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 Mining Helps Strengthen Your Instagram Marketing Strategies - In today's digital age, social media have become much more than social networking channels. Now, they're potent places for businesses to expand their reach and boost brand awareness with marketing videos, social posts, and any other content. Among these platforms, Instagram stands out as a vibrant marketplace where businesses can showcase their products or services to millions of users worldwide. However, with millions of posts being uploaded daily, standing out and reaching the right audience can be challenging. This is where data mining comes into play, offering valuable insights to help businesses increase sales on Instagram. Let's dive deeper into Instagram data mining and how it actually boosts your overall Instagram marketing strategies. In the social media realm, data mining involves extracting valuable information from social data. It goes beyond a company's or research firm's internal databases and systems. Also, this process typically entails gathering, refining, and examining raw data acquired from popular social media platforms like Facebook, Instagram, Twitter, TikTok, LinkedIn, YouTube, and more...
Data Mining Technology - Companies use data mining to weed through large caches of data, in search of new tools for market research. At its basest level, data mining is the multidimensional analysis of big data in search of correlations that could reveal historical trends to be further used to influence future decision making. Data mining allows managers to dynamically view the impacts of different factors on business operations, to increase productivity and lower downtime...
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 Modeling Tutorials - Learn about InetSoft's data layer capabilities with these data modeling tutorials to see how easy it is to leverage your data in the form of dashboards and reports...
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...