Intelligencia BI classes will train your company's employees how to understand and/or implement the following:
· Information delivery portal:
o Reporting: Provide the ability to create highly formatted, print-ready and interactive reports.
o Dashboards: A style of reporting that graphically depicts performance measures. Includes the ability to publish multi-object, linked reports and parameters with intuitive and interactive displays; dashboards often employ visualization components such as gauges, sliders, checkboxes and maps, and are often used to show the actual value of the measure compared to a goal or target value. Dashboards can represent operational or strategic information. Gamevision’s employees will learn how to build, run, and maintain a series of dashboards.
o Ad hoc report/query: Enables employees to ask their own questions of the data, without relying on IT to create a report. In particular, the tools must have a reusable semantic layer to enable users to navigate available data sources, predefined metrics, hierarchies and so on.
o Reports Design and Data-to-Reports Mapping.
· Analysis:
o Interactive visualization: Enables the exploration of data via the manipulation of chart images, with the color, brightness, size, shape and motion of visual objects representing aspects of the dataset being analyzed. This includes an array of visualization options that go beyond those of pie, bar and line charts, including heat and tree maps, geographic maps, scatter plots and other special-purpose visuals. These tools enable users to analyze the data by interacting directly with a visual representation of it.
o Search-based data discovery: Apply a search index to structured and unstructured data sources and map them into a classification structure of dimensions and measures that users can easily navigate and explore using a search interface.
o Embedded advanced analytics: Enables student to leverage a statistical functions library embedded in a BI server. Included are the abilities to consume common analytics methods such as Predictive Model Markup Language (PMML) and R-based models in the metadata layer and/or in a report object or analysis to create advanced analytic visualizations (of correlations or clusters in a dataset, for example). Also included are forecasting algorithms and the ability to conduct "what if?" analysis.
o Online analytical processing (OLAP): students will learn how ot analyze data with fast query and calculation performance, enabling a style of analysis known as “slicing and dicing.” Students will also learn how to navigate multidimensional drill paths. They will learn how to write-back values to a database for planning and “what if?” modeling. This capability could span a variety of data architectures (such as relational, multidimensional or hybrid) and storage architectures (such as disk-based or in-memory).
· Data Integration:
o BI infrastructure and administration: Students will learn all about the tools in the BI platform, including security, metadata, administration, object model and query engine, scheduling and the distribution engine.
o Metadata management: Students will learn about the tools that enable users to leverage the same systems-of-record semantic model and metadata. Students will learn how to create a robust and centralized way to search, capture, store, reuse and publish metadata objects, such as dimensions, hierarchies, measures, performance metrics/key performance indicators (KPIs), and report layout objects, parameters and so on.
o Business user data mashup and modeling: Students will learn how to create analytic models, such as user-defined measures, sets, groups and hierarchies. Advanced capabilities include semantic autodiscovery, intelligent joins, intelligent profiling, hierarchy generation, data lineage and data blending on varied data sources, including multi-structured data.
o Development tools: Students will learn how about the provided set of programmatic and visual tools as well as understand the development workbench used to build reports, dashboards, queries and analysis.
o Embeddable analytics: Students will learn about the BI tools, including a software developer's kit with APIs for creating and modifying analytic content, visualizations and applications, embedding them into a business process, and/or an application or portal.
o Support for big data sources: Students will learn how to support and query hybrid, columnar and array-based data sources, such as MapReduce and other NoSQL databases (graph databases, for example).
o Implement a nightly production process that automates a batch update of the data warehouse.
