Ubiq Services Training Development Developer Official Support Partner Services Company Solution Support in Doha Al Rayyan Qatar

Ubiq is a web-based reporting and business intelligence application. It enables users to analyze, explore and report data without programming or SQL knowledge. Users can create web-based reports and dashboards using a web browser; export them in multiple formats and collaborate with their team.

BI & Analytics Features

Platform

Administration via Windows App
Live Connection to External Data
ROLAP (SQL-layer querying)
Multi-Data Source Reporting (Blending)

Ubiq can be accessed on all major web browsers (Chrome, Firefox, Safari, IE 10+) on laptops, workstations, tablets and phones.

Supported Data Sources

PostgreSQL
MySQL

You can connect to local, remote and cloud databases. You can also connect to cloud based data warehouses like Amazon Redshift, Amazon RDS, Amazon EC2. Currently supports MySQL & PostgreSQL. More data sources to be added soon.

Standard Reporting Features

Pixel-perfect reports
Customizable dashboards

Drag & Drop report builder. Auto-refresh for reports. Dynamic filters for charts and reports using dropdowns/sliders. Real-time reporting to create reports from live data.

Ad-hoc Reporting Features

Drill-down analysis
Output formatting
Report sharing & collaboration

Report Output & Scheduling Features

Publish to Web
Publish to PDF
Output raw supporting data

Copy chart data to clipboard. Export charts as CSV. Export reports as PDF, PNG

Data Discovery & Visualization Features

Multiple pre-built visualization formats (heatmaps, scatter plots, etc.)

25 different types of visualization formats like table, line, bar (stacked, group, percent), scatter plots, stacked area, column (grouped, percent, stacked), pie, donut, bullet chart and funnel

Access Control and Security Features

Multi-user support (named login)
Role-based security model
Multiple access permission levels (Create/Read/Delete)

Mobile Capabilities

Responsive design for web access

Ubiq can be accessed on all major web browsers (Chrome, Firefox, Safari, IE 10+) on laptops, workstations, tablets and phones.

Data is a critical resource for every organization. We depend on data every day to keep records, produce reports, deliver information, monitor performance, make decisions, and much more. The data resource is on par with financial and human resources as a core component of doing business, yet data management practices are often quite casual. Data governance brings the same level of discipline to data management as is typical when managing financial and human resources.

Building a data governance program is a complex process that focuses people, processes, policies, rules, and regulations to achieve specific goals for a managed data resource. Successful and effective data governance depends on clear goals and well executed activities that match governance practices to your organization’s needs, capabilities, and culture. A continuously evolving program is necessary to keep pace with trends such as cloud services, big data, and agile development. This course provides fundamental understanding of data governance concepts and techniques that is essential to start a new governance program or evolve an existing program.

You Will Learn

Definitions and dimensions of data governance
Key considerations and challenges in building a data governance program
The practices, roles, skills, and disciplines essential to data governance
The qualities that make good data stewards and stewardship organizations
The processes of developing, executing, and sustaining data governance
Activities, issues, and options when building a data governance program
How maturity models are applied for data governance
The importance of adapting data governance for trends such as big data, cloud services, and agile development methods

Geared To

Data quality and data governance professionals; BI/DW managers, architects, designers, and developers; data stewards, data architects, and data administrators; anyone with a role in data governance or data quality management