Data Warehousing

The data needed on which to make business decisions is often scattered or in silos, inconsistent and unusable. This is often a result of a company making organizational changes, strategic acquisitions, legacy system modernization, or not having the right system in place. When a company uses multiple and inconsistent versions of data in different parts of their operations, they have trouble finding a complete view and struggle when it comes to planning future initiatives. KCM Solutions provides a complete Data Warehouse methodology which streamlines the process of accessing, discovering, cleansing and integrating data through:

Data Governance – Ability to quality control processes, policies, and standards required to manage and ensure availability, accessibility, quality, consistency and security. A Data Governance program includes pre-defined set of procedures and subsequently a plan to execute those same procedures. The first step involves defining the owners of the processes so as to have a defined body that would be held accountable for different aspects of the data. Lastly the process should also be compliant with existing government norms.

Data Consolidation – In this process various data sources are assimilated together to a single data store. This provides a realistic scope of coordinating and consolidating disparate data systems to provide a new view and unlock new business potential within an organization. The newly created data set represents a combination of both qualitative and quantitative data.Data consolidation processes decrease ineffectiveness of a data system by removing duplicate data which in turn helps in reducing the overall incurred cost.

Data Migration – In this procedure data on an old system is mapped to a new system for data loading. This reduces the costs of data migration projects by automating the processes, improving internal collaboration and incorporating proven best practices. Data Migration is a three step process comprised of Planning,Migration and Validation. Upon completion of Data Migration it is a good practice to validate statistics to determine data accuracy.

Data Quality – This process involves delivering correct data to everyone in the organization so there is complete trust with the information given. Data quality processes also includes, in some instance, statistical process control of data quality. Acceptable Data Quality is important to operational and transactional processes and to the reliability of Business Intelligence reporting. Maintaining Data Quality requires going through the data and amending or removing data in a database that is incorrect or duplicated.

Data Synchronization – The process includes synchronizing all operational and transactional systems with high-quality data in batch. The synchronization may either be a remote synchronization or a local synchronization. Data Synchronization is enabled through specialized software that tracks data version as they are created and utilized. The process ensures that regardless of data modifications all changes are merged with the original source.

Master Data Management (MDM) – Master Data,the non-transactional data entities of an organization are managed by MDM. This process creates a single version of the truth that recognizes inconsistent, duplicate data and resolves it to reveal relationships between business-critical data that will improve business performance. MDM enables the organization to link all of its important data to one file known as the master file. The master file is now used as a source for all references.