Video at: https://www.linkedin.com/video/live/urn:li:ugcPost:6705141260845412352/
In this talk, we will review some of the challenges related to Industry 4.0 or Factory of Future, and how can Artificial Intelligence help address them.
Examples include the use of semantic interoperability and integration to support the use of sensor collected data in decision making, the use of computer vision to identify deviations in the process and manage quality, and the use of predictive algorithms for device maintenance.
A Case Analysis on Involvement of Big Data during Natural Disaster and Pandem...YogeshIJTSRD
Big data is an upcoming technology and requires utmost care for an efficient and smooth implementation of the technology. In case of healthcare the most challenging part of big data is the privacy, data security, handling large volume of medical imaging data and data leakage. It can be useful to this sector when big data is made structured, relevant, smart and accessible and the managers should give importance to the strategic and business value of big data technology rather than only concentrating at the technological aspect of the implementation. The use of big data in natural disasters and pandemics helps to understand and make better decision with fast processing of the data that are collected through various sources such as social media, sensors and other internet activities. This paper tries to focus on effective involvement of Big Data in natural disaster and pandemic and also identify the current and future use of Big Data in health care sector. The paper identifies the critical aspects which are used for Big data implementation and describe ways to handle the challenges related to it. Mr. Bibin Mathew | Dr. Swati John "A Case Analysis on Involvement of Big Data during Natural Disaster and Pandemics and its Uses in the Health Care Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45049.pdf Paper URL: https://www.ijtsrd.com/management/other/45049/a-case-analysis-on-involvement-of-big-data-during-natural-disaster-and-pandemics-and-its-uses-in-the-health-care-sector/mr-bibin-mathew
In this advanced business analysis training session, you will learn Data Analytics Business Intelligence. Topics covered in this session are:
• What is Business Intelligence?
• Data / information / knowledge
• What is Data Analytics?
• What is Business Analytics?
• What is Big Data?
• Types of Data
• Types of Analytics
• What is Business Intelligence?
For more information, click here: https://www.mindsmapped.com/courses/business-analysis/advanced-business-analyst-training/
Predition Model for Stock Price on Big Data Analyticsijtsrd
Prediction in the stock market is very challenging in these days. Large datasets available from Twitter micro blogging platform and widely available stock market records. Machine learning was employ to conduct sentiment analysis of data and to estimate for future stock prices. The relation between sentiments and the stock value is to be determined. A comparative study of these algorithms Multiple linear Regression, Support Vector Machine and Artificial Neural Network are done. Thin Thin Swe | Phyu Phyu | Sandar Pa Pa Thein "Predition Model for Stock Price on Big Data Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26660.pdfPaper URL: https://www.ijtsrd.com/computer-science/database/26660/predition-model-for-stock-price-on-big-data-analytics/thin-thin-swe
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
Video at: https://www.linkedin.com/video/live/urn:li:ugcPost:6705141260845412352/
In this talk, we will review some of the challenges related to Industry 4.0 or Factory of Future, and how can Artificial Intelligence help address them.
Examples include the use of semantic interoperability and integration to support the use of sensor collected data in decision making, the use of computer vision to identify deviations in the process and manage quality, and the use of predictive algorithms for device maintenance.
A Case Analysis on Involvement of Big Data during Natural Disaster and Pandem...YogeshIJTSRD
Big data is an upcoming technology and requires utmost care for an efficient and smooth implementation of the technology. In case of healthcare the most challenging part of big data is the privacy, data security, handling large volume of medical imaging data and data leakage. It can be useful to this sector when big data is made structured, relevant, smart and accessible and the managers should give importance to the strategic and business value of big data technology rather than only concentrating at the technological aspect of the implementation. The use of big data in natural disasters and pandemics helps to understand and make better decision with fast processing of the data that are collected through various sources such as social media, sensors and other internet activities. This paper tries to focus on effective involvement of Big Data in natural disaster and pandemic and also identify the current and future use of Big Data in health care sector. The paper identifies the critical aspects which are used for Big data implementation and describe ways to handle the challenges related to it. Mr. Bibin Mathew | Dr. Swati John "A Case Analysis on Involvement of Big Data during Natural Disaster and Pandemics and its Uses in the Health Care Sector" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd45049.pdf Paper URL: https://www.ijtsrd.com/management/other/45049/a-case-analysis-on-involvement-of-big-data-during-natural-disaster-and-pandemics-and-its-uses-in-the-health-care-sector/mr-bibin-mathew
In this advanced business analysis training session, you will learn Data Analytics Business Intelligence. Topics covered in this session are:
• What is Business Intelligence?
• Data / information / knowledge
• What is Data Analytics?
• What is Business Analytics?
• What is Big Data?
• Types of Data
• Types of Analytics
• What is Business Intelligence?
For more information, click here: https://www.mindsmapped.com/courses/business-analysis/advanced-business-analyst-training/
Predition Model for Stock Price on Big Data Analyticsijtsrd
Prediction in the stock market is very challenging in these days. Large datasets available from Twitter micro blogging platform and widely available stock market records. Machine learning was employ to conduct sentiment analysis of data and to estimate for future stock prices. The relation between sentiments and the stock value is to be determined. A comparative study of these algorithms Multiple linear Regression, Support Vector Machine and Artificial Neural Network are done. Thin Thin Swe | Phyu Phyu | Sandar Pa Pa Thein "Predition Model for Stock Price on Big Data Analytics" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26660.pdfPaper URL: https://www.ijtsrd.com/computer-science/database/26660/predition-model-for-stock-price-on-big-data-analytics/thin-thin-swe
the influence of machine language and data science in the emerging worldijtsrd
The study describes the machine learning language with respect to big data sciences. The process of machine learning has evolved to have grown significantly to progress in information science. This progress has led to conquer different domains and are capable of solving myriad problems and upgrading the applicative properties. Hence, the present study is drafted to highlight the importance of machine learning process and language. Anitha. S "The Influence of Machine Language and Data Science in the Emerging World" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31907.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31907/the-influence-of-machine-language-and-data-science-in-the-emerging-world/anitha-s
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
This was first part of the presentation on "Road Map for Careers in Big Data" in Conjunction with Hortonworks/Aengus Rooney on 17th August 2016 in London. For those contemplating moving to Big Data from often Relational Background
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
Business Analytics and Optimization Introduction (part 2)Raul Chong
Technical introduction to Business Analytics and optimization. This is part 2. Part 1 can be found here: http://www.slideshare.net/rfchong/business-analytics-and-optimization-introduction
Big Data Update - MTI Future Tense 2014Hawyee Auyong
The Futures Group first wrote about the emerging phenomenon of Big Data in 2010 as it was about to enter the mainstream. It was envisaged that Big Data would create a demand for new skills (Google has identified statisticians as the “sexy job of the decade”) and generate new industries. This report updates on the industry value chain and business models for the data analytics industry, latest developments as well as the opportunities for Singapore.
TOWARDS BUILDING PEOPLE-CENTRIC AI FOR BUSINESS - THE LONG HAULBiplav Srivastava
Presentation given at New York University's Mini Conference -- AI in the Workplace: Future Directions in People Analytics, 2020;
Link: https://wp.nyu.edu/aiatwork/
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Certus Accelerate - Building the business case for why you need to invest in ...Certus Solutions
Becoming an analytically driven or cognitive business is a journey.
Businesses will be able to rapidly capitalize on new opportunities if they have invested in the foundations of their information management systems.
Do you want to understand the emerging new data-driven jobs? This presentation discusses the emerging roles of Data Science and Data Engineering, and how they are related to Business Intelligence and Big Data. We will talk about skills and background needed for the jobs, and what education and certification is important.
This was first part of the presentation on "Road Map for Careers in Big Data" in Conjunction with Hortonworks/Aengus Rooney on 17th August 2016 in London. For those contemplating moving to Big Data from often Relational Background
This presentation is an Introduction to the importance of Data Analytics in Product Management. During this talk Etugo Nwokah, former Chief Product Officer for WellMatch, covered how to define Data Analytics why it should be a first class citizen in any software organization
Business Analytics and Optimization Introduction (part 2)Raul Chong
Technical introduction to Business Analytics and optimization. This is part 2. Part 1 can be found here: http://www.slideshare.net/rfchong/business-analytics-and-optimization-introduction
Big Data Update - MTI Future Tense 2014Hawyee Auyong
The Futures Group first wrote about the emerging phenomenon of Big Data in 2010 as it was about to enter the mainstream. It was envisaged that Big Data would create a demand for new skills (Google has identified statisticians as the “sexy job of the decade”) and generate new industries. This report updates on the industry value chain and business models for the data analytics industry, latest developments as well as the opportunities for Singapore.
TOWARDS BUILDING PEOPLE-CENTRIC AI FOR BUSINESS - THE LONG HAULBiplav Srivastava
Presentation given at New York University's Mini Conference -- AI in the Workplace: Future Directions in People Analytics, 2020;
Link: https://wp.nyu.edu/aiatwork/
BA is used to gain insights that inform business decisions and can be used to automate and optimize business processes. Data-driven companies treat their data as a corporate asset and leverage it for a competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business, and an organizational commitment to data-driven decision-making.
Business analytics examples
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
Certus Accelerate - Building the business case for why you need to invest in ...Certus Solutions
Becoming an analytically driven or cognitive business is a journey.
Businesses will be able to rapidly capitalize on new opportunities if they have invested in the foundations of their information management systems.
Minggu-02 Big Data Business Model Maturity Index.pdfazkamuhammad11
Dalam rangka menyambur bisnis big data yang bisa dibelikan sebagai ambang penerapan sinergi yang memungkinkan big data berkecimpung pada brand clothing supermakepeace akan memberikan kunci sukses yang sangat ringan kepada rekan rekan yang mau terlibat terhadap brand supermakepeace ini, dengan itu kita bergerak secara konsiten=n denga brand cothing supermakepeace akan membuat kita masing2 berkecimpung di mirasa dan sekarang saya mengerjakan yang seharusnya tidak saya kerjakan karena dengan ini hidup terasa sangat hanya dengan mengantuk dengqan mengetik agar menemukan sedikit fb ads yang melanda kasih sayag dan dengan itu memvuat sayang;; dari pemerintah menjadi perintah alah siah botaaytn aaada acara berlingan iasng mata yang menebark
Better Living Through Analytics - Strategies for Data DecisionsProduct School
Data is king! Get ready to understand how a successful analytics team can empower managers from product, marketing, and other areas to make effective, data-driven decisions.
Louis Cialdella, a data scientist at ZipRecruiter, shared some case studies and successful strategies that he has used at ZipRecruiter as well as previous experiences. The purpose of this data talk was to enlighten people on how to make sure that analysts can successfully partner with other departments and get them the information they need to do great things.
Using Microsoft Excel in Your Next Internal and External Audit - Learning The...Jim Kaplan CIA CFE
Data analytics does not need to be difficult or time consuming to start and in this course, we will focus on the key learning blocks needed within Microsoft Excel to be an effective data analytic auditor or accountant. Using case studies and sample data / steps / macros, basic to intermediate Excel techniques will be discussed for completing a variety of analytics using specific audit program steps.
This program is in many ways a summary of all courses provided on the training calendar. Session highlights through case study instruction include learning how to:
Use a case study set of examples in payables, general ledger, and travel and entertainment spend to see how audit steps can be automated into analytics using Excel.
Discover how analytics can maximize the annual audit plan and better ensure focus is placed on organizational risk.
Use Excel as audit software, able to mimic practically all data analytic commands found normally in more expensive tools.
Get started with and quickly maximize Pivot Tables, turning them into effective data mining tools able to unearth almost any audit finding.
Learn to select a sample in Excel and consider scoring for improved hit rates and reduction of false positives.
Visualize and otherwise chart changes over time and/or other chart dimensions
Discover a key word analytic and the value of completing word and letter summaries.
Using analytics across the entire lifecycle from risk assessment, to planning, fieldwork, and reporting
The third webcast in this series focuses on ways to meet your health system’s specific needs and achieve a 360-degree view of your patients, processes, physicians, and costs without purchasing multiple, disparate solutions, and creating information silos.
Our speakers discuss their collective experience in working with organizations to create tailored platforms that provide convenient access to data collected by, and stored in, disparate clinical information systems and enabling that data to be securely used by users throughout the broader healthcare community. Actionable data – available to all users when they need it – serves as a foundation for analysis and decision-making aimed at improving how care is delivered.
You can find it online at http://www.informationbuilders.com/webevents/online/24637#sthash.RnwoH27x.dpuf
Netta Hollings (Programme Manager - Mental Health and Community Care) discusses how you can get the most out of the Maternity Services Data Set (MSDS) and the Child Health Data Sets.
The data sets provide comparative, mother and child-centric data that will be used to improve clinical quality and service efficiency; and to commission services in a way that improves health and reduce inequalities.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
Though the term machine learning has become very visible in
the popular press over the past few years—making it appear to be the newest shiny object—the technology has actually been
in use for decades. In fact, machine learning algorithms such as decision trees are already in use by many organizations for predictive analytics.
how i managed to Develop a Analytics story for services about 4 years back. Contains
Maturity Model, Business Potential, Services Structures Areas that analytics can be applied to
20150108 create time stamp
About Potato, The scientific name of the plant is Solanum tuberosum (L).Christina Parmionova
The potato is a starchy root vegetable native to the Americas that is consumed as a staple food in many parts of the world. Potatoes are tubers of the plant Solanum tuberosum, a perennial in the nightshade family Solanaceae. Wild potato species can be found from the southern United States to southern Chile
Synopsis (short abstract) In December 2023, the UN General Assembly proclaimed 30 May as the International Day of Potato.
RFP for Reno's Community Assistance CenterThis Is Reno
Property appraisals completed in May for downtown Reno’s Community Assistance and Triage Centers (CAC) reveal that repairing the buildings to bring them back into service would cost an estimated $10.1 million—nearly four times the amount previously reported by city staff.
Combined Illegal, Unregulated and Unreported (IUU) Vessel List.Christina Parmionova
The best available, up-to-date information on all fishing and related vessels that appear on the illegal, unregulated, and unreported (IUU) fishing vessel lists published by Regional Fisheries Management Organisations (RFMOs) and related organisations. The aim of the site is to improve the effectiveness of the original IUU lists as a tool for a wide variety of stakeholders to better understand and combat illegal fishing and broader fisheries crime.
To date, the following regional organisations maintain or share lists of vessels that have been found to carry out or support IUU fishing within their own or adjacent convention areas and/or species of competence:
Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR)
Commission for the Conservation of Southern Bluefin Tuna (CCSBT)
General Fisheries Commission for the Mediterranean (GFCM)
Inter-American Tropical Tuna Commission (IATTC)
International Commission for the Conservation of Atlantic Tunas (ICCAT)
Indian Ocean Tuna Commission (IOTC)
Northwest Atlantic Fisheries Organisation (NAFO)
North East Atlantic Fisheries Commission (NEAFC)
North Pacific Fisheries Commission (NPFC)
South East Atlantic Fisheries Organisation (SEAFO)
South Pacific Regional Fisheries Management Organisation (SPRFMO)
Southern Indian Ocean Fisheries Agreement (SIOFA)
Western and Central Pacific Fisheries Commission (WCPFC)
The Combined IUU Fishing Vessel List merges all these sources into one list that provides a single reference point to identify whether a vessel is currently IUU listed. Vessels that have been IUU listed in the past and subsequently delisted (for example because of a change in ownership, or because the vessel is no longer in service) are also retained on the site, so that the site contains a full historic record of IUU listed fishing vessels.
Unlike the IUU lists published on individual RFMO websites, which may update vessel details infrequently or not at all, the Combined IUU Fishing Vessel List is kept up to date with the best available information regarding changes to vessel identity, flag state, ownership, location, and operations.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
Monitoring Health for the SDGs - Global Health Statistics 2024 - WHOChristina Parmionova
The 2024 World Health Statistics edition reviews more than 50 health-related indicators from the Sustainable Development Goals and WHO’s Thirteenth General Programme of Work. It also highlights the findings from the Global health estimates 2021, notably the impact of the COVID-19 pandemic on life expectancy and healthy life expectancy.
Preliminary findings _OECD field visits to ten regions in the TSI EU mining r...OECDregions
Preliminary findings from OECD field visits for the project: Enhancing EU Mining Regional Ecosystems to Support the Green Transition and Secure Mineral Raw Materials Supply.
Jennifer Schaus and Associates hosts a complimentary webinar series on The FAR in 2024. Join the webinars on Wednesdays and Fridays at noon, eastern.
Recordings are on YouTube and the company website.
https://www.youtube.com/@jenniferschaus/videos
2. 2
Modernisation of the statistical production
• Reference models
Data collection
Data processing
Decent Work Indicators Reference Framework
Data dissemination
Evaluation
Metadata management
OUTLINE
4. 4
ModernStats World
Workshop
together with Sharing Tools Group
26-28 June, Geneva
Linking GSBPM
and GSIM
Core Ontology
for Official
Statistics
Alignment of
GSBPM OP with
GAMSO
Metadata
Glossary
GSIM e-training
5 November 2019
Activities in 2019 contribute de facto
to setting up a more integrated view of
the modernisation standards
HLG-MOS Activities 2019
5. 5
OVER-ARCHING
The GSBPM comprises eight main phases plusover-arching processes
GSBPM version 5.1 – Phases
1
Specify
Needs
2
Design
3
Build
4
Collect
5
Process
6
Analyse
7
Disse-
minate
8
Evaluate
PLANNING PRODUCTION
EVALUATION
Quality Management / Data & Metadata Management
7. 7
Mapping to GSBPM
In different circumstances sub-processes
- may occur in different orders
- may be revisited a number of times forming form iterative
loops (e.g. Process and Analyse )
- may be skipped (e.g. in regular processs/iterations)
9. 9
The traditional approach
DESIGN BUILD PRODUCTION DISSEM. EVAL
DESIGN
COLLECT PROCESS TABULATE PRINT
COLLECT – PROCESS
TABULATE
PLAN PUBLISH
ESTABLISHMENT SURVEY
HOUSEHOLD SURVEY
AD HOC SURVEY
• Statistical production seen as a "value chain“
• Different lines of production per statistical domain (“stove pipes”)
• Highly inefficient due to:
– duplication of infrastructure and resources
– lost economies of scale
– hard to get combined outputs
11. 11
Is GSBPM enough?
Modernisation of statistics requires:
• reuse and sharing of methods,
components, processes and data
repositories
• definition of a shared “plug-and-play”
modular component architecture
GSBPM helps in determining which
components are required.
Components will process information
Need for interfaces specification
GSIM
CSPA
12. 12
Generic Statistical Information Model
GSIM describes the information objects and flows
within the statistical business process.
• GSIM is a reference framework of information objects that sets
out definitions, attributes and relationships between them
14. 14
What’s the meaning of “VARIABLE”?
“An input to an indicator”
“The result of an equation”
“A column of a database”
“A place in memory to store values and operate with it”
“A field in a dataset”
“One element of a set that can change its value”
“Something that changes. Like the weather.”
- My mom
- Labour economist
- Developer (former Math teacher)
- Database Administrator
- IT Developer
- Labour analyst
- Statistical assistant
Why GSIM?
15. 15
What’s the meaning of “VARIABLE”?
“Variable is a characteristic of a unit to which a
numerical measure or a category from a classification
can be assigned”
Examples:
• Age of a person (measured in years since birthdate)
• Activity sector of an establishment (categorized according to ISIC)
• Occupation of a person (categorized according to ISCO)
• Total income of a household (measured in amount of money)
- GSIM
Why GSIM?
16. 16
Generic Statistical Information Model
GSIM is a conceptual model that provides a
standardized set of information objects that flow
through the process model in the creation of official statistics as
represented by the GSBPM.
It defines a common terminology across and between
statistical organisations.
It allows statistical organisations and standards
bodies (e.g. SDMX and DDI) to understand and map
common statistical information and processes.
Conceptual
mode
18. 19
• Mail interview: Questionnaire is sent by mail and requested to be sent back
• Personal Interview: Questions are asked face-to-face
• PAPI: Paper And Pencil Interviewing. Data obtained from the interview is filled in on a paper form
using a pencil.
• CAPI: Computer Assisted Personal Interviewing. This method is very much similar to the PAPI
method, but the data is directly entered into a computer programme instead of first using paper
forms.
• WAPI: Web Assisted Personal Interviewing. The respondents answer the questions online, but they
are also assisted online in doing so.
• TAPI: Tablet Assisted Personal Interviewing. This method is virtually identical to the CAPI method,
but the data is entered into a tablet instead of a computer/laptop.
• SAPI: Smartphone Assisted Personal Interviewing. With this method, the data is entered into a
smartphone by the interviewer.
• Telephone interview:
• CATI: Computer Assisted Telephone Interviewing. The questions are usually presented to the
interviewers on a computer screen, after which they ask them to the respondents. To ensure that
the correct questions are asked to each respondent, the specialised computer software uses
"skips": Certain answers can lead to the next question being different. This also prevents the
respondent from having to answer irrelevant questions.
• IVR: Interactive Voice Response. The interview is conducted by a computerized system.
Modes of data collection
19. 20
• CAWI: Computer Assisted Web Interviewing. Online research in which respondents are invited via e-mail
to answer the survey through online questionnaires. These questionnaires can be personalized so that the
correct questions are asked to each respondent.
• CASI: Computer Assisted Self Interviewing. The CASI method involves respondents taking place behind
the computer themselves in order to fill in the questionnaire. Audio or video recorded questions may be
included.
• TASI: Tablet Assisted Self Interviewing. This method is virtually identical to the CASI method, but the data
is entered into a tablet instead of a computer/laptop.
• SASI: Smartphone Assisted Self Interviewing. With this method, the data is entered into a smartphone by
the respondent.
• Multimodal approach = Mail + xASI + CATI(IVR) + xAPI
Modes of data collection
20. 21
Which modes of data
collection are used in your
institution?
Modes of data collection
21. 22
The Statistical Production
Quality Management / Data & Metadata Management
INFORMATIONSTORAGETOOLS
Quality Management / Data & Metadata Management
Documentation
Configuration
& Settings
Notes
22. 23
Data Validation
• Validation rules are applied to ensure the integrity and correctness of information entering the system
• Two types of validation:
• Structural: Correctness of data types, completness, codes, etc.
• Business rules: values’ ranges, totals, cross-referenced values, etc.
• Keep track of validation results (process/quality metadata)
Data validation, editing and transformation
Data Edition
• Correction of errors found during validation
• Structural errors may require going back to the collection phase
• Different procedures for correction:
• Imputation: Data is corrected automatically based on imputation methods: deductive, substitution,
estimator, cold/hot deck, nearest neighbour, etc.
• Interactive editing: By means of an Editor program (which can be the same used for data collection)
• Corrected data must be submitted back to validation.
Data Transformation
• Calculation of derived variables and/or aggregates.
• Generation of new datasets in different format
• Anonymization
VALIDATION
EDITING
ERROR TRANSFORMATION
OKCOLLECTION
DISSEMINATION
23. 24
The 2008 ILO Declaration on Social Justice for a Fair
Globalization recommends the establishment of appropriate
indicators to monitor and evaluate progress in the
implementation of the ILO Decent Work Agenda.
Decent work is considered central to sustainable poverty
reduction and is a means to achieve equitable, inclusive and
sustainable development.
In September 2008, the ILO convened an International Tripartite
Meeting of Experts to Develop a Decent Work Indicators
Framework, which was presented and adopted at the 18th
International Conference of Labour Statisticians in December
2008.
Decent Work Indicators Reference Framework
24. 25
The framework comprises ten substantive elements (and an
additional one on the economic and social context) corresponding
to the four strategic pillars of the Decent Work Agenda.
DECENT WORK AGENDA
Strategic Pillars
Full and productive employment
Rights at work
Social protection
Promotion of social dialogue
Decent Work Indicators Reference Framework
The Decent Work Agenda includes a cross-cutting objective of
gender equality. Thus, the Decent Work Indicators will be
disaggregated by sex, whenever possible.
25. 26
SUBSTANTIVE ELEMENTS OF DECENT WORK
1. Employment
opportunities
6. Stability and security
of work
2. Adequate earnings
and productive work
7. Equal opportunity and
treatment in
employment
3. Decent working time
8. Safe work
environment
4. Combining work,
family and personal life
9. Social security
5. Work that should be
abolished
10. Social dialogue,
employers’ and workers’
representation
Decent Work Indicators Reference Framework
26. 27
1.Employmentopportunities
INDICATOR DW SDG
Labour force participation rate
Employment to population ratio
Unemployment rate 8.5.2
Unemployment by education
Long term unemployment 19th
ICLS
Status in employment (ICSE)
Employment by occupation
(ISCO)
Wages in non-agriculture job
NEET 8.6.1
Labour underutilization
Informal employment rate 8.3.1
Decent Work Indicators Reference Framework
27. 28
Labour Force Participation Rate
𝐿𝑎𝑏𝑜𝑢𝑟 𝐹𝑜𝑟𝑐𝑒
𝑊𝑜𝑟𝑘𝑖𝑛𝑔−𝐴𝑔𝑒 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
x 100
– Working-Age Population and Labour Force should normally
correspond to persons aged 15 and above
– Labour force corresponds to persons either in employment or
unemployment
Sources: LFS, Census, other
28. 29
Persons outside the labour force
(inactivity rate)
Working age population – (persons in employment + persons unemployed)
𝑊𝑜𝑟𝑘𝑖𝑛𝑔 𝑎𝑔𝑒 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛
x 100
– Share of the working-age population that is not in the labour force (%)
– Includes discouraged
– It is the reverse side of the labour force participation rate (they sum to
1, cross check)
Sources: LFS, Census, other household surveys
29. 30
The Statistical Production
Quality Management / Data & Metadata Management
INFORMATIONSTORAGETOOLS
VTL
Quality Management / Data & Metadata Management
Documentation
Configuration
& Settings
Notes
31. 32
Technical metadata
• System’s parameters
– Govern automatic updates and scheduled batch processes
• Structural information
– Structural validation of data collection instruments
• User Access Control
– Roles, credentials, access rights, etc.
• Context information
– Dissemination website: language, country, subject
Metacontent
Methodology
Quality
Metadata
Technical
Process
Business
Structural
Reference
Descriptive
(conceptual)
Ext. Resources
Types of Metadata
32. 33
Process metadata (Paradata)
From the field
• Interview mode
– PAPI, TAPI, CAPI
• Date and time of the interview
• Geographical coordinates
• Number of attempts
Internal
• Consistency rules
• Formulas for derived variables or calculated indicators
Metacontent
Methodology
Quality
Metadata
Technical
Process
Business
Structural
Reference
Descriptive
(conceptual)
Ext. Resources
Types of Metadata
33. 34
Structural metadata
Business Structural metadata
• The “heart” of the metadata driven system.
– code lists for all the concepts in use
– definition of all the artefacts used for
• Data collection: questionnaires, DSD’s, etc.
• Dissemination: tables, charts, navigation menus, etc.
• Stored in a single metadata repository
– Shared by all the modules
– Single point of maintenance
Metacontent
Methodology
Quality
Metadata
Technical
Process
Business
Structural
Reference
Descriptive
(conceptual)
Ext. Resources
Types of Metadata
34. 35
Statistical metacontent
Business Reference metadata Descriptive metadata Statistical metacontent
• Typical “data about data”.
• Two classes of metacontent:
– Observation value status: sometimes called flag
– Notes: controlled vocabulary (coded at collection time) or free text
• Cleaning module
– Checking for mandatory and contradictory notes
• Calculation module
– “Operating” on notes for derived indicators
• Dissemination module
– Table metadata, flags and footnotes
Metacontent
Methodology
Quality
Metadata
Technical
Process
Business
Structural
Reference
Descriptive
(conceptual)
Ext. Resources
Types of Metadata
35. 36
Unlabeled stuff
Why is this metadata relevant?
Labeled stuff
The bean example is taken from: A Manager’s
Introduction to Adobe eXtensible Metadata Platform,
http://www.adobe.com/products/xmp/pdfs/whitepaper.pdf
Labeled stuff
36. 37
Methodological metadata
Business Reference metadata Descriptive metadata Methodological metadata
• Methodology used for data collection and processing
• Sampling procedures
• Primary source’s metadata is also pertinent to secondary data
– collect and disseminate this information in DDI-C
Metacontent
Methodology
Quality
Metadata
Technical
Process
Business
Structural
Reference
Descriptive
(conceptual)
Ext. Resources
Types of Metadata
37. 38
Quality metadata
Business Reference metadata Descriptive metadata Quality metadata
• Provides information about the quality of the data
– Ex.: After consistency checking data status can be “Error”,
“Ready for dissemination” or “Ready by allowance”.
– Additional quality related information attached as
comments/annotations
• Some of paradata is also quality metadata
– Ex.: Number of substitutions in a survey sample, Number of non-
responses
• Stored in the Workflow tables or “Administrative” modules
Metacontent
Methodology
Quality
Metadata
Technical
Process
Business
Structural
Reference
Descriptive
(conceptual)
Ext. Resources
Types of Metadata
38. 39
External resources
Business Reference metadata External resources
• Artefacts and documents related to the studies
– Questionnaires
– Methodological guidelines
– Reports
– Maps
– Computer programs
Metacontent
Methodology
Quality
Metadata
Technical
Process
Business
Structural
Reference
Descriptive
(conceptual)
Ext. Resources
Types of Metadata
40. 41
Survey Documentation using DDI-C
The data documentation, or reference metadata, helps
the researcher to:
– find the data they are interested in.
– understand what the data is measuring and how the data
has been created.
– assess the quality of the data.
41
41. 42
Questionnaires
Technical, analytical, administrative docs
• Sample selection information
• Listing forms
• Manuals, lists of codes, etc.
• Logistical documentation
• Personnel organization and structure
• Budget
• Any planning documentation
Metadata collection: Valuable resources
42. 43
Computer programs
• Data entry, editing
• Tabulations, computations
Tables, photos, maps
Reports
• Final reports
• Consultant reports
Others
• Press releases and media articles
• Other information or documentation
43
Metadata collection: Valuable resources
43. 44
The Statistical Production
Quality Management / Data & Metadata Management
INFORMATIONSTORAGE
Quality Management / Data & Metadata Management
Documentation
Configuration
& Settings
Notes