Nowadays, the vast majority of IT spending directed at business computing is on cloud solutions, as organizations recognize the significant advantages, including access, maintenance, reliability, and performance associated with cloud environments.
Visualize emergency services data and pinpoint problem areas using ESRI maps in SAP Analytics Cloud
Dashboards allow us to present data in a variety of creative ways which allow complex information to be analyzed at a glance. Along with charts and tables, maps are an increasingly common format, and with SAP Analytics Cloud it’s never been easier to create them, even without technical training. In this article we’ll look at the process for adding custom maps to SAP Analytics Cloud Stories, using a case study as an example.
The new Fiori BI LaunchPad will enchant BI 4.3 users in the first few clicks!
Ingredients for Success: The Future of Decision-Making with SAP Analytics Cloud
Let us take care of you
Did you know that you can lower your TCO with a best-in-class Hybrid Analytics environment?
A Hybrid Strategy for Best-in-Class Analytics
Travel with less baggage
Your current SAP BusinessObjects environment is key to your business. However, as many of our customers, you probably are carrying a bit of extra weight with too many licenses that are not used to their full potential. Why not review whether you can optimize your environment and convert some of the unused license maintenance to SAP Analytics Cloud?
Clariba takes home the prestigious SAP Innovation Award 2020
Technology Disruptor – Recognition for our customer co-innovation journey
If the current pandemic has made one thing clear, it is that organizations must become more agile and must be able to reimagine its business model, while at the same time count on resilient digital business processes and continuously course correct with operational and customer experience insights gained through real-time analytics.
You don’t need to be a data scientist to harness the power of Machine Learning, thanks to SAP Analytics Cloud
These days “machine learning” is a common phrase across a wide range of industries. The need to manage, analyse and report on large scale datasets is no longer restricted to tech companies - sectors from retail to manufacturing to healthcare rely on data scientists to make sense of the huge volumes of data gathered in the execution of their business, and attempt to use it to plan for the future, optimise processes and predict risks and opportunities.