How The Cloud Contributes To Agility And Data Interoperability

Howard Baldwin

One of the biggest issues companies face with innovation is integrating new systems into legacy systems. Everyone wishes they could simply start with a greenfield deployment and avoid that hassle – but in the past, that integration has been highly complex.

Now it’s becoming less complex, thanks to the cloud. Even so, whenever a company deploys a cloud, there’s always going to be the issue of eventual integration. How can companies use the cloud to “start from scratch” and still make it part of the enterprise architecture later?

Let’s start with what the cloud brings to the table when it comes in the form of software-as-a-service (SaaS). In a nutshell, it enables companies to take advantage of innovation more quickly than they would have been able to in the past. Procurement is certainly faster. Upgrades are easier, because they’re done once in a central location. And companies can even try out new cloud-based services to determine their efficacy. Overall, SaaS applications are less of a burden on IT, and lines of businesses get access to advanced application capabilities. The same advantages apply to infrastructure-as-a-service (IaaS) and platform-as-a-service (PaaS) scenarios as well.

Why do most companies want a greenfield? Because they want to take advantage of new technology. There is so much innovation in the field of technology these days, but companies need a way to work with it without making a deep and unending commitment. The cloud brings the ability to extend uses of new technology without forcing companies to incorporate new technology.

Consider the example of a product lifecycle management solution. In a hybrid scenario, some of the more-static information would be likely kept in a legacy system: when was it bought, how much it cost, part numbers, warranty and licensing information. But more dynamic information could remain in a cloud: usage, maintenance schedules, and other information. As long as the systems are linked by one element, such as a part number, they can exchange information.

For instance, when a piece of machinery is serviced, the PLM system can automatically notify its back-end inventory system to order more of the part that was used. A collective increase in parts usage may also trigger a proactive response regarding warranty or recall issues.

Or consider the opportunity provided by a new technology such as the Internet of Things. Adding sensors to devices helps companies manage a traditional process such as maintenance. A device has been used so many hours, so it needs to be serviced. But what about predictive maintenance? That’s a use case that’s an extension of the traditional business process relating to maintenance. Companies could use a cloud-based system to set up a new predictive analytics capability and understand how such a capability might help customers – some of whom might be price-sensitive and some of whom might be time-sensitive. The result: an extended business process that helps companies take advantage of new information.

The important thing to remember is the viability of a hybrid landscape. There’s too much data – both current and historical – in legacy systems to ignore. But it’s also not necessary to hew to traditional methods of tight integration. Thankfully, cloud architectures allow more loosely coupled integration through APIs, so that legacy systems and cloud-based systems can more easily exchange information.

The reality is that it’s probably not possible to automate everything, or predict everything perfectly. But hybrid cloud systems make it easier than ever before to experiment with greenfield solutions.

For more insight on cloud adoption strategies, see What’s The Return On Developing In The Cloud?


Howard Baldwin

About Howard Baldwin

Howard Baldwin has been writing about technology since OS/2 was released