Accessing Data Sources as a Web Service

Looking for reporting tools for accessing a data sources as a Web service? InetSoft offers Web-based BI software that can access almost any data source including Web and XML feeds or API-based data sources.

Data Warehousing BI Roadmap - You know I’ve heard this so long before, and it scares me a little bit that we are leaving out this discussion the reason of why the business is asking for this. It’s amazing how MDM is following the data warehousing BI roadmap. I am old enough but I remember the days when IT said, we build the data warehouse, and the business users will come and use our data and do all these analytics and all that good kind of stuff. And I am hearing kind of the same thing with master data management. I will build the customer repository. The business will figure out how we use it, and that really does worry me. They are going down that same path of building something and waiting for the business to figure out how to use it. Now you have to have a business reason for building this and make sure that you follow through and incorporate master data in the entire business infrastructure, whatever it is. Look out in your analytical environment, or look out in your IT environment where the business is complaining. People might say, I don’t trust the data within your analytical environment. I am not going to do my roll up of my monthly sales based on that because I think those numbers are false. And they try to do it themselves and put it together in an Excel spreadsheet, etc. It's just not the method or not the path to take. I think the IT people challenge is how to convince business they actually need it. It seems like it should be such as intuitive no-brainer to have integrated customer data and integrated product data. In some ways, it’s stunning to me that the business doesn’t get that. It's almost an issue of priority. And what we have heard from a lot of people is that you have to insert MDM into the projects they are getting...

 

Data Warehousing and Business Intelligence Solutions - InetSoft's data warehousing business intelligence solution allows administrators to extract information from virtually any type of data source and create detailed analysis and visually oriented reports on the spot. The software can access multiple, disparate databases, via any JDBC connector as well as OLAP cubes, flat files, Web services and other databases. What's more, the intuitive user interface provides a web like enviroment with simple point-and-click interaction and analysis, as well as drag-and-drop customization and design...

Data Warehousing Reporting Solution - Looking for data warehouse reporting tools? Since 1996 InetSoft has been making business intelligence software that is easy to deploy and use. InetSoft's server-based reporting application connects to many data warehouses including Microsoft SQL Server Analysis Services, Hyperion ESSbase, Oracle OLAP, and SAP NetWeaver. And it can also mash up data from other sources such operational databases, CRMs, and even Excel spreadsheets. The drag-and-drop design tools let you quickly build interactive dashboards accessible from any browswer and perfectly laid out pdf reports for scheduled email distribution...

Data Wrangling as an Enterprise BI Tool - Data wrangling is the process of cleaning, transforming, and shaping raw data into a format that can be used for analysis and decision-making. In recent years, data wrangling has become a critical component of enterprise business intelligence (BI) as organizations seek to leverage their data to gain insights and make informed decisions. One of the key benefits of data wrangling as an enterprise BI tool is that it enables organizations to overcome the challenges posed by disparate and siloed data sources. By integrating, transforming, and standardizing these sources, organizations can gain a single, unified view of their data, which can be used to drive more effective and efficient decision-making. Another key advantage of data wrangling is that it helps organizations to overcome the limitations of traditional BI tools. Many of these tools are designed to work with data that is already in a structured format, but in today's data-driven world, much of the valuable information is unstructured or semi-structured, such as social media posts, customer reviews, and sensor data. Data wrangling tools enable organizations to extract and structure this information, making it usable for BI purposes...

Database Schema Defintion - A database schema is the table structure of a database, independent of the data it contains. Database theory offers a mathematical description of database schemas, but from a practical perspective a schema specifies the table names, number of columns in each table, column names, and data types. The schema fully specifies the scope of data that can be read or written to the database, but does not include any data. The schema also specifies the certain columns are special 'key' columns for purposes of relating data. For example, the SALES_EMPLOYEES table below has a primary key column called EMPLOYEE_ID. This is the unique employee identifier. When this column appears within other tables, such as ORDERS, it is called a foreign key. A foreign key is simply a primary key from a different table...

Database Connection Pooling - InetSoft's query engine uses connection pooling for enhanced database performance. The default size of the pool is five connections. For enterprise level deployment, the number of connections can be increased to a more appropriate size by setting the property jdbc.connection.pool.size in sree.properties. Alternatively, an application can supply its own database connection pooling mechanism by implementing the ConnectionPool interface...

Database Fields and Types - A database is a set of data arranged in a particular way so that a computer program can use the necessary parts from it. Every database has several fields, records, and files. A database field refers to a set of values arranged in a table and has the same data type. A field is also known as a column or attribute. It is not necessary for the values included in a field to be in the form of text alone, as this is not a requirement. Some databases have the capability of having fields that contain images, files, and other types of media, while others have the capability of having data that links to other files that can be accessed by clicking on the field data. example of database fields in a table More Dashboard Examples In every database system, you can find three modes of fields. They are: Required Calculated Optional...

Database Management Software - Take your data warehousing capabilities to the next level with InetSoft's BI Solution. Style Intelligence enhances database management by employing a powerful data mashup engine, providing a convenient web-based platform, and offering efficient tools for creating compelling dashboards and reports, as well as database management tools such as database writeback...

Defining CORBA Data Sources - When defining a CORBA data source, the Data Modeler needs to import the IDL generated classes to analyze the method parameters. This requires the IDL definition to be properly compiled, and requires that classes generated from the IDL are accessible from the CLASSPATH. An IDL file can be compiled using the IDL compiler that comes with your CORBA software. The JDK1.2 and later comes with an IDL compiler. The IDL compiler in JDK 1.2.x is ‘idltojava’...

Defining Data Science Relative To Analytics - Thank you all for joining us today. I'm Abhishek Gupta, I am the Chief Data Scientist at InetSoft. We have four major points that we want to discuss today. The first one being the importance of data science and data scientists and bringing machine learning into organizations. The second one being we've all heard of the V's of big data, and we know that one is velocity, and we know that there's a lot of streaming data out there now. I feel like that's going to be a big part of organizational strategies moving forward. Point three, how an organization can keep creativity with machine learning. We have all of these different tools to choose from today, all of this different data, but we deal with regulation. We deal with documentation. We deal with productionizaling machine learning code. How do we keep infusing creativity into the machine learning workflow within an organization. Then, we've also heard a lot about the citizen data scientist recently, and just in general more and more people in organizations wanting to get involve with analytics and machine learning. So that's point four. Okay, so we're going to start our discussion here, is any of this really new? Is machine learning new? Is data science new? To me this is resounding no. In fact, machine learning has been studied at least since the 1950s, maybe before. Data science you could say goes back to John Tukey's 1962 Future of Data Analysis Paper. There's a great recent paper by Donoho out of Stanford that talks about 50 years of history of data science, and I urge you to read that...

Definition of Big Data - Let’s start with the definition of what is big data. Big data has been around for some time, but now it’s getting a little more attention. Big data is any dataset. Any one single chunk of data that exceeds the capability of most tools to use it. In other words, it gets beyond the common database toolsets, beyond the things that we’re all familiar with and that are popular. Whatever this data access tool is that we have, this big data will break it. This big data makes it hard to use that tool successfully. And you usually you see that in long response times. For instance, you try to use a business intelligence tool, and it takes 18 hours instead of 30 minutes. That is certainly something that slows you down. It makes your life difficult. Where does big data come from? Certainly over the years it began from storing historical data. But even if we look back just 10, 20 years, it was really all the transaction data and the call detail records. These types of data are producing huge datasets and huge amounts of data for some of our clients. Nowadays we’re seeing new forms of data and this is why we have the new term, big data. A good example is social media. So going out into Twitter and Facebook and all the blogs in the world and all these things and saying pull all this data in. Can we analyze it? Can we use it for some competitive advantage...

 

Definition of Data Friction - Any impediments or inefficiencies that prevent a company's data from flowing freely are referred to as data friction. Technical problems like mismatched systems, poor data quality, or ineffective data management techniques like manual data input, a lack of automation, or insufficient data storage and retrieval techniques are just a few examples of the many various ways that these barriers might appear. Organizational impediments, such as siloed data, when data is divided into many departments or systems and is challenging to access and exchange, may also cause data friction. Making strategic choices based on the whole picture might be difficult without a full perspective of the organization's data. Businesses may have severe effects from data friction, including lost opportunities, wasted time and resources, increased risk of data breaches or compliance violations, and poor decision-making as a result of erroneous or incomplete data...

Definition of a Data Pipeline - A data pipeline is a series of processes that move data from one system or source to another while transforming, enriching, or preparing it for analysis, storage, or operational use. It acts as the backbone of modern data engineering, enabling organizations to handle the increasing volumes and complexity of data efficiently. Key Components of a Data Pipeline: Data Sources: The starting point for any pipeline. These could be databases, APIs, IoT devices, log files, streaming platforms, or other systems that generate or store data. Ingestion: The process of collecting data from sources and bringing it into the pipeline. This could happen in batch mode (e.g., scheduled data transfers) or real-time/streaming mode (e.g., continuous data flow). Transformation: Data is often not ready for use in its raw form. Transformation involves cleaning, aggregating, filtering, standardizing, or enriching data to make it usable. Common frameworks for this include ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). Storage: Once processed, data is stored for analysis or future use. This could be in data warehouses, data lakes, or specialized storage systems optimized for fast querying and retrieval...

Definition of Data Product Management? - Data product management is a lot like making the perfect pizza. You want it to be delicious, but you also want to be efficient with your ingredients. This can be tricky when dealing with large data sets and tons of customer data. However, if you use these best practices from data product management, you will be able to enjoy a tasty pie in no time. What Is Data Product Management? Data product management is the process of creating, managing, and optimizing data products. Data products are a combination of data and analytics that can be used to make business decisions. A data product may be a report, presentation, or dashboard. Various departments in an organization create data products, including sales, marketing, HR, finance, and operations. Data product managers are responsible for coordinating these teams to create and manage their data products. They must have strong technical skills in both programming languages, such as Python or R and SQL, and be able to write well-crafted reports. They should also be able to work closely with other departments to ensure that the information is accurate and relevant for their users...

Definition of ETL and Its Advantages and Disadvantages - Today we are focusing on enterprise data integration methods. We will explain extract, transform, and load, better known as ETL technology. You will learn how ETL works, how it’s commonly used, as well as advantages and disadvantages of ETL. Our expert for this Webinar is Abhishek Gupta, product manager at InetSoft. Abhishek has experience in business intelligence, data integration, and data management. Now let’s hear Abhishek give a tutorial about ETL. ETL tools, in one form or another, have been around for over 20 years. The first question some people have is what ETL stands for. And that would be extract, transform, and load. Really, the history dates back to mainframe data migration, when people would move data from one application to another. So this is really one of the most mature out of all of the data integration technologies. ETL is a data movement technology specifically, where you are getting data from one application’s data store and moving it to another location rather than trying to interface to an application’s programming interfaces. So you are skipping all of the application’s logic, and going right through the data layer. And then, you have a target location where you are trying to land that data...

Definition of Digital Business Observability - The capacity of a company to acquire insight into the performance of its digital operations in real-time is referred to as "digital business observability." This implies that companies may continually monitor their digital systems, apps, and networks to find faults, solve difficulties, and find chances for improvement. Organizations may use observability to make data-driven choices that foster innovation, enhance the customer experience, and boost revenue. This article will explore the idea of "digital business observability," why it's important, and how businesses may use it to improve their online operations. Monitoring and analyzing the behavior of digital systems in order to find problems that affect performance, user experience, and business consequences is known as "digital business observability." This entails monitoring the behavior of software, networks, physical systems, and other digital assets to spot possible bottlenecks, flaws, and optimization possibilities. The objective of observability is to provide insight into intricate digital systems, which is essential for resolving problems, seeing trends, and coming to informed conclusions...

Deliver Real Time Data - And for really our fifth guest technically, we have got an all star cast here today. But it's a big topic. It's a big issue, and there are a lot of minds out there. So to give us one more perspective on the issue we have got Richard walker from Denodo. Welcome to DM Radio. Richard Walker: Thanks a lot, Eric, I really appreciate it, and to leverage what you, Philip, and the others have said, what we see is sort of the big trend. Around this real data people want to create an agile business, and that sort of means, how do I get incremental value, how do I deliver value quickly? But by project, as opposed to a big bang where I spend years to build it. Where it's flexible. Where you can make things iterative and self service, a little bit like Byron was talking about earlier and still deliver that real time data. To give the best example of a customer that was very similar to what Ian had brought up was they were always getting new data requests, and they were always building out another database or another materialized view or data model. This was taking them weeks to deliver that information and by the time they get it done, the requirements have changed, so what they have done is obsolete. Now the requirements comes in, and they use their data virtualization layer to actually immediately create a virtual view for every client that comes in, and they use that now to do a rapid prototyping with that client...

 

Demonstrating the Creation of Data Mashups - Let me just segue into demonstrating the creation of data mashups. We will come back to the dashboard side of things. So we use the same BI tool, still in the browser. The right-hand mashup development canvas looks the same, but the left-hand side is little bit different. Here are the actual atomic data sources that have been set up by IT. So IT defines, for instance, this connection to a database. This could be any relational database, many of the common OLAP servers, a Web service, or some of the popular ERP applications like SAP or JD Edwards. Then within the database is where you define...

Deploying a Data Science Solution - Abhishek: Sure, and as we're looking to the future, not only on the payoffs and the implications of where this is relevant, there's also some architectural changes with the way people are using a hybrid cloud technologies sourcing their data centers in different ways. Tell us a little bit about how you're deploying your data science solution. Are you using this all on premise? Do your security requirements make that necessary now? Are you into a hybrid mode, or how will that pan out in the future, and how might that impact how you can get more data into a compute mode that you can analyze? Jim: So, over time there's no doubt that the hybrid or deployment models were some of it will be on premise, and some of it will be in the cloud. This is going to happen in healthcare and healthcare claims, but still as people are seeing in the news, there is still a lot of apprehension around doing healthcare in the cloud, and so a lot of our requirements require us to keep data under lock and key...

Designing a Subquery - Sub-queries are queries used inside a query condition expression. The result of the sub-query is used when evaluating the expression. This functionality is only supported for hierarchical data sources like XML, SOAP, etc.. You cannot use this feature with a non-hierarchical data source like JDBC. This concept also exists in SQL, but there are a few important differences: • A sub-query can be used in an expression where a scalar or list value is expected. This is different from SQL sub-queries, which can only be used in a few specific types of expressions. • A sub-query is referenced by the query name. The definition of the sub-query is not included in the...

Developers Using Machine Learning for Mobile Apps - Conceived in 1959, machine learning entered the mainstream in recent years in combination with predictive analytics and artificial intelligence. Its developer, IBM's Arthur Samuel, had no mobile apps with which to use his concept, but today's programmers do. Increasingly, they leverage machine learning to provide their apps an edge. That edge - the ability to adapt, learn, and improve - lets the application continually develop without needing constant updates from the developer. A mobile app's ability to learn frees its programmers from the requirement of constant development of new releases. It also helps keep mobile apps small. It is impractical to expect a programmer to create code to address every possible scenario since any app that code-heavy would no longer fit on a cell phone or tablet. Machine Learning Defined The technology machine learning lets electronic devices process, analyze and self-actualize data. This learning extends to trend identification, pattern analysis and action implementation to fulfill an objective. One of its key benefits is increased efficiency resulting in updated programming without increased development costs or timeframes. Businesses investing in the use of machine learning are expected to double in the next three years, reaching an uptake of about 64 percent of businesses. Allied Market Research predicts the service market of machine learning will reach $5.537 million by 2023 and grow at a CAGR of 39 percent during the period 2017 to 2023...

Difference Between Machine Learning And Data Mining -I think there is a difference between machine learning and data mining. Machine learning is similar to data mining because a lot of the pioneers in data mining are still around, and they are pioneers in machine learning. In my mind part of the main difference was the emphasis even in the terms. One emphasizes mining. The other one emphasizes learning in terms of the branding. I think that's one of my observations. The other thing of course is the notion that we have to separate empirical results versus theory. So people in industry I guess they care about theory to some extent, but at the end of the day it's empirical results that matter. I think the connotations data mining sometimes brings up, and if that's historical consequence I don't know, is that, it's about torturing data. It's about mining it till it confesses with whatever the preconceived notions you had going into it. I think maybe that's a little bit unfortunate. Indeed I think that's probably more where we want to be than in that drill-till-you-find-something mentality. Machine learning has always been used as a part of data mining. Data mining involves all this data storage and data manipulation, and also machine learning or statistics is where we learn from the data. So I think a lot of comments are leading us towards this theme of automation. There is one more point that I wanted to emphasize which is actually for whatever reason, the techniques that statisticians have historically brought to the table became ill-suited at some point, because they didn't scale particularly to the number of variables...

Difference Between a Measure and a Metric? - Metrics and measures are frequently used interchangeably. They are frequently mistaken with one another and presented as the same item. It is easy to mix the two since, in some ways, a metric is a kind of measure, although a more useful and informative measure. While a measure is a basic number - for instance, how many kilometers you have drivenâ€"a metric contextualizes that measure - how many kilometers you have traveled per hour. This added information increases the usefulness of the same statistic by several orders of magnitude, particularly when looking at commercial KPIs. Conversions per thousand impressions are an illustration of a vital metric for an internet business. Understanding you have twenty conversions is a restricted measure in and of itself. A really positive KPI is knowing that those twenty conversions came from a hundred impressions. It is less beneficial if they came from a thousand impressions - context is crucial...

Different Business Groups Developing Different Data Models - A different level of complexity looks at the historical development of application, so it used to be that we would have one large amount of computer that was being used for all of our batch applications. But as time-sharing became more of the norm, and then came the evolution of workgroup computing, we ended up with a kind of divestment of centralized control of our management of our information. This meant that we have got different groups developing different data models for essentially the same context...

Different Ways You Can Highlight Data Using InetSoft's Software - Color Coding: Heat Maps: InetSoft's software provides heat maps, where colors represent values, making it easy to identify patterns and variations in data. Conditional Formatting: Users can set up rules for conditional formatting to dynamically change the appearance of data based on predefined conditions. For example, cells might change color if they meet certain criteria. Thresholds and Alerts: InetSoft allows users to define thresholds for key performance indicators (KPIs). When data exceeds or falls below these thresholds, the system could generate alerts or highlight the data for attention. Icons and Symbols: Users may have the option to use icons or symbols to visually represent specific data points or conditions, making it easier to interpret information at a glance. Data Bars and Sparklines: InetSoft's software supports data bars or sparklines to display trends or variations within a cell, providing a compact visual representation of data over a certain range. Grouping and Aggregation: The software allows users to group and aggregate data, and visual cues could be applied to these groups, helping to highlight trends or outliers in larger datasets...

Displaying Data From Multiple Data Sources in a Single View - I can even get data from multiple data sources for queries and display them in a single view. This is another very popular reporting structure called the Master Detail Report. Paginated reports are always developed against certain page size and page layout. In my Master Detail Report, I have some master information, some high level summary information, and I could break it down into details by different dimensions and use different visual forms. I can repeat this across again in dimension. My reports, just like my dashboards, can be exported to PDF, Excel, PowerPoint, in addition we also give you RTF and HTML. Now once you view reports, you can parameterize them. You take an input from the user and based on these inputs, you can filter your data and change the way the report is displayed. In the visualization module, it's all hands on. You had your little sliders, your selection lists...

 

Drilling Into This Idea of Data Discovery - Now I am moving to the second key issue, and really drilling into this idea of data discovery. It’s the biggest thing going on in my market. I have been working in this space now for 10-11 years and over the last I would say five or six of those years this idea of the disruptive impact of data discovery has been the most significant trend in the market. And it’s been very interesting to watch the success that vendors like us, QlikView and Tableau are having relative to the traditional vendors. This has really been quite a David and Goliath battle, and I think it’s becoming very clear that David is winning or even has won. I can almost use almost sort of past tense here. The market is sort of moving on in this direction. So let’s examine why that is. Ten years ago there were mainly the semantic layer based BI tools, the Cognos’ and Business Objects and OBIEEs. It was actually Siebel Analytics at the time and MicroStrategy. These were the top BI vendors. At the heart of them was a semantic layer. It was a place where you defined the dimensions and measures that describe your business, and all the reports and dashboards you built were built from those semantic layers that defined dimensions and measures of your business...

Dwarfing the Cost of the Original ETL Tool - Or is this spike useful even? I mean, I don’t want to hijack the conversation, but for example, you know, you see Sprint advertising that they won't slow down your telephone data downloads like the other providers will or charge you more for them, but that really has no bearing on whether this data is being used constructively, or if it's useful to begin with. I mean it's just the flip side of looking at bandwidth as a way of rationalizing the value of data or something I don’t know. Eric Kavanagh: Yeah. Well that’s a good point. So David I have just another couple of questions here and there for the rest of the segment. What are some of the biggest mistakes you have seen organizations make when trying to circumvent a bottleneck that they find? Maybe batch processes or what I used to think about batch windows being missed, that kind of thing, what are some mistakes that people make when trying to resolve those problems? David Inbar: It’s so interesting. There are several things you can often point to which are the originators of the problems. One of them, and I will just lay it straight out there, is poorly written software. The process runs slowly, and they just assume because it runs slowly it must be the hardware that’s the problem, and so they go out and write bigger checks for bigger hardware and hope to get around the problem that way...

Easier To Drive Data Exploration - I can simply drag in a control to make it a little easier to drive my data exploration. Being able to do selections, for example, and if you think about this, if I want to share this insight out, I’m coming through and doing the data discovery, but then for my other users in the organization, I’m making it available for them instantly. And we’ve put that together so pretty much anybody, my mom could come in here and figure this out. And we’ve also added some nice conditional elements here, so I can really see what’s important to me. So being able to line something up like my sales revenue, for example, and I want to know when it’s in the red, using the traffic light motif. There are some business metrics that I want to pay attention to, and simply drop them in, and when we’re finish that, we’ll add a green for good here. I can then save this to the server and instantly provide it to other users. So as soon as I’m finished and as soon as I decide that I’ve got the insight I want, users can come in here and play with the dashboard. But not only that, they can then use that as a jumping off point and then go off and do their own exploration...

Easy Big Data - Big Data and its potential are becoming a topic of greater interest for organizations of all types and sizes. However, many organizations do not know where to start when it comes to using Big Data effectively. Integrating Big Data sources with traditional data sources can also be a challenge. InetSoft has provided a solution that makes integrating Big Data into your BI a more easy and manageable task...

Easy Big Data Analytics Path - There is a dizzying array of big data solutions in the marketplace, and it's a daunting challenge to evaluate them all, determine which fit together, and hire the expertise to assemble the puzzle pieces to deliver a useful solution. InetSoft aims to solve this problem with a unified, easier-to-implement application. InetSoft offers a cloud-ready, fully scalable enterprise-grade platform that can access an existing big data source or use its own built-in, dedicated Spark/Hadoop cluster to turn an organization's data warehouses, relational databases, and almost every other on-premises or cloud-hosted data source into an integrated big data environment. With its powerful data mashup engine, data can be transformed and combined on the fly. With its visualization designer, interactive analyses, management dashboards, and production reports can be created. InetSoft's unified big data solution simplifies turning big data opportunities into actionable, self-service visual analytics...

Easy to Use Data Analysis Tools - Tired of sifting through loads of data that take dozens of clicks to get through static reports? Want to have data analysis tools that speed up analysis by condensing static reports into a single interactive, multi-dimensional analytical view? InetSoft offers powerful, yet intuitive data analysis tools that are easy, agile and robust to condense static reports into a single view. A visualization engine enables quick identification of trends and outliers for any business user. As an innovator in BI products since 1996, InetSoft's award-winning software has been deployed at thousands of organizations worldwide and integrated into dozens of other application providers' solutions serving vertical and horizontal markets of their own...

 

eCommerce Businesses Gain From Big Data - An amazing feature about being a writer on eCommerce is that one has to constantly keep on top of the latest concepts and technologies. On more than one occasion, this has led to me following the herd and falling for the hype. I wonder if that can possibly forgive the fact that I have thus far overlooked writing about the application of big data to eCommerce. It is time for me to right this wrong. What Is Big Data? A textbook definition would be much more rigorous, but think of big data this way: every time a user interacts with your website, you collect data. This data could be the kind that is entered in a form or created in the background. Some background data on an eCommerce website could be...

Educational Institutions Need to Depend Upon Data - Data is everywhere. For educational institutions, using business intelligence tools can seem like drinking from a fire hydrant. There is such a wealth of information coming from all directions without enough resources to manage it all. This results in the data getting lost along the way. Like other major industries such as healthcare and manufacturing that have fully embraced business intelligence, the education sector is realizing the need to depend on data. While schools and universities tend to be reluctant to make investments in technology, things are changing as educators ditch traditional holistic approaches for business intelligence. It is essential to think how to maximize results and bring about a positive change to make the most of business intelligence. Since education facilities are not unique, it is possible to use the tools to attain success. The insights generated can be used for a variety of purposes. This article takes a close look at the top three ways business intelligence can be used in education for bringing results...

Effective Data Discovery Solutions - From my perspective an effective data discovery solution comes down to the management of the data, and our platform’s speed and its ability to index and rapidly query information. But if you can build in processes that are compressed, it gives you the ability to do in memory analytics and process that information very quickly regardless of where it’s coming from, unstructured or structured, so that distinction doesn’t play so much a role, or at least it shouldn’t. For the best practices for data discovery and Unified Information Access you need to enable users to work with the growing data and content with less IT involvements. It is a key point. So the best practice is make it easier and faster to incorporate new data sources. Let users personalize discovery and visualization. Choose tools that leverage the value of big data. Looking at the kinds of data sources, they are growing. They are going to be important for decision makers in lines of business and throughout the organization. Establish managed self-service. And I think this is an area where IT certainly is important. To manage self service efforts, makes sure that the data is secure. Improve data quality...

Effectively Present Analytics Data - Presenting web analytics data to executives who have little or no experience data digging themselves can be a challenge for any analyst. There are dozens of metrics in your analytics account- page views, time on a site, unique visitors, cost per click, bounce rate, conversions-the list is endless. Reports containing all of these metrics are not only confusing but completely worthless. Some metrics in your analytics account will speak loudly to your online marketing team but will mean little to nothing to your CEO. When you communicate with the Big Guns you need to use persuasive presentation skills to make your arguments loud and clear...

Embedded Data Intelligence Solutions - There is a lot of demand growing for data intelligence being embedded inside processes, applications, solutions, Cloud services and devices. Obviously we've been talking about embedded for quite some time, forever really, but we're seeing these things changing how demand is looking. There is increased demand for data intensive applications. Setting aside just those kind of movements towards embedding intelligence in things, just to have from a user perspective that we get more users interact with more data, and they're more dependent on data insights. Because certainly as we begin to democratize business intelligence and analytics, and have more users involved in it, particularly non-technical users, managers and personnel, including frontline personnel who are not versed in how to access data and so forth and don't really have the time to do all the training and develop the skills to do it. This is becoming an issue in organizations. It's stressing the platforms. It's a stress on traditional systems that demand more of a standalone environment that requires a lot of training and expertise in using data.  Then as I mentioned, I think in the earlier slides, speed is a competitive advantage. Organizations want to be able to close the gap between the creation of the data and its availability for analysis and visualization...

Enabling Agile Data Access - It's all about agile BI and enabling agile data access. In much of that, which we will discuss in a moment, is oriented around virtualization and service-oriented architecture and similar approaches which enable maximum reuse of data assets. So really, in the yin and yang here of agile business technologies, you have agile BI, agile data access and really agile application infrastructure is the way to look at that lower ellipse. And that application infrastructure, of course, is a bit of a SOA, it’s Business Process Management, it’s business rules engines...

Enabling Big Data for Customer Use - Well, I mean, I think Byron said some really insightful points there. There are definitely a lot of cultural aspects of IT letting go of the front-end aspect and really focusing on the data infrastructure. But there is a kind of an interesting parallel of consumerization when it comes to the data layer. When we talk about Big Data, a lot of times we just talk about the data volumes or things like Hadoop. But the reality is that there is now a growing number of data sources that customers are deploying, whether it's the Hadoop stack or the NoSQL Stack or the Cloud data sources...

Enabling Real Time Data Exploration - Decision making is dynamic and complex, and the information has to lead to insights. Insights from data should be live and available at all levels. Business intelligence used to be available up in the C level office, but it needs to be accessible because there are people who can offer insights or offer analytical tools to help others understand and share that information. Data must be available in fast time if not real time to enable exploration. Data must be made available in a format that allows for sharing, exploring, and creating multidimensional insights that can support decision makers who have to act in a continually evolving landscape. Our decisions don’t happen in static worlds so we have to make sure that our information is reflecting our environment and we’re accessing insights as oppose to just raw data. How do we define a real time enterprise? Is it instantaneous? Is there some sort of delay? Is it the ability to make business decisions within an hour period or a 24-hr period? I think it depends on what your business presence is and what the decisions you’re trying to make. If you’re talking about making financial decisions, you can have them in milliseconds, so we’re talking real time, real time. Often times, we find a lag even there. You might have real time on the trading floor if you are a financial institution, but you’re not necessarily giving your customers real time. Maybe there is an hour lag...

 

Enterprise Information Integration - It’s often called data federation or enterprise information integration or data virtualization, of course. That’s critically important. That’s a key enabler. You also need to normalize access to this information meaning within your agile data access infrastructure, transform it, convert it to some set of canonical object models or schemas or views that can then be all rolled up...

ERWin Importer - During data model creation, the designer has to manually build physical and logical views of the database schema. Creating these views can be a time-consuming and tedious task, as schemas can be large and complex. ERWin is a widely used database modeling tool with reverse engineering functionalities, used for database design...

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