InetSoft Articles and Resources: Data Mashup Tool

Looking for a data mashup tool? InetSoft offers Web-based BI software that includes a powerful and flexible data mashup tool with direct access to disparate data sources of almost any type. Below are articles related to InetSoft's software.

Data Mashups and Governance - Especially when you're trying to work with many different data sets, and there’s the security angle here and there is also a data quality or data governance angle to this as well. But if you start talking about meta data, it really is important that you manage these definitions well because otherwise you start mixing and matching apples and oranges, and that cause all kinds of problems. Definitions are a little bit of an Achilles heel today in data management, and I think that to me, one of the important areas, and perhaps the other guests will be able to comment on this later in the show, is how the end user is going to interpret the mashup to make sure there are no issues in the way they are interpreting and using the mashup...

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Data Mashups Combining Enterprise Data and External Sources - Byron Igoe: Right, I mean, I may be somewhat bias given the kind of Bi technology that my company provides, but when I think about data mashups and the users being able to manipulate and combine not only the enterprise data but the external sources, the fact that it’s all still metadata, and you see the provenance, really trace it back to where that it is coming from, that you can really take that information for what it’s worth, you know, you can see that oh, okay they are just mashing up some of my sales reports from last quarter with a Twitter feed, so I will take that with a grain of salt. Jim Ericson: Taking it with a grain of salt, but also may be you make, may be you roll that out for people to be able to look at it generally more broadly. Eric Kavanagh: Feel free to send those tweets out there, lot of good tweets already today. Thank you folks for that, and we will be right back. Yeah, we heard that metadata bell in the opening segment there. That’s our favorite word because it’s just everywhere in this conversations, there it is. One of these days we will may be have some kind of a contest where the first person to tweet a bell something in there in it with referencing the metadata bell will wins a trip somewhere, maybe to New York, the Source Media Headquarters. Jim Ericson: Oh, I am too scared away exactly..

Data Mashups in an Ideal World - What do you think of this ideal world we have been discussing? You have this array of data marts and you can essentially mix and match to create your own dashboard on the fly. Within organizations, it probably makes a lot more sense. I know others are thinking about interoperability with externalities, vendors, external users and such, and how can this internal data I have be shared in a meaningful way with others. But within an enterprise for developing mashups of all of your internal data...

Data Mashup Product Demonstration - First, I am going to walk through what the end-user can do with our BI application, and so I will show a bunch of pre-built interactive dashboard examples. Then I will segue a little bit into the self-service BI capabilities, showing both data mashup and the interactive visualizations that again we are putting in the hands of the end-users. And then I can touch on a couple of the new features that are specific in Version 11 of our Style Intelligence application. Feel free to ask me questions as we go along. If you would like to see something or if you are curious about little bit more specifics behind a feature or user functionality, just let me know...

Data Mashups vs. Data Warehouses - With the typical data warehouse scenario, if one thing goes wrong, the whole system comes down to its knees. There’s a huge dependence on the IT side before any other business can get done. Whereas if you flip the situation on its head and focus more on the self-service you get the users to respond and react quickly in tune to their own needs and then worry about the performance of those queries after the fact with appropriate technologies like column-based databases, temporary caches, even data grids for processing things in parallel, something like a Map/Reduce technique...

Data Mashups vs. OLAP Cubes - But it seems to me that ideally the beauty of a mashup environment, if it is done properly, is that you can essentially, I don’t want to say circumvent IT, but you can avoid a lot of the painstaking work required for building specific OLAP cubes. I think that it’s certainly enables the end users to act in an agile way. But for me, I also think that situational awareness is also important which is, if I come in the morning, and I have a really good dashboard I can look at my production data landscape and just see very quickly that everything is OK. Or I can look at the previous example of the UN country mashups and just see what’s going on...

Data Mining DefinitionData mining is the process of analyzing data from different perspectives and summarizing it into useful information that can be used to increase revenue and cut costs. Data mining allows users to analyze data from a multidimensional standpoint in order to sort and summarize any relations that are derived. It can be interpreted as the process of finding correlations among a multitude of fields in large relational databases. Companies use data mining to sift through data for market research, report creation, and report analysis. Technology innovation continuously increases capacity for analysis whilst driving down costs...

Data Modeler - The Data Modeler included in InetSoft's business intelligence software, Style Intelligence, lays the foundation for InetSoft's patent-pending Data Block technology. The Data Modeler is used to connect to various data sources, define semantic layers, and create queries. These semantic layers (logical models) and queries are atomic data blocks hat can be manipulated and combined in a data worksheet, InetSof't term for where database fields are selected and transformed. The various data sources that you can access are databases, objects, and flat files. Databases include data warehouses, data marts, mainframes, operational data stores (ODS), multi-dimensional databases (OLAP), and transactional databases (OLTP). Objects include web services, XML, CORBA, EJBs, and plain old java objects (POJO). Flat files include spreadsheets, CSV, and text...

Data Monitoring Side of the Equation - Caching obviously is an old trick as well, but I think one of the keys with being able to use caching effectively is to know which datasets are useful, which datasets are valuable. You got to have that whole data monitoring side of the equation to see who is using which datasets, and when I say using, I mean not just actually receiving them and opening reports and so on and so forth, but then actually making decisions based on them. Because I have to think that you know if you are not focusing on the business value that someone is getting from these data feeds, you are going to be in trouble, and if you do focus on that in and of itself, that process can help you avoid bottlenecks by recognizing that Bob over in accounting really isn’t using this feed, but Jim over there in the senior executive’s office, he is on the stuff all the time so may be let’s find a way to cache certain pieces of data for him since he and his team use it a lot, and that really requires that monitoring side of the equation, right? Ian Pestel: Well yes that’s really a good point, and naturally yeah the monitoring can actually be through a process. So for example we have one customer, a big pharmaceutical company, and they have to provide data to the business intelligence community, people from a variety of sources. And one of the problems they were encountering was that they were trying to provide data out there, and there was a need to provide it very quickly. But also when they provided it, often there were problems where the data people were looking at had gone away...

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