Data problems cost enterprises money, customer confidence and lost business growth. Data success starts with the right data architecture, one that will scale, increase value over time, and integrate with existing assets. Solutions such as Enterprise Data Modeling,, Data Warehouse Design and various Data Integration Strategies support the mission, strategic objectives, decision processes and day-to-day operations of an enterprise.
Services include Enterprise Data Modeling, Database Design, Strategic Data Planning. Data Architecture (relational, big data, or hybrid), Data Warehouse Design, Conceptual Data Modeling, Logical Data Modeling, Physical Data Modeling, Reverse Engineering, Data Mapping, Data Governance, Data Standardization and Metadata Integration.
ENTERPRISE DATA MODELING
An Enterprise Data Model and Data Dictionary provide visibility into the use of the organization's information and facilitates
planning, re-engineering, development, and compliance.
DATA WAREHOUSE DESIGN
Inconsistent data between systems can be remedied by a data warehouse By providing a single version of the truth, a data warehouse improves business intelligence, saves time, enhances data quality, provides historical intelligence and generates a high ROI.
A well designed database is an excellent investment, it not only provides immediate ROI, it also adds to the long-term financial value of your business. A database built from a good data model can accommodate new ways of doing business, new lines of business, even new businesses.
Reverse engineering an existing database provides details on the types of information it contains as well as its structure. Design flaws can be corrected. Integration with other databases. or replacement with a newer structure, are facilitated.
Integrating data makes it easier to find and use information no matter where it resides, and intergrated datasets are easier to navigate. Inter-system data mappings and centralized data dictionaries open the doors to data integration.
Data Planning ensures that all aspects of data management are holistically explored at the start of a project. Based on the volume, frequency and structure of a project's data, the optimal data architecture will be a relational data arhitecture, a big data architecture or a hybrid.