A dashboard can be thought of as a type of business cockpit. The dashboard can be corporate i.e. shared with several users or it can be personal for the consumption of the owner only. A dashboard usually provides an information summary of the corporate or user’s view of the world with hyperlinks to more detailed reports.
Corporate information can be consumed and shared through many different types of dashboard visualization. Examples of the information presented on a dashboard might be a list of KPIs, a Balanced Scorecard, a Strategy Map, alerts on Statistical Process Charts, and Web Intelligence reports.
Although a dashboard looks like a single component it will usually consist of several different components using widgets and reports integrated together seamlessly. The foundation stone for building a dashboard with SAP BusinessObjects is Dashboard Builder.
The most powerful dashboards are also designed to be interactive. SAP BusinessObjects uses an interactive dashboard tool known as Crystal Xcelsius (now renamed SAP BusinessObjects Dashboards). Once the Xcelsius Dashboard has been created it can become one of the many components on the main dashboard. When Xcelsius is combined with Query as a Web Service the data can be refreshed at the click of a button.
Information Delivery & Discovery or IDD as it is frequently referred to can be thought of as the middle section of the SAP BusinessObjects BI stack. The two sections that sandwich it are Data Services and Enterprise Performance Management.
IDD comprises primarily of the Business Intelligence Platform , the Enterprise Reporting , and Query & Analysis components and Dashboarding
Biography – Russell Beech, Founder of BI System Builders & Cornerstone Solution®
I architect data solutions and superintend the solutions that I architect. I build teams that are usually a mix of full-time employees and sub-contractors. I ensure mentoring and knowledge transfer. The emergence of big data has opened the door to a rise in interest in predictive analytics aka machine learning. For me the technology changes have provided the opportunity to architect data solutions which combine my enterprise data warehousing experience with data lake concepts and to apply my knowledge of statistics to that data to deliver predictive analytics. To that end I’m putting work into understanding how the evolving technologies hook in to each other. I have a focused interest in learning about big data technologies such as Hadoop, Hive, GCP BigQuery and machine learning and their integration/virtualization with the enterprise data warehouse.
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