Data Management

Today, the volume of data is increasing exponentially. If companies do not have or source an ability to manage their data assets their compliance of regulatory requirements, accurate business forecasting, analysis, reporting can be in serious glitch All information assets have a lifecycle-defining their point of origin and point of disposition-in the organization.Successful data warehouse design starts with understanding the users and their needs. Data warehouse users can be divided into four categories: Statisticians, Knowledge Workers, Information Consumers and Executives. Each type makes up a portion of the user population as illustrated in this diagram.

At Inteliment we strive to design, develop and deploy solutions to serve as integrated and meaningful business information. We not only understand the applications that use data, but we also specialize in the methods to acquire and integrate data.

Data Migration

When upgrading to a new version of a database or application, or when switching to a new system, data needs to be preserved in the new system. The purpose of data migration is to transfer existing data to the new environment. It needs to be transformed to a format suitable for the new system, at the same time preserving the information present in the old.

Data Synchronization

The need for data synchronization can either be permanent (synchronization between operational systems), or temporary, for example during a migration. At Inteliment we start with data assessment, proceed to data cleansing, enhancement and consolidation steps to deliver:

• Data Profiling - Scrutinize Data for errors, such as discrepancy, duplication, completeness
• Data Quality - bring data into a homogeneous state and validate data
• Data Integration - Consolidate data from multiple data sources to provide users unified view of data
• Data Enrichment - Augments existing data from internal & external data sources to remove gaps and bring data to a more up to date stage.
• Data Monitoring - Data is perishable. It needs constant monitoring to ensure it is accurate, and relevant.

Data Profiling

This is a systematic review of the quality, scope and context of the data source from which the Extract Transform Load (ETL) system will extract data. A 'clean' data source will require minimal transformation, while a 'dirty' data source will require extensive data transformation. However, in the absence of proper planning unforeseen obstacles may arise, such as Data Profiling may reveal at the time of migration that an ETL process cannot support the 'dirty' data source. It is better to discover such issues in the planning stage rather than during implementation of the ETL system.