M&A activity in telecom companies has created vast data ecosystems with high complexity. Machine-centric approaches to scanning, cleansing, integrating and rationalizing data are pre-requisites to cost reduction initiatives.
Telecommunications organizations have the potential to significantly reduce their data management costs by rationalizing their data infrastructures.
These industry-specific versions of our products address multiple industry challenges related to:
Large telecom vendors have gone through decades of M&A activity to create sprawling IT infrastructures.
Marrying all these lines of business under one corporate umbrella brings with it distinctive challenges. For example, integrating disparate networks each containing hundred if not thousands of databases. The sheer amount of data consolidation can easily become overwhelming; telecom companies need a solution to keep their data ecosystems from spiraling out of control.
The Global IDs machine centric approach to master data management creates a foundation for firms to manage their data assets. Through automated discovery, data profiling, quality analysis and metadata documentation we allow companies to create transparency, enhance accuracy and reduce the resources required to manage data assets.
The goal of the software is to systematically reduce costs associated with enterprise data management. We believe that significant cost savings are possible at the data ecosystem level.
1) Data Ecosystem Profiling
When CIOs of telecom companies attempt to rationalize their redundant data environments, they must first have a good understanding of the data assets that are present in their enterprise databases. For organizations with thousands of databases, this can be a difficult task.
The Global IDs Data Profiling Product Suite was specifically created to help organizations automatically inventory their data assets. Large telecom companies can use the software to create transparency into their data ecosystem and use this knowledge to decommission, rationalize, merge and simplify their data infrastructure.
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