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Are You Using the Right Data Management Architecture for Your Enterprise?

Authors Photo Precisely Editor | August 24, 2020

Data is the lifeblood of modern enterprises. For a company to stay competitive in today’s business environment, it must consistently extract maximum value from its data resources. That requires having a well-structured data management architecture that defines the policies and practices governing how data is captured, transformed, consolidated, stored, and delivered to where it is needed to meet the business goals of the organization.

What data management must accomplish

The modern data management architecture must be able to aggregate and make widely usable information that resides in different locations or systems across your organization’s IT infrastructure. In many instances, those systems will have been crafted entirely independently of one another, each for its own individual purposes and with its own unique set of workflows, processes, and data structures.

Your company’s data management system should be architected to intelligently (and automatically) access, transform, and correlate those diverse data streams into a single virtual pool of information that users throughout your organization can easily access, through a common user interface, to achieve their own business purposes.

Achieve more with data

If you’d like to ensure that your company has a data management architecture that will allow it to fully leverage its information resources, Precisely can help. Learn more about our products and how they can help you go further and achieve more with data.

Such a data management architecture will include effective processes in the following areas:

  • Data Collection: The architecture is configured to ingest data streamed from a number of different sources and with a variety of formats. For many use cases, data collection must be accomplished in near real time.
  • Data Harmonization: Intelligent record matching is performed to link together records from different sources that refer to the same logical entity. This creates a single master record (whether physical or virtual) for each entity.
  • Data Transformation: Data objects from varied sources are restructured into a common format.
  • Data Centralization: Information from all sources is consolidated into a single, common, virtual repository.
  • Data Synchronization: The architecture enables scanning of data from each source on an appropriate schedule (including, as necessary, continuous scanning for real-time acquisition) so that consolidated records remain current and reflect changes in source datasets as they occur. This allows the repository’s master records to be kept reliably up-to-date.
  • Data Security: There is constant monitoring of all parts of the system to detect anomalous behaviors and generate security alerts as necessary. Encryption is employed to protect data both at rest and in transit. Effective access protection protocols are implemented to ensure that only authorized users can access system data or processes.
  • Data Quality: Validation processes incorporating relevant business rules are used to ensure that data quality standards, such as accuracy, consistency, completeness, timeliness, and uniqueness, are met.
 

Characteristics of an effective data management architecture

For maximum effectiveness, your data management system should be designed around the following principles:

  • Flexible: The system is easily reconfigured to accommodate changes in source datasets or in business requirements for how the consolidated data can be used.
  • Scalable: It is able to keep up with the exponential data growth many modern enterprises are experiencing.
  • Self-Service: The system allows ordinary users to extract the information they need for their own business purposes. The workflow allows users to define and access the particular data items they need without having to be concerned with where those items originate or how they are delivered.
  • Simple UI: The user interface is intuitive and usable by individuals who have no specialized technical expertise.
  • Automated: the entire workflow operates with a minimum of human intervention. This is especially important for anomaly detection and alert generation.
  • Compliant: It meets regulatory standards for protection of personally identifiable information and allows tracking of data lineage, with a reliable and complete audit trail.

Where to start

If you’d like to ensure that your company has a data management architecture that will allow it to fully leverage its information resources, Precisely can help. Learn more about our products and how they can help you go further and achieve more with data.