The second benefit of data visualization is speed of thought. And we see this a lot, whether it’s in a meeting in person or on a phone call with someone via Gotomeeting or WebEx. Data is on the screen, and people are trying to slice it different ways, and they do not necessarily know where they want to go with it. They need to have a much ad-hoc experience than a report can give.
And we see with many our clients that a more collaborative speed of thought approach eliminates the cycle of pain. By that I mean avoiding the other way to get that information where the user makes the custom report request. It goes it back to IT who writes some queries, runs them, and gets some results to come back. It takes a few days.
But then you didn’t get the answer you thought. It goes back through the cycle. The other somewhat less painful version is where you get a download from the source system into Excel. You get some flattened data. You try to slice a dice it, but that’s hard because it’s hard to go through a lot of fields using Excel.
Finally you’ve done all of the hard data manipulation and create a summary to show people. Then the Excel sheet lives on because it’s hard to get in the first place, so you keep it. The shadow data mart is born, but it’s painful to manage when you need updated data and so on.
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There is a whole set of problems we’ve seen visualization apply to about risk reduction. And that could be a case where I’ve got 20 million customers. I want to find the right list of 40,000 to send a mailing to. That’s a perfect case for this. I’m an HR manager, and I want to find the at-risk employees in my firm. I’ve got 300,000 employees I don’t know how many are there at-risk, but I want to get a list of them out.
Well in this case we’re going to be fund raiser and our president is going to South Florida. We want to find the right people to invite to a dinner. So let’s take a look at how that might go like. I am opening another viewsheet, and again this is a web based analytics solution so it can work in browsers. It can work on iPads. It can work on client PC’s.
There are 93,000 prospects. There is a list of them all. There are some filters on the first page. We organize analytical views on tabs and bookmarks. Here is a map giving history. You can select anywhere and it updates everywhere. So let’s do a little brainstorming for our dinner in South Florida.
We want high rated prospects who maybe have been staff. We go to the ratings view. Here is the data on the same 93,000 people, the same list summarized. There is a smart chart which shows ratings. So these people are rated highest as red, and there are 166 of them. So I might say let’s just grab the three highest rated and sweep over them with the mouse. The like count is 953 of them. Here the staffing level is shown on a bar at the top. These are the ones who weren’t staffed. It tells we had 224. The lower number of unstaffed is in those top rating groups out of 81,000 total unstaffed. That’s actually not enough.
So I am going to go back and say I cut this the wrong way. Lets add in this next rating group, the one to five million group. I control click, and it’s added in. Now I have got 3,000 total people. I have 711 in this unstaffed group. That’s better. Let’s get rid of everybody else. So now I am just looking at my top rating groups. I can assume the bar and because at this level I actually wanted to see all the labels of the top of this group.
So I am seeing a bunch of these are staffed. The unstaffed are these 711. I am using coloring consistently so I see I’ve got a mix of red and orange across the ratings here I’ve got some of the red as the highest, some yellow okay, so I have a mixed bag. Let’s grab this group, and get rid of everybody else. And so there was 711. Let’s go to the map view and see where they live.
There is a big bar up here in New York so I’ve got a cluster in New York, 85 of them. Up there I’ve got some in Boston, Cambridge and the suburbs around. There are not a whole lot in Chicago. It looks like I have a cluster in the Bay Area, not a lot in LA and I actually have a lot in South Florida. So you might actually wanted to go back and do some dinners up here, but doing one in South Florida for sure.