Artis Consulting has experience with a wide range of technologies and solution implementations. The following are some examples of key business intelligence technologies:

Online Analytical Processing (OLAP)
This technology allows evaluation of data in a way the user thinks about it (multidimensional). It allows users to ask “what if” questions in many different ways without requiring separate reports for each question. OLAP data or cubes are separate from production transactional (OLTP) databases and the associated dependencies. OLAP technology is at the core of most analytical solutions and for this reasons, Artis Consulting has extensive experience with this technology. Data is built into cubes by products from companies like Microsoft and Cognos and then evaluated by tools from companies like ProClarity, Panorama, and Cognos.

Analytical Applications
Analytical Applications are similar to OLAP but with the purpose of answering specific questions about the business on a regular basis, where a defined process exists. They may be delivered in the form of a scorecard or a complex report that a department needs to perform a certain function. Analytical Applications can be delivered by a custom solution using a technology like Microsoft ASP.NET or with off-the-shelf solutions from product companies like ProClarity, Microsoft, or Clarity Systems.

Data Mining
Data mining is the process of analyzing large volumes of data to find correlations, trends, or patterns. This is accomplished by evaluating the data from different perspectives and summarizing it into useful information. This information can then be used to increase revenue and cuts costs. With such large amounts of data available today, it is almost impossible to perform this type of analysis manually. There are solutions available today that help make data mining simpler by including built in analysis features such as statistics, neural networks, and visualizations. Example products: SQL Server Data Mining, SAS, and SPSS.

Extract, Transform, & Load (ETL) Technology
ETL, on the surface, often seems like a simple process of just getting data from the source systems into a useable target database. However, there are many aspects of this process that must be considered to ensure the process executes reliably. Some of the considerations are: exactly what data to retrieve (e.g., change data scheme), approach and timing for retrieving the source data, approach and timing for loading the target data, exactly how to transform each data field (e.g., merge fields, look ups, formulas), and how to handle exceptions. Depending on the technical environment and the resources to support the solution, this may be accomplished using a custom solution with technologies like T-SQL or PL/SQL or using a packaged solution from companies like Informatica, Microsoft SSIS, or IBM/Ascential.

Data Warehousing and Data Marts
Data Warehouses (enterprise level) and Data Marts (department or subject level) are repositories of integrated information (i.e., from disparate sources), available for queries and analysis. Data Warehouses are not just a larger or extended version of the source system. Unlike source systems which are typically transactional databases (OLTP) where the primary objective is to get transactions into the database as efficiently as possible, data warehouses are designed to make getting information out of the database as efficient and easy as possible (star schema or dimensional model). A data warehouse should be planned and architected to serve as the “single version of the truth” for the enterprise or the department that it is serving. All intended information consumers should use the same solution. Another important note is that the data warehouse is only part of the solution. Tools and processes must be established for getting the information out of the data warehouse and to the right person, at the right time, in the right format. Example database products: Microsoft SQL Server, Oracle.

Data Cleansing, Quality, and Enhancement
Many times the source data used to feed a Business Intelligence (BI) solution has issues such as incomplete fields, non-standard formats, and inconsistent values. Choosing the right data quality and enhancement strategy can mean the difference between a successful BI solution and a total failure. There are many techniques for addressing issues like these such as name and address standardization, householding of customers (duplicate detection and merging), reference or look up validation, and exception handling. Data can also be enhanced by extending the original data with geographic and demographic information. There are a variety of options including packaged solutions from companies like IBM/Ascential and Informatica, custom solutions using ASP.NET and PL/SQL, and third party service providers like Group1.

Reporting
Reporting can quickly become a massive, never-ending effort. There are many reporting solutions on the market such as Microsoft Reporting Services, Cognos ReportNet, and Crystal Enterprise. However, the challenge for an overwhelming majority of companies is understanding who the users (information consumers) are, what their needs are, and then developing an appropriate solution that meets those needs, nothing more and nothing less.

Portals, Dashboards, and Mobile Solutions
Providing key decision makers with access to critical information in a concise and timely manner; that is what Portal and Dashboard solutions are all about. This can be done via a desktop web browser or through a mobile delivery solution such as a BlackBerry or Treo phone. Instead of being overwhelmed with reports, these users just want to see critical information and then have the ability to drill down into more detail if needed. Example products are: Microsoft SharePoint and ProClarity Dashboard.

To learn more about potential technology solutions and Artis Consulting, contact us.