Mark Flaherty: So again, this is what we mean by Self-Service BI. It's the ability to work with this data that’s much larger than where you can traditionally work with In-Memory right on our laptop. You can also directly connect to your databases because in some cases, you may have invested in very fast databases like a teradata or vertica database and you want be able to leverage that fast infrastructure. So you can connect directly in InetSoft too and you can actually switch back and forth between your data grid cache and your live data source connection. And in both cases, you can create this kind of ad-hoc explorations of your data just as we are doing here.
Another thing that’s very important when you are working with Big Data is the ability to mash two data sources together. In BI we have been somewhat siloed in saying okay we have got some data over here in this database and some data over here in other databases around Excel Spreadsheets. And we can do this analysis and that analysis but would never combine the two. And that’s really limiting because when you think about having sales data and operational data, there are all kinds of questions that you may wanted to answer by mashing up those two data sources.
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So I am going to give you an example of how you can do that with InetSoft. Let me go ahead and create a new sheet. And I am going to go ahead and connect to a new data source, and let me just tie this up here. And I am going to click “Connect to an Excel Workbook”. And it's a workbook of State Parks I am going to connect live. So I am not really sure what’s in there right now so I can just explore this data and say okay we are in the State Parks, I have got the name, I have got the state. This is probably the long name of the state and so on. So the state data, let’s just check and make sure that’s data abbreviation. So it looks like these are all the states where there are national parks and monuments in the United States.
And we can drag the number of records off the side and say okay, well most of the national parks and monuments are actually down here in the Southwest and that’s an interesting result, but quite a few up here in Alaska as well. But if I wanted to look at the states and parks and mashed that up with some of the airline data we are looking at and saying, “I would love to go and visit some of the state parks and monuments but I don’t want to be stuck at the airport the whole time.” So with InetSoft, if you have fields in common between two datasets, they are automatically mashed up. And so I am going go ahead and rename destination state to State. And when I do that InetSoft is going to realize that both fields have “state” in common and you can see that with a little red icon here it says, you have got two different data sources in plan, I know that they have “state” in common.
So I can actually take arrival delay, from the second data source and mash it up with this first data source. Again let’s change their color to the skin that we have been using, the orange blue so that you can see the biggest delays in orange. And now all of the sudden you can see where the biggest delays, the biggest average delays, let me go ahead and change that average again, where the biggest delays are around the country. And it shows you that the east coast is very much more delayed than the west coast and it’s also left to your park. So if you are really looking at going to a place with great parks you can actually come right here to Utah, where there are 11 parks and monuments. And there is very small average delay or you can go to New Jersey where there is only one state park and monument and the average delay is about 14 minutes. So kind of a fun example but you can see right here that we have mashed up a live connection to an Excel Spreadsheet which just has some parked data.
And using a data grid cache, the 70 million rows or 66 million rows of flight data are accessible right here in InetSoft. And that’s an extremely powerful tool to be able to take deep and dive into different datasets, and mash them up. You can do this with different warehouses, different databases, different Excel Sheets. We see this being used very much by our customers, not only to look deeply to different databases but some times just augment data as I have done here, augment some flight delayed data with some additional data that you might want to pull in.
You could see yourself doing that by modeling out sales territories for example. You might have your entire sales database and you want to know what things would look like if you just change your sales territory, slightly. You might not want to change that in your massive database just to try something out but you can augment that database within an Excel Sheet of the new territories to see what those sales look like in each territory. That’s just another example of what you could do. We have created the dashboard showing us where different delays in the country are and mashed up this airline delay data with some data in an Excel Spreadsheet.