This document discusses tools to support continual service improvement (CSI) activities. It defines CSI as a process under ITIL's CSI process group that is used to define initiatives to improve service and process quality based on results from other ITSM processes. The document explains that there are three types of metrics needed to support CSI: technology metrics related to components and applications, process metrics around critical success factors and KPIs, and service metrics that measure end-to-end performance using individual technology and process metrics. It provides examples of each type of metric and describes how metrics are measured and used within specific domains.
Controlling consists of verifying whether everything occurs in conformities with the plans adopted, instructions issued and principles established. Controlling ensures that there is effective and efficient utilization of organizational resources so as to achieve the planned goals. Controlling measures the deviation of actual performance from the standard performance, discovers the causes of such deviations and helps in taking corrective actions.
Presentation by Dawn McGeachy, BAccS, FCUIC, ACUIC, FCGA, LPA
Member, IFAC SMP Committee at the International Federation of Accountants & Institute of Chartered Accountants of Jamaica Business Development Conference, March 18, 2014
Risk Based Quality Management System AuditingAQSS-USA
All organizations have challenges in their businesses, But these internal and external challenges pose a threat to our goals and risk of their nonfulfillment.
Controlling consists of verifying whether everything occurs in conformities with the plans adopted, instructions issued and principles established. Controlling ensures that there is effective and efficient utilization of organizational resources so as to achieve the planned goals. Controlling measures the deviation of actual performance from the standard performance, discovers the causes of such deviations and helps in taking corrective actions.
Presentation by Dawn McGeachy, BAccS, FCUIC, ACUIC, FCGA, LPA
Member, IFAC SMP Committee at the International Federation of Accountants & Institute of Chartered Accountants of Jamaica Business Development Conference, March 18, 2014
Risk Based Quality Management System AuditingAQSS-USA
All organizations have challenges in their businesses, But these internal and external challenges pose a threat to our goals and risk of their nonfulfillment.
Nagios Conference 2014 - Jorge Higueros - Making KPIs Component Work For You ...Nagios
Jorge Higueros's presentation on Making KPIs Component Work For You With Nagios.
The presentation was given during the Nagios World Conference North America held Oct 13th - Oct 16th, 2014 in Saint Paul, MN. For more information on the conference (including photos and videos), visit: http://go.nagios.com/conference
ITIL Practical Guide - Continual Service Improvement (CSI)Axios Systems
To view this complimentary webcast in full, visit: http://forms.axiossystems.com/LP=272
This video provides a run through of the lifecycle stage, which manages the day-to-day operation of IT services for the identification and reporting of interruptions in the delivery of services and handling of service requests at agreed levels.
A Comprehensive Guide to Measuring Success with Test Automation KPIs.pdfkalichargn70th171
Test automation has become indispensable in today's Agile software development landscape, bringing speed, efficiency, and replicability to testing processes. Despite widespread acknowledgment of its merits, evaluating test automation's effectiveness remains a debated topic in software testing.
This whitepaper provides some meaningful examples on metrics along with purposes of metrics (targets).
The whitepaper focuses on metrics in relation to the status of the ISMS and its output. These are also the outputs, which feeds into the management reporting.
This Slideshare presentation is a partial preview of the full business document. To view and download the full document, please go here
https://flevy.com/browse/business-document/itil-process-assessment--service-design-xls-3668
DOCUMENT DESCRIPTION
This Excel spreadsheet system with approx. 400 Questions allows you to conduct a Assessment of ITIL v3 Service Design processes:
1 Design Coordination
2 Service Catalogue Management
3 Service Level Management
4 Supplier Management
5 Availability Management
6 Capacity Management
7 IT Service Continuity Management
8 Information Security Management
Assessment highlights areas that require particular attention and gives you idea on process maturity. It can also be used as a benchmarking mechanism and a boost in creating continual improvement culture for your ITSM / ITIL processes.
The assessment is based on Process maturity framework (PMF), (as recommended in ITIL Service Design book). Maturity rating levels are:
Level 1: Initial
Level 2: Repeatable
Level 3: Defined
(Level 3 +: Deployed )
Level 4: Managed
Level 5: Optimizing
The use of the PMF in the assessment of service management processes relies on an appreciation of the IT organization growth model. At the process level, assessment covered following groups of questions regarding process attributes to establish process maturity:
1. Process performance (outcomes achieved)
2. Performance Management ( activities performed)
3. Work product management ( inputs/outputs)
4. Process Definition ( roles documentation)
5. Process deployment( accepted, performed)
6. Process Measurement
7. Process control
8. Process innovation
9. Process optimisation
ITIL foundations - Complete introduction to ITIL phases, lifecycle and processesRichard Grieman
ITIL V3 Foundations introduction for certification study, classroom and training. Includes terms, objectives, functions and resource requirements for all five ITIL phases: Service Strategy, Service Design, Service Transition, Service Operation and Continuous Service Improvement. Study guide for ITIL training and certification
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. What is CSI
Definition of CSI Initiatives is one of the main processes under ITIL’s Continual Service Improvement
(CSI) process group of IT Service Management (ITSM) framework.
This Process is also known as “Definition of Improvement Initiatives”.
This process is used to define specific initiatives aimed to improve the quality of services and
processes.
These initiatives are planned based on the results received from Service Review and Process
Evaluation processes respectively.
3. Different types of metrics in CSI
It is important to remember that there are three types of metrics that an organization will need to
collect to support CSI activities as well as other process activities.
Technology metrics:
Process metrics:
Service metrics:
4. Technology metrics:
These metrics are often associated with component and application based metrics such as
performance, availability soon.
5. Process metrics:
These metrics are captured in the form of critical success factors (CSFs), KPIs and activity metrics for
the service management processes. They can help determine the overall health of a process. KPIs can
help answer four key questions on quality, performance, value and compliance of following the
process.
CSI would use these metrics as input in identifying improvement opportunities for each process.
6. Service metrics:
These metrics are a measure of the end-to-end service performance. Individual technology and
process metrics are used when calculating the end-to-end service metrics.
In general, a metric is a scale of measurement defined in terms of a standard, i.e. a well-defined unit.
Metrics are a system of parameters or ways of quantitative assessment of a process that is to be
measured. Metrics define what is to be measured.
Metrics are usually specialized by the subject area, in which case they are valid only within a certain
domain and cannot be directly bench marked or interpreted outside it. However, Generic metrics can
be aggregated across subject areas or business units of an enterprise.
