- Business Data Network - Reference Data Manager - Dashboard and Monitor Viewers
- Data Enrichment - Data Integration - Data Monitoring - Data Profiling - Data Quality - Entity Resolution - Metadata Analysis - MDM Foundations
The DataFlux Data Management Methodology is a step-by-step process for performing data management tasks, such as data quality, data integration, data migrations and master data management (MDM). When organizations plan, take action on and monitor data management projects, they build the foundation to optimize revenue, control costs and mitigate risks.
No matter what type of data you manage, DataFlux technology can help you gain a more complete view of corporate information.
The planning stage of any data management project starts with this essential first step. This is where the people, processes, technologies and data sources are identified. Roadmaps are built that include articulating the acceptable outcomes. Finally, the cross-functional teams across business units and between business and IT communities are created to define the data management business rules.
A quick inspection of your corporate data would probably find that it resides in many different databases, managed by many different systems, with many different formats and representations of the same data. This step of the methodology lets you explore metadata to verify that the right data sources are included in the data management program – and create detailed data profiles of identified data sources to understand their strengths and weaknesses.
After completing the first two steps, this phase allows you to take the different structures, formats, data sources and data feeds, and create an environment that accommodates the needs of your business. At this step, business and IT users build workflows to enforce business rules for data quality and data integration, and create data models to house data in consolidated or master data sources.
Once business users have established how the data and rules should be defined, the IT staff can install them within the IT infrastructure and determine the integration method – real-time, batch or virtual. These business rules can be reused and redeployed across applications, helping increase data consistency in the enterprise.
This step of the methodology allows users to define and enforce business rules to measure the consistency, accuracy and reliability of new data as it enters the enterprise. Reports and dashboards on critical data metrics are created for business and IT staff members. The information gained from data monitoring reports is used to refine and adjust the business rules.
The final stage in a data management project involves examining any trends to validate the extended use and retention of the data. Data that is no longer useful is retired. The project’s success can then be shared throughout the organization. The next steps are communicated to the data management team to lay the groundwork for future data management efforts.