Master Data Management

Deliver a single, unified and accurate view of enterprise information

Of all the factors that drive an organization’s success, customers and products are at the top of the list. To increase revenue, it’s critical to know who your customers are, what they purchase and how to sell more to them. And you need to know what you’re spending on materials across all business units to control costs.

Master data management (MDM) is the creation of a single, accurate and unified view of corporate data, integrating information from various data sources into one master record. This master data is then used to feed information back to the applications, creating a consistent view of information across the enterprise.

The insights offered by MDM allow companies to:

• Improve revenue generation
• Reduce risk
• Enhance customer relationships
• Uncover opportunities to maximize spend

As the capacity to collect and use data continues to expand, so does the complexity of managing that data. Data can be a company's greatest asset – the knowledge gained from product, customer or any other type of data can enhance relationships, streamline operations and improve effectiveness. But to unlock this knowledge, your organization must first unlock the potential of your data.

MDM lets you harness this potential of your data, letting you:

• Work from a consistent unified view of all corporate data
• Make more informed, predictive decisions
• Provide everyone in your organization with the data they need, when it's needed

Profile and Discover Metadata

Data profiling and discovery are key components of the DataFlux Data Management Platform, allowing business users to develop business rules to identify and correct issues and then extend these rules across the enterprise.

DataFlux technology enables automated data profiling and discovery for metadata management though:

• Metadata analysis – Gain a clear view of the information contained in your enterprise data resources, accurately grouping related data and excluding irrelevant data
• Outlier detection – Detect data that falls outside normal limits, identify incorrect attributes and drill down to the data to make a more in-depth determination about the data
• Data validation – Verify that the data in your tables matches its description, catalog metadata across an organization and validate that your data follows standardized patterns