photo by BotheredByBees
At Baseline Consulting we’ve been talking for several years about the concept of a data supply chain. But IT executives are only now starting to catch on to its importance.
Over the past 15 years there has been a big push to standardize on off-the-shelf software. This allowed IT organizations to buy instead of build. We’ve migrated from proprietary architectures to Windows and Linux standards. We’ve gone from custom-built applications to packaged CRM and ERP applications. IT adopted this approach because its value is automating business processes and supporting analysis– not inventing new technologies. The problem is that moving data between all of these “packaged systems” still requires custom code.
There’s no question that middleware provides value: it delivers the pre-built data pipes. Unfortunately, these are toolkits requiring developers to write code to connect their packages to the pipes. Most CIOs are blissfully unaware of the amount of custom coding middleware requires. Trust me: IT spends an enormous amount of money on supporting such data migration solutions. Many IT shops still view middleware as sacred ground.
The data warehousing w…
photo by BotheredByBees
At Baseline Consulting we’ve been talking for several years about the concept of a data supply chain. But IT executives are only now starting to catch on to its importance.
Over the past 15 years there has been a big push to standardize on off-the-shelf software. This allowed IT organizations to buy instead of build. We’ve migrated from proprietary architectures to Windows and Linux standards. We’ve gone from custom-built applications to packaged CRM and ERP applications. IT adopted this approach because its value is automating business processes and supporting analysis– not inventing new technologies. The problem is that moving data between all of these “packaged systems” still requires custom code.
There’s no question that middleware provides value: it delivers the pre-built data pipes. Unfortunately, these are toolkits requiring developers to write code to connect their packages to the pipes. Most CIOs are blissfully unaware of the amount of custom coding middleware requires. Trust me: IT spends an enormous amount of money on supporting such data migration solutions. Many IT shops still view middleware as sacred ground.
The data warehousing world has enthusiastically adopted ETL tools to reduce custom coding so they can focus on the issues of data accuracy and usability. One fact lost in translation is that ETL integrates data– it’s more than just a pipe. The application world has adopted EAI, ESB, and orchestration to move data quicker. However, there’s no integration. Each application is responsible for integrating the data they receive.
So, there’s even more custom code. Code to connect an application to the pipes. Code to integrate and cleanup the data they receive from the pipes.
Custom code to move data around isn’t the answer. Orchestration, message passing, and data movement just creates a labyrinth of pipes. There are no economies of scale. The data doesn’t get better.
Walmart learned years ago that it was impractical to have a custom (and separate) distribution system for every supplier. They knew the cost benefits of a standard distribution system; this meant they needed to standardize the size of the trailers, the size of the boxes, and the way the boxes were packed and shipped. The benefits of a supply chain is that standardization occurs at the most cost effective point: the source. Walmart’s distribution success was measured by its ability to accept new suppliers and manage more shipments.
Most CIOs don’t recognize that they have a data supply chain. Instead of building a custom distribution system for each suppler (each business application), they should be focused on a single data supply chain. Middleware supports the creation of custom distribution solutions, but not the standardization of data. A data supply chain can only be successful if the data is standardized. Otherwise everyone is forced to write custom code to standardize, clean, and integrate the data.