The CA Service Management 17.0 documentation builds on the CA Service Management 14.1.03 documentation.
We encourage you to take advantage of the collaborative features of DocOps. Leave comments, rate pages, and get involved. We monitor DocOps closely and look forward to optimize your experience with the documentation.
Today, we are pleased to announce the availability of CA Service Management 17.0.
Aligned with our strategic themes of targeted user experience, simplification, and unification of solution components, this release includes the following key features:
Contextual Collaboration: Analysts using xFlow can collaborate with other analysts and subject matter experts in context of a ticket. Conversations and files are retained in the ticket timeline to speed up resolution of similar future tickets.
Ticket Follow-up: xFlow now enables analysts to schedule follow-up on tickets along with reminders.
Quick Profile: xFlow now provides Support Analysts with enhanced in-context profile and environment information about end users (customers).
New consistent look & feel: CA Service Desk Manager, CA Service Catalog, and CA Asset Portfolio Management are updated with a new, consistent UI theme, unifying the look and feel of CA Service Management solution components, as well as xFlow.
Improved accessibility compliance for Service Catalog: CA Service Catalog now offers significantly improved accessibility (section 508) compliance for End Users and Administrators.
Easy upgrade: CA Service Desk Manager includes a Customization Upgrade Utility that semi-automatically merges customizations with out-of-the-box files enabling a quicker, reliable upgrade of CA Service Desk Manager.
Improved compatibility: This release supports recent versions of Operating Systems, Databases, third-party technologies, and other interoperable CA Technologies products.
This release also includes several quality, stability and security improvements, and bug fixes.
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.
The Digital Transformation of Information in the Application Economy: Part Four of Five
New apps support business strategy and provide valuable mechanisms for interacting with users. The knowledge management challenge they present is that IT has to track, process and manage the data—yet another way the app economy has changed the info lifecycle.
Management needs this data at their disposal, since they have to be more nimble in responding to users. But management doesn’t need data, per se; it’s knowledge. With that in mind, let’s agree on these definitions:
Knowledge=Information in context
You Need Plan A—and Plan B
IT has to ensure that management gets the knowledge they need to make nimble business decisions. In formulating a knowledge management plan, answer these questions:
Does IT understand each manager/department’s knowledge needs and enterprise-wide knowledge needs?
Is the knowledge being delivered current? Is that evident to the knowledge users?
Can management readily get the knowledge when needed?
Is knowledge delivered in a format that best supports management?
Is mission-critical knowledge securely stored and accessed, well defined and backed up frequently?
If the IT infrastructure fails, do you have a Plan B for accessing critical knowledge?
Perhaps Even Plan C
We also need to remember that individual departments retain department-unique knowledge that may not be mission-critical enterprise-wide. Such repositories are usually met with almost immediate adoption, but unless a skilled knowledge manager is identified, and that role is in their job description, most departmental repositories become large files of artifacts that accumulate with no logical plan.
Things don’t need to unspool like that. When IT provides guidance and support for department repositories, they will likely provide value: information in context that is accessible when needed, so departments can be nimble enough to contribute to the bottom line.
The World is Your Audience
In the app economy, we also support external end users who conduct business via smart phone: banking transactions, purchasing goods and services, arranging travel—the list goes on.
Users expect the right data, delivered in protected mode, to be readily visible and understandable on a small screen. They also expect their responses to be accurate and secure.
When IT gives users information in context, users can readily make informed decisions, and the company will be an industry leader.
Next, we’ll look at the underlying technical details of managing data. Until then, we’d love to hear from you if you’ve encountered knowledge management challenges other than those discussed here.
In case you missed them, here are the links to posts one, two and three.
The Digital Transformation of Information in the Application Economy: Part Three of Five
We all know that sound business decisions require a single version of the truth. And anyone who has worked on large-scale enterprise data warehouse projects knows that capturing data presents numerous challenges.
Data from business units don’t necessarily provide sufficient context because the data aren’t centrally managed. Another challenge: Shadow IT departments in separate business units often lack governance and serve only unit needs.
Of course, the ultimate goals are a targeted, enterprise-wide growth strategy that works for all units, ensuring efficiency across all units and aligning to corporate data standards, quality and sources.
Compliance with Data Regulations is Key
Another challenge is regulatory compliance, a critical requirement for most companies. Because non-compliance may mean financial penalties, all units, including IT, need to adopt compliance policies.
Data privacy challenges are especially daunting, and the data privacy discipline is experiencing a digital transformation. Smartphone apps capture personal identifiable data that app creators must protect; encryption safeguards can be established, but collection and storage are in digital format by default.
Data’s Business Value Can Change the Game
Organized data drives the strategic analysis and decision-making that fuel growth. Business leaders must understand their entire service portfolio and draw the line on what’s in and what’s out.
Decisions should emphasize market conditions, the demand for services, and the cost of business assets. The challenge, of course, is in the details—the data we need to collect, store and analyze.
In the first blog in this series, Rob Zuurdeeg and I wrote that success in the app economy depends on managing the information that powers our apps. Our second blogconcentrated on information lifecycle design. Next, we’ll discuss data organization and knowledge management best practices.
Until then, we’d love to hear from you if you’ve encountered challenges other than those discussed here.
As part of the ongoing updates to the Idea backlog, we will be moving several Ideas to the "Not Planned" stage due to lack of support across the Service Management community. As such, we have determined that Ideas still in a "New" or "Under Review" stage that have not been updated since 2015 and have a score of 10 or less will be moved to "Not Planned".
If you find that an idea that is moved to "Not Planned" should be reconsidered, we welcome you to create a new idea but ask for you to please consider searching the community first to find a synergistic idea and vote on it. If you need to create a new idea, please be sure to document the business problems that would be solved, the use case, the type of user, and the business value expected from having this feature.