Product Information: Logical Data Models

This is a table of contents of useful information about building logical data models using InetSoft's business intelligence software for dashboards, reporting, and data mashups, Style Intelligence:

How Data Management Professionals Can Manage Data Complexity - The role of data management professionals has become increasingly challenging with multiple audiences, multiple business units and departments, multiple tools and applications and multiple databases. In this Webinar we will look at these challenges and explore how organizations can better manage their data complexity. You will also discover how a consistent, integrated view of critical data assets can turn data complexity into information advantage. When both the business and technical stakeholders have a common view of information, they can visualize the power of their data...

Click this screenshot to view a two-minute demo and get an overview of what InetSoft’s BI dashboard reporting software, Style Intelligence, can do and how easy it is to use.

How Data Virtualization Supports Business Agility - Today, enterprise architectures need agility to support business initiatives, and data virtualization can play a key role in this. In this webinar we will provide insights on how data virtualization provides information agility to your enterprise architecture project to deliver business value and significant competitive event. The focus of this webinar is on how to think about different information delivery strategies to help enterprise architecture as a whole be very business focused. So while we will get somewhat technical to address some of the key challenges, the idea is really to provide the framework primarily, and we will talk about some of the other ways. Today what we will talk about is what is the evolution of this whole area of business and enterprise architecture. Obviously those are joined at the hip. There are different perspectives on this, and they are changing. Based on this context we want to think of what is the role of information in this coming together of business and enterprise architecture...

How Do Advertising Agencies Use Big Data? - Audience Segmentation: Big data enables advertising agencies to segment audiences with precision. By analyzing large datasets, agencies can identify specific demographics, behaviors, and interests of potential customers. This allows for highly targeted advertising campaigns tailored to individual segments. Customer Profiling: Agencies use big data to create detailed customer profiles. These profiles include information such as purchasing history, browsing behavior, social media interactions, and more. This helps in understanding customer preferences and predicting future behavior. Personalized Messaging: With the insights gained from big data, advertising agencies can craft personalized messages that resonate with specific audience segments. This personalization can significantly increase engagement and conversion rates. Predictive Analytics: By analyzing historical data, agencies can use predictive analytics to forecast future trends and consumer behavior. This enables them to stay ahead of the curve and adjust campaigns accordingly...

How Do Tobacco Product Companies Use Big Data? - Market Research and Consumer Insights: Tobacco companies analyze big data to gain deep insights into consumer preferences, behaviors, and trends. This information helps in understanding the target audience and designing products that resonate with them. Product Development and Innovation: Big data can inform the development of new products or the modification of existing ones. Through consumer feedback and market trends, companies can refine their offerings to meet changing consumer demands. Regulatory Compliance: The tobacco industry is subject to strict regulatory guidelines and compliance requirements. Big data can be used to track and ensure adherence to these regulations, helping companies avoid legal issues and maintain a positive reputation. Supply Chain Optimization: Big data analytics can be applied to optimize the supply chain, ensuring that the production, distribution, and delivery of tobacco products are efficient and cost-effective...

How Do You Best Utilize Your Data Visualization Tool - With the tools of today, it’s not so hard to simply go to that trial and error process when mixing and matching data sets in Big Data. It works the best in business scenarios such as business analytics...

How Does the Apparel Manufacturing Industry Use Big Data? - The apparel manufacturing industry has increasingly embraced big data to enhance various aspects of their operations. Here's a detailed look at how they leverage big data: Demand Forecasting Big data analytics helps apparel manufacturers analyze historical sales data, social media trends, and other relevant factors to predict future demand for specific products. This enables them to optimize production schedules, reduce overstocking or understocking, and improve inventory management. Market Trend Analysis By monitoring social media, fashion blogs, and e-commerce platforms, apparel manufacturers can identify emerging fashion trends and consumer preferences. This information guides the design process and helps in producing styles that are in high demand. Personalized Product Recommendations Through data analytics, manufacturers can gather insights on individual customer preferences and behaviors. This allows for the creation of personalized product recommendations and targeted marketing campaigns...

How Does Data Mashup Eliminate the Need for ETL Processes? - Data mashup is a technique used to integrate data from multiple sources or formats into a single dataset for analysis or visualization purposes. While data mashup can offer benefits in terms of agility and flexibility, it does not necessarily eliminate the need for Extract, Transform, Load (ETL) processes entirely. Instead, it complements traditional ETL processes and can be used in conjunction with them to streamline data integration workflows. Here's how data mashup and ETL processes compare and how they can work together: Data Mashup: Agile Integration: Data mashup allows users to quickly combine data from different sources or formats without the need for complex transformations or pre-defined schemas. It often involves using visual tools or self-service BI platforms to blend data interactively. Ad Hoc Analysis: Data mashup is well-suited for ad hoc analysis or exploratory data tasks where users need to combine and analyze data on-the-fly without formal ETL processes. User Empowerment: Data mashup empowers business users and analysts to perform data integration tasks without heavy reliance on IT or data engineering teams. It promotes self-service analytics and enables users to access and blend data as needed. ETL Processes: Structured Data Pipelines: ETL processes involve structured pipelines for extracting data from source systems, transforming it according to predefined business rules or requirements, and loading it into a target data warehouse or data lake...

How Does a Developer Use InetSoft to Build Virtual Data Models? - Building virtual data models with InetSoft involves creating a representation of data from various sources that can be manipulated and analyzed in a unified manner. While InetSoft is primarily a business intelligence tool, it can be utilized to create virtual data models by leveraging its data integration, transformation, and visualization capabilities. Data Source Integration Connect InetSoft to various data sources including databases, spreadsheets, web services, and more. InetSoft supports a wide range of data formats and protocols. InetSoft's extensive data source compatibility makes it a versatile choice for handling diverse data sets. Data Extraction and Transformation Extract data from the connected sources using InetSoft's data extraction tools. Apply necessary transformations to the data, such as cleaning, filtering, aggregating, and combining datasets. InetSoft provides robust data preparation capabilities, allowing developers to refine and structure data for modeling...

How to Find the Difference Between Dates - This section discusses several basic date functions: computing the difference between dates, computing a date in the past or future, formatting a date, and extracting date components. Use the 'dateDiff()' function to find the difference between two dates in terms of days, months, or years. Use the 'dateDiff()' function to find the difference between two dates in terms of days, months, or years. For example, if a table displays the column 'Birth Date', you can create a formula column to calculate the current age of an individual by subtracting the 'Birth Date' from today's date...

How Is a Data Lake Different from a Data Warehouse? - A data lake and a data warehouse are both storage systems used in big data and analytics. However, they serve different purposes and have distinct characteristics. Here is a comparison between the two: Data Lake: Storage Paradigm: Raw and Unstructured Data: A data lake stores vast amounts of raw, unstructured data in its native format. This can include anything from text and images to log files and social media data. Schema-On-Read: In a data lake, data is stored with no predefined structure. The schema is applied at the time of analysis, allowing for flexibility in data handling. Scalability: Data lakes are highly scalable and can handle massive volumes of data. They can accommodate both structured and unstructured data types. Data Processing: Data processing in a data lake often involves batch processing or real-time processing using tools like Apache Spark, Apache Flink, or Hadoop. These technologies are designed to work directly on the raw data. Cost-Effective Storage: Storing data in a data lake is typically more cost-effective than in a data warehouse, especially for organizations dealing with extremely large datasets. Use Cases: Data lakes are well-suited for scenarios where organizations need to store and process large volumes of raw data for future analysis, such as in machine learning, data science, and exploratory analytics...

How Is HTML5 Better Than Flash? - HTML5 brought several advantages over Adobe Flash, which contributed to its widespread adoption and eventual displacement of Flash. Here are some key ways in which HTML5 outperforms Flash: Open Standard: HTML5 is an open standard developed by the World Wide Web Consortium (W3C) and the Web Hypertext Application Technology Working Group (WHATWG). This means it is not controlled by a single company, making it more inclusive and accessible to developers and browsers alike. In contrast, Flash was a proprietary technology owned by Adobe. Built-in Multimedia Support: HTML5 natively supports audio and video playback, eliminating the need for additional plugins like Flash. This makes multimedia content seamless and efficient, enhancing the overall user experience. Compatibility with Mobile Devices: One of the most significant advantages of HTML5 over Flash is its compatibility with mobile devices, including smartphones and tablets. Flash was not supported on iOS devices, which was a major drawback in an increasingly mobile-centric world. Improved Performance: HTML5 is generally more resource-efficient compared to Flash. It consumes fewer system resources, leading to faster loading times, better battery life on mobile devices, and a smoother overall browsing experience. Better Security: HTML5 was designed with security in mind from the outset. Flash, on the other hand, was plagued by numerous security vulnerabilities over the years, making it a target for cyberattacks...

View a 2-minute demonstration of InetSoft's easy, agile, and robust BI software.

How to Leverage Key Performance Indicators to Measure and Enhance Data Protection Effectiveness - In the modern business world, every organization collects some form of data. To make use of this data, it's important for businesses to implement effective data protection strategies to help protect sensitive information from loss, damage or corruption. The impact of data loss can be quite significant. Statistics show that 94% of organizations that experience severe data loss rarely recover. Because of this, businesses set up key performance indicators (KPIs) to help them gauge whether the data protection strategy in place is effective. These quantifiable metrics can help determine whether the company is performing optimally and areas that need improvement. In this article, we'll look at the different ways of measuring KPIs for effective data protection processes...

How to Manage Data Distribution Across the Enterprise - Now we have all sorts of different large scale inter-organizational requirements whether it's for customer relationship management, enterprise resource planning, whether it's for supply chain management, whether it's data warehousing, business intelligence, integrated analytics, complex event processing, a significant amount of repurposing and data reuse is now required and the data is being distributed across the organization. We don’t really have a good handle on our data investments, and that gives us a little bit of pause...

How to Map Business Terms to Data Elements - I am going to show a little bit of a picture here, because what we said is that we have got the same business terms that are being used. We have got the same structures that are being used or different structures, etc., but how do we map all these things together? And from a conceptual standpoint, the first thing that we might want to look at is, what are the business terms that are being used and what’s the context of those business terms? What’s the business term? How is it being used? What's its definition? How is that mapped with a data element concept...

How Much Data Can You Visualize In-Memory - That concludes the formal part of the webinar. We committed to stay over a little bit to answer questions. So we are going to pick up questions now, and let’s take a look at what we have? And, yeah, feel free to email additional questions to info@inetsoft.com. Here we have got one question about how much data can you visualize in-memory, which comes up every time. In-memory technology has expanded tremendously. Our compression algorithms are pretty good. We are fitting tens of millions of rows on a normal windows class machine whether it's a client or server, and we’re often pulling in 60, 80, 100 tables from something like an Oracle database on a nightly basis and linking and joining them and doing roll ups and whatever else we have to do. We are not going to put you of Wal-Mart transactions in-memory. But if you’re doing analysis you probably don’t want to look at that anyway you probably roll up the transaction level to the day level if you’re looking at three years of data. And if you are looking at products, probably you could roll up to the class or the sub class instead of the SKU...

View live interactive examples in InetSoft's dashboard and visualization gallery.

How to Perform Data Mashup for Comprehensive Analytics in Higher Education - A data mashup can provide a thorough view of what's happening within higher education institutions and show what people should change to maintain the best student outcomes. Since a data mashup combines information from multiple sources into one platform, it can help people uncover trends and identify actionable steps faster than they otherwise might. However, getting the best results from data-mashing efforts requires following a particular process. Administration leadership must start by determining what they want to achieve by creating the data mashup. One smart way to narrow down the possibilities is to consider how they're already used in higher education. A business intelligence dashboard is one of the most common types. Higher education professionals use BI platforms to identify high-risk students who may only graduate with extra support. In contrast, the data could highlight characteristics shared by the happiest or best-performing learners. Considering stakeholders' expectations can also help set meaningful goals for a data mashup. Would they care more about student retention, attracting new learners or something else? Narrowing down the top data mashup objectives helps people stay on track throughout the process...

IFrame Integration - The simplest way to integrate reports with a third party Web application is to present the reports inside an IFrame. A report embedded in an IFrame can be freely viewed and manipulated, even when the report engine runs in a separate container. This eliminates the need for a proxy. The basic syntax for integrating an individual report into an IFrame is shown below, where ReportName is the name of the desired report, and remotehost is the remote server name. The path should start with the username for a user scope viewsheet. Also, an identifier parameter can be provided in place of path, and an edit=true parameter can be used to load the viewsheet in the Visual Composer...

IFrame Integration Example - As a further example, the HTML markup below generates a simple Web page with two divs, the top div containing a heading and some text, and the bottom div containing an IFrame with embedded report. To run this example, replace “remotehost” below with the address of the remote server on which the report engine is running. IFrame integration is the recommended alternative to JSP-based integration, and there are several advantages that make IFrames the preferred approach in most cases: IFrames are easier to use, and Style Intelligence's report design architecture is geared toward IFrame integration...