Module #4 Flashcards
True or false
The use of multiple tools and platform drives the need for a common information and data model across the organization, as well as the need for effective integration
True
What can happen to information as digital transformation progresses?
it can become a constraint on the effective delivery of services
An information model makes it possible for an organization to develop a shared understanding of their
Information, technology, systems, and structure
What are the core elements of an information model?
- Definitions of key facts, terminology, activities, and practices within the org
- Structural representations of key components of the organizations technology and business services, and the relationship between them
What are the two ways an information model can be created?
- From scratch
- by adopting an established model like Common Information Model, or Frameworx
an ___________ can be a key enablement tool for
- Transforming processes and practices
- integrating technologies
- Gaining an accurate overview of strengths and weaknesses in the service framework
- Driving informed decisions at all levels of the org
- enabling results based measurements
Information model
These types of toolsets do the following:
- Provide real time dynamic ability to manage volumes of work
- Automate records and workflow management
- act as engagement and communication tools
- Support a holistic information model for service management
- provide reporting and business analytics on performance, trends, improvements, costs and risks.
- Offer accountability and audit trails
Service management toolsets
These toolsets are often designed to automate the service management practices recommended by ITIL. These toolsets are constantly evolving to adopt new technologies
Service Management
What are service management toolsets most commonly used as?
- Systems of record
- Systems of engagement
What are service management toolset expectations?
- effective automation of workflows
- effective inventory, monitoring, and event management
- effective integration with organizations toolsets, other information systems, social networks and communication channels
- a high level of service warranty
- conformance to evolving architectural and technical requirements and standards
- advanced analytics and reporting
Service design frequently relies on what?
Integration between multiple systems
What are signs of good integration?
- enables and reinforces the processes that underpin the delivery of value
- ensure compliance with regulatory obligations
Considerations for integrations
- Reliability
- Fault tolerance
- Cost
- Complexity
- expected evolution
- Security
- Observability
What are the two Integration topologies?
- Point to point
- Publish - subscribe
What is a point to point integration topology?
- Involves directly linking pairs of systems
what is a publish subscribe integration topology?
messages are published by systems to an event broker, which forwards the message to the systems that have been designated as its recipients
What are the 3 integration approaches?
- Big bang
- Incremental delivery
- Direct integration
What is big bang integration approach?
Big bang - every integration is delivered at once
What is incremental delivery integration approach?
Integrations are introduced separately in a predefined order
What is direct integration integration approach?
Individual integrations are deployed as soon as they are ready, in no predetermined order
This is the method of examining data sets, often using specialized software, in order to draw conclusions about the information they contain
Data analytics
Data analytics technology and techniques can:
- Enable orgs to make informed business decisions
- Help to disprove scientific models, theories, and hypotheses
This is the autonomous or semi autonomous examination of data or content using high-level techniques and tools
Advanced analytics
A term that describes large volumes of structured, semi-structured, and unstructured data
Big Data
Extracting meaningful data requires
Processing power, analytics capabilities, and skill
Criteria for assessing data complexity
Data size data structure data type query language data sources data growth rate
The more complex the data…
the bigger the challenge in finding value within it
What are the steps to generate useful dashboards and reports
Connect to data sources
extract, transform, and load data
query the centralized data
perform data visualization
Agile ways of working
making work visible working in topic based forums mapping workflows working in small teams and sprints collaboration using simple feedback mechnisms
Agile tools
communication walls kanban boards topic based forums value stream maps event surveys portals self help social media features
These increasingly provide the capability to build, map, and management process workflows dynamically within the products via a locked down dev interface
IT and service management tools
How are changes to workflow elements made for IT and service management tools?
Without the need for scripts or coding
by less frontline individuals
These support collaboration, because they reduce lead times for changes, and present workflows in simple graphical formats
What is RPA?
Robotic Process Automation
RPA uses:
- Software robots to automate repetitive tasks
- allows for consistent, reliable, and predictable processes to be implemented in a cost-efficient way
RPA can yield the most benefit when processes are:
High volume, error prone, sensitive to faults
Rules based and do not require complex decision making
True or false
Incorporating machine learning and artificial intelligence enables RPA tools to replace a rote-based approach for one that can adapt and react to a variety of inputs
RPA allows orgs to:
strealine business operations, lower staffing costs, increase throughput, reduce errors, and deploy resources to higher value activities
What are some drawbacks of RPA
- It can be fragile
- even simple changes can have unexpected consequences
- design and development plays a part in how robust it is when dealing with change
- development requires configuration and scripts to define inputs and outputs
- testing, config management, and change enablement apply
Types of RPA
Process automation
enhanced and intelligent process automation
cognitive platforms
RPA process automation
Focuses on automated tasks that depend on structured data
RPA enhanced and intelligent process automation
works with unstructured data. Can learn from experience and apply knowledge
RPA cognitive platforms
understands customers queries and can perform tasks based on that
RPA considerations
it needs planning, analysis, design and governance
A highly advanced automation that demonstrates capabilities of general reasoning, learning, and human-like intelligence. A branch of computer science and engineering focused on simulating intelligent behavior in computer systems
Artificial intelligence
true or false
AI solutions are now available as cloud based services or on demand via API.
true
Some AI driven options include
conversational tools for end users and support agents
automated classification and routing
language tools like translation or sentiment analysis
AI architecture options
Vendor:
designed for service management
more immediate value
underpinned by the vendors AI software
Onsite:
significant computing power and processing time
possibly high charges
at scale may be more economical to use on site hardware
Common applications of AI
process and decision automation natural language processing conversational interfaces predictive analysis discovery
This involves combining big data, analytics, and machine learning in the field of IT operations
AIOps
what is AI ops?
Algorithmic IT Operations
What is AIops?
assumed to mean artificial intelligence for IT operations
what is a benefit of AI ops?
- instead of siloed teams monitoring their own parts, all important monitoring data is collected in one place
- machine learning used to identify patterns and detect abnormalities
- issue detection and prediction
- proactive system maintenenace and tuning
- threshold analysis - enabling more accurate picture of the normal range of operation of a system
AI ops allows IT operations to
Identify and resolve high-severity incidents faster
detect potential problems
automate routine tasks and focus on strategic work
AIOps can do what?
harness data platforms and machine learning
collect observational data and engagement data
draws insight by applying cognitive or algorithmic processing to it.
This is an applied form of artificial intelligence, based on the principle of systems responding to data, and adapting their actions and outputs as they are continually exposed to more of it
Machine learning
Types of machine learning
Supervised - a supervisor determines the learning algorithm and the sample data set used to train the machine. The inputs and expected outputs are defined.
Unsupervised - the machine learns from the input data alone
Benefits of Machine learning
- ability to derive valuable results from quantities of data which would be difficult for humans to process
- enables improvements in the efficiency or accuracy of decision making
- enables the automation of entirely new data driven decisions
Limitations of machine learning
performance of the system is depend on its data, the algorithms used, and the quality of training for supervised systems
- results can be distorted if the input data contains inherent bias
What is deep learning?
a subset of machine learning based on artificial neural networks, it relies on computing systems modeled on the biological neural networks found in animal brains
What is CI
Continuous integration - an approach to integrating, building, and testing code within the software development environment
(Checked in code is validated, through a set of automated tests, then merged into a shared code branch for deployment to production)
What is Continuous Delivery?
Continuous delivery - an approach that focuses on ensuring that software can be released to prod at any time
frequent deployments are possible, but deployment decisions are taken case by case, due to a preference for a slower rate of deployment
What is Continuous deployment?
an approach in which changes go through the pipeline and are automatically put into the prod environment, enabling multiple prod deployments a day
Details on CD/CI
- it is a methodology for delivering software in an agile manner
- commonly known as the opposite of a more linear waterfall approach (waterfall defines the process as linear series of phases)
- sometimes confused with DevOps, but devops contains more context on team orgs and culture in addition to system delivery
CI/CD goals & measurements
- Smaller, high frequency deployments of changes to systems
- reduced risk, making each deployment less complex
- increased velocity of value co-creating, by enabling useful changes to be delivered more quickly to consumers.
small changes are easier to comprehend, consume, test, troubleshoot and roll back
CI/CD teams focs on
- Identification and removal of bottlenecks that reduce the speed of delivery
- automating delivery that require manual effort
- ensuring each change maintains quality
- automating validation and testing of each change
True or false
a significant focus for organizations using CI/CD is the reduction of toil (work requiring manual effort)
true
This is a set of tools, integrations, practices, and guardrails that allow a continuous and substantially automated flow of changes, from their initial design and development through to deployment into production
CI/CD pipeline
The three stages of work flowing through a CI/CD pipeline
- Build automation (the CI phase) - includes version control to merge multiple dev changes into one branch
- Test automation - each change is tested and validated as part of the flow chain from dev
- deployment automation - the automation of the actual process of moving code from pre prod to prod
ITIL guiding principles
Focus on value start where you are progress iteratively with feedback collaboration and promote visibility think and work holistically keep it simple and practical optimize and automate
Agile approaches are good for which situations?
- there is uncertainty about present and future requirements
- risks associated with errors or failure are low impact or managed quickly
- iterative nature enables the ongoing development to understand customer demands and respond to them
