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.
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...Alan McSweeney
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.
Estimating The Size of the Irish PopulationAlan McSweeney
The various sources of population-related data are inconsistent with one another. There has been past issues with determining the extent of immigration. This in turn creates an issue with the size of the population of Ireland.
This analysis has identified one possible set of inconsistencies relating to the size of the Irish population. It may well be that the population of Ireland is greater that than counted by the CSO in the census.
Population sizes at various ages determine the demand for different societal resources. People are, after all, the direct and indirect buyers and users of products and services, both public and private sector. People drive demand. Changes in the profile of people – numbers and ages – will change the demand profile.
Discrepancies between other data sources from which population data can be inferred and the CSO’s population data indicate that there may be ongoing errors.
Consistency checking between multiple sets of related data is a standard technique to identify potential quality data issues that should then be the subject of further analysis. Detailed consistency checking is hampered by the limited set of information made publically available by various state agencies.
This analysis has looked at the following sets of data with a view to identifying potential data conflicts:
1. DEASP PPSN Registration Numbers
2. CSO PPSN Numbers
3. CSO Migration Numbers
4. CSO Population Numbers
5. Revenue Income Tax Numbers
6. Department of Education Third-Level Numbers
7. DEASP Pensioner Numbers
8. DEASP Live Register/Disability/Work Activation Numbers
9. Irish Naturalisation and Immigration Service (INIS) Statistics
The purpose of this analysis is to assess trends in residential renting and rent prices.
This is a macro-level analysis based on a variety of publically available data sources.
The issue of prices for rental property in Ireland and, more particularly in Dublin and the other larger cities, and their rate of increase have dominated property-related discussions.
The data publically available on residential renting is patch, disparate and, in some case, of poor quality.
All the rent indices demonstrate the same pattern of increase.
Demand for rental properties is driven by increased population. A large part of the population increase in the renting age groups is due to net immigration who almost exclusively rent. It may also be that the recorded population number underestimates the actual population and thus the actual demand for rented accommodation
Most renters are aged between 25 to 44 – 60.7% in 2011 and 60.5% in 2016.
Nationally private landlords accounted for 68.0% of lettings in 2011 and 65.9% in 2016. In Dublin private landlords accounted 71.1% in 2011 and 69.3% in 2016.
RTB has records for 124,574 tenancies in the Dublin area with 272,981 bedrooms in March 2017. This excludes Local Authorities and a range of holiday, informal and family property lettings. The number of tenancies registered with the RTB has increased slightly indicating no drop in supply.
In the five and half years from Jun 2012 to Dec 2017, the number of BTL mortgages dropper by 27,821 or 18.52%. The number of repossessions in the interval was 4,897. So the number of BTL mortgages is dropping. This may be due to the group of people to who this lending relates – accidental landlords – selling their investment properties.
The last 10 years has seen the growth of the institutional residential property investor, especially in Dublin. Around 75% of large-scale multiple unit residential property purchases occurred in Dublin. These probably represent around 9,500 residential units. This represents a significant change in the rental sector, especially in Dublin.
There is a belief that residential institutional letters change a higher rent than other residential landlords. This may be one driver of increased rents.
In the last nine years, only 15,408 new property purchases were registered in Dublin. This illustrates the lack of new property supply in Dublin to accommodate a growing population and a demand for rental accommodation.
Airbnb rentals represent only 2.45% of the 124,574 registered tenancies and 2.08% of the 272,981 bedrooms in those tenancies and so is not significant.
Problems with availability and affordability of suitable residential rental accommodation represents a potential systemic economic risk.
Analysis of Possible Excess COVID-19 Deaths in Ireland From Jan 2020 to Jun 2020Alan McSweeney
This analysis seeks to determine if there are excess deaths that occurred in Ireland in the interval Jan – Jun 2020 that can be attributed to COVID-19. Excess deaths means deaths in excess of the number of expected deaths plus the number of deaths directly attributed to COVID-19. On the other hand a deficiency of deaths would occur when the number of expected deaths plus the number of deaths directly attributed to COVID-19 is less than the actual deaths.
This analysis uses number of deaths taken from the web site RIP.ie to generate an estimate of the number of deaths in Jan – Jun 2020 in the absence of any other official source. The last data extract from the RIP.ie web site was taken on 3 Jul 2020.
The analysis uses historical data from RIP.ie from 2018 and 2019 to assess its accuracy as a data source.
The analysis then uses the following three estimation approaches to assess the excess or deficiency of deaths:
1. The pattern of deaths in 2020 can be compared to previous comparable year or years. The additional COVID-19 deaths can be added to the comparable year and the difference between the expected, actual from RIP.ie and actual COVID-19 deaths can be analysed to generate an estimate of any excess or deficiency.
2. The age-specific mortality rates described on page 16 can be applied to estimates of population numbers to generates an estimate of expected deaths. This can be compared to the actual RIP.ie and actual COVID-19 deaths to generate an estimate of any excess or deficiency.
3. The range of death rates per 1,000 of population as described in Figure 10 on page 16 can be applied to estimates of population numbers to generates an estimate of expected deaths. This can be compared to the actual RIP.ie and actual COVID-19 deaths to generate an estimate of any excess or deficiency.
How Many Net New Residential Units Are Really Available In Ireland?Alan McSweeney
The CSO announced in June 2018 that they are publishing a new set of data series called New Dwellings Completed. The purpose of this new data is to create realistic statistics on the number of new dwelling completions in Ireland.
Counting the number of new dwellings while important needs to be conducted in a wider context where factors that affect the reduction in the number of dwellings and demographic changes that affect demand for dwellings.
Focussing on the narrow issue of new dwellings may be a distraction on the wider problem of an increasing population and thus a greater demand for residential accommodation and changes that cause a reduction in dwellings.
There was virtually no net increase in the number of dwellings between the 2011 and 2016 census. There was a net increase of just 8,800 new dwellings while the population increased by 348,404.
Based on the assumption that around 85% of the number of planned units become actual units in 15 months, the current stock of planned residential units will translate roughly into just under 25,000 new units by mid-2019. This is a very small increase. This does not take into account any loss of residential housing stock during the same interval.
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 ChartsAlan McSweeney
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.
This case study examines what linking Migrant Worker Scan and Personal Demographic Service data together can tell us about when and how new international migrants appear on different data sources. The slides summarise the findings on registration lags and explore differences between EU and non-EU nationals. The case study looks at both the lag between arrival and registration for a NINo, and arrival and registration with the NHS. The findings from this case study provide important insights that will be key to the successful development of a population and migration statistics system based on administrative data sources.
Comparison of COVID-19 Mortality Data and Deaths for Ireland March 2020 – Mar...Alan McSweeney
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.
Estimating The Size of the Irish PopulationAlan McSweeney
The various sources of population-related data are inconsistent with one another. There has been past issues with determining the extent of immigration. This in turn creates an issue with the size of the population of Ireland.
This analysis has identified one possible set of inconsistencies relating to the size of the Irish population. It may well be that the population of Ireland is greater that than counted by the CSO in the census.
Population sizes at various ages determine the demand for different societal resources. People are, after all, the direct and indirect buyers and users of products and services, both public and private sector. People drive demand. Changes in the profile of people – numbers and ages – will change the demand profile.
Discrepancies between other data sources from which population data can be inferred and the CSO’s population data indicate that there may be ongoing errors.
Consistency checking between multiple sets of related data is a standard technique to identify potential quality data issues that should then be the subject of further analysis. Detailed consistency checking is hampered by the limited set of information made publically available by various state agencies.
This analysis has looked at the following sets of data with a view to identifying potential data conflicts:
1. DEASP PPSN Registration Numbers
2. CSO PPSN Numbers
3. CSO Migration Numbers
4. CSO Population Numbers
5. Revenue Income Tax Numbers
6. Department of Education Third-Level Numbers
7. DEASP Pensioner Numbers
8. DEASP Live Register/Disability/Work Activation Numbers
9. Irish Naturalisation and Immigration Service (INIS) Statistics
The purpose of this analysis is to assess trends in residential renting and rent prices.
This is a macro-level analysis based on a variety of publically available data sources.
The issue of prices for rental property in Ireland and, more particularly in Dublin and the other larger cities, and their rate of increase have dominated property-related discussions.
The data publically available on residential renting is patch, disparate and, in some case, of poor quality.
All the rent indices demonstrate the same pattern of increase.
Demand for rental properties is driven by increased population. A large part of the population increase in the renting age groups is due to net immigration who almost exclusively rent. It may also be that the recorded population number underestimates the actual population and thus the actual demand for rented accommodation
Most renters are aged between 25 to 44 – 60.7% in 2011 and 60.5% in 2016.
Nationally private landlords accounted for 68.0% of lettings in 2011 and 65.9% in 2016. In Dublin private landlords accounted 71.1% in 2011 and 69.3% in 2016.
RTB has records for 124,574 tenancies in the Dublin area with 272,981 bedrooms in March 2017. This excludes Local Authorities and a range of holiday, informal and family property lettings. The number of tenancies registered with the RTB has increased slightly indicating no drop in supply.
In the five and half years from Jun 2012 to Dec 2017, the number of BTL mortgages dropper by 27,821 or 18.52%. The number of repossessions in the interval was 4,897. So the number of BTL mortgages is dropping. This may be due to the group of people to who this lending relates – accidental landlords – selling their investment properties.
The last 10 years has seen the growth of the institutional residential property investor, especially in Dublin. Around 75% of large-scale multiple unit residential property purchases occurred in Dublin. These probably represent around 9,500 residential units. This represents a significant change in the rental sector, especially in Dublin.
There is a belief that residential institutional letters change a higher rent than other residential landlords. This may be one driver of increased rents.
In the last nine years, only 15,408 new property purchases were registered in Dublin. This illustrates the lack of new property supply in Dublin to accommodate a growing population and a demand for rental accommodation.
Airbnb rentals represent only 2.45% of the 124,574 registered tenancies and 2.08% of the 272,981 bedrooms in those tenancies and so is not significant.
Problems with availability and affordability of suitable residential rental accommodation represents a potential systemic economic risk.
Analysis of Possible Excess COVID-19 Deaths in Ireland From Jan 2020 to Jun 2020Alan McSweeney
This analysis seeks to determine if there are excess deaths that occurred in Ireland in the interval Jan – Jun 2020 that can be attributed to COVID-19. Excess deaths means deaths in excess of the number of expected deaths plus the number of deaths directly attributed to COVID-19. On the other hand a deficiency of deaths would occur when the number of expected deaths plus the number of deaths directly attributed to COVID-19 is less than the actual deaths.
This analysis uses number of deaths taken from the web site RIP.ie to generate an estimate of the number of deaths in Jan – Jun 2020 in the absence of any other official source. The last data extract from the RIP.ie web site was taken on 3 Jul 2020.
The analysis uses historical data from RIP.ie from 2018 and 2019 to assess its accuracy as a data source.
The analysis then uses the following three estimation approaches to assess the excess or deficiency of deaths:
1. The pattern of deaths in 2020 can be compared to previous comparable year or years. The additional COVID-19 deaths can be added to the comparable year and the difference between the expected, actual from RIP.ie and actual COVID-19 deaths can be analysed to generate an estimate of any excess or deficiency.
2. The age-specific mortality rates described on page 16 can be applied to estimates of population numbers to generates an estimate of expected deaths. This can be compared to the actual RIP.ie and actual COVID-19 deaths to generate an estimate of any excess or deficiency.
3. The range of death rates per 1,000 of population as described in Figure 10 on page 16 can be applied to estimates of population numbers to generates an estimate of expected deaths. This can be compared to the actual RIP.ie and actual COVID-19 deaths to generate an estimate of any excess or deficiency.
How Many Net New Residential Units Are Really Available In Ireland?Alan McSweeney
The CSO announced in June 2018 that they are publishing a new set of data series called New Dwellings Completed. The purpose of this new data is to create realistic statistics on the number of new dwelling completions in Ireland.
Counting the number of new dwellings while important needs to be conducted in a wider context where factors that affect the reduction in the number of dwellings and demographic changes that affect demand for dwellings.
Focussing on the narrow issue of new dwellings may be a distraction on the wider problem of an increasing population and thus a greater demand for residential accommodation and changes that cause a reduction in dwellings.
There was virtually no net increase in the number of dwellings between the 2011 and 2016 census. There was a net increase of just 8,800 new dwellings while the population increased by 348,404.
Based on the assumption that around 85% of the number of planned units become actual units in 15 months, the current stock of planned residential units will translate roughly into just under 25,000 new units by mid-2019. This is a very small increase. This does not take into account any loss of residential housing stock during the same interval.
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 ChartsAlan McSweeney
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.
This case study examines what linking Migrant Worker Scan and Personal Demographic Service data together can tell us about when and how new international migrants appear on different data sources. The slides summarise the findings on registration lags and explore differences between EU and non-EU nationals. The case study looks at both the lag between arrival and registration for a NINo, and arrival and registration with the NHS. The findings from this case study provide important insights that will be key to the successful development of a population and migration statistics system based on administrative data sources.
Flash Report - Government Deficit - 6 April 2018OTP Bank Ltd.
2017-ben a kormányzat előzetes bejelentésének megfelelően 2% volt a költségvetés hiánya. Az erős bér- és fogyasztás-bővülés miatt gyorsan nőttek az adóbevételek. A kiadási oldalon a legnagyobb mértékben, 54%-kal a beruházások nőttek, illetve az év végi, diszkrecionális döntéseknek köszönhetően a dologi kiadások. A beruházások várhatóan átmeneti megugrása és az év végi diszkrecionális döntések nélküli egyenleg továbbra is egyensúly közeli. Az államadósság – amely immár az Exim Bank adatait is tartalmazza – a GDP 73.6%-ára mérséklődött az előző évi 76%-ról, dacára annak, hogy az EU-s projektek előfinanszírozása az eredmény-szemléletűnél érdemben magasabb pénzforgalmi deficitet eredményezett. Előretekintve a kockázatok – részben a választásokhoz kötődő bizonytalanság miatt – a deficit növekedése irányába mutatnak.
"Highlights":
* Inflation stabilizes, main risk stems from oil price fluctuations
* Manufacturing retains high growth momentum
* Exports of goods increase volume and diversity
"In Focus":
* Tax reform, autors: Kārlis Vilerts
Our coverage of the Americas this month includes a new report on Dominican Republic, where the country’s wildly popular incumbent president, Danilo Medina, has given his blessing to a bid by his supporters in the governing Dominican Liberation Party (PLD) to secure a constitutional amendment that will permit him to stand for immediate re-election in 2016. That decision is a crushing blow to the
This case study examines what we can discover about circular patterns of movement into and out of the UK for non-EU nationals in Home Office data. This research has shown that people’s travel patterns can be complex and further examination is needed to understand what these patterns mean. The findings from this case study provide important insights that will be key to the successful development of a population and migration statistics system based on administrative data sources.
"Highlights"
* Manufacturing growth accelerated in the second half of 2016
* Retail trade reported higher growth at the end of 2016
* Consumer prices pick up reflecting an increase in commodity prices
"In Focus":
* Reshaping of Latvia's energy sector continues, autors: Igors Kasjanovs
Highlights:
* Fastest GDP growth in 6 years
* Inflation slightly down
* Dynamic year for retail trade
In Focus.
Four years in the euro area – have the promises come true? author: Egils Kaužēns
Highlights:
- Broad-based growth drives GDP acceleration
- Inflation has reached its upswing potential
- Increase in external demand accelerates external trade
In Focus:
- "The importance of high value added services exports is growing for Latvia's economy" by Linda Vecgaile
Data Digest #9: Vietnam Stock Market: Embracing New Normal amidst COVID!FiinGroup JSC
COVID-related impacts on the Value could be somehow predictable. In this Report, we conduct an in-depth analysis on factors determining SUPPLY in correlation with DEMAND, instead of purely analyzing corporate fundamentals like before. Under the current circumstance, factors determining DEMAND or affecting money flow and investor sentiment, in our view, are the most important and need taking into serious consideration.
We are trying to make a plenty of data-driven comparisons on impacts of different COVID waves (the first in Q1-2020 and the fourth now) to support you in having assessments on your own. Accordingly, this Report aims to give in-depth analysis and data-driven findings on which sectors or companies could be beneficiaries from the pandemic, especially once the “Embracing the Covid-19” strategy is confirmed.
Download our full report: https://bit.ly/FiinPro-Digest-9-EN
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...Alan McSweeney
This analysis seeks to validate published COVID-19 mortality statistics using mortality data derived from general mortality statistics, mortality estimated from population size and mortality rates and death notice data
Matthew Edwards - CMI COVID-19 Inquiry SubmissionILC- UK
Presented at the "Pandemics and longevity: Will we die, survive, or thrive next time?" webinar, by ILC-UK
Date: Thursday 16 March 2023
Time: 1.30pm – 3.00pm (GMT)
Flash Report - Government Deficit - 6 April 2018OTP Bank Ltd.
2017-ben a kormányzat előzetes bejelentésének megfelelően 2% volt a költségvetés hiánya. Az erős bér- és fogyasztás-bővülés miatt gyorsan nőttek az adóbevételek. A kiadási oldalon a legnagyobb mértékben, 54%-kal a beruházások nőttek, illetve az év végi, diszkrecionális döntéseknek köszönhetően a dologi kiadások. A beruházások várhatóan átmeneti megugrása és az év végi diszkrecionális döntések nélküli egyenleg továbbra is egyensúly közeli. Az államadósság – amely immár az Exim Bank adatait is tartalmazza – a GDP 73.6%-ára mérséklődött az előző évi 76%-ról, dacára annak, hogy az EU-s projektek előfinanszírozása az eredmény-szemléletűnél érdemben magasabb pénzforgalmi deficitet eredményezett. Előretekintve a kockázatok – részben a választásokhoz kötődő bizonytalanság miatt – a deficit növekedése irányába mutatnak.
"Highlights":
* Inflation stabilizes, main risk stems from oil price fluctuations
* Manufacturing retains high growth momentum
* Exports of goods increase volume and diversity
"In Focus":
* Tax reform, autors: Kārlis Vilerts
Our coverage of the Americas this month includes a new report on Dominican Republic, where the country’s wildly popular incumbent president, Danilo Medina, has given his blessing to a bid by his supporters in the governing Dominican Liberation Party (PLD) to secure a constitutional amendment that will permit him to stand for immediate re-election in 2016. That decision is a crushing blow to the
This case study examines what we can discover about circular patterns of movement into and out of the UK for non-EU nationals in Home Office data. This research has shown that people’s travel patterns can be complex and further examination is needed to understand what these patterns mean. The findings from this case study provide important insights that will be key to the successful development of a population and migration statistics system based on administrative data sources.
"Highlights"
* Manufacturing growth accelerated in the second half of 2016
* Retail trade reported higher growth at the end of 2016
* Consumer prices pick up reflecting an increase in commodity prices
"In Focus":
* Reshaping of Latvia's energy sector continues, autors: Igors Kasjanovs
Highlights:
* Fastest GDP growth in 6 years
* Inflation slightly down
* Dynamic year for retail trade
In Focus.
Four years in the euro area – have the promises come true? author: Egils Kaužēns
Highlights:
- Broad-based growth drives GDP acceleration
- Inflation has reached its upswing potential
- Increase in external demand accelerates external trade
In Focus:
- "The importance of high value added services exports is growing for Latvia's economy" by Linda Vecgaile
Data Digest #9: Vietnam Stock Market: Embracing New Normal amidst COVID!FiinGroup JSC
COVID-related impacts on the Value could be somehow predictable. In this Report, we conduct an in-depth analysis on factors determining SUPPLY in correlation with DEMAND, instead of purely analyzing corporate fundamentals like before. Under the current circumstance, factors determining DEMAND or affecting money flow and investor sentiment, in our view, are the most important and need taking into serious consideration.
We are trying to make a plenty of data-driven comparisons on impacts of different COVID waves (the first in Q1-2020 and the fourth now) to support you in having assessments on your own. Accordingly, this Report aims to give in-depth analysis and data-driven findings on which sectors or companies could be beneficiaries from the pandemic, especially once the “Embracing the Covid-19” strategy is confirmed.
Download our full report: https://bit.ly/FiinPro-Digest-9-EN
Validating COVID-19 Mortality Data and Deaths for Ireland March 2020 – March ...Alan McSweeney
This analysis seeks to validate published COVID-19 mortality statistics using mortality data derived from general mortality statistics, mortality estimated from population size and mortality rates and death notice data
Matthew Edwards - CMI COVID-19 Inquiry SubmissionILC- UK
Presented at the "Pandemics and longevity: Will we die, survive, or thrive next time?" webinar, by ILC-UK
Date: Thursday 16 March 2023
Time: 1.30pm – 3.00pm (GMT)
451 Research - Data Law 2015 - The Outlook for Data E-Discovery, Privacy and ...Roshan Methananda
Increasing concern over data breaches and privacy made data law a critical concern in 2014, and its importance is only set to increase. However, our research shows corporate organizations are not giving data governance the attention it deserves. Only a third of senior executives believe information governance is very important.
Każdy Amerykanin konsumuje 34 GB danych dziennie.
Coroczny raport Uniwersytetu Kalifornia bada przyswajanie tych danych via sieć, tv, radio, konsole, czytając, itd.
To pokazuje ogrom danych jakie docierają do każdego konsumenta. Można śmiało przypuszczać, że w przypadku europejczyków te dane są zbliżone.
On Thursday, 28 May 2020, Connor O'Toole hosted a webinar which presented the findings from the report 'Quarterly Economic Commentary, Summer 2020'. The report assesses the future prospects for the Irish economy under three different scenarios: Baseline (“New normal with ongoing physical distancing”, Severe (“Second wave requiring strict lockdown”) and Benign (“Successful disease suppression”).
The webinar featured a presentation by Conr O'Toole and was followed by a Q&A session with co-author Kieran McQuinn.
To view the report, visit our website here: https://www.esri.ie/publications/quarterly-economic-commentary-summer-2020
To watch a video of the webinar, visit our Youtube here:
https://www.youtube.com/watchv=FQl91wpY_bQ&feature=emb_title
NCL Consumer Data Insecurity Report: Examining Data Breaches June 2014nationalconsumersleague
The National Consumers League #DataInsecurity Project has released a new survey of identity fraud victims, which finds that Americans are urgently calling out for government action on the growing threat posed by data breach and identity theft. The study, conducted in partnership with Javelin Strategy & Research, shows that the consumer impact of data breach is indeed severe: 61 percent of data breach victims surveyed reported that the breached information was used to commit fraud against them. What’s more, nearly half of victims--49 percent--do not know where the information used to defraud them was compromised.
The OECD Business and Finance Scoreboard contains indicators and data related to corporate performance, banking, capital markets, pensions and investments. It supports analysis of developments in the financial markets and corporate sector. The Scoreboard is a sister publication to the OECD Business and Finance Outlook.
Find out more: http://www.oecd.org/daf/oecd-business-and-finance-scoreboard.htm
Home Pharma & Healthcare ATV And UTV Audio Systems Market
ATV And UTV Audio Systems Market Report 2023 (Global Edition)
Companies: MB Quart, MTX Audio, Froghead Industries, Rockford Fosgate, BOSS Audio, KICKER Audio, Pyle, NOAM, Actiway China
Type: Speakers, Amplifiers, Head Units
Application:
OEM, Aftermarket
WIPAC Monthly is the monthly magazine of the LinkedIn Group Water Industry Process Automation & Control. This is the edition from July 2016.
In this edition we have articles on Smart Water Networks, Area Velocity Flow Measurement, Smart Water Semantics and the Cyber Security of devices connected to the Industrial Internet of Things. All things that will be covered in the IWA Conference at WWEM this November.
Enjoy
Regulating for a Digital Economy: Understanding the Importance of Cross-Borde...accacloud
Cross-border data access, usage, and exchange are essential to economic growth in the digital age. Every sector—including manufacturing, services, agriculture, and retail—relies on data and on the global flow of that data. Whether directly, or by indirectly taking advantage of global-scale data infrastructure such as cloud computing, global connectivity has enabled cross-border economic activity, allowing individuals, startups, and small businesses to participate in global markets. However, while the economic and trade opportunity from connectivity and data flows are significant, governments are increasingly introducing measures which restrict data flows—data localization measures.
This report reviews the various mechanisms by which governments are attempting to manage their digital economy. It covers the issues of data localization and data residency, clarifies cross-border data flow restrictions by developing a typology of data localization mechanisms like privacy, cybersecurity, law enforcement, digital protectionism, and levelling the playing field for businesses.
Sponsored by the Asia Cloud Computing Association, this report was independently researched and published by the Brookings Institution and TRPC Pte Ltd.
For more information, visit us at http://www.asiacloudcomputing.org
Precision cardiology industry to be one of the most rapidly evolving and dynamic markets and is predicted to grow at a CAGR of 12.90% over the forecast period of 2021-2031.
2012 Jordan ICT & ITES Industry Statistics Yearbook
Jordan’s ICT and IT Enabled Services (ITES) sector has come a long way in the past years and has achieved a great deal of accomplishments in which we can all take great pride. ICT and ITES are listed amongst the government’s highest priorities, and are expected to continue to contribute to the Jordanian economy.
To demonstrate the sector’s growth in terms of numbers and to determine the growth in market size, exports, investments, and employment, the Information Technology Association of Jordan (int@j) and the Ministry of Information and Communications Technology (MoICT) have completed the ICT and ITES Sector Classification and Statistics for 2012 aiming to provide clear and accurate references on Jordan's ICT and ITES sector size and magnitude.
Similar to Analysis of Irish Mortality Using Public Data Sources 2014-2020 (20)
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.
Solution Architecture and Solution Estimation.pdfAlan McSweeney
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.
Analysis of the Numbers of Catholic Clergy and Members of Religious in Irelan...Alan McSweeney
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.
IT Architecture’s Role In Solving Technical Debt.pdfAlan McSweeney
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.
Solution Architecture And Solution SecurityAlan McSweeney
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.
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Alan McSweeney
This paper describes how technologies such as data pseudonymisation and differential privacy technology enables access to sensitive data and unlocks data opportunities and value while ensuring compliance with data privacy legislation and regulations.
Data Privatisation, Data Anonymisation, Data Pseudonymisation and Differentia...Alan McSweeney
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.
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 Architecture And (Robotic) Process Automation SolutionsAlan McSweeney
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)
Data Profiling, Data Catalogs and Metadata HarmonisationAlan McSweeney
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.
Analysis of Decentralised, Distributed Decision-Making For Optimising Domesti...Alan McSweeney
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.
Operational Risk Management Data Validation ArchitectureAlan McSweeney
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
Data Integration, Access, Flow, Exchange, Transfer, Load And Extract Architec...Alan McSweeney
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
Review of Information Technology Function Critical Capability ModelsAlan McSweeney
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
Critical Review of Open Group IT4IT Reference ArchitectureAlan McSweeney
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
This presentation describes systematic, repeatable and co-ordinated approach to agile solution architecture and design. It is intended to describe a set of practical steps and activities embedded within a framework to allow an agile method to be adopted and used for solution design and delivery. This approach ensures consistency in the assessment of solution design options and in subsequent solution design and solution delivery activities. This process leads to the rapid design and delivery of realistic and achievable solutions that meet real solution consumer needs. The approach provides for effective solution decision-making. It generates options and results quickly and consistently. Implementing a framework such as this provides for the creation of a knowledgebase of previous solution design and delivery exercises that leads to an accumulated body of knowledge within the organisation.
Solution Architecture and Solution AcquisitionAlan McSweeney
This describes a systematised and structured approach to solution acquisition or procurement that involves solution architecture from the start. This allows the true scope of both the required and subsequently acquired solution are therefore fully understood. By using such an approach, poor solution acquisition outcomes are avoided.
Solution architecture provides the structured approach to capturing all the cost contributors and knowing the true solution scope.
There is more packaged/product/service-based solution acquisition activity. There is an increasing trend of solutions hosted outside the organisation. Meanwhile solution acquisition outcomes are poor and getting worse.
Poor solution acquisition has long-term consequences and costs.
The to-be-acquired solution needs to operate in and co-exist with an existing solution topography and the solution acquisition process needs to be aware of and take account of this wider solution topography. Cloud-based or externally hosted and provided solutions do not eliminate the need for the solution to exist within the organisation solution topography.
Strategic misrepresentation in solution acquisition is the deliberate distortion or falsification of information relating to solution acquisition costs, complexity, required functionality, solution availability, resource availability, time to implement in order to get solution acquisition approval. Strategic misrepresentation is very real and its consequences can be very damaging.
Solution architecture has the skills and experience to define the real scope of the solution being acquired. An effective structured solution acquisition process, well-implemented and consistently applied, means dependable and repeatable solution acquisition and successful outcomes.
Creating A Business Focussed Information Technology StrategyAlan McSweeney
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.
Describing the Organisation Data LandscapeAlan McSweeney
Outlines an Approach to Describing the Organisation Data Landscape to Assist with Data Transformation Analysis and Planning
The Data Landscape is a representation of the organisation’s data entities and their relationships, interfaces and data flows. Data entities are data asset components that perform data-related functions, from data storage to data transfer and data processing within the Data Landscape.
The objective of developing a Data Landscape model is to define an approach for formally and exactly defining the operation and use of data at a high-level within the organisation and to plan for future changes. It allows the enterprise data fabric to be defined and modelled.
Creating a data landscape view is important as data underpins the operation of information technology solutions and business processes. Data breathes life into solutions as its flows through the organisation. The optimum and most cost-effective design of the data landscape is therefore important. Similarly, solutions that are developed or acquired and deployed on the data landscape
The nature of the organisation data landscape is changing as organisations are undergoing a data transformation.
Shadow IT And The Failure Of IT ArchitectureAlan McSweeney
The continued existence and growth of shadow IT gives IT architecture the opportunity show leadership. IT architecture can be the gateway for business IT solution requirements, from initial solution concept through to solution realisation.
Shadow IT is 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. There are many aspects of shadow IT:
• Shadow Projects
• Shadow Data
• Shadow Sourcing
• Shadow Development
• Shadow Solutions
• Shadow Support Arrangements
Shadow IT takes many forms and types
1. CUST – customised solution developed by a third-party
2. DEV – personal devices used to access business systems or authenticate access to hosted solutions used for business
3. DIY – end-user computing application developed by the business
4. HOME – organisation data sent to home devices to be worked on
5. MSG – public messaging and data exchange platforms
6. OPEN – open-source software used as a stand-alone solution or incorporated into other solutions
7. OUT – outsourced service solution
8. PROD – software product acquired by the business and implemented on organisation infrastructure
9. PUB – accessing organisation applications and data using public devices or networks
10. STOR – public data storage and exchange platforms
11. SVC – hosted software solution
Uncontrolled shadow IT represents a real risk to organisations. The experience from previous shadow IT examples is that they have resulted in real financial losses. IT architecture can and should take the lead in implementing structures and processes to mitigate risks while taking maximising the benefits of shadow IT.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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).
Analysis of Irish Mortality Using Public Data Sources 2014-2020
1. Analysis of Irish
Mortality Using
Public Data Sources
2014-2020
An analysis of patterns and trends in
mortality in Ireland using public death notice
information
Alan McSweeney
February 2021
http://ie.linkedin.com/in/alanmcsweeney
2. Contents
Introduction ........................................................................................................................................................4
Notes on RIP.ie Web Site Data ............................................................................................................................4
Mortality Analysis................................................................................................................................................7
Deaths By Day of the Week .............................................................................................................................7
Daily Deaths....................................................................................................................................................7
Deaths By Week ..............................................................................................................................................9
Deaths by Month ...........................................................................................................................................11
Deaths by Quarter..........................................................................................................................................12
Deaths by County ..........................................................................................................................................13
Names............................................................................................................................................................22
Deaths by Surnames...................................................................................................................................22
Deaths by First Names...............................................................................................................................23
Deaths of Clergy and Members of Religious Orders..........................................................................................26
3. Analysis of Irish Mortality Using Public Data Sources 2014-2020
Page 3
List of Figures
Figure 1 – Differences Between CSO and RIP.ie Mortality Data............................................................................5
Figure 2 – Deaths by Day of Week 2014 to 2020....................................................................................................7
Figure 3 – Daily Number of Deaths by Day Number From 2014 to 2020 ...............................................................8
Figure 4 – Daily Number of Deaths Aligned to Weekday From 2014 to 2020 .........................................................8
Figure 5 – Range of Deaths for 2014 to 2019 Overlaid With Deaths for 2020..........................................................9
Figure 6 – Deaths by Week 2014 to 2020.............................................................................................................10
Figure 7 – Deaths by Month 2014 to 2020 ...........................................................................................................12
Figure 8 – Deaths by Quarter 2014 to 2020 .........................................................................................................12
Figure 9 – Proportion of County Populations That Died 2014 to 2020..................................................................14
Figure 10 – Difference in County Death Rate in 2020 from the Average of 2014-2019...........................................15
Figure 11 – County Death Rates 2014.................................................................................................................18
Figure 12 – County Death Rates 2015.................................................................................................................19
Figure 13 – County Death Rates 2016.................................................................................................................19
Figure 14 – County Death Rates 2017.................................................................................................................20
Figure 15 – County Death Rates 2018.................................................................................................................20
Figure 16 – County Death Rates 2019.................................................................................................................21
Figure 17 – County Death Rates 2020.................................................................................................................21
Figure 18 – Proportions of the Top 100 Surnames in Death Notices 2014 to 2020 .................................................22
Figure 19 – Proportions of the Top 100 First Names in Death Notices 2014 to 2020 .............................................23
Figure 20 – Numbers of Deaths by Religious Title in Ireland 2014 to 2020...........................................................28
Figure 21 – Numbers of Deaths by Religious Title for all Locations 2014 to 2020 .................................................29
4. Analysis of Irish Mortality Using Public Data Sources 2014-2020
Page 4
Introduction
This document describes the use of published death notices on the web site www.rip.ie as a substitute to officially
published mortality statistics.
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.
Notes on RIP.ie Web Site Data
This analysis uses data from RIP.ie for the years 2014 to 2020.
RIP.ie is a web site that contains death notices that are also published in newspapers. The information available on
a death on RIP.ie includes:
• Name of deceased
• Six-digit reference
• Date the death notice was published
• Date of death
• County of the deceased
• Town of the deceased (this is frequently blank)
• Text of death notice
The information must be scraped from the web pages containing death notices displayed by the site. This extracted
information has then to be parsed to make it usable.
The data is informal. RIP.ie is the only publicly available source of detailed current death data in Ireland.
However, the information needs to be filtered to make it usable. This processing is not exact and so the exclusion of
deaths that have occurred outside Ireland may be problematic.
There information is not present in the RIP.ie data that would be useful such as age at death and sex. Sex could be
inferred from the name of deceased and the use of pronouns in the text of the notice.
This results in inevitable differences between the officially published data and that extracted from RIP.ie. The
RIP.ie data has a number of advantages over the officially published data: it is available immediately and contains
a much greater level of granularity.
The following schematic illustrates the key differences between the CSO and RIP.ie datasets.
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Figure 1 – Differences Between CSO and RIP.ie Mortality Data
The CSO data is taken from deaths registered in Ireland. This includes anyone who died in Ireland. The reporting
of deaths is mandatory so the data can be assumed to be very accurate. A small number of deaths may occur that
may not be registered but this is likely to be very small.
The use of RIP.ie is not mandatory for deaths. The RIP.ie data has a number of issues including:
• There are multiple occurrences of the same notice with slightly different values for County and Town.
• There are duplicate notices for the same death with different reference numbers.
• There are notices for deaths that occur in Northern Ireland.
• There are notices for deaths of Irish people that occurred outside Ireland other than Northern Ireland. These
notices can apply to deaths of Irish people who died while outside Ireland temporarily or Irish people who are
resident outside Ireland long-term.
• Death notices may not be published for foreign nationals who have died in Ireland, including both visitors,
tourists and temporary workers but whose deaths will appear in the CSO data.
• Death notices are frequently not published for the deaths of very young children but whose deaths will appear
in the CSO data.
• Some deaths will not have a death notice but these deaths will appear in the CSO data.
• Deaths for which a notice is published in one year may have occurred in a prior year. In some cases, the gap
can be several years.
• There are data errors. For example, the date of death in some cases is after the date of publication of the death
notice. In this analysis death notices were extracted up to the end of January 2021 to cater for this. There may
be deaths that occurred in 2020 that were not published until after that date.
• Some death notices have a date published value but no date of death value. In these cases, I have assumed
that the date is death is the date of publication.
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• A small number of notices refer to more than one death.
The following table summarises the results of the processing of the RIP.ie data that was performed to address these
issues.
Year RIP.ie Death
Notices Published
Calculated
Individual Deaths
Reduction
2014 34,564 28,351 6,213
2015 37,168 29,385 7,783
2016 39,473 30,499 8,974
2017 40,239 30,313 9,926
2018 42,003 31,158 10,845
2019 42,459 31,163 11,296
2020 44,074 32,090 11,984
Total 279,980 212,959 67,021
The following table summarises the differences between the RIP.ie and CSO VSQ04 time series1 for the numbers of
deaths in the four quarters for the years 2014 and 2020 and overall for each year. At the time this analysis was
generated (Feb 2021), the CSO data is only available up to 2020Q2.
Year 2014 2015 2016 2017 2018 2019 2020
Quarter 1
CSO 8,110 8,604 8,609 9,067 9,278 8,618 8,674
RIP.ie 7,854 8,249 8,462 8,436 9,227 8,257 8,459
Difference 256 355 147 631 51 361 215
Difference % 3.26% 4.30% 1.74% 7.48% 0.55% 4.37% 2.54%
Quarter 2
CSO 7,197 7,565 7,697 7,315 7,592 7,519 8,582
RIP.ie 6,667 7,019 7,129 6,959 7,305 7,558 8,477
Difference 530 546 568 356 287 -39 105
Difference % 7.95% 7.78% 7.97% 5.12% 3.93% -0.52% 1.24%
Quarter 3
CSO 7,001 6,851 7,129 6,987 7,143 7,358
RIP.ie 6,581 6,689 6,921 6,877 6,976 6,959 7,060
Difference 420 162 208 110 167 399
Difference % 6.38% 2.42% 3.01% 1.60% 2.39% 5.73%
Quarter 4
CSO 6,787 6,932 6,955 7,115 7,103 7,639
RIP.ie 7,249 7,428 7,987 8,041 7,650 8,389 8,094
Difference -462 -496 -1,032 -926 -547 -750
Difference % -6.37% -6.68% -12.92% -11.52% -7.15% -8.94%
Annual
CSO 29,095 29,952 30,390 30,484 31,116 31,134
RIP.ie 28,351 29,385 30,499 30,313 31,158 31,163 32,090
Difference 744 567 -109 171 -42 -29
Difference % 2.62% 1.93% -0.36% 0.56% -0.13% -0.09%
Averages
Average CSO
Deaths Per Day
79.71 82.06 83.03 83.52 85.25 85.30
Average RIP.ie
Deaths Per Day
77.67 80.51 83.33 83.05 85.36 85.38 87.92
2016 and 2020 were leap years so for each of these years, there will be an additional day’s deaths added to the first
quarter and to the year total.
The two sets of numbers are generally quite close.
The implicit assumption here is that the CSO death statistics are completely accurate.
1 VSQ04 Total Births, Deaths and Marriages Registered https://data.cso.ie/table/VSQ04
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Mortality Analysis
Deaths By Day of the Week
The following chart shows the recorded number of deaths by day of week.
Figure 2 – Deaths by Day of Week 2014 to 2020
The recorded number of deaths is generally the same across all days of the week. In general, about one seventh of
the deaths occur on each of the days of the week. For some but not all years the proportion of deaths on Saturdays
and Sundays is slightly lower than on other days.
The day of the week is potentially important as each year starts on different weekdays.
Year Start Day
2014 Wed
2015 Thu
2016 Fri
2017 Sun
2018 Mon
2019 Tue
2020 Wed
Daily Deaths
The following chart shows the number of deaths per day. In this chart the days for each year are not aligned to
weekdays.
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Figure 3 – Daily Number of Deaths by Day Number From 2014 to 2020
The following charts shows the same daily number of deaths with the days for each year aligned to the same
weekday.
Figure 4 – Daily Number of Deaths Aligned to Weekday From 2014 to 2020
At a superficial level, there is no difference with the pattern of deaths when the series for each starts on the same
weekday or not.
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The increase is recorded deaths from around Thu 26 Mar 2020 to around Fri 1 May 2020 reflects the increased
number of deaths due to COVID-19.
Outside this interval the daily variation in number of deaths. The following chart shows the range of daily numbers
of death in the years 2014 to 2019 and the deaths for 2020.
Figure 5 – Range of Deaths for 2014 to 2019 Overlaid With Deaths for 2020
There are many reasons why the number of deaths varies throughout the year and between years, such as:
• Changes in mortality rates
• Change in the size of the population
• Changes in the age profile of the population
• Seasonal factors that vary both cyclically within and between years such as seasonal influenza and
temperature
Deaths By Week
The following chart shows the deaths by week. The values for the first and last weeks are lower than for other
weeks as these are generally partial weeks.
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Figure 7 – Deaths by Month 2014 to 2020
Again, this shows excess deaths for April 2020. This does not occur consistently for the other months of 2020.
Deaths by Quarter
The following chart shows the deaths by month from 2014 to 2020.
Figure 8 – Deaths by Quarter 2014 to 2020
As before, this shows excess deaths for second quarter of 2020. This does not occur consistently for the other
quarters of 2020.
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Deaths by County
The following table shows the numbers of deaths by county from 2014 to 2020.
County 2014 2015 2016 2017 2018 2019 2020
Carlow 379 394 387 412 470 516 495
Cavan 531 551 554 573 671 637 753
Clare 861 899 913 815 1,013 944 899
Cork 3,351 3,460 3,734 3,647 3,957 3,976 4,012
Donegal 1,176 1,162 1,221 1,310 1,330 1,318 985
Dublin 6,813 7,061 6,853 7,378 8,394 8,406 8,536
Galway 1,560 1,520 1,638 1,576 1,659 1,715 1,622
Kerry 1,171 1,273 1,318 1,235 1,259 1,244 1,339
Kildare 743 775 833 789 850 876 994
Kilkenny 552 663 753 675 732 674 732
Laois 433 490 505 503 425 427 487
Leitrim 281 311 325 321 302 314 308
Limerick 1,490 1,441 1,521 1,461 1,387 1,364 1,448
Longford 286 313 330 303 297 310 307
Louth 716 795 740 811 793 866 900
Mayo 1,194 1,206 1,283 1,276 1,157 1,091 1,096
Meath 744 779 834 809 772 732 868
Monaghan 376 363 391 418 373 389 494
Offaly 451 505 515 486 464 473 522
Roscommon 535 597 600 523 481 465 544
Sligo 515 526 548 537 471 475 534
Tipperary 1,270 1,362 1,505 1,345 1,189 1,186 1,250
Waterford 785 761 856 782 702 743 753
Westmeath 595 573 610 596 437 438 540
Wexford 834 865 974 992 951 906 958
Wicklow 709 740 758 740 622 678 714
Total 28,351 29,385 30,499 30,313 31,158 31,163 32,090
The following chart shows the proportion of the population of counties that died from 2014 to 2020.
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Figure 9 – Proportion of County Populations That Died 2014 to 2020
The following charts shows the difference in the death rate by county for 2020 compared to the average of the
preceding six years.
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Figure 10 – Difference in County Death Rate in 2020 from the Average of 2014-2019
The proportion of population that died in 2020 is lower than the average for the preceding six years for the
following counties:
Clare
Donegal
Galway
Leitrim
Limerick
Longford
Mayo
Roscommon
Tipperary
Waterford
Westmeath
Wicklow
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These proportions are based on the following county population estimated from 2014 to 2020 produced by the
CSO2. These are estimates and may not be accurate. So any information based on these estimates may not be
accurate.
There are wide variations in the proportions of county populations that died from 2014 to 2020. Kildare and Meath
have consistently low proportions. Leitrim and Mayo have consistently high proportions.
County 2014 2015 2016 2017 2018 2019 2020
Carlow 55,156 55,430 57,040 57,628 58,284 59,023 59,535
Cavan 73,379 73,357 75,985 76,870 77,820 78,579 78,806
Clare 118,149 118,555 118,063 119,219 120,540 121,195 122,001
Cork 532,339 531,940 540,635 545,684 552,184 557,386 565,678
Donegal 160,672 160,624 158,615 160,462 162,445 164,029 164,504
Dublin 1,286,409 1,315,318 1,335,861 1,350,018 1,370,508 1,395,618 1,417,654
Galway 246,511 247,128 253,444 256,062 259,238 262,603 266,091
Kerry 147,117 147,008 144,348 145,696 147,431 148,821 151,035
Kildare 215,352 217,909 223,059 226,425 229,922 233,593 236,820
Kilkenny 96,497 96,975 98,672 99,688 100,824 102,102 102,988
Laois 82,832 83,207 85,011 86,096 87,345 88,349 88,078
Leitrim 32,084 32,075 32,143 32,517 32,919 33,240 33,336
Limerick 191,832 192,492 194,013 195,913 198,084 199,160 200,484
Longford 40,284 40,466 41,182 41,707 42,313 42,799 42,668
Louth 126,202 127,701 128,956 130,902 132,924 135,046 136,911
Mayo 130,236 130,561 129,320 130,656 132,276 133,994 135,773
Meath 189,869 192,124 196,259 199,221 202,297 205,527 208,366
Monaghan 61,038 61,019 61,792 62,512 63,284 63,901 64,086
Offaly 78,630 78,986 78,330 79,329 80,480 81,405 81,156
Roscommon 64,346 64,507 64,750 65,419 66,230 67,090 67,981
Sligo 64,841 64,821 64,767 65,521 66,331 66,978 67,172
Tipperary 161,791 161,791 161,791 161,791 161,791 161,791 161,791
Waterford 114,752 115,321 115,674 116,865 118,196 119,695 120,734
Westmeath 88,081 88,481 88,853 89,987 91,292 92,341 92,059
Wexford 147,100 147,829 149,785 151,328 153,051 154,991 156,337
Wicklow 139,944 141,606 142,625 144,777 147,013 149,360 151,424
Total 4,645,443 4,687,231 4,740,973 4,792,293 4,855,022 4,918,616 4,973,468
The proportions of the county populations that died from 2014 to 2020 are contained in the following table.
County 2014 2015 2016 2017 2018 2019 2020
Carlow 0.69% 0.71% 0.68% 0.71% 0.81% 0.87% 0.83%
Cavan 0.72% 0.75% 0.73% 0.75% 0.86% 0.81% 0.96%
Clare 0.73% 0.76% 0.77% 0.68% 0.84% 0.78% 0.74%
Cork 0.63% 0.65% 0.69% 0.67% 0.72% 0.71% 0.71%
Donegal 0.73% 0.72% 0.77% 0.82% 0.82% 0.80% 0.60%
Dublin 0.53% 0.54% 0.51% 0.55% 0.61% 0.60% 0.60%
Galway 0.63% 0.62% 0.65% 0.62% 0.64% 0.65% 0.61%
Kerry 0.80% 0.87% 0.91% 0.85% 0.85% 0.84% 0.89%
Kildare 0.35% 0.36% 0.37% 0.35% 0.37% 0.38% 0.42%
2 DHA06 Population: Denominator Data https://data.cso.ie/table/DHA06
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County 2014 2015 2016 2017 2018 2019 2020
Kilkenny 0.57% 0.68% 0.76% 0.68% 0.73% 0.66% 0.71%
Laois 0.52% 0.59% 0.59% 0.58% 0.49% 0.48% 0.55%
Leitrim 0.88% 0.97% 1.01% 0.99% 0.92% 0.94% 0.92%
Limerick 0.78% 0.75% 0.78% 0.75% 0.70% 0.68% 0.72%
Longford 0.71% 0.77% 0.80% 0.73% 0.70% 0.72% 0.72%
Louth 0.57% 0.62% 0.57% 0.62% 0.60% 0.64% 0.66%
Mayo 0.92% 0.92% 0.99% 0.98% 0.87% 0.81% 0.81%
Meath 0.39% 0.41% 0.42% 0.41% 0.38% 0.36% 0.42%
Monaghan 0.62% 0.59% 0.63% 0.67% 0.59% 0.61% 0.77%
Offaly 0.57% 0.64% 0.66% 0.61% 0.58% 0.58% 0.64%
Roscommon 0.83% 0.93% 0.93% 0.80% 0.73% 0.69% 0.80%
Sligo 0.79% 0.81% 0.85% 0.82% 0.71% 0.71% 0.79%
Tipperary 0.78% 0.84% 0.93% 0.83% 0.73% 0.73% 0.77%
Waterford 0.68% 0.66% 0.74% 0.67% 0.59% 0.62% 0.62%
Westmeath 0.68% 0.65% 0.69% 0.66% 0.48% 0.47% 0.59%
Wexford 0.57% 0.59% 0.65% 0.66% 0.62% 0.58% 0.61%
Wicklow 0.51% 0.52% 0.53% 0.51% 0.42% 0.45% 0.47%
The proportions of county populations that die are related to the age profile of the county and the proportions in
older age groups3. The following table shows the proportions of county populations aged 85 and older compared
with proportions of county populations that died in 2020.
The values for the counties Kildare and Meath (lowest county death rates) and May and Leitrim (highest county
death rates) in 2020 are highlighted.
County in
Descending Age
Order
85 Years and
Over
Age Weighting
Rank
Age Weighting
Rank
County in
Descending
Proportion of
Population Died
Proportion of
Population Died
Kildare 1.06% 1 2 Meath 0.4166%
Laois 1.17% 2 4 Kildare 0.4197%
Meath 1.22% 3 1 Wicklow 0.4715%
Westmeath 1.36% 4 5 Laois 0.5529%
Offaly 1.45% 5 11 Westmeath 0.5866%
Louth 1.46% 6 12 Donegal 0.5988%
Wicklow 1.47% 7 3 Dublin 0.6021%
Longford 1.49% 8 15 Galway 0.6096%
Dublin 1.56% 9 7 Wexford 0.6128%
Carlow 1.59% 10 23 Waterford 0.6237%
Cork 1.64% 11 13 Offaly 0.6432%
Wexford 1.64% 12 9 Louth 0.6574%
Limerick 1.65% 13 16 Cork 0.7092%
Waterford 1.70% 14 10 Kilkenny 0.7108%
Monaghan 1.76% 15 18 Longford 0.7195%
Cavan 1.77% 16 26 Limerick 0.7223%
Galway 1.78% 17 8 Clare 0.7369%
Donegal 1.85% 18 6 Monaghan 0.7708%
3 This does not take into account the slight regional variations in life expectancy that exist in Ireland. See Table 4, Life
expectancy by sex, age, NUTS3 region and year of Irish Life Tables 2015-2017
https://www.cso.ie/en/releasesandpublications/er/ilt/irishlifetablesno172015-2017/.
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Clare 1.86% 19 17 Tipperary 0.7726%
Kilkenny 1.86% 20 14 Sligo 0.7950%
Kerry 1.93% 21 24 Roscommon 0.8002%
Tipperary 2.01% 22 19 Mayo 0.8072%
Sligo 2.02% 23 20 Carlow 0.8314%
Mayo 2.23% 24 22 Kerry 0.8865%
Leitrim 2.40% 25 25 Leitrim 0.9239%
Roscommon 2.46% 26 21 Cavan 0.9555%
The following charts show the proportions of the county populations that died in the years 2014 to 2020.
Figure 11 – County Death Rates 2014
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Figure 12 – County Death Rates 2015
Figure 13 – County Death Rates 2016
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Figure 14 – County Death Rates 2017
Figure 15 – County Death Rates 2018
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Figure 16 – County Death Rates 2019
Figure 17 – County Death Rates 2020
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Names
This section looks at the first and last names contained in the death notices.
Deaths by Surnames
The following diagram illustrates the proportions of the top 100 surnames contained in the death notices from 2014
to 2020.
Figure 18 – Proportions of the Top 100 Surnames in Death Notices 2014 to 2020
The information in this chart is contained in the following table.
Murphy 1.71% Gallagher 0.42% Kenny 0.31% Buckley 0.25% Lyons 0.20%
Kelly 1.32% Smith 0.41% Moore 0.31% Browne 0.25% Moloney 0.20%
Byrne 1.08% Fitzgerald 0.40% Keane 0.31% Ward 0.25% Cunningham 0.20%
O'Brien 1.06% Carroll 0.39% Brady 0.30% Sweeney 0.25% Sheehan 0.19%
Walsh 1.05% Flynn 0.38% Reilly 0.30% Maguire 0.24% Higgins 0.19%
Ryan 1.01% Power 0.38% Duffy 0.30% Smyth 0.24% Flanagan 0.19%
O'Sullivan 0.92% O'Connell 0.38% Moran 0.29% Butler 0.23% Barrett 0.19%
O'Connor 0.91% Kavanagh 0.38% Hayes 0.29% McDonnell 0.23% Curran 0.19%
Doyle 0.72% Farrell 0.37% Barry 0.29% McCormack 0.23% Cahill 0.19%
McCarthy 0.66% Collins 0.36% Roche 0.28% Griffin 0.22% McLoughlin 0.19%
O'Neill 0.60% Connolly 0.36% O'Keeffe 0.28% Cronin 0.22% McDonagh 0.19%
Lynch 0.57% Quinn 0.35% Fitzpatrick 0.27% Egan 0.22% Mooney 0.19%
Dunne 0.50% Clarke 0.34% Foley 0.27% Delaney 0.22% O'Rourke 0.18%
Murray 0.50% O'Donnell 0.34% Casey 0.27% Hogan 0.22% Crowley 0.18%
Brennan 0.48% O'Leary 0.33% O'Mahony 0.27% Hughes 0.21% O'Driscoll 0.18%
Burke 0.48% Whelan 0.33% Martin 0.26% O'Donoghue 0.21% McDermott 0.18%
Daly 0.45% McGrath 0.33% O'Callaghan 0.26% Hickey 0.21% Molloy 0.18%
O'Reilly 0.44% Healy 0.32% McMahon 0.26% White 0.21% O'Toole 0.18%
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Nolan 0.44% Doherty 0.32% Maher 0.26% Cullen 0.21% King 0.18%
Kennedy 0.43% O'Shea 0.32% O'Donovan 0.25% Keogh 0.21% Corcoran 0.17%
These 100 surnames account for 36.43% of all deaths.
This illustrates an historical cultural homogeneity and the cultural lag that is to be expected from the generally
older age cohorts that dominate the number of deaths.
Deaths by First Names
The following diagram illustrates the proportions of the top 100 first names contained in the death notices from
2014 to 2020.
Figure 19 – Proportions of the Top 100 First Names in Death Notices 2014 to 2020
The information in this chart is contained in the following table.
Mary 5.82% Peter 0.85% Seamus 0.48% Anna 0.32% Susan 0.19%
John 5.15% Teresa 0.80% Ellen 0.47% Nancy 0.31% Hugh 0.19%
Patrick 4.58% Martin 0.78% Gerard 0.47% Alice 0.31% Eamonn 0.19%
Michael 3.66% Patricia 0.78% Josephine 0.46% Stephen 0.30% Geraldine 0.19%
Margaret 2.99% Joan 0.73% Carmel 0.45% Christina 0.30% Agnes 0.19%
Thomas 2.88% Edward 0.73% Rita 0.44% Bernard 0.30% Nuala 0.19%
James 2.51% Marie 0.70% Brian 0.44% Vincent 0.28% Andrew 0.18%
Kathleen 2.12% Nora 0.68% Helen 0.42% Donal 0.28% Eamon 0.18%
Elizabeth 1.52% Noel 0.64% Christopher 0.41% Gerry 0.27% Una 0.18%
Anne 1.47% Sheila 0.63% Robert 0.40% May 0.27% Eugene 0.18%
Joseph 1.46% David 0.62% Bernadette 0.38% Brigid 0.27% Desmond 0.18%
Eileen 1.30% Denis 0.58% Philomena 0.38% Phyllis 0.26% Hannah 0.18%
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William 1.28% Brendan 0.55% Jimmy 0.36% Philip 0.26% Julia 0.18%
Catherine 1.19% Bridie 0.54% Breda 0.36% Noreen 0.25% Charles 0.18%
Bridget 1.13% Maura 0.54% George 0.36% Maurice 0.22% Monica 0.18%
Sean 1.07% Daniel 0.53% Rose 0.35% Timothy 0.22% Evelyn 0.17%
Ann 0.97% Paul 0.52% Frances 0.34% Christy 0.21% Oliver 0.17%
Anthony 0.95% Kevin 0.49% Angela 0.34% Dermot 0.21% Jeremiah 0.17%
Maureen 0.95% Richard 0.49% Sarah 0.32% Theresa 0.21% Laurence 0.17%
Francis 0.87% Liam 0.49% Pauline 0.32% Matthew 0.20% Veronica 0.17%
These 100 first names account for 73.38% of all deaths.
As before, this illustrates a cultural homogeneity that is to be expected from the generally older age cohorts that
dominate deaths. These first names can be compared with the current top baby names4. The top male name John
represented in death notices is currently at position 26. The top female name Mary is currently at position 82.
Boys' Name Rank in 2020 Rank In Death
Notices
Girls' Names Rank in 2020 Rank In Death
Notices
Jack 1 Not present Grace 1 233
James 2 7 Fiadh 2 Not present
Noah 3 1664 Emily 3 192
Daniel 4 36 Sophie 4 618
Conor 5 199 Ava 5 1232
Finn 6 921 Amelia 6 724
Liam 7 40 Ella 7 453
Fionn 8 883 Hannah 7 92
Harry 9 104 Lucy 9 196
Charlie 10 111 Mia 10 1232
Cillian 11 803 Olivia 11 402
Adam 12 392 Lily 12 145
Darragh 12 463 Ellie 13 420
Luke 14 224 Anna 14 61
Rian 15 1115 Emma 14 343
Oisin 16 687 Eabha 16 Not present
Michael 17 4 Chloe 17 803
Tadhg 18 430 Sophia 18 1034
Thomas 19 6 Molly 19 181
Sean 20 16 Saoirse 20 1232
Alex 21 364 Sadie 21 170
Patrick 22 3 Evie 22 2229
Jamie 23 440 Kate 23 215
Cian 24 564 Aoife 24 468
Oliver 25 97 Freya 24 Not present
John 26 2 Isla 26 2229
Bobby 27 338 Caoimhe 27 1115
Dylan 28 618 Holly 28 1115
Leo 28 147 Robyn 29 1115
Ryan 30 587 Katie 30 227
Oscar 31 1034 Sarah 30 59
Ben 32 329 Roisin 32 334
4 Irish Babies' Names 2020 https://www.cso.ie/en/releasesandpublications/ep/p-
ibn/irishbabiesnames2020/babiesnames2020tables/
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Boys' Name Rank in 2020 Rank In Death
Notices
Girls' Names Rank in 2020 Rank In Death
Notices
David 33 31 Alice 33 63
Mason 34 2229 Ruby 34 541
Theo 34 850 Sofia 35 Not present
Tommy 36 Not present Cara 36 1034
Jacob 37 1388 Ada 37 587
Callum 38 850 Eva 37 260
Matthew 39 80 Isabelle 39 1388
Alexander 40 440 Sadhbh 39 1664
Alfie 41 453 Bonnie 41 1388
Max 42 921 Erin 41 1034
Tom 43 Not present Willow 43 Not present
Ollie 44 541 Zoe 44 850
Aaron 45 420 Millie 45 747
Jake 46 772 Clodagh 46 641
Donnacha 47 1664 Leah 46 772
Ethan 47 1115 Ciara 48 440
Evan 49 664 Charlotte 49 353
Benjamin 50 687 Emilia 50 1664
Senan 51 554 Isabella 50 488
William 51 13 Rosie 52 317
Sam 53 343 Annie 53 Not present
Shay 53 413 Eve 54 641
Logan 55 2229 Maya 54 Not present
Joshua 56 921 Layla 56 Not present
Nathan 57 709 Maisie 57 227
Kai 58 Not present Sienna 57 2229
Archie 59 803 Jessica 59 747
Joseph 59 11 Ailbhe 60 1034
Jayden 61 1664 Clara 60 724
Luca 61 1664 Lauren 62 921
Billy 63 101 Harper 63 Not present
Arthur 64 152 Abigail 64 972
Danny 65 141 Faye 65 1664
Theodore 66 1388 Amber 66 2229
Samuel 67 296 Aoibhin 67 2229
Cathal 68 275 Mila 67 2229
Lucas 68 2229 Maria 69 138
Aidan 70 119 Zara 70 1388
Freddie 71 Not present Elizabeth 71 9
Rory 72 329 Rose 72 56
Robert 73 50 Aria 73 2229
Eoin 74 319 Alannah 74 2229
Frankie 75 Not present Bella 74 664
Leon 75 772 Ivy 74 772
Muhammad 77 Not present Daisy 77 687
Ruairi 77 1115 Julia 77 92
Eoghan 79 664 Meabh 77 1388
George 80 54 Amy 80 440
Isaac 81 921 Lara 80 2229
Louis 81 272 Mary 82 1
26. Analysis of Irish Mortality Using Public Data Sources 2014-2020
Page 26
Boys' Name Rank in 2020 Rank In Death
Notices
Girls' Names Rank in 2020 Rank In Death
Notices
Odhran 81 2229 Nina 83 641
Sonny 81 453 Penny 83 850
Kyle 85 1034 Mollie 85 386
Rian 86 1115 Luna 86 Not present
Shane 87 203 Niamh 86 402
Henry 88 116 Evelyn 88 96
Sean 88 16 Laura 88 218
Edward 90 26 Abbie 90 747
Martin 90 23 Cora 90 307
Mark 92 107 Rebecca 92 380
Andrew 93 87 Fiadh 93 Not present
Anthony 93 18 Heidi 93 1388
Kayden 93 Not present Croia 95 Not present
Odhran 93 2229 Elsie 95 269
Christopher 97 49 Hazel 95 346
Sebastian 97 1388 Pippa 95 2229
Hugo 99 618 Maeve 99 159
Joey 99 573 Paige 99 1664
The use of the name Jack as a proper name rather than as a diminutive for John is a relatively new phenomenon.
Deaths of Clergy and Members of Religious Orders
One additional possible insight that can be gained from an analysis of the mortality information from RIP.ie
relates to the deaths of clergy and members of Catholic religious orders.
The age profile of this population cohort is much older than the general population5.
These numbers can be simply obtained by analysing the name text strings included in death notices for one of the
following words:
5 Archdiocese of Dublin Projection of Position in 2030 https://www.dublindiocese.ie/wp-content/uploads/2016/01/Results-
meeting-unprotected.pdf September 2015.There are 386 priests in the Dublin Archdiocese aged under 75 and 33 aged from 75 to
80.The expected number of new joiners to the priesthood up to 2030 is 16.
DIOCESAN PRIEST AGE PROFILE 2013 https://www.catholicbishops.ie/wp-content/uploads/2014/05/2013-age-profile-
quick-report-revised-May-2014.pdf contains the following number and age profile of priests in all dioceses, including Northern
Ireland that are not included in this analysis. These numbers do not include retired priests.
Age Group Numbers of
Priests in All
Dioceses 2013
Proportion General
Population
Proportion 2013
25-34 32 1.55% 15.45%
35-44 214 10.35% 15.34%
45-54 480 23.22% 12.85%
55-64 519 25.11% 10.28%
65-74 561 27.14% 7.12%
75-84 231 11.18% 3.90%
85 + 30 1.45% 1.32%
Total 2,067
The General Population Proportion is derived from PEA07 - Estimated Population (Persons in April)
https://data.cso.ie/table/PEA07.
27. Analysis of Irish Mortality Using Public Data Sources 2014-2020
Page 27
"SR."
"SR "
"SISTER "
"MOTHER "
"FR "
"FR."
"FATHER"
"BR "
"BR."
"BROTHER"
"REV "
"REV."
"REVD "
"REVD."
"REVEREND"
"CANON "
"MONSIGNOR "
"MGR "
"MGR."
"BISHOP "
"CARDINAL "
The words “CANON “ and “BROTHER” can occur in surnames and must be filtered. The word “MOTHER “ can
occur in surnames and as a term of endearment and must also be filtered.
The title “CANON” and “REVEREND” can occur in both Catholic and Protestant religions. Filtering this is
subjective.
More than one of these titles can occur in the same notice such as: “Very Rev. Fr.” and “Very Reverend Canon”.
The titles are not be double counted. Where a deceased has more than one title, the first one is used.
The numbers of deaths in the various categories of religious titles from 2014 to 2020 are shown in the following
table.
Religions Title
(and its
Variants)
2014 2015 2016 2017 2018 2019 2020 Total
Sister 261 278 295 276 287 250 287 1,934
Father 118 117 116 122 111 128 163 875
Brother 28 31 25 30 25 21 21 181
Reverend 37 36 29 30 26 26 20 204
Canon 6 4 3 1 3 12 17 46
Monsignor 4 2 6 7 3 5 10 37
Bishop 3 4 3 1 4 4 3 22
Cardinal 0 0 0 1 0 0 0 1
Total 457 472 477 468 459 446 521 3,300
These numbers include active and retired clergy and members of religious orders. In the Catholic Church, priests
retire on the 30th of June following their 75th birthday. From age 66 to 75, priests work at a reduced level.
28. Analysis of Irish Mortality Using Public Data Sources 2014-2020
Page 28
Figure 20 – Numbers of Deaths by Religious Title in Ireland 2014 to 2020
This shows a decline in the numbers of Catholic religious. There is a noticeable increase in the number of deaths of
priests (title of Father) in 2020. This probably relates to increased deaths of retired priests and mirrors the increase
in nursing home deaths due to COVID 19.
The reduction in the numbers of religious orders reflects the diminishing influence and position of Catholic Church
in Irish society. There is limited information available on the numbers of religious6. The following table summarises
information on the numbers of Clergy from the 2016 census.
2011 2016
Total Number At Work 3,536 3,377
Average Age At Work 57.2 58.5
While this analysis is concerned with deaths in Ireland, a characteristic of Irish members of religious order is that
they frequently move outside Ireland. When the analysis is extended to include these categories that were
previously excluded, the numbers of deaths in the various sets of religious titles from 2014 to 2020 are shown in the
following table.
Religions Title
(and its
Variants)
2014 2015 2016 2017 2018 2019 2020 Total
Sister 272 296 309 293 305 268 313 2,056
Father 134 144 127 138 137 150 180 1,010
Brother 31 31 28 32 29 22 21 194
Reverend 37 42 26 33 28 27 31 224
Canon 7 6 5 5 6 16 9 54
Monsignor 6 3 11 9 6 8 13 56
Bishop 1 2 2 1 2 2 2 12
Cardinal 0 0 0 1 0 0 0 1
Total 488 524 508 512 513 493 569 3,607
6 EB068 - Average Age of Population Aged 15 Years and Over At Work 2011 to 2016 https://data.cso.ie/table/EB068
EZ054 - Population Aged 15 Years and Over At Work 2011 to 2016 https://data.cso.ie/table/EZ054
29. Analysis of Irish Mortality Using Public Data Sources 2014-2020
Page 29
This shows an additional 10% of deaths of clergy and members of religious orders over the numbers for Ireland
only.
Figure 21 – Numbers of Deaths by Religious Title for all Locations 2014 to 2020
30. Analysis of Irish Mortality Using Public Data Sources 2014-2020
Page 30
For more information, please contact:
http://ie.linkedin.com/in/alanmcsweeney