SOLUTIONS

ComFrame’s methodology and project approach is based on decades of project experience and the use of a variety of toolsets and approaches through the years.  These experiences have culminated in reasoned, business value centered methodology that we believe mitigates risk and delivers tangible value more quickly than the approach of many others in the space. Our methodology is driven by business requirements and implemented in technology solutions, not the other way around. Our BI Architects are well grounded in both business & technology concepts, they are Information Architects, not IT architects.

Strategic Analysis and Planning

The depth of strategic analysis and planning can vary greatly depending on the maturity level of our client’s reporting infrastructure, data consumption strategies and goals for the engagement.  In this stage, our project teams explore the existing and desired future state components and temporal concerns of strategic goals and existing and desired consumption methods (reports, graphs, dashboards, PDA) required to monitor progress toward, or away from, strategic goals.  This stage may also include technical analysis of primary data sources and existing transition and storage techniques.

Information Infrastructure

Establishment of a clear and business centered information roadmap and strategy during analysis and planning provides the required detail for setting of the information infrastructure required to meet objectives.  ComFrame’s methodology utilizes specific infrastructure components and techniques to maximize the effectiveness of our solutions while reaping the greatest benefit from Microsoft’s business intelligence stack.  Our position as a National Systems Integrator (NSI) provides us direct access to Microsoft’s near and long term strategy, enabling us to “future proof” your information infrastructure.

Extract, Transform, and Load (ETL)

ETL is the first tier of the required information infrastructure. It is the ETL process that routinely extracts and loads the relational data warehouse (or datamart(s)).  This phase builds on information collected during analysis and planning regarding back end systems holding required data and business rules that may need to be applied as the data is loaded to ensure data consistency downstream.

This is generally accomplished via SQL Server Integration Services (SSIS) packages that execute processes that collect data from various organizational data sources during periods of minimal activity. The packages are physically stored on relational database server and operate in a ‘pull mode’. By executing the packages on the database server we allocate the majority of the ETL workload onto the data warehouse server and not on the company’s production servers. If high volumes of data are continuously being processed in the ETL you can optionally allocate a separate SSIS server.

Dimensional Data Marts

Our methodology is based upon the rapid creation of business value through incremental delivery of independent data marts.  Data Marts are analytical data structures, associated with a specific business process, which facilitate storage of historical data, in a business (not technical) context.  At the core of every data mart are measures.  These are generally numeric values associated with a business process or functional activity. For example, a retail sales data mart may have measures that include:

• Units Sold
• Revenue
• Number of Sales Transaction (customer count)

Finally, hierarchies serve to organize groups of dimensions.  For instance all activities for a the calendar date representing January 1, 2009 would be included in aggregate totals for the first week of 2009, the entire month of January, 2009, the first quarter of 2009 and the entire year of 2009.  Hierarchies make possible the drilldown analysis by users to breakdown measures into their smallest atomic contributing measure.
Dimensional data marts provide the added advantage of allowing incremental delivery of business value over time.  Business Intelligence projects no longer require an enterprise scope of modeling and construction to be successful.  Instead, the enterprise data warehouse can be constructed over time by the release of individual, independent data marts. 

In addition to delivering incremental business value through the release of dimensional data marts, ComFrame’s Business Intelligence methodology provides the following benefits:

• Predictable Schedule, by decomposing the enterprise data warehouse into smaller projects of discrete, business-aligned functionality, software release risk is minimized.
• Additional functional areas can be incorporated with minimal additional effort.

Cubes

OLAP Cubes are logical objects, physically implemented in binary files that provide information consumers with fast analysis of Dimensional Models. By leveraging Cubes you can provide information to both various reporting formats (traditional, scorecards, dashboards, and charts) as well as analytical tools for exploratory (slice-and-dice) functionality. Cubes must have their content kept up to date by using a technique known as Processing. Cube Processing must occur after the ETL processes run.

Data Consumption

Ultimate realization of the corporate goals associated with business intelligence lies in the method, frequency and organizational depth of data access, or data consumption.  Delivering the right data is one thing, but providing that data in a fashion that is geared to the consumer and supports the organizational goal behind that consumer’s access is critical.  Today, we are provided with more tools than ever before to view, analyze and model the information held within our organizations.  ComFrame’s methodology places high value on consumption planning and utilizes the options below, and more, to craft a data consumption strategy for clients.

Scorecards, Dashboards, Excel Services

In order to take advantage of the investments made in Strategic Analysis and Information Infrastructure information must be provided to the right user at the right time.   This is addressed by using best practices around usability and the use of Scorecards & Dashboards which provide defined Key Performance Indicators to those in decision making positions.   
Since its inception the spreadsheet has been a foundation for business analysis.   Many organizations have extensive expertise and have invested heavily in building business decisioning systems entirely this technology.   This experience can be taken advantage of through technologies such as Excel Services which presents business data through a standard web browser.   Access to business logic and data sources can be protected while the information itself can be shared in a familiar environment.

Web Services