Connecting Your Data Islands Through Data Virtualization

By Ehrar Jameel, Director Data Management and Analytics, American Tire Distributors

“Imagine a world where all the data scattered across your organization and external sources is easily accessible and united in a single location. A world where you can see the complete journey of your inventory, from start to finish, without the hassle of physically visiting each data island or duplicating the data on each one. This world is possible through the magic of data virtualization.

Data virtualization allows you to create a virtual bridge connecting all the different data islands in your company. It enables you to access and combine data from multiple sources as if it were a single, unified data store without replicating or moving the data physically. This allows you to get a complete, end-to-end view of the flow of products and identify any bottlenecks or inefficiencies in the process.

Ours is a data-savvy distribution & logistics company with a vision to be the most connected and insightful automotive service provider. The company has multiple data sources, including transportation management systems, warehousing systems, and other logistics systems, that are scattered, both within and outside the organization. It is difficult to get a complete, end-to-end view of the flow of goods, and to identify bottlenecks and inefficiencies in the supply chain.

When all is said and done, data virtualization is a powerful tool that can help you to see the complete journey of your products and optimize your operations.

As the head of Supply Chain & Merchandising Analytics, I needed to ensure that I enable the business to have the right amount of product in the right place at the right time, I evaluated various methods and decided to implement data virtualization within our Data Strategy to improve its supply chain management since the data lake buildout was lengthy and time-consuming, and businesses would not wait for answers until the Data Lake build was complete. Data virtualization allows us to create a virtual bridge that connects all the different data sources scattered across the supply chain. This enables the organization to access and combine data from multiple sources as if it were a single, unified data store, without the need to physically replicate or move the data.

By using data virtualization, we were able to: Get a complete, end-to-end view of the flow of goods across the supply chain, Identify bottlenecks and inefficiencies, and exceptions in the supply chain and take corrective action, Improve data accessibility by providing a single point of access to data from multiple sources, enhance analytics by allowing us to access and combine data from multiple sources.

Overall, data virtualization helped us improve our supply chain management and make more informed decisions about optimizing our operations.

But data virtualization isn’t just useful for the supply chain management. It can also improve data accessibility, reduce the cost and time of data integration, improve data governance, increase agility, and speed up the process of gaining insights from data.

However, data virtualization isn’t the right fit for every organization or use case. It’s important to consider your specific needs and goals carefully before deciding whether to include them in your data strategy. Factors to consider include the number and variety of data sources, your data integration requirements, your data governance, and security needs, and your analytic needs.

Implementing data virtualization also comes with its own set of challenges. It can be complex to set up and maintain and can impact the performance of data queries if not properly optimized. It’s also important to ensure the quality of the source data and the security of the virtualized data.

There are many tools available in the market that can help you with data virtualization, such as Denodo, IBM InfoSphere, TIBCO, AWS Glue, Red Hat Jmax, and Cdata. Each has its own unique set of features and capabilities, so it’s important to carefully consider your needs and do your research before choosing the right one for your organization.

I’m hoping that my knowledgeable friends, connections, and readers will weigh in to point out the distinctions and nuances. When all is said and done, data virtualization is a powerful tool that can help you to see the complete journey of your products and optimize your operations. It’s worth considering as part of your data strategy but be sure to weigh the benefits against the potential challenges and choose the right tool for your needs. Data leaders must do a much better job of articulating the value and benefits of the capabilities of data virtualization in plain business language, in terms of business value and clear business benefits, if they ultimately want to be successful in building data-driven companies.”