Data-Related Presentations

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This summarises the various data-related presentations I have written and relationships between them.

Designing
An
Enterprise
Data Fabric
https://www.slideshare.n
et/alanmcsweeney/desig
ning-an-enterprise-data-
fabric
Forget Big
Data. It's All
About
Smart Data
https://www.slideshare.n
et/alanmcsweeney/forget
-big-data-its-all-about-
smart-data-71160531
Translating
Big Raw
Data Into
Small
Actionable
Information
Real Time
Data
Strategy and
Architecture
Review of
Data
Management
Maturity
Models
Data Audit
Approach To
Developing An
Enterprise Data
Strategy
Data
Governance:
Keystone of
Information
Management
Initiatives
Data, Information And
Knowledge Management
Framework And The Data
Management Book Of
Knowledge (DMBOK)
Data
Warehouse
Programme
Notes
https://www.slideshare.n
et/alanmcsweeney/transl
ating-big-raw-data-into-
small-actionable-
information
https://www.slideshare.n
et/alanmcsweeney/transl
ating-big-raw-data-into-
small-actionable-
information
https://www.slideshare.n
et/alanmcsweeney/data-
audit-approach-to-
developing-an-enterprise-
data-strategy
https://www.slideshare.n
et/alanmcsweeney/revie
w-of-data-management-
maturity-models
https://www.slideshare.n
et/alanmcsweeney/data-
governance-keystone-of-
information-
management-initiatives
https://www.slideshare.n
et/alanmcsweeney/data-
information-and-
knowledge-management-
framework-and-the-data-
management-book-of-
knowledge-dmbok-
3366885
https://www.slideshar
e.net/alanmcsweeney/
data-warehouse-
programme-notes
Data-Related Presentations

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Was ist angesagt?

This presentation describes a structured approach to creating a business-focussed information technology strategy. An effective business-oriented IT strategy is an opportunity to resolve the disconnection and to ensure the IT function is able to and does respond to business needs and is trusted by the business to provide IT solutions. The IT strategy will consist of static structural elements relating to the organisation of the IT function: • Capabilities – skills and abilities the IT function should possess and be able to use effectively and efficiently • IT Function Structure – the organisation and arrangement of the sub-functions and their responsibilities and relationships • Operating Model – how the IT function work and delivers value and the processes it implements and operates • Staffing And Roles – the numbers of people, their roles, responsibilities, expected skills, experience and abilities, workload, reporting structures and expected ways of operating It will also include dynamic elements relating to initiatives, both enabling initiatives within the IT function and specific business initiatives required to achieve the business strategy.

Creating A Business Focussed Information Technology Strategy

Creating A Business Focussed Information Technology Strategy

Creating A Business Focussed Information Technology Strategy

Data lineage is a regulatory and internal requirement with potential to deliver significant operational and business benefits, but financial institutions can find it difficult to implement and complex to maintain as systems and regulatory requirements themselves, change quickly. The importance of understanding where the true source of the data is coming from, where the data flows to and what has changed cannot be overstated. The webinar defines data lineage and discuss implementation through the eyes of those that have implemented and sustained successful lineage solutions with significant benefits. Listen to the webinar to find out about: - Data management for data lineage - Winning buy-in for projects - Best practice implementation - Operational and business benefits - Expert practitioner advice

The art of implementing data lineage

The art of implementing data lineage

The art of implementing data lineage

A solid data management foundation to support big data analytics and more importantly a data-driven culture is necessary for today’s organizations. A mature Data Management Program can reduce operational costs and enable rapid business growth and development. Data Management program must evolve to monetize data assets, deliver breakthrough innovation and help drive business strategies in new markets.

Enterprise Data Management Framework Overview

Enterprise Data Management Framework Overview

Enterprise Data Management Framework Overview

Marcoccio10 22

Business intelligence and analytics

Business intelligence and analytics

Business intelligence and analytics

For the last few years, analytics, data science and data management have achieved tremendous exposure on all the media channels. Big Data has become a major topic of discussion, catalyzing attention among the C-Level executives and driving investments and projects inside the enterprise. However, it is really interesting that just a selected group of business has created successful data teams and has mastered the skills to manage it. What we have seen is that most companies still do not know how to create, implement and manage a data and analytics organization. Above all, if data has become an strategic asset and is being considered the new oil for the 21st century economy, what your strategy to handle it ? This webinar will help to bring some concepts and ideas to enlighten your path to create and manage an analytics organization, providing some real life examples on companies which did it.

How to Create and Manage a Successful Analytics Organization

How to Create and Manage a Successful Analytics Organization

How to Create and Manage a Successful Analytics Organization

Presentation on Business Intelligence (BI)

Presentation on Business Intelligence (BI)

Presentation on Business Intelligence (BI)

TargetStateFutureArchitect - DV

TargetStateFutureArchitect - DV

TargetStateFutureArchitect - DV

Bhavendra Chavan

Trillium software garp march 2014 presentation bfast briefing

Trillium software garp march 2014 presentation bfast briefing

Trillium software garp march 2014 presentation bfast briefing

Trillium Software

The importance of data lineage has escalated in recent years in response to regulatory demand and increased business understanding of the benefits it can deliver. Like all capital markets technology, data lineage presents both challenges and opportunities, so how best can it be implemented and sustained? And how can your organisation reap the rewards of successful implementation? This webinar will outline data lineage, its progress towards automation, and why it is so important from both a regulatory and business perspective. It will also provide advice on how to select a solution and step-by-step guidance on how to implement and integrate data lineage. Finally, the webinar will discuss how to manage data lineage to ensure regulatory compliance, deliver business benefits and plan for the future. Register for the webinar to find out more about: -The importance of data lineage in capital markets -How to select a solution for your organisation -Approaches to implementation and integration -How to achieve sustainable regulatory compliance -The business benefits of successful implementation

Data lineage to drive compliance and as a business imperative

Data lineage to drive compliance and as a business imperative

Data lineage to drive compliance and as a business imperative

Business Analytics

Business Analytics

Business Analytics

Gartner Business Intelligence & Analytics Summit Brochure

Gartner Business Intelligence & Analytics Summit Brochure

Gartner Business Intelligence & Analytics Summit Brochure

Ict Vision And Strategy Development

Ict Vision And Strategy Development

Ict Vision And Strategy Development

Business has a consistently poor experience of the internal IT function. It is now all too easy for the business to bypass the central IT function. There is a business shift to cloud service providers offering infrastructure-less solutions with no perceived IT involvement. Outsourcing and the divestment of IT functions in response to business wishes to remove the overhead. The business need to respond to the interrelated developments of digital, mobile and social computing and perceived inability of the central IT function to respond. If the IT function cannot react to the requirements of the business due to business pressures, the business will go elsewhere. Shadow IT - the acquisition of IT solutions outside the control of the IT function - is an unpleasant and common reality. 50% of IT expenditure is routinely spent outside the control of the IT function. Shadow IT is a symptom of a post-IT world. The central IT function loses relevance and control. Businesses reduce their reliance on the core IT function. IT architecture should act as a glue joining the business strategy to the IT strategy. IT architecture needs to operate as an internal business consulting And advisory function. An effective business oriented IT architecture function can get the correct balance between too little and too much, too slowly and too quickly. The IT Architecture team needs to operate as a team rather than a set of siloed internally focussed IT roles, involving business as well as technologists.

The Centre Cannot Hold: Making IT Architecture Relevant In A Post IT World

The Centre Cannot Hold: Making IT Architecture Relevant In A Post IT World

The Centre Cannot Hold: Making IT Architecture Relevant In A Post IT World

Ensuring Data Quality and Lineage in Cloud Migration - Dan Power

Ensuring Data Quality and Lineage in Cloud Migration - Dan Power

Ensuring Data Quality and Lineage in Cloud Migration - Dan Power

Molly Alexander

Gartner: The BI, Analytics and Performance Management Framework

Gartner: The BI, Analytics and Performance Management Framework

Gartner: The BI, Analytics and Performance Management Framework

Reference Data Management

Reference Data Management

Reference Data Management

Axis Technology, LLC

Creating Your Data Governance Dashboard

Creating Your Data Governance Dashboard

Creating Your Data Governance Dashboard

Trillium Software

Business process based analytics

Business process based analytics

Business process based analytics

Korea Advanced Institute of Science and Technology

Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards

Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards

Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards

TechSoup Canada

Was ist angesagt? (20)

Creating A Business Focussed Information Technology Strategy

Creating A Business Focussed Information Technology Strategy

Creating A Business Focussed Information Technology Strategy

The art of implementing data lineage

The art of implementing data lineage

The art of implementing data lineage

Enterprise Data Management Framework Overview

Enterprise Data Management Framework Overview

Enterprise Data Management Framework Overview

Marcoccio10 22

Business intelligence and analytics

Business intelligence and analytics

Business intelligence and analytics

How to Create and Manage a Successful Analytics Organization

How to Create and Manage a Successful Analytics Organization

How to Create and Manage a Successful Analytics Organization

Presentation on Business Intelligence (BI)

Presentation on Business Intelligence (BI)

Presentation on Business Intelligence (BI)

TargetStateFutureArchitect - DV

TargetStateFutureArchitect - DV

TargetStateFutureArchitect - DV

Trillium software garp march 2014 presentation bfast briefing

Trillium software garp march 2014 presentation bfast briefing

Trillium software garp march 2014 presentation bfast briefing

Data lineage to drive compliance and as a business imperative

Data lineage to drive compliance and as a business imperative

Data lineage to drive compliance and as a business imperative

Business Analytics

Business Analytics

Business Analytics

Gartner Business Intelligence & Analytics Summit Brochure

Gartner Business Intelligence & Analytics Summit Brochure

Gartner Business Intelligence & Analytics Summit Brochure

Ict Vision And Strategy Development

Ict Vision And Strategy Development

Ict Vision And Strategy Development

The Centre Cannot Hold: Making IT Architecture Relevant In A Post IT World

The Centre Cannot Hold: Making IT Architecture Relevant In A Post IT World

The Centre Cannot Hold: Making IT Architecture Relevant In A Post IT World

Ensuring Data Quality and Lineage in Cloud Migration - Dan Power

Ensuring Data Quality and Lineage in Cloud Migration - Dan Power

Ensuring Data Quality and Lineage in Cloud Migration - Dan Power

Gartner: The BI, Analytics and Performance Management Framework

Gartner: The BI, Analytics and Performance Management Framework

Gartner: The BI, Analytics and Performance Management Framework

Reference Data Management

Reference Data Management

Reference Data Management

Creating Your Data Governance Dashboard

Creating Your Data Governance Dashboard

Creating Your Data Governance Dashboard

Business process based analytics

Business process based analytics

Business process based analytics

Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards

Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards

Finding Meaning in the Numbers: Tools for Data Analysis & Dashboards

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BPM is established, tools are stable, many companies use it successfully. However, today's business processes are based on data from relational databases or web services. Humans make decisions due to this information. Companies also use business intelligence and other tools to analyze their data. Though, business processes are executed without access to this important information because technical challenges occur when trying to integrate big masses of data from many different sources into the BPM engine. Additionally, bad data quality due to duplication, incompleteness and inconsistency prevents humans from making good decisions. That is status quo. Companies miss a huge opportunity here! This session explains how to achieve intelligent business processes, which use big data to improve performance and outcomes. A live demo shows how big data can be integrated into business processes easily - just with open source tooling. In the end, the audience will understand why BPM needs big data to achieve intelligent business processes.

How to create intelligent Business Processes thanks to Big Data (BPM, Apache ...

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Gartner Top Technology Trends

Gartner Top Technology Trends

Gartner Top Technology Trends

Building Products with Data at Core

Building Products with Data at Core

Building Products with Data at Core

Sandeep Adwankar

Traditional data management is changing rapidly, attributed to significant changes brought on by evolving big data environments. IT complexity is on the rise as businesses choose the technologies they need to support their big data strategies and targeted business outcomes. Now, more than ever, we need IT management tools that can accommodate and effectively manage these evolving, complex environments to ensure that enterprises can move forward with their preferred technology and vendor choices. For more information on Mainframe solutions from CA Technologies, please visit: http://bit.ly/1wbiPkl

Big Data Management: A Unified Approach to Drive Business Results

Big Data Management: A Unified Approach to Drive Business Results

Big Data Management: A Unified Approach to Drive Business Results

CA Technologies

Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Solving The Data Growth Crisis: Solix Big Data Suite

Solving The Data Growth Crisis: Solix Big Data Suite

Solving The Data Growth Crisis: Solix Big Data Suite

Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information. While metadata has been managed for decades, new strategies and approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies and technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption and use.

DAS Slides: Best Practices in Metadata Management

DAS Slides: Best Practices in Metadata Management

DAS Slides: Best Practices in Metadata Management

Enabling 360-degree Business Insights with SAP Data

Enabling 360-degree Business Insights with SAP Data

Enabling 360-degree Business Insights with SAP Data

Enterprise Management Associates

Pres_Big Data for Finance_vsaini

Pres_Big Data for Finance_vsaini

Pres_Big Data for Finance_vsaini

Vandana Saini (Vinnie)

Four Key Considerations for your Big Data Analytics Strategy

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Four Key Considerations for your Big Data Analytics Strategy

Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace from digital transformation, to marketing, to customer centricity, population health, and more. This webinar will help de-mystify data strategy and data architecture and will provide concrete, practical ways to get started.

DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...

DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...

DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...

On behalf of SBI Consulting I’ve made a webinar on September 25th about Data Monetization. In the post covid-19 era, transformation of businesses to govern their data more as an asset will become of huge importance. Becoming more data driven and digital will only increase at an unseen pace. The essence of this transformation and the emphasis will be on Data Monetization. Monetizing your data assets will be of vital importance if you’d want to remain competitive and survive & thrive in the new normal. In this webinar “Data Monetization in a post-Covid era”, I cover topics such as: What does Data Monetization entails Why Data Monetization is important for your business How does the post-Covid era impacts this monetization process What do we mean with Infonomics and Data Debt The 5 key takeaways to get started with Data Monetization The outcome? A good understanding of Data Monetization and practical insights to get going immediately!

Data Monetization in a Corona Era

Data Monetization in a Corona Era

Data Monetization in a Corona Era

Mathias Vercauteren

Big data analytics overview

Big data analytics overview

Big data analytics overview

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace, from digital transformation to marketing, customer centricity, population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Organising the Data Lake - Information Management in a Big Data World

Organising the Data Lake - Information Management in a Big Data World

Organising the Data Lake - Information Management in a Big Data World

DataWorks Summit/Hadoop Summit

DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?

DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?

DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

IBM Governed Data Lake

IBM Governed Data Lake

IBM Governed Data Lake

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How to create intelligent Business Processes thanks to Big Data (BPM, Apache ...

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Gartner Top Technology Trends

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Gartner Top Technology Trends

Building Products with Data at Core

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Building Products with Data at Core

Big Data Management: A Unified Approach to Drive Business Results

Big Data Management: A Unified Approach to Drive Business Results

Big Data Management: A Unified Approach to Drive Business Results

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Solving The Data Growth Crisis: Solix Big Data Suite

Solving The Data Growth Crisis: Solix Big Data Suite

Solving The Data Growth Crisis: Solix Big Data Suite

DAS Slides: Best Practices in Metadata Management

DAS Slides: Best Practices in Metadata Management

DAS Slides: Best Practices in Metadata Management

Enabling 360-degree Business Insights with SAP Data

Enabling 360-degree Business Insights with SAP Data

Enabling 360-degree Business Insights with SAP Data

Pres_Big Data for Finance_vsaini

Pres_Big Data for Finance_vsaini

Pres_Big Data for Finance_vsaini

Four Key Considerations for your Big Data Analytics Strategy

Four Key Considerations for your Big Data Analytics Strategy

Four Key Considerations for your Big Data Analytics Strategy

DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...

DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...

DAS Slides: Building a Data Strategy — Practical Steps for Aligning with Busi...

Data Monetization in a Corona Era

Data Monetization in a Corona Era

Data Monetization in a Corona Era

Big data analytics overview

Big data analytics overview

Big data analytics overview

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Organising the Data Lake - Information Management in a Big Data World

Organising the Data Lake - Information Management in a Big Data World

Organising the Data Lake - Information Management in a Big Data World

DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?

DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?

DAS Slides: Building a Future-State Data Architecture Plan - Where to Begin?

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

Unleashing the Power of Cloud-Based Big Data Analytics.pptx

IBM Governed Data Lake

IBM Governed Data Lake

IBM Governed Data Lake

Mehr von Alan McSweeney

The data architecture of solutions is frequently not given the attention it deserves or needs. Frequently, too little attention is paid to designing and specifying the data architecture within individual solutions and their constituent components. This is due to the behaviours of both solution architects ad data architects. Solution architecture tends to concern itself with functional, technology and software components of the solution Data architecture tends not to get involved with the data aspects of technology solutions, leaving a data architecture gap. Combined with the gap where data architecture tends not to get involved with the data aspects of technology solutions, there is also frequently a solution architecture data gap. Solution architecture also frequently omits the detail of data aspects of solutions leading to a solution data architecture gap. These gaps result in a data blind spot for the organisation. Data architecture tends to concern itself with post-individual solutions. Data architecture needs to shift left into the domain of solutions and their data and more actively engage with the data dimensions of individual solutions. Data architecture can provide the lead in sealing these data gaps through a shift-left of its scope and activities as well providing standards and common data tooling for solution data architecture The objective of data design for solutions is the same as that for overall solution design: • To capture sufficient information to enable the solution design to be implemented • To unambiguously define the data requirements of the solution and to confirm and agree those requirements with the target solution consumers • To ensure that the implemented solution meets the requirements of the solution consumers and that no deviations have taken place during the solution implementation journey Solution data architecture avoids problems with solution operation and use: • Poor and inconsistent data quality • Poor performance, throughput, response times and scalability • Poorly designed data structures can lead to long data update times leading to long response times, affecting solution usability, loss of productivity and transaction abandonment • Poor reporting and analysis • Poor data integration • Poor solution serviceability and maintainability • Manual workarounds for data integration, data extract for reporting and analysis Data-design-related solution problems frequently become evident and manifest themselves only after the solution goes live. The benefits of solution data architecture are not always evident initially.

Data Architecture for Solutions.pdf

Data Architecture for Solutions.pdf

Data Architecture for Solutions.pdf

Solution architects and the solution architecture function are ideally placed to create solution delivery estimates Solution architects have the knowledge and understanding of the solution constituent component and structure that is needed to create solution estimate: • Knowledge of solution options • Knowledge of solution component structure to define a solution breakdown structure • Knowledge of available components and the options for reuse • Knowledge of specific solution delivery constraints and standards that both control and restrain solution options Accurate solution delivery estimates are need to understand the likely cost/resources/time/options needed to implement a new solution within the context of a range of solutions and solution options. These estimates are a key input to investment management and making effective decisions on the portfolio of solutions to implement. They enable informed decision-making as part of IT investment management. An estimate is not a single value. It is a range of values depending on a number of conditional factors such level of knowledge, certainty, complexity and risk. The range will narrow as the level of knowledge and uncertainty decreases There is no easy or magic way to create solution estimates. You have to engage with the complexity of the solution and its components. The more effort that is expended the more accurate the results of the estimation process will be. But there is always a need to create estimates (reasonably) quickly so a balance is needed between effort and quality of results. The notes describe a structured solution estimation process and an associated template. They also describe the wider context of solution estimates in terms of IT investment and value management and control.

Solution Architecture and Solution Estimation.pdf

Solution Architecture and Solution Estimation.pdf

Solution Architecture and Solution Estimation.pdf

Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...

Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...

Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...

This analysis looks at the changes in the numbers of priests and nuns in Ireland for the years 1926 to 2016. It combines data from a range of sources to show the decline in the numbers of priests and nuns and their increasing age profile. This analysis consists of the following sections: • Summary - this highlights some of the salient points in the analysis. • Overview of Analysis - this describes the approach taken in this analysis. • Context – this provides background information on the number of Catholics in Ireland as a context to this analysis. • Analysis of Census Data 1926 – 2016 - this analyses occupation age profile data for priests and nuns. It also includes sample projections on the numbers of priests and nuns. • Analysis of Catholic Religious Mortality 2014-2021 - this analyses death notice data from RIP.ie to shows the numbers of priests and nuns that have died in the years 2014 to 2021. It also looks at deaths of Irish priests and nuns outside Ireland and at the numbers of countries where Irish priests and nuns have worked. • Analysis of Data on Catholic Clergy From Other Sources - this analyses data on priests and nuns from other sources. • Notes on Data Sources and Data Processing - this lists the data sources used in this analysis.

Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...

Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...

Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...

Technical debt is an overworked term without an effective and common agreed understanding of what exactly it is, what causes it, what are its consequences, how to assess it and what to do about it. Technical debt is the sum of additional direct and indirect implementation and operational costs incurred and risks and vulnerabilities created because of sub-optimal solution design and delivery decisions. Technical debt is the sum of all the consequences of all the circumventions, budget reduction, time pressure, lack of knowledge, manual workarounds, short-cuts, avoidance, poor design and delivery quality and decisions to remove elements from solution scope and failure to provide foundational and backbone solution infrastructure. Technical debt leads to a negative feedback cycle with short solution lifespan, earlier solution replacement and short-term tactical remedial actions. All the disciplines within IT architecture have a role to play in promoting an understanding of and in the identification of how to resolve technical debt. IT architecture can provide the leadership in both remediating existing technical debt and preventing future debt. Failing to take a complete view of the technical debt within the organisation means problems and risks remained unrecognised and unaddressed. The real scope of the problem is substantially underestimated. Technical debt is always much more than poorly written software. Technical debt can introduce security risks and vulnerabilities into the organisation’s solution landscape. Failure to address technical debt leaves exploitable security risks and vulnerabilities in place. Shadow IT or ghost IT is a largely unrecognised source of technical debt including security risks and vulnerabilities. Shadow IT is the consequence of a set of reactions by business functions to an actual or perceived inability or unwillingness of the IT function to respond to business needs for IT solutions. Shadow IT is frequently needed to make up for gaps in core business solutions, supplementing incomplete solutions and providing omitted functionality.

IT Architecture’s Role In Solving Technical Debt.pdf

IT Architecture’s Role In Solving Technical Debt.pdf

IT Architecture’s Role In Solving Technical Debt.pdf

This describes an approach to embedding security within the technology solution landscape. It describes a security model that encompasses the range of individual solution components up to the entire solution landscape. The solution security model allows the security status of a solution and its constituent delivery and operational components to be tracked wherever those components are located. This provides an integrated approach to solution security across all solution components and across the entire organisation topology of solutions. It allows the solution architect to validate the security of an individual solution. It enables the security status of the entire solution landscape to be assessed and recorded. Solution security is a wicked problem because there is no certainly about when the problem has been resolved and a state of security has been achieved. The security state of a solution can just be expressed along a subjective spectrum of better or worse rather than a binary true or false. Solution security can have negative consequences: prevents types of access, limits availability in different ways, restricts functionality provided, makes solution harder to use, lengthens solution delivery times, increases costs along the entire solution lifecycle, leads to loss of usability, utility and rate of use.

Solution Architecture And Solution Security

Solution Architecture And Solution Security

Solution Architecture And Solution Security

Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...

Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...

Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...

Your data has value to your organisation and to relevant data sharing partners. It has been expensively obtained. It represents a valuable asset on which a return must be generated. To achieve the value inherent in the data you need to be able to make it appropriately available to others, both within and outside the organisation. Organisations are frequently data rich and information poor, lacking the skills, experience and resources to convert raw data into value. These notes outline technology approaches to achieving compliance with data privacy regulations and legislation while providing access to data. There are different routes to making data accessible and shareable within and outside the organisation without compromising compliance with data protection legislation and regulations and removing the risk associated with allowing access to personal data: • Differential Privacy – source data is summarised and individual personal references are removed. The one-to-one correspondence between original and transformed data has been removed • Anonymisation – identifying data is destroyed and cannot be recovered so individual cannot be identified. There is still a one-to-one correspondence between original and transformed data • Pseudonymisation – identifying data is encrypted and recovery data/token is stored securely elsewhere. There is still a one-to-one correspondence between original and transformed data These technologies and approaches are not mutually exclusive – each is appropriate to differing data sharing and data access use cases The data privacy regulatory and legislative landscape is complex and getting even more complex so an approach to data access and sharing that embeds compliance as a matter of course is required. Appropriate technology appropriately implemented and operated is a means of managing and reducing risks of re-identification by making the time, skills, resources and money necessary to achieve this unrealistic. Technology is part of a risk management approach to data privacy. There is wider operational data sharing and data privacy framework that includes technology aspects, among other key areas. Using these technologies will embed such compliance by design into your data sharing and access facilities. This will allow you to realise value from your data successfully.

Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...

Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...

Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...

Solution architects must be aware of the need for solution security and of the need to have enterprise-level controls that solutions can adopt. The sets of components that comprise the extended solution landscape, including those components that provide common or shared functionality, are located in different zones, each with different security characteristics. The functional and operational design of any solution and therefore its security will include many of these components, including those inherited by the solution or common components used by the solution. The complete solution security view should refer explicitly to the components and their controls. While each individual solution should be able to inherit the security controls provided by these components, the solution design should include explicit reference to them for completeness and to avoid unvalidated assumptions. There is a common and generalised set of components, many of which are shared, within the wider solution topology that should be considered when assessing overall solution architecture and solution security. Individual solutions must be able to inherit security controls, facilities and standards from common enterprise-level controls, standards, toolsets and frameworks. Individual solutions must not be forced to implement individual infrastructural security facilities and controls. This is wasteful of solution implementation resources, results in multiple non-standard approaches to security and represents a security risk to the organisation. The extended solution landscape potentially consists of a large number of interacting components and entities located in different zones, each with different security profiles, requirements and concerns. Different security concerns and therefore controls apply to each of these components. Solution security is not covered by a single control. It involves multiple overlapping sets of controls providing layers of security.

Solution Security Architecture

Solution Security Architecture

Solution Security Architecture

Automation is a technology trend IT architects should be aware of and know how to respond to business requests as well as recommend automation technologies and solutions where appropriate. Automation is a bigger topic than just RPA (Robotic Process Automation). Automation solutions, like all other technology solutions, should be subject to an architecture and design process. There are many approaches to and options for the automation of business activities. Too often automation solutions are tactical applications layered over existing business systems The objective of all IT solutions is to automate manual business processes and their activities to a certain extent. The requirement for RPA-type applications arises in part because of automation failures within existing applications or the need to automate the interactions with or integrations between separate, possibly legacy, applications. One of the roles of IT architecture is to always seek to take the wider architectural view and to ensure that solutions are designed and delivered within a strategic framework to avoid, as much as is practical and realistic, short-term tactical solutions and approaches that lead to an accumulation of design, operations and support debt. Tactical solutions will always play a part in the organisation’s solution landscape. The objective of these notes is to put automation into its wider and larger IT architecture context while accepting the need for tactical approaches in some instances. These notes cover the following topics: • Solution And Process Automation – The Wider Technology And Approach Landscape • Business Processes, Business Solutions And Automation • Organisation Process Model • Strategic And Tactical Automation • Deciding On The Scope Of Automation • Digital Strategy, Digital Transformation And Automation • Specifying The Automation Solution • Business Process Model and Notation (BPMN) • Sample Business Process – Order To Cash • RPA (Robotic Process Automation)

Solution Architecture And (Robotic) Process Automation Solutions

Solution Architecture And (Robotic) Process Automation Solutions

Solution Architecture And (Robotic) Process Automation Solutions

These notes discuss the related topics of Data Profiling, Data Catalogs and Metadata Harmonisation. It describes a detailed structure for data profiling activities. It identifies various open source and commercial tools and data profiling algorithms. Data profiling is a necessary pre-requisite activity in order to construct a data catalog. A data catalog makes an organisation’s data more discoverable. The data collected during data profiling forms the metadata contained in the data catalog. This assists with ensuring data quality. It is also a necessary activity for Master Data Management initiatives. These notes describe a metadata structure and provide details on metadata standards and sources.

Data Profiling, Data Catalogs and Metadata Harmonisation

Data Profiling, Data Catalogs and Metadata Harmonisation

Data Profiling, Data Catalogs and Metadata Harmonisation

This document compares published COVID-19 mortality statistics for Ireland with publicly available mortality data extracted from informal public data sources. This mortality data is taken from published death notices on the web site www.rip.ie. This is used a substitute for poor quality and long-delayed officially published mortality statistics. Death notice information on the web site www.rip.ie is available immediately and contains information at a greater level of detail than published statistics. There is a substantial lag in officially published mortality data and the level of detail is very low. However, the extraction of death notice data and its conversion into a usable and accurate format requires a great deal of processing. The objective of this analysis is to assess the accuracy of published COVID-19 mortality statistics by comparing trends in mortality over the years 2014 to 2020 with both numbers of deaths recorded from 2020 to 2021 and the COVID-19 statistics. It compares number of deaths for the seven 13-month intervals: 1. Mar 2014 - Mar 2015 2. Mar 2015 - Mar 2016 3. Mar 2016 - Mar 2017 4. Mar 2017 - Mar 2018 5. Mar 2018 - Mar 2019 6. Mar 2019 - Mar 2020 7. Mar 2020 - Mar 2021 It focuses on the seventh interval which is when COVID-19 deaths have occurred. It combines an analysis of mortality trends with details on COVID-19 deaths. This is a fairly simplistic analysis that looks to cross-check COVID-19 death statistics using data from other sources. The subject of what constitutes a death from COVID-19 is controversial. This analysis is not concerned with addressing this controversy. It is concerned with comparing mortality data from a number of sources to identify potential discrepancies. It may be the case that while the total apparent excess number of deaths over an interval is less than the published number of COVID-19 deaths, the consequence of COVID-19 is to accelerate deaths that might have occurred later in the measurement interval. Accurate data is needed to make informed decisions. Clearly there are issues with Irish COVID-19 mortality data. Accurate data is also needed to ensure public confidence in decision-making. Where this published data is inaccurate, this can lead of a loss of this confidence that can exploited.

Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...

Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...

Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...

This analysis looks at the potential impact that large numbers of electric vehicles could have on electricity demand, electricity generation capacity and on the electricity transmission and distribution grid in Ireland. It combines data from a number of sources – electricity usage patterns, vehicle usage patterns, electric vehicle current and possible future market share – to assess the potential impact of electric vehicles. It then analyses a possible approach to electric vehicle charging where the domestic charging unit has some degree of decentralised intelligence and decision-making capability in deciding when to start vehicle charging to minimise electricity usage impact and optimise electricity generation usage. The potential problem to be addressed is that if large numbers of electric cars are plugged-in and charging starts immediately when the drivers of those cars arrive home, the impact on demand for electricity will be substantial.

Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...

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Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...

This describes a structured approach to validating data used to construct and use an operational risk model. It details an integrated approach to operational risk data involving three components: 1. Using the Open Group FAIR (Factor Analysis of Information Risk) risk taxonomy to create a risk data model that reflects the required data needed to assess operational risk 2. Using the DMBOK model to define a risk data capability framework to assess the quality and accuracy of risk data 3. Applying standard fault analysis approaches - Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) - to the risk data capability framework to understand the possible causes of risk data failures within the risk model definition, operation and use

Operational Risk Management Data Validation Architecture

Operational Risk Management Data Validation Architecture

Operational Risk Management Data Validation Architecture

These notes describe a generalised data integration architecture framework and set of capabilities. With many organisations, data integration tends to have evolved over time with many solution-specific tactical approaches implemented. The consequence of this is that there is frequently a mixed, inconsistent data integration topography. Data integrations are often poorly understood, undocumented and difficult to support, maintain and enhance. Data interoperability and solution interoperability are closely related – you cannot have effective solution interoperability without data interoperability. Data integration has multiple meanings and multiple ways of being used such as: - Integration in terms of handling data transfers, exchanges, requests for information using a variety of information movement technologies - Integration in terms of migrating data from a source to a target system and/or loading data into a target system - Integration in terms of aggregating data from multiple sources and creating one source, with possibly date and time dimensions added to the integrated data, for reporting and analytics - Integration in terms of synchronising two data sources or regularly extracting data from one data sources to update a target - Integration in terms of service orientation and API management to provide access to raw data or the results of processing There are two aspects to data integration: 1. Operational Integration – allow data to move from one operational system and its data store to another 2. Analytic Integration – move data from operational systems and their data stores into a common structure for analysis

Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...

Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...

Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...

This analysis compares some data areas - Economy, Crime, Aviation, Energy, Transport, Health, Mortality. Housing and Construction - for Ireland for the years 2019 and 2020, illustrating the changes that have occurred between the two years. It shows some of the impacts of COVID-19 and of actions taken in response to it, such as the various lockdowns and other restrictions. The first lockdown clearly had major changes on many aspects of Irish society. The third lockdown which began at the end of the period analysed will have as great an impact as the first lockdown. The consequences of the events and actions that have causes these impacts could be felt for some time into the future.

Ireland 2019 and 2020 Compared - Individual Charts

Ireland 2019 and 2020 Compared - Individual Charts

Ireland 2019 and 2020 Compared - Individual Charts

This describes the use of published death notices on the web site www.rip.ie as a substitute to officially published mortality statistics. This analysis uses data from RIP.ie for the years 2014 to 2020. Death notice information is available immediately and contains information at a greater level of detail than published statistics. There is a substantial lag in officially published mortality data.

Analysis of Irish Mortality Using Public Data Sources 2014-2020

Analysis of Irish Mortality Using Public Data Sources 2014-2020

Analysis of Irish Mortality Using Public Data Sources 2014-2020

This analysis compares some data areas - Economy, Crime, Aviation, Energy, Transport, Health, Mortality. Housing and Construction - for Ireland for the years 2019 and 2020, illustrating the changes that have occurred between the two years. It shows some of the impacts of COVID-19 and of actions taken in response to it, such as the various lockdowns and other restrictions. The first lockdown clearly had major changes on many aspects of Irish society. The third lockdown which began at the end of the period analysed will have as great an impact as the first lockdown. The consequences of the events and actions that have causes these impacts could be felt for some time into the future.

Ireland – 2019 And 2020 Compared In Data

Ireland – 2019 And 2020 Compared In Data

Ireland – 2019 And 2020 Compared In Data

IT Function critical capabilities are key areas where the IT function needs to maintain significant levels of competence, skill and experience and practise in order to operate and deliver a service. There are several different IT capability frameworks. The objective of these notes is to assess the suitability and applicability of these frameworks. These models can be used to identify what is important for your IT function based on your current and desired/necessary activity profile. Capabilities vary across organisation – not all capabilities have the same importance for all organisations. These frameworks do not readily accommodate variability in the relative importance of capabilities. The assessment approach taken is to identify a generalised set of capabilities needed across the span of IT function operations, from strategy to operations and delivery. This generic model is then be used to assess individual frameworks to determine their scope and coverage and to identify gaps. The generic IT function capability model proposed here consists of five groups or domains of major capabilities that can be organised across the span of the IT function: 1. Information Technology Strategy, Management and Governance 2. Technology and Platforms Standards Development and Management 3. Technology and Solution Consulting and Delivery 4. Operational Run The Business/Business as Usual/Service Provision 5. Change The Business/Development and Introduction of New Services In the context of trends and initiatives such as outsourcing, transition to cloud services and greater platform-based offerings, should the IT function develop and enhance its meta-capabilities – the management of the delivery of capabilities? Is capability identification and delivery management the most important capability? Outsourced service delivery in all its forms is not a fire-and-forget activity. You can outsource the provision of any service except the management of the supply of that service. The following IT capability models have been evaluated: • IT4IT Reference Architecture https://www.opengroup.org/it4it contains 32 functional components • European e-Competence Framework (ECF) http://www.ecompetences.eu/ contains 40 competencies • ITIL V4 https://www.axelos.com/best-practice-solutions/itil has 34 management practices • COBIT 2019 https://www.isaca.org/resources/cobit has 40 management and control processes • APQC Process Classification Framework - https://www.apqc.org/process-performance-management/process-frameworks version 7.2.1 has 44 major IT management processes • IT Capability Maturity Framework (IT-CMF) https://ivi.ie/critical-capabilities/ contains 37 critical capabilities The following model has not been evaluated • Skills Framework for the Information Age (SFIA) - http://www.sfia-online.org/ lists over 100 skills

Review of Information Technology Function Critical Capability Models

Review of Information Technology Function Critical Capability Models

Review of Information Technology Function Critical Capability Models

This reviews the Open Group’s IT4IT Reference Architecture (https://www.opengroup.org/it4it) with respect to other operational frameworks to determine its suitability and applicability to the IT operating function. IT4IT is intended to be a reference architecture for the management of the IT function. It aims to take a value chain approach to create a model of the functions that IT performs and the services it provides to assist organisations in the identification of the activities that contribute to business competitiveness. It is intended to be an integrated framework for the management of IT that emphasises IT service lifecycles. This paper reviews what is meant by a value-chain, with special reference to the Supply Chain Operations Reference (SCOR) model (https://www.apics.org/apics-for-business/frameworks/scor). the most widely used and most comprehensive such model. The SCOR model is part of wider set of operations reference models that describe a view of the critical elements in a value chain: • Product Life Cycle Operations Reference model (PLCOR) - Manages the activities for product innovation and product and portfolio management • Customer Chain Operations Reference model (CCOR) - Manages the customer interaction processes • Design Chain Operations Reference model (DCOR) - Manages the product and service development processes • Managing for Supply Chain Performance (M4SC) - Translates business strategies into supply chain execution plans and policies It also compares the IT4IT Reference Architecture and its 32 functional components to other frameworks that purport to identify the critical capabilities of the IT function: • IT Capability Maturity Framework (IT-CMF) https://ivi.ie/critical-capabilities/ contains 37 critical capabilities • Skills Framework for the Information Age (SFIA) - http://www.sfia-online.org/ lists over 100 skills • European e-Competence Framework (ECF) http://www.ecompetences.eu/ contains 40 competencies • ITIL IT Service Management https://www.axelos.com/best-practice-solutions/itil • COBIT 2019 https://www.isaca.org/resources/cobit has 40 management and control processes

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Corporate and higher education May webinar.pptx

Rustici Software

Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

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