 Keore, mae nestaigi kaunai i au ganau i girاio zoto?」 Aotelebi'i datumia kautama a algodosri tlirai ata garaiai pa mlea i datatiai. Here's a quick visualisation I've done of an Excel spreadsheet It's not actually that useful but it's something you'll be familiar with. What we'll be looking at today is some different ways to view that kind of data. So typically there's three steps that you'll go through. You'll often have to take your existing data and get it into some generic format. That might just be an Excel spreadsheet. You might then have to do some kind of cleanup or manipulation of that data to make it best suit the type of visualisation that you're wanting to do. Lastly, you bring it across to the tool that you've got and start analysing and viewing it. Excel, in fact, is one of the tools that you could use in that cleanup step. Here I've used a couple of basic Excel functions to split a text column into multiple columns and pull out just the year from the acquisition date column in this data. This data is actually all of the works acquired by Art Gallery of New South Wales in the 1990s. Google's Open Refines, another product you can use with that cleanup step. There's a website, The Programming Historian, that's written a really good blog post about how you can use Open Refine to clean up your data. So you might, for example, have map coordinates for some of your collection items. You could choose to import that data as a layer in Google Maps, which is a free option. And so here I've got some collection items. And in this case, this is helping us spot the trees in the collection. Or in this case, not a tree, a magnificent kiwi caught on its migration to its breeding grounds in the North Pacific Ocean. Now that actually seems a bit odd. So if I drill down into the data, I see it's merely a mistake of somebody putting in a latitude as just a positive number instead of negative for the southern hemisphere. So I can correct it and move that kiwi back to the steward island where he belongs. It also helps us see the forest. In this case, we're looking at all three quarters of a million specimens from Queensland Museum. And they've tapped into visualization tools that a bigger project is built. In this case, the Atlas of Living Australia. And we can immediately get a sense of where all of that collection was actually collected from. Google Analytics can let you check through data about visitors to your website and visualize that in different ways. And in fact, now Digital NZ is adding a similar matrix dashboard to give you basic stats about items you provide to them. So that's yet another incentive contributing to some of these bigger projects. Excel itself also has some basic tools, pivot tables and charts. And that can let you take this raw data and then build it into an interactive chart. So here, I've combined the media of those art gallery items with the period they were made. And I can drill down into an individual media, in this case, ceramic items. And I can see what periods they'd come from. So a lot of this particular collection comes from the Ming dynasty. There's a number of new tools that are evolving in this market. And one of those is IBM's product, Watson Analytics. So I can import that same data in here and it immediately gives me some information about the quality of it. If it's messy, it'll say you're a bad, bad cataloger. It doesn't quite say that, but it gives me some indication that in this case, my date field's pretty consistent. One clever thing that Watson Analytics does is it prompts you with questions that it thinks it can answer based on the data you've given it. But you can also prompt it with questions of your own following its standard syntax. So, for instance, I might take that same collection data and ask it to plot by year. And it's immediately apparent that 1990 was a big year for acquisitions in the Art Gallery of New South Wales. I might then break that down further, perhaps by departments within the museum. And I can see that in 1990, a large portion of that collection were from the Australian Art Department. In fact, if I drill down further, it's the acquisition of a large print collection. And so Watson provides a whole range of different ways of presenting that data so I can really easily see what portion of the collection is a particular media, for example. It lets you build interactive dashboards from these different chart tools. So I could have several different ways to drill down into that data. Unfortunately, it's only with the paid version you can then share those dashboards to other people. But you can download any of those charts in standard formats and you might, for instance, include it in an annual report. There are also some really simple tools. So this is the Wordle website. You can just paste in a whole block of text and it'll show you how popular each of those words are. And this is a whole set of museum mission statements that I copied and pasted. And we can see what kind of words people are repeatedly using in their mission statements. Everybody wants to inspire. Now, this last example is using Tableau Public. In this case, we're looking at which songwriters contributed over time to the different Beatles albums. And we can see George Harrison over time gradually growing in his contributions. Tableau is probably the most complex product that we've looked at, but it does produce some really beautiful visualisations. So I've put all of this up on Slide Share. There's links to all of the products that I've mentioned today. Thank you very much.