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Designing a marketplace of help for vulnerable people, with Simon Hopkins
Episode 625th April 2022 • Fibonacci, the Red Olive data podcast • Red Olive
00:00:00 00:47:03

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Hello and welcome to Fibonacci, the Red Olive data podcast, all about data and analytics where we hear from leading specialists and get their take on the industry. We are joined today by Simon Hopkins, Director of Resources at Blind Veterans UK. This provides free support and services to vision-impaired, ex-Armed Forces and National Service personnel. 

Simon has previously held a variety of roles in the third sector at CFO, CEO and COO levels and started his career in professional practice, qualifying as a chartered accountant with KPMG in London. 

Simon is a regular columnist for Charity Finance magazine, publishes and speaks regularly on subjects ranging from financial sustainability to digital disruption. 

Here are the main topics of conversation with their timecodes:

  • Learning how to rethink the relationship between money and data when moving to the charity sector (3m)
  • Understanding that the customer is more sophisticated and multi-layered in the charity sector than people think (4m 41s)
  • Integrating data, and the idea of one platform going out of fashion (6m)
  • How important it is for a single view of data at the top is in bringing an organisation with you (6m 52s)
  • Simon’s work at Blind Veterans (10m)
  • Formulating a data strategy (11m 28s)
  • Connecting with other organisations to help the vulnerable (16m)
  • Data-rich solutions that could connect multiple sources into a marketplace of help (22m)
  • Thinking of the GDPR as an asset (27m 44s)
  • Where a marketplace of help could emerge from (31m)
  • A map of veterans’ needs and how it helps anticipate what their future needs will be (33m)
  • Using data from patients’ smartphones to help determine when interventions are needed (34m)
  • The skills that you need to enter a career in the data industry (40m)
  • Working strategically with Red Olive (44m)

Transcripts

Speaker:

- Hello, and welcome to

"Fibonacci:

The Red Olive Data Podcast",

"Fibonacci:

all about data and analytics,

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where we hear from leading specialists

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and get their take on the industry.

"Fibonacci:

I'm your host, Nicky Rudd.

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Today I'm joined by Simon Hopkins,

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director of resources at Blind Veterans U.K.,

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a charity that provides free support and services

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to vision-impaired ex-armed forces

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and National Service personnel.

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Simon has previously held a variety of senior roles

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in the third sector,

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and started his career in professional practise,

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qualifying as a chartered accountant with KPMG in London.

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He's a regular columnist for "Charity Finance Magazine",

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publishes and speaks regularly on subjects

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ranging from financial sustainability to digital disruption.

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He's a passionate and occasionally outspoken advocate

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for financial professionals

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shaking off the back office label and embracing their role

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as outward-looking organisational leaders.

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But where does data fit in with this vision?

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Let's find out more.

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(driving rock music)

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Just wanted to start our discussion really

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and cover off your background,

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'cause obviously with your role that you're in now,

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which we'll come to, it's very, very different.

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So do you want to tell me about your past?

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- I've had a classic sort of commercial accountant's career,

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bit like Jefferson at Red Olive, funnily enough.

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And 15 years ago, I had the chance to do one of those

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leap of faith moves,

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where you take yourself right out of your comfort zone.

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And I became a senior civil servant in Whitehall,

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under the Brown government.

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And I worked there for three years.

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It was a classic classic mid-career move.

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And for me, with hindsight,

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it was a really really important step

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to moving into the charity sector,

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because it helped me to think in a different currency.

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It helped me to think about money and data

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through the lens of social change,

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rather than through the lens of profit.

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My move was heavily driven by curiosity,

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and I think anybody that's interested in data

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has to be fueled by intense curiosity.

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When I'd been in the civil service sort of

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2 1/2, three years,

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I had the chance to come into the charity sector.

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I came into the charity sector in early 2010.

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And for me, it felt like a perfect combination

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of the commerciality, and innovation, and drive,

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that I was used to in industry and commerce,

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allied to that sense of success being measured

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in non-financial terms that I'd learned in Whitehall.

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I'd loved to claim that there was this grand plan

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to come to the charity sector, but there wasn't.

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It was just a really lucky synchronicity for me.

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And I still feel like that 12 years on,

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having been in the sector for 12 years.

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That sense of the alliance of the commerciality

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and the public benefit, which you only find, in many ways,

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in civil society.

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It all felt like I was leading up

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to being part of this sector.

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I've had to learn how to rethink money,

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but I've also had to learn how to rethink data.

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And because success is measured in terms of social change

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and transforming lives,

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and not about giving a rate of return to shareholders,

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it's got massive implications

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for the way that we as a sector think about data,

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and utilise and exploit data.

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- What was the biggest learning,

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and kind of rethinking your brain?

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Obviously with your background in finance,

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it's completely different, but, you know,

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was there big sort of technology steps

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that you were like actually,

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it's much easier for me to kind of step into

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and understand it, or kind of better visualisation tools,

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or anything like that

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that sort of helped with that transition?

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- I think there's two things in particular.

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I mean, one is, as I've already mentioned,

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this idea about success is measured

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in a completely different way.

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And it's in a way that you can't always quantify.

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That certainly, in the charity sector in particular,

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that mixture of quantitative

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and qualitative measures of success,

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metrics and storytelling, is really, really critical.

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Most charities, when they do their impact report,

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will probably use what I would refer to as

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the mixed economy.

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So they include metrics of the number of people

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that they've helped, et cetera, et cetera,

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but they'll tell stories as well.

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And that's the way we articulate success.

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It wasn't as like that working for a massive bank.

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It was about weighted risk assets and return on investment.

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Highly, highly metricated.

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The other thing is the understanding of the customer,

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and it's far more complicated in the charity sector

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than I think most people might instinctively think.

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So one of the things that's happened in the charity sector,

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really positively over the last decade,

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has been a real focus on single customer view.

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But you might get a customer who is a beneficiary,

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who raises funds for the organisation,

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who is a member of a support group

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and therefore a campaigner,

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and who also might be a volunteer

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and may even be a member of staff as well.

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So the number of dimensions or lenses

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through which you relate to the same person,

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can be very, very complex and very, very sophisticated.

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So the challenge, but the opportunity,

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on things like single customer view,

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are so much more multi-layered in the charity sector.

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Now to a data nerd like me,

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that's really, really fascinating and stimulating,

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but it isn't necessarily intuitive

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until you come into the sector and you realise that

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your sense of customer is so multilayered.

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- I'm presuming has its own challenges

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and its own opportunities as well.

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- It does.

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I mean, the challenge is in how you integrate the data.

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And it was quite fashionable 10 or 15 years ago

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to think about this idea of one CRM.

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I think it's gone out of fashion because

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the cost of this idea of a single platform

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that can deal with all of these perspectives,

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is just prohibitively expensive.

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It's prohibitively expensive to put in,

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and also to maintain.

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So the idea of data integration,

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and my background is commercial finance not technology.

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So don't ask me how you write code,

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'cause this might be a very short conversation.

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But that opportunity to move the sector

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from the transactional to the relational is massive.

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And as a sector, we are starting to achieve that

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and we continue to achieve that.

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And I find that really, really exciting.

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- How important do you feel that having that view at the top

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is what's needed to bring everybody along

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on that sort of journey?

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I mean you and I were talking earlier about

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15 years ago or so, when people started thinking,

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or charities started thinking about marketing,

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it was very much a kind of, okay,

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we're in third sector but we need to have this right,

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'cause we're up against the commercial sector

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and they're throwing stacks of cash at it,

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and kind of really really, it's shifting,

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it's moving really fast.

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And I kind of feel that that's where data is

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within the charity sector now.

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But I can see that because there are so many layers of it,

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actually you need somebody who's steering the project

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and who's got a very, very clear set of objectives

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of where they want the charity to sort of be

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in a certain amount of time on that journey.

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Would you say that the leadership side of it,

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with all the digital technology,

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the disruptive technology that's around,

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and also all the financial side of it as well,

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basically need to be pulled along by somebody, if you like,

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to hold their hands and bring them with you?

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- It is critical.

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I would always characterise

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the evolution of data in the charity sector

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as somebody who doesn't claim to be

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a sort of a black belt expert, but yes,

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you need the leadership, you need that sense of digital

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and data being mission-critical.

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But at the same time,

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you have to have a really strong sense of being informed

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and being receptive to what's happening bottom up.

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Not least because if you're the chief exec,

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or the chief operating officer,

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or the chief financial officer or CIO of a charity,

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you're not gonna have day to day contact with beneficiaries.

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So if it's purely top down,

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there's a risk that you lose the organisation

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acting as a sponge to that voice of the client.

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And so it has to be both top down and bottom up.

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Now, the place where I think it comes together,

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which has got nothing to do with technicalities

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and everything to do with culture,

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and I think we're starting to get really right

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at Blind Veterans, is that sense that

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leadership articulate that it's important

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and it gives permission.

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And then the content comes from the front line.

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It comes from the people dealing with suppliers.

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It comes from people dealing with supporters and volunteers.

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So what the job of leadership is to do

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is to give permission and empower,

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not to try and solve everything.

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And that's a cultural consideration.

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It basically says data is really, really important.

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And as a leader,

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my role is to create a culture of curiosity.

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- With that then, let's just come back to Blind Veterans.

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So what attracted you to the role there?

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And obviously you've been working with Red Olive

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for a while, but let's talk a little bit about

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the charity's work, and how data sort of

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flows around the organisation,

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and what steps you've put in place

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to make that work better for you.

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- There are a number of things that attracted me here.

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I've worked in the charity sector for over a decade,

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and that work has given me a real sort of epiphany

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on how much work we have to do

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as a society around disability.

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And when the job with Blind Veterans came up

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three years ago, there was this sense that

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here's something that really, really matters to me,

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and I have a chance in a leadership position

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to do something about it.

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So there was a natural kind of meeting of things

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that was quite instinctive for me.

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I have to say on top of that,

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when I started looking at Blind Veterans,

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I identified an organisation that I thought

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this is really, really well placed

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to be innovative and disruptive and bold,

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around the data and digital agenda.

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And again, I felt that there was something

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I could help with, not at the level of explaining

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how we might use NFTs or blockchain,

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'cause I don't really know very much about those things,

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but just at the bigger picture level on things like

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single customer view,

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on things like having a coherent data strategy

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or coherent digital strategy.

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They were things that I thought I could help facilitate

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and bring along.

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So part of it was around data,

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but the key thing was around mission.

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- What was the first steps when you kind of

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sort of stepped into the role?

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How did you kind of go about setting out that data strategy

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and putting those pieces in play?

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- For me, it was the same as coming into

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any sort of leadership role.

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Your single biggest tool, as an incoming senior person,

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is to listen.

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So what I did was just let people unload.

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And in many ways, the data strategy

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that we finally landed upon

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where we talk about things like single customer view,

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we talk about the principle of line of sight, for instance,

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where everything from kind of atomic level data,

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all the way through to reporting,

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through to performance management framework,

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through to things like strategic impact measurement,

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has to be systematically integrated.

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That data strategy emerged quite organically,

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because we just listened to what people

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much much more expert than me

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across the organisation was saying.

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All I did, which I think was the right thing to do,

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was to create a space where we could bring it together

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and say, right, we're gonna capture some of this

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and turn it into a data strategy.

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And it's not a massively complex data strategy.

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It just articulates the big principles

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that are most important to us,

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that we know will help further our work

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and further the impact we have on society.

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- Have you had to sort of pull together

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different people within the organisation,

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to sort of run with that?

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I mean, I know the way that you're now working with

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Red Olive is almost like an outsource data partner,

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but I'm guessing that actually

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there's a lot of people within the organisation

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that know that they are the go-to person,

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if they need that information for a particular report or,

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whether they're looking at different aspects

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of the business.

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How has that kind of evolved over the time

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that you've been working with them?

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- We've deliberately used a cross-functional data group

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and that's drawn from all parts of the organisation.

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So we have people in there

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who represent the service delivery side.

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We have people in there who represent

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the information services team,

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the research entity that we've set up within the group,

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the senior person who runs

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the performance management framework.

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We've deliberately had this very eclectic group,

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with very different frames of reference,

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and quite wide cognitive diversity.

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And the data strategy that we now have in place

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was developed by embracing that sort of eclectic mix

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of experiences and that cognitive diversity.

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And if it was simply the case of I'd sat in the corner

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with the chief exec and the head of information services

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and written it, we'd have probably come up with rubbish.

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By dint of the fact that

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we went for something really inclusive,

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I think got us to a better data strategy

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and allowed us to think quite holistically

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and quite strategically about things like

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single customer view.

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Our single customer view is really quite sophisticated,

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and we now have that programme mobilised, for instance.

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We can quite often have somebody who's a member of staff

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and a member, we don't refer to them as beneficiaries,

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we refer to them as members, and a volunteer,

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and a fundraiser.

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So the relational view of that person

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is really sophisticated, but if we get it right,

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it's remarkably rich.

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Similarly, we've just overhauled our procurement framework

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and you might think what's that got to do with

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single customer view?

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Well, one of the things we now make a virtue of

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is that for our strategic suppliers, we now say to them,

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one of the things that gives you the licence

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to operate with us,

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is you need to tell us how you're going to contribute

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to our social mission.

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So suddenly we have a situation where

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we have people who have a record as a supplier

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in the back end of the accounting system,

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but we also need a record of how they are helping us

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campaign or fundraise,

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or how they are becoming more accessible

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as an employer themselves.

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So there's quite a lateral and sophisticated

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relational set of data we need,

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to describe that relationship

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that we might historically not have had.

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So the single customer view thing in particular

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is a really massive, massive part of our data strategy,

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and something that we have set up deliberately

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as a flagship programme,

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because how important we realise that it is.

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- I guess that, like you were saying,

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it's your wider network then of partners and suppliers

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who are also helping you tell that sort of

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social value story, if you like.

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Jefferson's mentioned to me that you have a vision

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for interconnected organisations working together

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to help the vulnerable.

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I'm guessing that's part of that.

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- It is.

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This is something I've been campaigning on for years,

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or making a nuisance of myself about for years,

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pick your version of it.

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So if you bear with me, let me tell you a story on this.

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- Yeah, sure.

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- One of the things that attracted me to Blind Veterans U.K.

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was I saw how well positioned they were

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to be a really active participant,

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a really active catalyst in this.

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So if you look at areas like

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financial hardship and disability,

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really, really central themes to the charity sector,

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those individuals and families that we help as a sector,

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will often have very complex needs.

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And issues like disability and financial hardship,

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for instance, very often go together.

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Probably more so than most of the public would realise.

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If you have a family where there's a disability

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in the immediate family,

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statistically, that family is two or three times more likely

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to be struggling financially,

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than one where that isn't an issue.

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Now to me, as somebody in a leadership position

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in civil society, I am totally intolerant of that situation.

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It's a moral disgrace.

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It is not good enough.

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So my question to myself is what am I gonna do about it?

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'Cause I'm not a campaigner,

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I don't work in service delivery.

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I'm a person who does a classic director of resources role.

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So hold that thought.

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What I've also learned is that the help that is needed

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to deal with those sometimes kind of

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collective complex needs, is very often all out there,

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but more often than not, it's very fragmented.

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So let me paint a picture to illustrate.

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And because we're talking about something

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that I think can be transformed

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through a very data-led solution,

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I'm gonna describe a persona,

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'cause I think that's the right term.

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So we've got a family dealing with disability.

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They're struggling financially, despite being in work.

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The health diagnosis that wraps around that disability

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is still quite new.

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So they're still processing it,

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and they need advice on things like

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access to local services,

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just in terms of how do we adapt life

"Fibonacci:

to deal with this new variable that is life-changing?

"Fibonacci:

They're finding the benefit system around disability

"Fibonacci:

totally impenetrable, because it is.

"Fibonacci:

They would benefit massively from being part of

"Fibonacci:

a local support group, just in terms of coping.

"Fibonacci:

What I've described there is a very, very common picture,

"Fibonacci:

as a set of complex things.

"Fibonacci:

The package of support, if you think holistically,

"Fibonacci:

if you think whole person,

"Fibonacci:

the package of support that would help that family

"Fibonacci:

is quite likely all out there, but it's in multiple places.

"Fibonacci:

So let's say there's help from the local authority.

"Fibonacci:

There's a benefits helpline

"Fibonacci:

that will help them navigate the disability benefits maze.

"Fibonacci:

There's a charity out there that might give them a grant

"Fibonacci:

to adapt a bedroom or a bathroom,

"Fibonacci:

to physically adapt the family home.

"Fibonacci:

There's a local support group out there

"Fibonacci:

where there's mutual support for people

"Fibonacci:

living with the same condition.

"Fibonacci:

And there might be a local charity out there

"Fibonacci:

that provides things like talking books for people

"Fibonacci:

who are experiencing sight loss.

"Fibonacci:

Now my premise is this.

"Fibonacci:

If that package of things can be bundled up,

"Fibonacci:

that family will see a transformational change

"Fibonacci:

for the better.

"Fibonacci:

It's an holistic set of interventions.

"Fibonacci:

If you can help them holistically,

"Fibonacci:

if you can deal whole person,

"Fibonacci:

then that family will not just cope, but they will thrive.

"Fibonacci:

And that's good for that family,

"Fibonacci:

but it's good for society as well.

"Fibonacci:

Traditionally, this is a sort of thing

"Fibonacci:

that an experienced case worker would bundle up.

"Fibonacci:

A case worker would sit down with that family and say,

"Fibonacci:

have you considered this, and did you know about this,

"Fibonacci:

and you can possibly be eligible for that.

"Fibonacci:

Casework is expensive and it's not actually that scalable.

"Fibonacci:

As casework capacity increases, casework cost increases.

"Fibonacci:

It's a pretty straight line extrapolation,

"Fibonacci:

'cause it's a capacity issue.

"Fibonacci:

So it's actually quite expensive.

"Fibonacci:

And similarly, not everybody does casework.

"Fibonacci:

Now you might say at this point,

"Fibonacci:

well we live in a consumer society,

"Fibonacci:

and if all that help is out there,

"Fibonacci:

we've all got a smartphone.

"Fibonacci:

We've probably most of us got a laptop or a tablet.

"Fibonacci:

Why can't that family just research this stuff?

"Fibonacci:

In a consumerist society,

"Fibonacci:

the responsibility for bundling and packaging

"Fibonacci:

sits with the family, doesn't it?

"Fibonacci:

So far so good, but there's a problem with that argument.

"Fibonacci:

It doesn't actually work.

"Fibonacci:

It relies on perfect consumerist behaviour.

"Fibonacci:

We aren't dealing with somebody

"Fibonacci:

who's buying a pair of shoes, or booking a train ticket.

"Fibonacci:

What we're dealing with here

"Fibonacci:

is something that's far more important at a human level,

"Fibonacci:

than purchasing a commodity.

"Fibonacci:

And it brings with it a level of complexity,

"Fibonacci:

not least because we're talking about

"Fibonacci:

interacting with people

"Fibonacci:

when they are at their most vulnerable.

"Fibonacci:

One thing I have learned from years in the charity sector

"Fibonacci:

is that when you see these kind of cases

"Fibonacci:

where people have to package from very multiple sources,

"Fibonacci:

is that they cut out after about the second or third search,

"Fibonacci:

or the second or third phone call,

"Fibonacci:

because the process of getting help

"Fibonacci:

actually adds to the burden and stress.

"Fibonacci:

That can't be right.

"Fibonacci:

That's completely counterintuitive.

"Fibonacci:

So my question and my premise is this.

"Fibonacci:

What if the wider system of help could take the strain

"Fibonacci:

in a much more coordinated,

"Fibonacci:

and dare I say it predictive way,

"Fibonacci:

because we've established a persona.

"Fibonacci:

And that's how personas can work.

"Fibonacci:

There simply has to be in this day and age a data-led,

"Fibonacci:

data-rich solution to all of this.

"Fibonacci:

What if we could connect multiple sources of help,

"Fibonacci:

primarily charities, but local public services as well,

"Fibonacci:

through really really smart, interconnected use of data.

"Fibonacci:

And to my mind, what we're talking about here

"Fibonacci:

is the charity sector not thinking like

"Fibonacci:

a traditional benevolent organisation,

"Fibonacci:

but thinking more like Airbnb or Trip Advisor.

"Fibonacci:

The classic consolidator disruptor,

"Fibonacci:

fragmented marketplace of supply, significant demand.

"Fibonacci:

They're the broker in the middle.

"Fibonacci:

We can do this.

"Fibonacci:

What if we could use digital technology

"Fibonacci:

to create what I call a marketplace of help?

"Fibonacci:

And that's kind of how I've been describing it for years.

"Fibonacci:

I should probably trademark it,

"Fibonacci:

but that feels like the most appropriate description.

"Fibonacci:

What if there was a broader virtual system

"Fibonacci:

sitting behind thousands of charities,

"Fibonacci:

so that when somebody makes that first connection,

"Fibonacci:

wherever they first present as a sector,

"Fibonacci:

the wider virtual system simply kicks into gear.

"Fibonacci:

The system, whatever it is, technically,

"Fibonacci:

does the process of bundling.

"Fibonacci:

And as I've already said,

"Fibonacci:

that notion of holistic help is critical

"Fibonacci:

because it gets people past simply coping, to thriving.

"Fibonacci:

Now make no mistake,

"Fibonacci:

there is a lot of phenomenal work going on

"Fibonacci:

in the charity sector,

"Fibonacci:

between charities making really effective referrals,

"Fibonacci:

signposting to complementary services.

"Fibonacci:

And that stuff is very, very data-led.

"Fibonacci:

So we're not starting from scratch.

"Fibonacci:

We're starting from a very, very strong position.

"Fibonacci:

My point here is that that stuff

"Fibonacci:

that you tend to typically see is quite bilateral.

"Fibonacci:

The marketplace philosophy is based on something

"Fibonacci:

being consolidated and multilateral.

"Fibonacci:

So in other words, rather than everybody setting up

"Fibonacci:

lots of bilateral partnerships,

"Fibonacci:

even though they work very, very effectively,

"Fibonacci:

what if we all collectively use data

"Fibonacci:

to create a single marketplace,

"Fibonacci:

to which we all then subscribe.

"Fibonacci:

It's a industrial scale form of consolidation and brokerage,

"Fibonacci:

but the social impact implications

"Fibonacci:

could be unbelievably transformational.

"Fibonacci:

The potential for that system

"Fibonacci:

growing and learning organically is huge.

"Fibonacci:

You get a bit of pretty basic machine learning

"Fibonacci:

embedded in this,

"Fibonacci:

and it starts to work like Amazon Marketplace.

"Fibonacci:

Other online retailers are clearly available.

"Fibonacci:

I have a massive data footprint with Amazon,

"Fibonacci:

'cause I've been using them for years.

"Fibonacci:

So when I buy a pair of gloves,

"Fibonacci:

it says, would I like to buy a scarf as well?

"Fibonacci:

And it does that because of two things.

"Fibonacci:

It does that because it has established

"Fibonacci:

a behavioural pattern on my part as a regular visitor.

"Fibonacci:

But it's also established through

"Fibonacci:

what I think is probably machine learning,

"Fibonacci:

but I'm not an expert,

"Fibonacci:

something that says 79% of people who needed that

"Fibonacci:

also needed that.

"Fibonacci:

So it sees me not just as an individual,

"Fibonacci:

but it sees me through the lens of a typical persona.

"Fibonacci:

Now, if you can do that on help,

"Fibonacci:

and you can have something that learns organically,

"Fibonacci:

it constantly gets better,

"Fibonacci:

it constantly gets more effective.

"Fibonacci:

Now I'm not a technical expert,

"Fibonacci:

as you will have worked out by now,

"Fibonacci:

but I've got a sense

"Fibonacci:

that there's an enormous opportunity here.

"Fibonacci:

And this opportunity requires a number of things.

"Fibonacci:

So it's going to require a critical mass,

"Fibonacci:

and an approach and a philosophy

"Fibonacci:

that are highly highly multilateral in nature.

"Fibonacci:

Really really importantly,

"Fibonacci:

it has to start from the premise

"Fibonacci:

that this isn't about making money.

"Fibonacci:

So that classic classic process

"Fibonacci:

of commercial intermediation doesn't apply.

"Fibonacci:

Now that is actually really, really helpful,

"Fibonacci:

'cause it means we don't have to worry about

"Fibonacci:

the money thing.

"Fibonacci:

And that probably sounds a bit surprising,

"Fibonacci:

coming from a commercial accountant,

"Fibonacci:

but hopefully it reflects the fact that by now

"Fibonacci:

I have learned to think in a different currency.

"Fibonacci:

- I was gonna say, that's some journey you've been on.

"Fibonacci:

- I hope it is because that's my job.

"Fibonacci:

It's my job to think like that.

"Fibonacci:

Because it's not about making money,

"Fibonacci:

you don't need a common front end.

"Fibonacci:

So you're not saying you have to go to help.org.uk,

"Fibonacci:

or whatever you're gonna call it.

"Fibonacci:

That isn't needed.

"Fibonacci:

What if we could all act as effective referral agents

"Fibonacci:

for each other, through this bigger collective system.

"Fibonacci:

If you like, what about if everybody

"Fibonacci:

is everybody else's front door,

"Fibonacci:

because the person that we're helping benefits from that.

"Fibonacci:

It will need gold standard levels of data security and DP.

"Fibonacci:

We are talking about the data of people

"Fibonacci:

at their most vulnerable.

"Fibonacci:

So if a bit of my record on a online retail platform

"Fibonacci:

happens to reveal to somebody

"Fibonacci:

that I bought a pair of slippers,

"Fibonacci:

in the great scheme of things it doesn't matter.

"Fibonacci:

But the sort of data

"Fibonacci:

we're talking about here matters intensely.

"Fibonacci:

So the design of this thing that I'm talking about

"Fibonacci:

has to be gold standard, in terms of security.

"Fibonacci:

GDPR is actually a massive asset in this.

"Fibonacci:

You probably find that most people don't talk about GDPR

"Fibonacci:

as an asset, but it really, really is.

"Fibonacci:

GDPR presents us with something

"Fibonacci:

phenomenally valuable in this,

"Fibonacci:

because it helps to define a benchmark level

"Fibonacci:

of practise and behaviour for anybody wanting to participate

"Fibonacci:

in the marketplace of help.

"Fibonacci:

And if we ever got this going as a sector,

"Fibonacci:

there's a very careful balancing act

"Fibonacci:

between wanting as many agencies as possible participating,

"Fibonacci:

because the richness of what's available is enhanced.

"Fibonacci:

But having barriers to entry about minimum standards of data

"Fibonacci:

and digital maturity, that balancing act would be critical.

"Fibonacci:

Somebody's gonna have to go first.

"Fibonacci:

By definition, it can't be somebody acting on their own,

"Fibonacci:

it's got to be a first cohort who'll just give it a go

"Fibonacci:

and prove the concept.

"Fibonacci:

And of course, somebody's gonna have to fund it.

"Fibonacci:

Somebody's gonna have to fund the initial investment

"Fibonacci:

in creating this thing, whatever it is.

"Fibonacci:

That can be funded in money, or it could be funded in kind.

"Fibonacci:

It's a sort of thing that actually big tech could say, okay,

"Fibonacci:

we're gonna take some of our bright people,

"Fibonacci:

and we're not gonna send you a donation,

"Fibonacci:

but we're gonna build the infrastructure

"Fibonacci:

around the marketplace for help,

"Fibonacci:

and invite participation and invite organisations

"Fibonacci:

to plug in.

"Fibonacci:

So there's lots of considerations to getting it going,

"Fibonacci:

but I've been convinced by this idea

"Fibonacci:

for the last six or seven years.

"Fibonacci:

And the more I think about it, the more I think this idea,

"Fibonacci:

if brought to fruition, could be quite extraordinary,

"Fibonacci:

because it recognises how complex needs work.

"Fibonacci:

- Yeah.

"Fibonacci:

I think the thing is, like you say,

"Fibonacci:

it's, you can see it's a great idea and how it could work,

"Fibonacci:

but again, it's one of those real challenges

"Fibonacci:

as of who would drive that.

"Fibonacci:

'Cause that is obviously societal change, isn't it?

"Fibonacci:

Is it the Charity Commissioner,

"Fibonacci:

or is it actually government, or is it, you know,

"Fibonacci:

sort of a lot of public organisations working together?

"Fibonacci:

But I think the idea as well of using some machine learning,

"Fibonacci:

or having it so that actually you've got

"Fibonacci:

that predictive modelling within it,

"Fibonacci:

you're not just looking at what you've got.

"Fibonacci:

You're looking at it sort of future-wise.

"Fibonacci:

Obviously you've got a,

"Fibonacci:

even not being a techy person in your own words,

"Fibonacci:

you've got that kind of vision of how data could be used,

"Fibonacci:

and how it could completely change the way that people

"Fibonacci:

can benefit from and access services.

"Fibonacci:

But I wonder how many charities are that data-driven?

"Fibonacci:

There was a report that came out a couple of years ago

"Fibonacci:

that basically said that they were thinking they performed

"Fibonacci:

in using, managing and analysing data,

"Fibonacci:

not particularly brilliantly.

"Fibonacci:

I mean sort of 88% of them saying

"Fibonacci:

they're fair to poor at it.

"Fibonacci:

So again, I think it's that getting the right person

"Fibonacci:

to drive it with that vision.

"Fibonacci:

So I don't know whether that's going to be your next

"Fibonacci:

I'm going to aim for.

"Fibonacci:

It's visionary.

"Fibonacci:

Because like you say, you know,

"Fibonacci:

it's great when you can see data being a sort of an enabler,

"Fibonacci:

and technology being used disruptively for a greater good,

"Fibonacci:

but there is still, isn't there,

"Fibonacci:

some sort of steps to move that, to progress it on.

"Fibonacci:

I don't know kind of what your thoughts are on that.

"Fibonacci:

- I think it will happen,

"Fibonacci:

and I think it will happen organically.

"Fibonacci:

It's going to have to come from the sector.

"Fibonacci:

It's not a fair thing to chuck at our regulator.

"Fibonacci:

It's not something we should reasonably expect

"Fibonacci:

the Charity Commission to do.

"Fibonacci:

It's actually not fair on them.

"Fibonacci:

And I don't think it's gonna come from government.

"Fibonacci:

It's going to be something that as the sector evolves

"Fibonacci:

and matures collectively, it's gonna happen.

"Fibonacci:

Data is really more and more central

"Fibonacci:

to what the sector is doing.

"Fibonacci:

We are absolutely on a journey, as a sector.

"Fibonacci:

The fact that we actually have specialist

"Fibonacci:

conferences and networks on things

"Fibonacci:

like innovation around data,

"Fibonacci:

this stuff is picking up a head of steam

"Fibonacci:

and it's actually really, really encouraging.

"Fibonacci:

I think the sector has really upped its game

"Fibonacci:

in the last decade.

"Fibonacci:

Have we got to the promised land?

"Fibonacci:

No we haven't.

"Fibonacci:

But there is a sense that this is a really critical thing.

"Fibonacci:

And I think the whole idea, for instance,

"Fibonacci:

of the marketplace for help,

"Fibonacci:

I think it will come organically

"Fibonacci:

out of that unstoppable momentum.

"Fibonacci:

You know, if you look at Blind Veterans U.K.,

"Fibonacci:

we now have a strategic objective on data.

"Fibonacci:

So we have six strategic objectives, one of which says

"Fibonacci:

we will use data and digital

"Fibonacci:

to make a bigger difference in the world.

"Fibonacci:

It's completely out there, it's in the public domain.

"Fibonacci:

And it's very deliberate on our part,

"Fibonacci:

because we want to say to the world, this is who we are.

"Fibonacci:

This is how we work.

"Fibonacci:

There are some really great examples in the sector

"Fibonacci:

of how this thing is just hurtling forward positively.

"Fibonacci:

I've already talked about single customer view.

"Fibonacci:

There's been massive, massive progress on that

"Fibonacci:

across the sector.

"Fibonacci:

And again, as I said earlier,

"Fibonacci:

helping charities to be more relational

"Fibonacci:

and less transactional, that's really priceless.

"Fibonacci:

It's really, really positive.

"Fibonacci:

There's a lot of phenomenal work happening on data analysis,

"Fibonacci:

to understand the bigger picture.

"Fibonacci:

And again, it's data analysis

"Fibonacci:

that is constantly reinforcing this idea

"Fibonacci:

of the very sophisticated interconnectivity

"Fibonacci:

between charities, and it's that kind of platform

"Fibonacci:

and that kind of ethos that will make marketplace of help

"Fibonacci:

happen one day.

"Fibonacci:

So if I give you an example,

"Fibonacci:

there's been a really phenomenal piece of work done

"Fibonacci:

over the last couple of years by,

"Fibonacci:

I think it's the University of Northumbria,

"Fibonacci:

working with veterans' charities,

"Fibonacci:

and it's to create a thing called the map of veterans needs.

"Fibonacci:

That's a really powerful piece of shared analysis

"Fibonacci:

that helps the whole veteran sector

"Fibonacci:

plan and target more effectively.

"Fibonacci:

And it's painting a really rich picture

"Fibonacci:

of the U.K. population,

"Fibonacci:

so it's telling us where veterans are,

"Fibonacci:

where there's regional variation,

"Fibonacci:

what their current welfare needs is,

"Fibonacci:

but it's also anticipating

"Fibonacci:

what their future needs are going to be.

"Fibonacci:

Now for us, as a charity

"Fibonacci:

that's not just a sight loss charity

"Fibonacci:

but a veteran charity as well, this is gold dust

"Fibonacci:

because it helps us get our strategy and our plans right.

"Fibonacci:

And if we get those things right, we'll do more good.

"Fibonacci:

It's that simple.

"Fibonacci:

You know, I look at what our research charity is doing

"Fibonacci:

around digital phenotyping.

"Fibonacci:

We've all got a smartphone, or pretty well most of us

"Fibonacci:

have got a smartphone.

"Fibonacci:

Smartphones are increasingly being used

"Fibonacci:

to collect health information.

"Fibonacci:

Digital phenotyping,

"Fibonacci:

which is something that our research subsidiaries

"Fibonacci:

is working on,

"Fibonacci:

is all about using data collected from smartphones

"Fibonacci:

to build a picture

"Fibonacci:

about an individual's lifestyle and health.

"Fibonacci:

And so what they do is, patients download and launch an app

"Fibonacci:

that collects both active data from things like surveys

"Fibonacci:

and passive data from things like the stepometer.

"Fibonacci:

So we've all got a stepometer on here.

"Fibonacci:

You've got one in your pocket, I've got one in my pocket.

"Fibonacci:

And those measurements can then be correlated

"Fibonacci:

with self-reported data and findings

"Fibonacci:

from clinics and clinicians.

"Fibonacci:

And it creates opportunities to conceptualise

"Fibonacci:

large volumes of patient-generated health data,

"Fibonacci:

which can be used to aid clinical decision-making.

"Fibonacci:

Now you can take it further as well.

"Fibonacci:

So if a person's vision starts to weaken,

"Fibonacci:

they might start going out less

"Fibonacci:

when the evenings get darker.

"Fibonacci:

Care providers can be alerted through digital phenotyping,

"Fibonacci:

by these digital signals, and determine very early on

"Fibonacci:

whether an intervention's needed.

"Fibonacci:

And the beauty of this, to an accountant,

"Fibonacci:

is that this is really, really low cost,

"Fibonacci:

because the physical infrastructure is already there,

"Fibonacci:

'cause we've all got it in our hand or in our bag

"Fibonacci:

or in our pocket.

"Fibonacci:

Now the point there, to go back to your point

"Fibonacci:

about that sense of the way we were becoming

"Fibonacci:

more and more interconnected,

"Fibonacci:

something like digital phenotyping

"Fibonacci:

is at its most transformational and most powerful,

"Fibonacci:

when it's operated on a multi-agency basis.

"Fibonacci:

When you take the data in multiple sources,

"Fibonacci:

cutting across organisational boundaries

"Fibonacci:

and put it together,

"Fibonacci:

and there's the two plus two equals five effect.

"Fibonacci:

So I look at those kind of things and I think, well,

"Fibonacci:

I think the marketplace of help idea

"Fibonacci:

will happen organically,

"Fibonacci:

because it's where we are gravitating as a sector.

"Fibonacci:

There's a lot of opportunities to take things further,

"Fibonacci:

like I say, but I see a really consistent commitment

"Fibonacci:

across the third sector.

"Fibonacci:

One of the other things I see as well

"Fibonacci:

is a real commitment to invest in data talent

"Fibonacci:

and thought leadership.

"Fibonacci:

One of the best things about working in the charity sector

"Fibonacci:

is that 'cause we aren't really competing with each other,

"Fibonacci:

that thought leadership is really viral.

"Fibonacci:

So we have people in this sector like Zoe Amar,

"Fibonacci:

who's the chair of the charity Digital Code.

"Fibonacci:

Got people like Jo Kerr who is the instigator and driver

"Fibonacci:

of a thing called the Digital Maturity Matrix.

"Fibonacci:

We've got people like Tris Lumley

"Fibonacci:

at New Philanthropy Capital,

"Fibonacci:

who's constantly challenging the sector

"Fibonacci:

to think very, very laterally about using data

"Fibonacci:

to create better beneficiary pathways.

"Fibonacci:

If anybody's listening to this, look these people up,

"Fibonacci:

you will not be disappointed.

"Fibonacci:

They are so fresh thinking and so wise.

"Fibonacci:

And we've got things like, you know, a few years ago,

"Fibonacci:

the sector set up this thing called

"Fibonacci:

the Digital Leaders Programme,

"Fibonacci:

which is run through the School for Social Entrepreneurs.

"Fibonacci:

And I was lucky enough to be part of the

"Fibonacci:

inaugural graduate cohort in 2016

"Fibonacci:

that went through this programme.

"Fibonacci:

So there's this really collective recognition that we need,

"Fibonacci:

not just to invest in talent,

"Fibonacci:

but to invest in the kind of thought leadership

"Fibonacci:

that will make these more transformational initiatives,

"Fibonacci:

they'll happen naturally.

"Fibonacci:

There is a but to all this, and that but is,

"Fibonacci:

sometimes it feels like when the really big conversations

"Fibonacci:

around big data and similar subjects are happening,

"Fibonacci:

that we get forgotten about as a sector.

"Fibonacci:

Sometimes it feels like

"Fibonacci:

it's the exclusive preserve of industry

"Fibonacci:

and the public sector.

"Fibonacci:

We need to be at that table,

"Fibonacci:

because when you're talking about

"Fibonacci:

stuff that affects people at a human level,

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charities have a unique perspective

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that for all its strengths,

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public services sometimes don't have.

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I used to be a senior civil servant,

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so I'm not gonna criticise the public sector,

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but we bring a degree of intimate understanding,

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and therefore nuanced data,

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that can inform some of those really big conversations

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about big data, about innovation, about connectivity.

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And I think we do tend to make a general plea that says,

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please let us be at the table by default.

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And if we think not just cross-agency but cross-sector,

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some of the opportunities that are available

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that are very, very data-driven, are huge.

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I thought for years we could run payroll giving

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through the tax system.

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In fact, I'm convinced that you can run payroll giving

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through the tax system.

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When people leave a job,

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usually 'cause they get a better job,

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their payroll giving declaration lapses.

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The system assumes

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that they no longer wish to be charitable.

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Well, that doesn't make sense.

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So lapsed payroll giving declarations

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cost the charity sector millions of pounds every year.

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If you integrate that with the tax system,

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and by the way you can 'cause we did it with student loans,

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it can work.

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If you integrate it with the tax system,

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you make it universal and portable.

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You're gonna fund stuff that's gonna change lives.

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You're gonna fund stuff through that retention of income,

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that might save the life of somebody that you or I love.

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That's what's at stake,

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and that is completely driven by data.

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So if we have a seat at that table,

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the transformational outcomes we can create,

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even through simple things like that,

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simply by being smart about simple data solutions,

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the impact is gonna be unbelievably good.

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- It brings me on nicely to my last sort of question,

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which is for people who are maybe thinking about

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entering into either a data career,

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or actually a career within the charitable sector.

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What kind of skills do you think

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that they should be trying to get on to their CV?

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I mean, I've always sort of said that

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within the data sort of space,

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there's been a sort of massive change.

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Instead of it just being very, very technical people,

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it's the people who are inquisitive.

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Like you say, you're curious about why things work

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in the way they should.

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Or there's also then the kind of,

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sort of reporting the analytics field, visualisation field.

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But what would you say are particular skills

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that if somebody was thinking of coming and

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sort of moving into either of those sectors,

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would be top of the list?

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- I'm not a data professional.

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So there are people who are far far more qualified

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to talk about that than me.

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What I can talk about is if you go into a role

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that is data-rich within the charity sector,

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what do you need?

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And number one is you have to have

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that intense unstoppable curiosity.

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We did a piece of work, about 18 months ago,

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where we took the data that comes from

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the voice of our members.

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And we took this very, very big data set.

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And the scope of that data set was very very deliberately

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just what the members were telling us.

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It was very unfiltered,

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but it excluded any of the kind of organisational biases

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that we might have put onto it.

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It was purely coming from the members.

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And we produced this thing called the ecosystem map,

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and it actually tells us what's really important.

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Now that's been really powerful

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in helping us plan and helping us create

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really robust strategy.

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That was a piece of work I sponsored,

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but the substantive work was done by a colleague of mine,

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chap called Matt Lee, and he won't mind me mentioning him,

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'cause he's given me permission to mention him.

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Matt is a serious, serious expert

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in complex data interrogation interpretation,

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but he also is driven by an intense curiosity.

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So it's technique

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and it's kind of behavioural attitudinal as well.

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And the final analysis we got on our ecosystem map,

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which is actually proving really, really vital by the way,

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in helping us define good partnership strategy,

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so it's forcing us to take very deliberate action.

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It's not just an exercise for its own intellectual sake.

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It's making us do stuff.

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We wouldn't have got to the quality of interpretation

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and conclusion that we got to on that,

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if the field work and the actual research

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hadn't been done by somebody

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who's just known for being powered through curiosity.

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And if you've got somebody who has the ability

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to assimilate and balance data from multiple sources,

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and who can look at it objectively,

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who can have an intense curiosity to say

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what else might I bring into this thinking,

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what other data sources, be they internal or external are,

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and has just that natural curiosity gene,

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and who cares deeply about the work that's going to be done

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off the back end of it.

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So there has to be the brain,

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but there has to be the heart as well.

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Somebody coming into a data-rich job in the charity sector

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needs those three things.

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But if somebody does have those three things,

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they'll probably be a really, really big success,

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'cause they're gonna be made of the right stuff

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that catalyses more of these sort of

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transformational ideas and transformational programmes

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that we've been talking about.

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- Obviously at the moment you're working with Red Olive,

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and they're like an outsource data department for you.

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From a strategic point of view,

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what was the sort of thinking behind that

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and how is it working?

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- It's working pretty well.

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It was driven very much by our information services team

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who are phenomenally good technical experts.

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We have an information services team

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that's been drawn from a very, very sort of

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wide range of experiences.

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And we have people who've worked on

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three different continents.

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We have people who've worked in multiple industries.

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So again, you know what I was talking about earlier

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in terms of the data group

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being a group of people with very high cognitive diversity,

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our information services team have the same.

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And the Red Olive relationship was very much

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their brainchild, because they saw an organisation

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that would get us, if that's not too cliched a term.

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And again, it goes back to something else I was saying

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that says if we are going to be brave

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and imaginative and creative,

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we need something that's not transactional.

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We need a relationship like the one we have with Red Olive,

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where we can just say, do you know what,

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we're just gonna share some ideas,

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and it feels like a single virtual team.

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And I think that's why our relationship

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with Red Olive works, because it's not just transactional.

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We're not just saying dear Red Olive,

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perform this set of tasks.

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There is that sense that when we're talking about

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innovation and progress and implementing new things,

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that it is one team.

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I have that relationship with Jefferson.

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We just meet up once a quarter and talk about

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how the charity sector might use data better.

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And actually that's how I ended up talking to you today.

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The key thing for me to sum up

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is that if it's just transactional, yeah,

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it'll work after a fashion.

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But for an organisation as critical in terms of what they do

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as Red Olive does for us,

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they have to understand what makes us tick,

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and Red Olive do understand what makes us tick,

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and that's why it's healthy.

"Fibonacci:

- A really interesting take on the importance

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of how data could be the driver

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for making society a better place for all.

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Join us for the next episode of

"Fibonacci:

The Red Olive Data Podcast",

"Fibonacci:

where we'll be joined by another expert

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sharing their thoughts on the latest trends

"Fibonacci:

in AI and big data.

"Fibonacci:

Make sure you subscribe to

"Fibonacci:

The Red Olive Data Podcast",

"Fibonacci:

from wherever you get your podcasts,

"Fibonacci:

to make sure you don't miss out.

"Fibonacci:

That's all for today.

"Fibonacci:

Thanks for listening.

"Fibonacci:

I've been your host, Nicky Rudd.

"Fibonacci:

I'll see you next time.