[ ] Yeah, that’s a fantastic question. One of the things about data is that – a good friend of mine describes it as a team sport. The data team alone cannot do that; the strategy needs buy-in from the entire organization. That’s one of the reasons why I think I always push for the lead of the data team to be part of the exec team, because they need to span across the entire organization…

But in order to develop the strategy, there’s several things you need. Firstly, you need to make sure you understand the organizational strategy, and by that I mean you need to know the objectives of the organization, the boundaries of scope and the approach… And usually, those organizational strategies are hidden in reams and reams of documentation, and you’ve gotta just try and simplify it, because data also needs an objective to work towards.

So when building the strategy, the first thing that we did was made sure that we could disseminate the organizational strategy into a sentence that everybody could understand… And also that we could work very easily with data, so it was a little more discreet in its numbers, it had very clear numeric objectives, a timeline which it was working with, a bounded scope, so it wasn’t just any free idea, and also a clear advantage that we were using.

For example, at Just Giving, the advantage that we had is that we had millions of causes on the site, and nobody else had that. So we had to work with that piece of content, rather than just coming up with something arbitrary at the time.

So getting the business strategy right was one of the most critical things we needed to do in order to get the data strategy. It was then from there that we went on to start looking at the possible use cases, and those use cases were really disseminating those decisions, as I said, to try and understand “What are the decision we’re making operationally?” For example, sending an email about a new campaign - let’s say there was an earthquake… For example, we had the earthquake in Haiti a while ago - so who exactly are we gonna send that email to? Because every time you send an email, as with any decisions, there are tradeoffs; something happens, you send the email, and there are people who will unsubscribe. That means there are less people available for us to email for the next cause, so we need it to be personal.

So that was a decision that we had to make, “Who do we send that email to?” and that’s where we could apply AI. So that helps with the use cases.

Then also looking at the decisions that are being made externally by our audience… So when someone comes onto the site, are they deciding how much they want to give? Are they deciding who they wanna give to? Are they making a decision on whether they want to come just read, or absorb content? So trying to understand those and support those decisions…

And the last thing, actually – so I said the strategy, the use cases… So the last thing in the data strategy was understanding where you are as an organization, so looking at it on almost two spectrums. The first spectrum is what capabilities do we have to develop any of these data solutions? Are we at the stage where we can only say what’s happened and why it’s happened, or can we build algorithms that can predict what’s happening and even prescribe?

And the second spectrum we were looking at was “How well do we know our decisions?” Once we get an indication of where we are, you almost have a gameplan, a roadmap of how you’re gonna get to the desired destination. It was a lot there, so let me know if I need to go through any of it again.