The Integration Imperative

Now that you have a sense of the different types of cloud services, it’s time to think about how you bring services together — essentially, to integrate those services — to create a hybrid next-generation computing environment that offers the flexibility and cost control organizations are beginning to demand. Standards will have to emerge so that there is a consistent approach to integration across a hybrid computing environment. Foundational requirements necessary to make this type of integration work include

A service-oriented approach: Creating an environment that allows services to be linked together requires modular components with well-defined interfaces. A service-oriented approach facilitates integration at the process level.

Well-defined data: Organizations need to combine data from different cloud environments. This data must be designed to support common definitions.

Service-level integration: When creating a hybrid environment, services need to be managed in a way that allows them to perform according to customer expectations.

Service-oriented integration

An important element of a flexible hybrid computing environment is the ability to easily link services together to create a virtual environment. Of course, not every element of a computing environment needs to be combined. There are clearly situations where the integration takes place only at the data level so that a data record can be moved from a SaaS environment to a system of record, such as an Enterprise Resource Planning (ERP) system. However, often organizations want to use a set of modular services from public and private services that, when combined, add a new and more flexible value to the company — without having to do massive programming whenever something changes.

One of the most important aspects of this service-oriented approach happens at the process level. The systems that have the data you value include business logic and processes that control the way that data is managed. So, you can’t simply connect data elements or business logic together without a deep understanding of how these systems behave from a business process perspective.

It’s helpful, for example, if you can graphically define the flow of data between source and target applications. In this context, you can graphically define all the steps needed to extract purchase order data from your ERP-specific system and send it to a different system (that is, a specific CRM system).

Unless your business is standing still, you can expect to see the SaaS vendor improve the underlying application. It may find a more efficient way to manage a certain business process that affects how you connect the logic among various systems. However, the typical SaaS vendor doesn’t make arbitrary changes. The typical vendor bases its approach to integration on best practices in integration patterns and often reuses these common patterns. By understanding these patterns and watching for changes, your organization is better able to withstand changes in the implementation details.

Data integration

The most common way organizations create hybrid environments is through integration at the data level. To get a better idea of what this looks like, consider the example of a manufacturing company that has embraced cloud computing. The company set up a private cloud for its developers across three continents and established a private exchange to support its parts suppliers. At the same time, to support the global sales team, the company selected a cloud-based CRM system. To support the Human Resources department, it selected a SaaS-based HR platform. The company continues to manage its own ERP system in its data center.

Integrating information across multiple public- and private-based siloed data sources can be challenging, even if all your information assets are tightly controlled in an internal data center. However, when you begin to incorporate data from public cloud sources — such as a SaaS application or with data stored in a private cloud, such as a commerce system — the complexity of the integration process increases. Maintaining the integrity of your information is at great risk unless you’re able to consistently integrate across your hybrid environment.

Information is the heart of how companies differentiate themselves from competitors. Companies must approach cloud integration with these fundamental concepts of information management in mind:

To truly innovate, you need a complete understanding of all the information about your customers, partners, and suppliers.

You need a full understanding of all aspects of your relationship with your value chain. This information has to be accurate, timely, and in the right context.

You need to gain an understanding of what your customers are buying or the status of orders.

You must understand what your customers and partners are saying and how satisfied they are with your products and services.

In addition, your customers and partners need to trust the shared information. These collective information assets must be secured and managed according to business, governmental, and industry rules and regulations.

In many ways, the need for integration remains the same as it has been for decades — providing an organization with a clear understanding of the transactions, services, and other critical information about the business. Departments such as finance, operations, human resources, and sales all typically use applications designed specifically to support their unique business processes. These applications are likely to have unique and independent sources of data.

Regardless of the technical means used to integrate data across these systems — whether in the data center or in private or public clouds — business and IT must collaborate to identify inconsistencies in data definitions and apply best practices to maintaining the quality of its data. For example, prior to integrating data across internal data sources, IT may need to account for variability in data definitions, data formats, and data lineage (like a family tree for the data element), how that data is derived, and its relationship to other data elements.

What has changed? In addition to integrating data across legacy applications in the data center, you may need to integrate data managed in multiple private and public cloud platforms. For example, say that a manufacturing company implemented salesforce.com to provide CRM support to over 500 customer sales representatives across the globe. Initially, the sales team found that its productivity decreased under this new system because they spent a lot of time manually comparing sales order and invoice information from SAP, the company’s internal ERP system, against prospect and customer information in salesforce.com. Without timely insight into the company’s orders, shipments, and invoices managed in SAP, the sales team couldn’t perform up to expectations.

The company also recognized that other internal systems managing billing and inventory were dependent on some of the same data that needed to be reconciled between SAP and salesforce.com. The solution is for the IT organization to leverage technology that provides consistent connectors between its internal ERP system and its cloud-based CRM system in a way that will allow for consistency and repeatability across multiple systems.