The concept behind Query & Analysis is really about empowering the Business User and Analyst to interact with data on the fly in a Self-service BI environment. There a few tools available from SAP BusinessObjects that fulfil this criteria. First of all, here’s a little background, so as to avoid any confusion around what the products are.
Historically, the original Query & Analysis and Reporting tool was simply called ‘BusinessObjects’. Back then if you were to ask someone what reporting tool they were working with they would just say ‘BusinessObjects’. That was the only tool, until sometime later the first version of Web Intelligence was released. After BusinessObjects acquired Crystal Decisions the ‘BusinessObjects’ reporting tool was renamed Desktop Intelligence, sometimes also known as Deski. Running alongside Desktop Intelligence the thin client tool Web Intelligence was being developed.
Over the years Web Intelligence has grown to become SAP BusinessObjects’ flagship tool that allowing non-technical users to create new queries, slice and dice, and then drill down through their data all via a web browser. The advancement in Web Intelligence means that Desktop Intelligence is now coming to its end of life.
A second tool available is Voyager. Voyager is an Online Analytical Processing Tool (OLAP). In the BI 4.0 release Voyager is superseded by SAP BusinessObjects Analysis edition for OLAP and SAP BusinessObjects Analysis edition for Microsoft Office. It should be used when analysis is required against large data sets.
We also find it useful to think of a third tool under the Query & Analysis category. That is the search tool named Explorer. The original name for Explorer was Polestar.
SAP BusinessObjects Web Intelligence makes Self-service BI a reality by providing access to data in an intuitive information analysis product. By simply dragging and dropping objects into their reports users can gain business insights and turn these into true Business Intelligence for effective decision making. A business user can quickly create a query from scratch with a few mouse clicks. This is frequently termed ad hoc analysis or creating queries on the fly. Users can then format the retrieved information and analyze it to understand underlying data trends and root causes. The Web Intelligence interface is intuitive in design allowing business analysts and non-technical information consumers to ask spontaneous and iterative business questions of their data using their everyday business language. For example, users could easily create a query that allowed them to compare the performance of their top customers this year over last year. The user could then create their own variables, for example a forecast algorithm and add this to their report. Web Intelligence allows trends to be quickly identified along with sub-optimal areas. Users can drill down and drill across on their data. Web Intelligence access to the data is via a robust semantic layer known as the SAP BusinessObjects universe. The universe objects make query building easy and shields business users from needing to understand database technicalities. Multiple universes from different data sources can be combined in a single report.
The earliest versions of Web Intelligence were only available in thin client. In other words Web Intelligence had to be accessed via InfoView and a web browser. Web Intelligence is now available in the Rich Client version. This is loaded on to the users local machine. A report can now be viewed or developed on the local machine and then published to the BI Platform and shared and viewed as thin client via InfoView. This is great news for people working from home or on a train etc. They can now make changes to their Web Intelligence document off-line and then publish their changes next time they connect.
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. The technology changes have provided the opportunity to architect data solutions which combine enterprise data warehousing experience with data lake concepts and to apply knowledge of statistics to that data to deliver predictive analytics. To that end I’m putting effort into understanding how the evolving technologies hook in to each other. I have a focused interest in big data technologies especially on the Google Cloud Platform. Technologies such as Hadoop, Spark, Apache Beam, BigQuery and Tensorflow (machine learning) and their integration/virtualization with the enterprise data warehouse.
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