R is a computer program that lets you analyze data. By “analyze” we mean, first, read the data into the program and then operate on it – drawing graphs and charts, manipulating values, fitting statistical models, and so on. R is both a statistical “environment” and also a programming language, and it is very widely used both in commercial and academic settings. R is free and open-source and runs on Windows, Apple, and Linux operating systems. It is maintained by a group of volunteers who release bug fixes and new features regularly.

Companies in different sectors may not share core business strategies. They are also likely to focus on different aspects of their businesses to improve efficiency and drive revenue. However, there is a set of use cases that is applicable to the majority of companies. This includes use cases in the areas of sentiment analysis, brand reputation management, human resources and retention analytics, and financial analytics. For companies and government agencies that are public facing and consumer or citizen focused, customer churn prevention is also a common analysis that plays a crucial role in expanding market share, increasing revenue, and improving profit margin.

Traditional business intelligence falls short of meeting the full analytical needs of business users. According to various industry reports from TDWI and IDC, more and more enterprises are looking into establishing data discovery capabilities to reduce operational cost, proactively discover potential threats and issues, and gain competitive advantage. Data discovery removes the obstacle between business users and the data they need to analyze through self-service data provisioning and easy-to-use advanced visualization. But for all its promise to fulfill long-awaited analytics needs, organizations must carefully consider their approach to data discovery. Here are some of the best practices.

We have yet to meet an organization that has told us it has a serious data collection problem, but we’ve heard from countless organizations that they can’t understand the mountains of data they collect. If you listen closely to most organizations’ data challenges, they will admit they are data-rich and information-poor. In other words, they have a real-time data collection strategy, but they only understand their data in stale time.

In this example, you will use publicly available data for airline on-time statistics and delay causes to demonstrate two more excellent tools for data visualization: the pivot table and the stacked bar chart. This data is available at the U.S. Department of Transportation Bureau of Transportation Statistics web site. This is a topic familiar to most of us who travel on airlines, and the data available groups flight delays into several groups:

• Delays caused by the airlines, such as late-arriving flight crews
• Delays caused by airport security
• Delays caused by weather
• Delays caused by National Aviation System delay

• Desirable Functionality in Desktop-Based OLAP Reporting

  Earlier in my blog, we mentioned that desktop-based reporting is most often done from within Microsoft Excel. This means that you can take advantage of many of the features that come standard with Excel, including formatting, sorting, and other spreadsheet-related functionality. When selecting a desktop-based reporting tool (instead of a web-based tools) - whether it is from Oracle, a third-party, or created in-house - you should make sure that you are getting the OLAP functionality you need to produce the types of reports you want.

  Desirable functionality for desktop-based reporting includes:

• Developing Analytical Capabilities with Oracle on examples

  The IT industry has seen many evolutions, and it is in the midst of another major paradigm shift. Few technologies have captured more attention than big data, and there is tremendous interest in business use cases featuring big data and analytics. Gartner highlighted the top ten technologies and trends that will be strategic for most organizations in 2018. Strategic big data and actionable analytics were among these ten trends. In 2019, Gartner released its top ten IT trends again. This time, the list included mobile, Internet of Things, and smart machines. Big data and analytics become enablers - a hidden force that’s behind the scenes driving these businesses and IT innovations.

• Endeca Information Discovery Integrator

  This blog provides more detail about the major components of Oracle Endeca Information Discovery Integrator. The Endeca Information Discovery Integrator consists of the following five components:

  • Integrator ETL
  • Integration Server
  • Integrator Acquisition System
  • Web Acquisition Toolkit
  • IKM SQL to Endeca Server

  • Healthcare Analytics Use Case Overview

    Most healthcare organizations are faced with disparate data sources, including electronic medical records (EMRs) such as patient records, procedures, medica tions, and labs; departmental research databases; clinical data warehouses; medical ontology; and significant unutilized, unstructured content, including doctor’s notes, tests, and results summaries. Their analytical requirements range from measuring required key performance indicators (KPIs), including readmission rates, mortality rates, hospital-acquired infections, and surgical care improvement, to answering new questions such as the following: What does our patient population look like? What is the geographical distribution of patients and clinics? Where are we using high-cost medications? Are there correlations with procedures and nursing facilities for re-admission?

  • Key Big Data analytical use cases in a variety of industries

    McKinsey Global Institute published a report on big data in five industries, including healthcare, public sector, retail, manufacturing, and personal-location data. It found that big data generated tremendous value in each of these domains. Many believe that the innovative use of big data is crucial for leading companies to outperform their peers. In this blog, we’ll describe key big data analytical use cases in a variety of industries.

  • Oracle Endeca Server overview

    Endeca Server is at the heart of Oracle Endeca and is the core search-analytical database. In Endeca Server, data is organized using a highly flexible data model known as a faceted data model. With this data model, it is not necessary to define a unified schema before loading and analyzing data; data models are derived from the data that is stored in the database, and every record has its own schema based on its own generated attributes. This is irrespective of the data source or whether the source is structured or unstructured.