is your experience in getting data from others - what works/what doesn’t
- Large Group Discussion: What are best practices and lessons learned from the discussions?
- Relevant resources on this subject:
- What resources are helpful for community foundations?
- What did you want to get out of the workshop on February 1st?
Who’s here: Andrew Ridgewell, Adam Lopardo, Dirk Slater,
Raw notes:
Challenges 0
Adam - integrating data from other datasets -
How uniform there are - seven that they regular they work - five local authorities. The gaps - here’s a region-wide - local authority. How they make statements - rather than specific -
The external datasets may answer the first question but not often the second question.
Andy - Local authority has a statistician - things don’t seem to get updated - ONS Data - pull us- rather than the national.
They pick up local.
Data is useful at using the whole questions - depravation indexices - they can give good dataad
Strengths in their own data -
Combining data -
The qualitative stuff is where the richness is -
Urban/Rural Bias - they have the smallest city in England, so it’s mostly rural
Northumberland is largely rural. Just Newcastle as a city.
You can have a ridiculously rich person living next to a very poor person. Even the wealthiest postcode has areas of deprivation?
Local knowledge - affluent -
One of the community foundations jobs is to figure it out.
Edit postcodes - trying to keep aware -
As part of the northeast -
Gather insights up to a point to see where the money
Categorising grants at the UKCF doesn’t work.
Identifying cold spots -
Categorise grant data on success categorise - and A,B,C and U. This works good with donors that aren’t concerned - (dead ones)
20 grants - just give them 20 to
Demonstrate good practice and then granting based on Trust. THen you don’t have to monitor and do the paperwork on a whole bunch of grants.
A whole checking process - people that are consistently failing, we want to find out why, and they want to help them build there capacity.
Get the data -
Authoritative - I want something to
Changed the monitoring process a few years ago - you goes visit.
The visit process - SOe
Narrative reporting -
How flexible can you be in granting.
How do we keep our data fit for purpose?
We were asking for all this data and no-one is usual
We need data to change - overblown justification -
Something valuable -
Resource Library - you can learn from them or
What people have found useful and why?
Other getting people to play with it?
Resource Library - Champions -
Simplicity - visualisation tools -
Maps, pictures and diagrams