Develop a structured database to support quality, reliability and integration with other products and databases
Global data is growing rapidly, but IT Departments do not have the budgets to organize and structure this data. This harsh reality poses severe problems for both the CIO and CEO if the CIO wants to seek an improvement in data quality, reliability and processing efficiencies, and the CEO is looking to launch new products where seamless data integration is required with existing products and databases.
To improve data quality and reliability, and to integrate seamlessly with other products and databases, it is essential that organizations develop their data landscape in a structured manner. Else be prepared to forsake business agility due to data “spaghetti”.
To inspire confidence in their data, organizations build data models that represent the data used by their business processes and applications. These models are representations of either the logical or the physical structures that define the data stored in IT applications for a business. A logical model consists of business entities, their attributes and their relationships independent of how they are physically organized. This logical model leads to the creation of a physical model that consists of the real structure in how the entities and their attributes are organized, including specificities like length of data that can be stored in each attribute, type of data etc.
One of the starting points for building a data model is the analysis of data that is embedded in the existing application portfolio. If an organization can understand what data is currently in use then they can begin to develop plans to make the best use of this data and to optimize that data to ensure that the information presented is consistent across the organization.
In most cases, it is highly unlikely that an organization will retain the logical data model that leads to the physical data structures due to the manual effort required to keep both the logical and physical models in sync, whether those be a database or a series of flat files.
Therefore, the first step in any process is to develop a cohesive organizational data model and one approach is to understand the current physical data structures and relationships. Once these structures and relationships have been developed then they can be used by subject matter experts (SMEs) to view the data that lets the organization exploit and develop the data it uses and needs.
EvolveWare’s Intellisys™ Platform supports this approach by documenting the data structures and relationships when examining an application’s database files and the data accesses embedded in that application’s source code. Entity Relationship Diagrams that include non-relational and relational data structures are produced to assist in understanding the data used by applications. Relational constraints are also visualized to provide a better understanding of data relationships and interdependence. The model, whilst being developed from a physical source, is presented by the Intellisys™ Platform in a vendor neutral manner removing the complexity often times introduced by a vendor’s physical implementation.
By using the Intellisys™ Platform, organizations can be confident in the organization and reliability of their data, and use that knowledge to make better decisions throughout the life cycle of their business processes and applications.
ADABAS/Natural | ADSO | ASP | Assember | C | C# (.Net) | CA Gen | COBOL (AS/400, HP3000, z/OS, Unisys, etc.) | Easytrieve | Forte | FoxPro | Java |
PACBASE | PL/1 | PL/SQL | PowerBuilder | T SQL | Universe Basic | VB 5.0 & 6.0. | VB (.Net)
Files Inventory | Source Code Logistics | Source Code Complexity | Dead Code Details | Data Dictionary | System Details Diagram | Business Logic Connectivity Diagrams | Data Flow Diagram | Program Flow Diagram | Data Model in SQL/DDL Format | Program Logic | Business Logic.
Trace flow of variables and entities | Generate Entity Relationship Diagram