View all Posts Supply Chain Optimization , Data Integration

This Kind of Data Integration Is Key for Supply Chain Optimization

There are several different types of data integration, but only a few of them are appropriate for effective logistics and supply chain optimization.

Few people get excited about data integration. This comes as no surprise. It's easy to imagine the process as too complex and boring to think about, so why bother? And, since its effects are more enjoyable than seeing how it works (you'd rather savor your double fudge sundae than pore over the logistics of how your ice cream made it to the shop, right?), most people don't spend enough time wondering what role data integration plays in supply chain optimization.

Data integration is the collection and consolidation of information into one location for the purpose of creating an aggregated picture of it and what it means. Historically, integration of data takes place in three steps:

  • Extract
  • Transform
  • Load

Through this process, called ETL after the steps' initials, data is retrieved from a source, taken to a staging area to be cleaned and converted for storage, then "loaded" into a repository. As cloud technology grows in sophistication and popularity, a variation of this process has taken shape. ELT, the newer version of ETL, places the load step before transform, and so in this model data is placed in the target location before transformation occurs.

So, what's all this mean? You came to the right place.

Isn't integration just consolidation?

Although the terms "integration" and "consolidation" are often used interchangeably when referring to data, they don't mean quite the same thing. Consolidation follows the traditional integration process of combining data, removing its errors and redundancies, then aggregating it in one location — like a pool or warehouse. Consolidation relies on ETL/ELT technology and is great if you need to standardize your data and bring it under one roof. However, data integration has evolved to mean so much more than that.

Today, data integration has different definitions as it comes in multiple types. The three primary types of data integration are:

  • Consolidation
  • Virtualization
  • Replication

The latter type simply involves cloning data to be shared between systems, and it doesn't support real-time analysis. Replication works best when you have few data sources and don't mind lacking up-to-the-minute precision in your reporting. If you're looking to optimize your supply chain, you'll want to embrace data virtualization.

Virtualization vs. consolidation

As they both fall under the umbrella of data integration, consolidation and virtualization bear many similarities. They're each used to bring data together from separate sources and make it accessible in one place. However, whereas consolidation employs the ETL/ELT process to move data into a single repository, virtualization does not.

What sets virtualization apart is a virtual data layer that provides a continuous audit of your information, wherever it lives, allowing for real-time monitoring and analysis. Virtualization doesn't replicate data, nor does it actually move data. In fact, the ETL/ELT process that defines consolidation plays no role in it. Rather, virtualization pulls data from disparate sources together into an integrated virtual view that's accessible across organizations.

For companies that require immediate updates on pricing, inventory, shipping, yields, trends, and anything else in their network, virtualization is the preferred style of data integration. It supports end-to-end visibility, so anyone who needs updated data can access it, at any time, from where they are. In a nutshell, data virtualization empowers users throughout the network to get real-time data, whenever it's needed, with the benefit of real-time analysis to inform their decisions. Pretty cool, huh?

Data virtualization in supply chain optimization

There are a few methods to integrate your data, and they each lend themselves well to different types of integration. To determine the method of data integration best suited for supply chain optimization, consider the strengths and weaknesses of each one and the specific needs of your particular situation.

The five methods for integrating data are:

  • Manual
  • Middleware
  • Application-based
  • Uniform data access
  • Common storage

Virtualization is not possible via manual or common storage integration, and so you'll want to compare middleware, app-based, and uniform data access integration. Let's take a look:

Middleware data integration

This partially automated approach transforms data to make it compatible with newer systems. It's especially helpful for connecting legacy solutions with the latest software. One downside to middleware, though, is that it entails having an experienced developer to install and maintain it. On top of that, not all systems are capable of being linked with middleware integration, and so its potential for deployment is limited.

Application-based integration

A fully automated process, app-based integration is useful for enterprises relying on hybrid cloud environments. Open-source tools and pre-configurations power the software to extract, convert, and transport your data to the target destination. The result is a consistent transfer of data between separate departments and systems. However, despite the full automation, you'll likely want a technical expert on site to deal with on-premise applications.

Uniform data access integration

Perhaps the best-suited method for data virtualization, this strategy establishes a set of views to display data in a virtual dashboard. Users can access and analyze information from multiple locations while it stays put in whatever source is housing it. This adds a layer of security to the system as data remains stored in its original source location. Uniform data access also makes possible real-time reporting and analysis without the need for a single warehouse of integrated data.

One drawback of uniform data access integration is that it only works if your data sources are similar to each other. Ensuring a standardized set of databases would be wise if you plan to take advantage of this method to optimize your supply chain. With integrated data and real-time analysis that's accessible to your whole network, you'll have a sound strategy in place for supply chain optimization. To find out more about optimizing your supply chain, contact our team.

New call-to-action

On Demand Supply Chain Blog

Continued Reading

Stay up-to-date with supply chain news and articles by reading more posts written by our team at D.W. Morgan.