Phocas Business Intelligence Services Training Support

Phocas helps users discover data and provides results in real time. It is designed for non-technical users and delivers a simple yet powerful analytical capability that quickly turns data into a chart, graph or map. It brings up data on local, regional or global sales, inventory, forecasts, prices, profit margins, budgets and more.

Phocas, based in the United Kingdom, is a business intelligence firm with offices in the UK, the United States, and Australia. The vendor says that by offering an innovative data discovery solution, it has maintained a 97 percent customer retention rate.

Phocas Business Intelligence Features

BI Platform Features

Administration via Web Interface
Snapshot of External Data
In-memory data model
OLAP (Pre-processed cube representation)
ROLAP (SQL-layer querying)
Multi-Data Source Reporting (Blending)
Data warehouse / dictionary layer
ETL Capability
ETL Scheduler

Supported Data Sources Features

Data sources that can be consumed by the application.

MS Excel Workbooks
Text Files (CSV, etc)
Oracle
MS SQL Server
IBM DB2
Postgres
MySQL
ODBC
Cloudera Hadoop
Hortonworks Hadoop
EMC Greenplum
IBM Netezza
HP Vertica
ParAccel
SAP Hana
Teradata
Sage 500
Salesforce
SAP
Google Analytics

BI Standard Reporting Features

Standard reporting means pre-built or canned reports available to users without having to create them.

Customizable dashboards
Report Formatting Templates

Ad-hoc Reporting Features

Ad-Hoc Reports are reports built by the user to meet highly specific requirements.

Drill-down analysis
Formatting capabilities
Predictive modeling
Report sharing and collaboration

Report Output and Scheduling Features

Ability to schedule and manager report output.

Publish to PDF
Output Raw Supporting Data
Report Versioning
Report Delivery Scheduling

Data Discovery and Visualization Features

Data Discovery and Visualization is the analysis of multiple data sources in a search for pattern and outliers and the ability to represent the data visually.

Pre-built visualization formats (heatmaps, scatter plots etc.)
Location Analytics / Geographic Visualization
Predictive Analytics

Access Control and Security Features

Access control means being able to determine who has access to which data.

Multi-User Support (named login)
Role-Based Security Model
Multiple Access Permission Levels (Create, Read, Delete)
Report-Level Access Control
Table-Level Access Control (BI-layer)
Field-Level Access Control (BI-layer)

Mobile Capabilities Features

Support for mobile devices like smartphones and tablets.

Responsive Design for Web Access
Dashboard / Report / Visualization Interactivity on Mobile

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