The Digital Transformation of Information in the Application Economy: Part 5 of Five
Before we can manage data, we have to understand and define it. Here we’ll highlight a couple of aspects of data management across these lifecycles. Our new book, ITIL and the Information Lifecycle, gives more detail.
IT organizations need a clear understanding of what data means to its users. Whether data comes from a tablet, smartphone, or even a mainframe, data definition needs to be obvious and meaningful to everyone concerned.
The Big Picture: Your Application Portfolio
Apps are written to manage and transform data into knowledge. When designing data for an app, developers work from a vision of the data that the app will use. For example:
- A customer orders a tee shirt online
- The customer registers a complaint online: the shirt arrived in the wrong size
- After the customer returns the shirt, the customer service rep initiates the process to send a new shirt in the correct size
A data model—a blueprint of an app’s entities, attributes and relationships—helps us visualize those entities, attributes, and relationships and communicate how the data work together:
- Entity: an object such as a customer, product (the tee shirt), or flight reservation
- Attributes: the entity’s characteristics, like the shirt’s size
- Relationships: associations among entities, such as between a customer and the online customer service rep.
The challenge is designing apps that integrate with and add value to a company’s app portfolio. When one app’s business rules or end-user requirements differ from another department’s, the apps’ data models are incompatible.
That’s when developers need to step back to gain a broader vision and establish corporate-wide data definitions, which are essential in enforcing standards that facilitate application integration. A data model can provide a relatively simple abstraction of a real-world environment—in this case, your app portfolio.
Application Data Models and IT Service Management
Data models are stored in database management systems. Objects (“entities” in databases and “services” in CMDBs) run in memory in web and application servers; they are secured and distributed across the infrastructure. IT departments manage and deliver these assets in the form of services.
To manage service quality, IT departments use best practices known as IT Service Management. For instance, if the service desk needs to assess a change’s impact on an application, it uses a service-level configuration model, a specific type of data model in a CMDB that aligns relationships and attributes to services.
Since the model is at the service level, it helps IT assess the change’s impact, plan releases and orchestrate migration of apps across environments.
So data models help us visualize and communicate a real-life application environment and configuration models help us understand relationships among services, applications, and infrastructure. Data flow across application development and IT Service Management help us meet service-level targets, capture changes to an application or service, improve adherence to standards, and identify costs.
In case you missed it, here are the links to part one, two, three and four.
Questions? Comments?