Reporting & Analysis
Centralized, Conformed Enterprise Reporting and Analysis.
Trusted, Reconciled Business Intelligence.
Auditable, Secured Business, Legal, & Regulatory Reporting.
- Reporting Hierarchy and Drill-Through Analysis.
Reporting and analysis services are the business intelligence component of a relational data warehousing environment. They deliver paginated reporting and On-line Analysis Processing (OLAP) solutions by provisioning data from enterprise data warehouses and conformed data marts. Sourcing data from centrally managed data warehousing environments ensures consistently reported information of the highest quality and integrity, ensuring consistently interpreted decisions. Eliminating the costs, delays, and inefficiencies experienced in attempting to reconcile independently generated reporting silos within cross-functional decision making environments.
IT managed reporting and analysis environments are integrated into the overall information systems infrastructure to protect and secure sensitivity corporate information. Assuring only users with the required privileges and accessibility rights are permitted access to the informational resources.
Managed reporting and analysis services are implemented with the capabilities to trace the lineage of the data supply chain from initial sourcing, to eventual publication of the delivered information. An essential requirement for complying with external financial, legislative, and regulatory audits; and internal operational, management, and executive governance reporting.
Paginated reporting solutions are developed with technologies such as SQL-Server Reporting Services (SSRS), which allow for the delivery of textual and graphically rich content. The reports can be prepared ready for consumption by the recipient, or published in a form that allows them to interactively customize the contents of the reported information for themselves.
OLAP technologies such as SQL-Server Analysis Services (SSAS) are an alternative to paginated reporting when the quantities of data that needs to be queried results in poor response times. OLAP cubes are designed to accommodate highly responsive querying of large quantities of historical data.They’re especially efficient at ‘slicing’ (filter) and ‘dicing’ (group) data across a range of reporting business entities. Decision makers have the ability to ‘drill-up’ and ‘drill-down’ through the data cube in navigating through the summary and detail levels of hierarchically structured reported information.
OLAP Cubes can be implemented to serve the informational needs of either individual, or cross-functional business departments. An OLAP architecture consists of a landscape of conformed data cubes, seamlessly integrated to allow ‘drill-through’ capabilities between the cubes. Providing decision makers with cross-functional reporting capabilities without the need to duplicate either the underlying data or the organizational reporting hierarchies.
OLAP Cubes provide are a highly responsive means of conducting ‘period over period’ historical analysis in reference to organizational reporting hierarchies. They allow to analyze the reported information across a range of business dimensions such as customer, product, location, and service channels. Enabling an efficient means of responding to questions such as ‘who bought what where, how, and when?’.
OLAP cubes can be implemented in a number of ways. They include proprietary Multi-dimensional (MOLAP) cubes, relational database (ROLAP) star schemas, and cloud hosted real-time analysis solutions such as Microsoft Azure SSAS.
MOLAP cubes contain pre-computed, pre-summarized data. Pre-processing the data enables highly responsive means of grouping, filtering, and navigating through the reported data. Functionality which can be resource intensive and less responsive when executed via relational querying. MOLAP cubes are generated through off-line processing, and are current as of the data available at the time they are created. They are good solutions for reporting static periodic data such as month-end reporting. Not so much for reporting constantly arriving data. ROLAP is a better alternative in such cases.
ROLAP cubes are implemented within relational databases as star and snowflake data structures. The relational data structures are populated with static historical snapshots which can be periodically refreshed to accommodate arriving data. ROLAP cubes are alternative to MOLAP solutions when the reported data needs to be updated in-between the periods the MOLAP cubes are regenerated.
The introduction of the cloud hosted Microsoft Azure SSAS technology implements a relational-like ‘Tabular Model’ OLAP solution. In this case the data is cached and processed in memory, allowing for real-time analysis of Microsoft Azure residing data sources.