telephone:InetSoft Product Information: Data Mashups and Their Applications in Enterprises
Learning about data mashups? InetSoft is a pioneer in data mashups, and its BI software makes mashups from disparate data sources possible with a drag and drop mashup tool. Read articles below for more information, or view the enterprise data mashup solutions page.
Advent of Big Data - Companies have been analyzing data for years. That’s not new. Before we get too far into this topic, let’s set the stage for this topic. Let’s explain what has changed. What does ‘big data’ mean? It’s actually a difficult area to define. It’s actually a little bit like cloud computing. Any discussion of cloud usually spends the first 45 minutes arguing what everyone means by it. I am going to try to avoid that when we’re talking about big data. Companies have had big databases for years, and some of them are very, very large. One of the things that can be implied by big data is that the database has become so large that it is no longer easily dealt with, managed, reported on, or analyzed with traditional database tools. That’s only part of the problem though. One of the other parts is sometimes traditional database tools can look at a really large data set, but they can’t do it particularly fast. So maybe if you want to look at a smaller data set, but get the data in real time, that would be another instance where you might want to use the new big data tools...
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Advice on Data Mashups - That’s a very good point. William, what Malcolm has just been talking about is very interesting, this source data analysis stuff, I wonder, you know we had a show a couple of weeks ago on data profiling and the importance of data profiling, I wonder if mashups can be used in that whole process. Especially when you have these very large organizations that have so many data sources. It can be a real mess trying to sort them out. And as Malcolm just said, sometimes you sort them out, and then they forgot, and you have to sort them out again. Is there a role for mashups in that very process of source analysis and understanding even what is out there? If there is, one of the vendors could speak to that more. I don’t see it. I see a perpetual cycle, continuing cycle of rolling up your sleeves, getting your hands dirty, and discovering. Business changes so quickly through mergers and acquisitions, through regulatory compliance, the speed of business is too quick. So I don’t see the silver bullet approach for getting your hands dirty. Where I am at now, we still have people going through COBOL copy books that are twenty-five years old, asking what does it all mean to really figure out the ontology of the data...
Alphabet Soup of Data Integration Technologies - Today we are going to talk about data integration, and attempt to sort out the alphabet soup of data integration technologies. We’ll learn the differences between ETL, EAI, EII, EIM, and explain how things like SOA help data integration. We’ll provide some insight into why companies choose one method of data integration over another? While some terms like ETL or Extract, Transform and Load have been around for a while, other terms like EII, or Enterprise Information Integration, are fairly new. Even when you know what the abbreviation stands for, it doesn’t always shed much light onto what they do and how and why people choose different integration technologies. We are talking about data integration, and I think that most people know it will be great to have all their data sources highly integrated. What is really forcing companies to take data integration seriously? There are a lot of business drivers now for companies to do data integration. Suddenly there are government regulations such as Sarbanes-Oxley which is putting a lot of pressure on the financial transparency demands and stockholders. There are industry regulations and initiatives across other industries such as HIPAA and Basel II Accord...
Answer Is Data Mashup - So this shift to commodity super computing drives a change from traditional BI where you had upfront data modeling that then results in a company’s relatively slow response time to change in the outside world, to more of a model-as-you-go approach which then allows a fast response to change. We will talk about what that means with a particular story. These new market requirements demand a new approach, and of course we have a particular way to address these new requirements. For this new requirement of data and content from any type, format or source and bringing that all together, our answer data mashup. It’s the data access engine at the heart of out platform. For this second requirement of interactive applications for business users, our response to that is providing a toolset to build highly interactive BI applications in Flash. This enables data exploration, ad hoc information access and data discovery. Our BI technology is not only used for decision making among employees but it is also used for improving consumer decisions. We know that this approach works, and we are now bringing those innovations to the enterprise so that decision making can be improved...
Best Practices in Data Mining - When you talk about best practices in data mining, what are some of the first things that you tell people to keep in mind? Flaherty: What I really try to stress is to think about the data. Actually spend some time trying to understand it, trying to go beyond what you can get just out of the box using some analytical software. It seems like there is so much capability there that sometimes, we are tempted to turn our brains off when we get close to a data set. Moderator: Right. So you really have to kind of think about the data and the context of the data. You really have to focus on what is the goal that you have in mind, right? Flaherty: Absolutely, you need to really start at it from both ends like you said, the goal of where you want to get to. Think about where you are starting from. What is the population of data I am working with? Is it my customers? Am I thinking about sites for retail locations? Am I thinking about production jobs that I am trying to run out of manufacturing plant? All of those things might be my population or the level of analysis that I am trying to do. And you really have to put that into the proper context. I always find myself asking, “compared to what.” Now if I look at a set of customers, you think okay well, I have got a lot of one gender, for instance. Well, how do I know that? Compared to what...
Best Practices in Data Warehousing - We have some really excellent best practices in data warehousing, and we carefully study to be sure we find just the right sources. Be very careful with data transformations to make sure that we are not losing any value from the data as we transform it. We make models that are really going to be useful. With those models we really want to have hefty metadata management. Nowadays there is master data management and other things that help us to document and beef up the semantics around data. I could go on and on. You have heard this before. This is sacrosanct stuff. And you start contradicting these careful steps, there are people who push back and say no, no, no wait, I have built my career on this rather slow moving but high quality process. So you ask, ‘how do we speed it up the data delivery process without losing the quality of data, without losing the intelligence of models and so on and so forth’ That’s one of the really hard parts. In so much of the work we do in BI or data warehousing, the data modeling has been front-loaded. We have got this planning process, the requirements gathering, and across the board I am seeing a lot of people just pushing real fast to get to some kind of prototype. And once you get to the prototype that’s when the iterations kick in. That’s when things we associate with agile kick in...
Better Tools for Delivering Data or Accessing Data - Ladies and gentleman, hello and welcome out there everybody. It’s DM Radio. My name is Eric Kavanagh, and I will be your humble if excitable host for the show that is designed after all to peel away the marketing veneer so we can get down to brass tacks and hopefully get a better hand for what is going on in the field of information management. And there is plenty going on obviously. It is the Information Age. And the topic for today really deals with some old problems but some new problems as well. Of course, we are always talking about moving data around one way or another. And the topic for today is avoiding bottlenecks and hurdles in data delivery. And we also have one of my best friends in the business, Philip Russom of TDWI, who is going to be talking to us in just about one minute. And we use a (#) Hashtag @DMRadio. Folks, thanks so much for all the tweets already. We do appreciate that, getting the word out there. And feel free to send your questions there, you can also send your questions to me at dmradio@sourcemedia.com, but without further ado, let’s bring in Jim Ericson, our Editorial Director and Co-host. Welcome back to DM Radio...
Big Data Analytics - There has been a lot of talk this year about the wonderful world of big data and big data analytics. What is driving big data today? When we talk about big data, we’re often talking about volumes of data, and what is driving that tremendous increase in volume. I’ve heard people call it tsunami of data that is now. Things that are driving that volume are a lot of new channels or new sources of data coming into the enterprise. This includes digital data, data like click stream traffic, things like mobile applications. It also includes a lot of sensor data that is coming off our vehicles or coming off of production lines. All of these news sources of data are generating not only large volumes of data. To be honest database companies have solved the issue of large volumes of data for some time now. What we’re talking about now is how do we solve some of the new challenges around big data analytics. That includes both being able to take in these news sources of data, which are not well structured...
Big Data Technologies to Improve a Company's Performance - So when you talk about what is the opportunity for big data technologies to improve a company’s performance, and what we don’t see organizations doing much of, yet, what you might call the next frontier, is not just analyze the transactions that happen from those interactions online but get a better sense of customer behavior and customer interactions that precede a transaction. The state of the art for most marketing teams today is looking at last click attribution. Whatever Web page you were on right before making a product purchase, that Web page gets a hundred percent of the credit for the marketing campaign measurement for driving that transaction. We know that is just not true. Most consumers, no matter what they’re buying, whether they’re buying a pair of shows, a new pair of pants, it’s going to take them six to eight different touches from an organization before they make a purchasing decision. So getting smarter about how we not only get visibility into consumer behavior, but how we actually quantify what impact interactions have had with the consumer before is really important to bringing the science of data into the art of marketing...
Build Flexibility Into Data Mining Programs - I think the first thing to think about when you think about flexibility is what is your business response time? If you are talking about a business response time where the amount of time that you can react with something is a week or so, then your flexibility has to be tied to that kind of environment. There is also the possibility that you might talking about online customer service requests which demands showing quick results. Your CSR’s or your end users or actual customers are interacting with your predictive models through instantaneous predictions in a Web site. In that case the models actually can change in a real-time basis without having to bring your infrastructure down. So you would want to have a system in place where you can deploy new models seamlessly in the backend that impact your customers or your CSRs or whatever interact with those models in real time. And those systems are fairly convenient to put together with today’s technology so that you can actually update your response rate immediately. So if you see changes in the market, and you have done the data analysis to see how those changes in the market can impact your models and impact the patterns that the models are using, you can actually apply those in real-time while people are interactively using your systems...
Building a Business Case for Master Data Management - Today we are talking about ‘Building a Business Case for Master Data Management.’ Our customers have told us that this is not always an easy process since it requires calculating the cost of bad data to justify the pricing of potentially multiyear master data management project. And if you haven't attempted to calculate these costs, don’t worry you are not alone. One research study recently surveyed over 500 businesses to determine the state of master data management maturity. Almost 50% of those surveyed indicated they have not even attempted to calculate the cost of bad data. And over 6% estimated that their costs are reaching well beyond $11 million. And we have heard that implementing master data management isn’t always easy either. Experts tell us you can't just throw money or software at a master data problem. Organizations also need to budget for data governance, data stewardship and other process improvements. So today’s Webinar is intended to sort this all out and give some tips and advice about building a business case for master data management...
Combining Data Analysis - 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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