These videos demonstrate how Peak Indicators would approach an actual customer project. Data can be accessed from a variety of sources, such as CSV’s, AWS, Oracle DBs, Google Analytics feeds. Once the data has been modelled we would carry out a Value Based Design workshop to identify what your crucial business requirement are, what are the drivers of these KPI’s, and the best way to display this data. The Peak objective is to give the user the right data, in the right format, at the right time.
Peak InfoGraphicViz plugin
This video demonstrates how the InfoGraphicViz plugin can be used to display fantastic graphic visuals within Oracle Data Visualization.
Supporting the use of your own custom images and bringing to life dashboards and data insights.
Oracle BI Load Tester
Peak Indicators' OBI Load Tester performs automated and repeatable load tests for Oracle BI. It has been designed to deliver realistic user volume testing on the Oracle Business Intelligence platform.
The MapInfoViz Plugin enables you to overlay 'map info' onto your existing map visualizations within Oracle Data Visualization.
Hosted on the Oracle Cloud, Workforce Retention Analytics provides a range of analytical and predictive capabilities to enable organisations of all sizes to optimise their workforce:
- In-depth historical analysis
- Predictive BI
- Unstructured/textual data
- What-if Analysis
The issue of student retention is a major problem for higher education institutions worldwide, and within the UK, universities of all sizes can lose millions of pounds each year due to students withdrawing early from their courses.
Hosted on the Oracle Cloud, Student Retention Analytics provides a range of analytical and predictive capabilities that enable universities to protect their valuable funding by improving their levels of student retention.
Higher Education Predictive
This short demo shows how Birst, seamlessly integrated with R-based analytic models, can be used to address student success rates in a Higher Education institution. It proves how historical data can be prepared in Birst, and used to train a model in R.
For the business user, the result is a prediction on which of the current year's students might fail or withdraw. Targeted support can then be provided to those at-risk students to improve the chances of them succeeding and, for the institution, mitigate the financial and organizational impact of drop-outs
This demo looks at sales dashboards from the point of view of an executive at a UK based car dealership. We start off looking at an overview of all the dealerships, before diving into the data to gain more insight into this year's performance.
Throughout the demo we see the seamless integration of multiple Birst charting options including maps and pixel-perfect reports, in a smooth interactive experience helped by visual filtering and drill downs.