Six steps to successful data virtualization

Data Virtualization

The advancement in the field of virtualization and cloud computing has made Data Virtualization the next big thing. The fact that businesses are experiencing the need for increased computing abilities and reduced time to access data has made firms offering IT consulting Virginia Beach develop their understanding on data virtualization. Most businesses are making the switch with the technical assistance from IT specialist.

But data virtualization is easier said than done. If not planned and deployed properly, it can do a business more harm than good. Mismanagement of data, reduced data quality, and poor performance are some of the consequence of a poorly planned data virtualization effort.  As per technology professionals from IT VA firms, businesses can successfully implement data virtualization by following these steps.

  • Architect from an Enterprise Perspective:
    The fundamental goal of implementing data virtualization solutions is to meet the ever-evolving needs and requirements of businesses and organizations. When more layers of applications and systems are added to the network, data virtualization process can become unresponsive, unproductive and challenging to manage. When there is a higher level of dependencies and duplication, it takes longer to troubleshoot performance issues and run tests. These challenges can be resolved by working closely with the data architects and considering approaches to mitigate problems.
  • Coordinate with your Data Governance organization:
    Data virtualization serves as a gateway to corporate data assets thus it is recommended that your data virtualization plans are aligned well with your data governance policies.
  • Establish usage guidelines and train Development, teams:
    It is a must to establish a guideline on how and when to utilize data virtualization technologies to access the data. Since no two organization and their data sources are same, there is no set approach to controlling access to the data. When implementing data virtualization, developers must have basic training and knowledge of how to leverage the capabilities of virtualization technologies. When initiating data development plans consider data virtualization to maintain cooperation throughout the process.
  • Determine organizational responsibilities for the Data Virtualization platform:
    With data virtualization, businesses have the capability to deploy web applications, query operational systems and data integration for analysis. But this often creates issues in determining who is responsible for supporting the applications. Businesses can adopt a matrixed approach as it allows identifying which department is the owner and has the responsibility to administrate data virtualization software.
  • Coordinate with Information Security:
    Data security has a direct implication on how DV is managed. With data virtualization, it becomes easier to expose data sources to others. In case a data is exposed to a new user type, it is essential to determine how to regulate the data access.
  • Collaborate with your Data Warehouse/Business Intelligence department:
    An organization’s DW/BI teams must have some knowledge of DV and undergo training if necessary. Data Warehouse/Business Intelligence usually formulate guidelines that define when and how much data virtualization techniques to be used. The practice of determining and clarifying the frequency and usage of DV ensures there is no conflict amongst the organizations.