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An 8 Step guide to Successful Data Virtualization

According to studies, data virtualization (DV) is going to stay here for a long time. It is driven by factors like the maturation of different vendor technologies, such as higher availability, row-level security, heterogeneous join optimization, reduction in the time end-users and an enhancement in the computing power.

In recent years, the business enterprises provide the prerequisite opportunity to the business organization to expand the data virtualization into different industries. It also enables the option for Agile BI or Business Intelligence. Such business enterprises are integrating with different cloud applications like Workday, Salesforce.com, and MS Dynamics. It is also responsible for integration with different cloud data solutions like LexisNexis, Windows Azure, and data.gov.

Besides, it offers access to a plethora of data, such as email, video, JSON documents, and different big data resources which are stored in different platforms. Data virtualization solutions helps in streamlining the majority of the challenges. It offers a singular point of data access for the unstructured and structured data. It helps in unifying data security across the business enterprise.

Data virtualization increases the development team's agility as they try to embark on different data integration projects. Besides this, it is useful in decoupling the analytics and applications from the different physical data structure. Besides this, it enables the options for changes in the data infrastructure, which reduce the end-user impact. Data virtualization confers real-time cleansed and processed data and operational data, which offers support to the different up to the minute data needs. In this write-up, you will find an 8 step guide to the successful data virtualization:

Architect from the business enterprise perspective

It is a prerequisite that data virtualization solutions are needed for meeting the evolving and dichotomous needs of the potential users across the business enterprise. The development of data virtualization might become lesser performant, lesser agile owing to which it becomes difficult to manage, with the addition of extra objects and layers.

As extra business dependencies and logic is present, the testing cycles become longer and harder owing to which it becomes difficult to troubleshoot different performance problems. If you want to mitigate such types of challenges, you should ensure to work with the data architecture teams.

In this aspect, you should implement the layered view approach, which helps in isolating the business logic. The data virtualization is useful in creating different development standards that are inclusive of different common rules and standards for layer isolation and reusability.

Coordination with the data governance business organization

In case you possess the data governance organization currently, it is recommended that you should socialize different data virtualization capabilities and concepts, before the start and leverage other processes, standards, business rules, and data definitions, which are defined already. The data virtualization offers gateways into different corporate data assets. It is deployed in the cooperation of this business organization.

Establishing of different usage guidelines and training other development teams

Business organizations are generating different guidelines regarding the use of other data virtualization techniques. As the business enterprise is unique, you can be ensured that no singular approach is going to work for everyone. In this regard, a reasonable approach is starting the first data virtualization project and implementation by using different tool-specific best practices after which the approach should be refined in due course of time, to suit the business organization needs.

Determination of different organizational responsibilities for the platform of Data Virtualization

The data virtualization offers the capabilities to deploy operational query systems and web services. It offers integrated data for the analysis. Business organizations are known to struggle to determine and understand who are responsible for supporting the platform. It is recommended to consider the matrixed approach across different groups such as DBA's, Data architects, application developers, ETL administrators, system administrators, production support, to name a few.

Such a matrixed approach is useful in recognizing the group with primary accountability and ownership for the data virtualization software administration. Besides this, it is useful in the identification of the group, which features the primary responsibility for the data virtualization objects production migration.

Coordination with information security

Data security is known to have a more substantial effect on the management of data virtualization security. So, it becomes easy to expose the different data and data resources to potential users. As the data gets exposed to the new types of users, the data security helps in determining the kind of regulations.

Collaborating with the Business Intelligence and Data Warehouse department

The Business Intelligence and Data Warehouse teams need to be aware of different DV capabilities. Business Intelligence and Data warehouse organizations should produce guidelines regarding how DV technologies are used. There are two different approaches to use data virtualization to decrease the costs and enhance the agility.

Exposing the metadata of data virtualization to the potential audience

There is a wide assortment of data virtualization tools capable of showcasing and exporting data lineage information. It is highly beneficial to the business users and data developers when it is necessary to find the source of specific data. This type of lineage information contributes to being a metadata piece which offers value to the business enterprise. As the business enterprise is leveraging the different metadata applications and standards, you should consider how the DV metadata is going to fit within the overlying strategy.

Considering how data fits

It is possible to leverage data virtualization to confer secured and controlled access to the operational data. It offers an opportunity for different Data Quality teams. It provides access to the controlled source system. It is possible to leverage data quality for the analysis and resolution of varying data quality problems. As the business enterprise intends to manage the quality of data down the stream from the specific data source. Exposure of operational data will resurface the resolved the different quality problems.

Summary

The business enterprise can gain a lot of benefits with the implementation of data virtualization success. It contributes to being an enabling technology. It is advantageous as data virtualization is a suitable choice for the enterprise-wide deployment.