These are slides for my talk "Data Quality as a prerequisite for you business success: when should I start taking care of it?" I delivered as an invited keynote for HackCodeX Forum that gathered international experts to share their experience and knowledge on the emerging technologies and areas such as Artificial Intelligence, Security, Data Quality, Quantum Computing, Sustainability, Open Data, Privacy etc.

1. HackCodeX Forum
5.06.2023, Riga, Latvia
DATA QUALITY AS A PREREQUISITE
FOR BUSINESS SUCCESS:
WHEN SHOULD I START
TAKING CARE OF IT?
Anastasija Nikiforova
Assistant Professor of Information Systems, Faculty of Science and Technology,
Institute of Computer Science, Chair of Software Engineering, University of Tartu
European Open Science CLoud (EOSC) Task Force “FAIR metrics and data quality”

2. PHD IN COMPUTER SCIENCE – DATA PROCESSING SYSTEMS AND DATA NETWORKING
RESEARCH INTERESTS: DATA MANAGEMENT WITH A FOCUS ON DATA QUALITY, OPEN
GOVERNMENT DATA, SMART CITY, SOCIETY 5.0, SUSTAINABLE DEVELOPMENT, IOT, HCI,
DIGITIZATION.
✔ASSISTANT PROFESSOR AT THE UNIVERSITY OF TARTU, FACULTY OF SCIENCE AND TECHNOLOGY, INSTITUTE OF COMPUTER SCIENCE,
CHAIR OF SOFTWARE ENGINEERING
✔EUROPEAN OPEN SCIENCE CLOUD TASK FORCE “FAIR METRICS AND DATA QUALITY”
✔EDSC AMBASSADOR (EUROPEAN DIGITAL SKILLS CERTIFICATE, AS PART OF ACTION 9 OF THE DIGITAL EDUCATION ACTION PLAN (2021- 2027) –
JRC/SVQ/2022/OP/0013)
✔IFIP WG8.5 ON ICT AND PUBLIC ADMINISTRATION MEMBER
✔ASSOCIATE MEMBER OF THE LATVIAN OPEN TECHNOLOGY ASSOCIATION
✔EXPERT OF THE LATVIAN COUNCIL OF SCIENCES IN (1) NATURAL SCIENCES – COMPUTER SCIENCE & INFORMATICS, (2) ENGINEERING & TECHNOLOGY-
ELECTRICAL ENGINEERING, ELECTRONICS, ICT, (3) SOCIAL SCIENCES – ECONOMICS & BUSINESS
✔EXPERT OF THE COST – EUROPEAN COOPERATION IN SCIENCE & TECHNOLOGY
✔ASSISTANT PROFESSOR AT THE UNIVERSITY OF TARTU, FACULTY OF SCIENCE AND TECHNOLOGY, INSTITUTE OF COMPUTER SCIENCE,
CHAIR OF SOFTWARE ENGINEERING
✔EUROPEAN OPEN SCIENCE CLOUD TASK FORCE “FAIR METRICS AND DATA QUALITY”
✔EDSC AMBASSADOR (EUROPEAN DIGITAL SKILLS CERTIFICATE, AS PART OF ACTION 9 OF THE DIGITAL EDUCATION ACTION PLAN (2021- 2027) –
JRC/SVQ/2022/OP/0013)
✔IFIP WG8.5 ON ICT AND PUBLIC ADMINISTRATION MEMBER
✔ASSOCIATE MEMBER OF THE LATVIAN OPEN TECHNOLOGY ASSOCIATION
✔EXPERT OF THE LATVIAN COUNCIL OF SCIENCES IN (1) NATURAL SCIENCES – COMPUTER SCIENCE & INFORMATICS, (2) ENGINEERING & TECHNOLOGY-
ELECTRICAL ENGINEERING, ELECTRONICS, ICT, (3) SOCIAL SCIENCES – ECONOMICS & BUSINESS
✔EXPERT OF THE COST – EUROPEAN COOPERATION IN SCIENCE & TECHNOLOGY
✔VISITING RESEARCHER AT THE DELFT UNIVERSITY OF TEHNOLOGY, FACULTY TECHNOLOGY POLICY AND MANAGEMENT (TPM)
✔ASSISTANT PROFESSOR AT THE FACULTY OF COMPUTING, UNIVERSITY OF LATVIA
✔RESEARCHER IN THE INNOVATION LABORATORY, FACULTY OF COMPUTING, UNIVERSITY OF LATVIA
✔IT-EXPERT AT THE LATVIAN BIOMEDICAL RESEARCH AND STUDY CENTRE, BBMRI-ERIC LV NATIONAL NODE
✔ADVISOR FOR THE INSTITUTE FOR SOCIAL AND POLITICAL STUDIES, UNIVERSITY OF LATVIA
✔DATA SECURITY SOLUTIONS, LATVIA
✔VISITING RESEARCHER AT THE DELFT UNIVERSITY OF TEHNOLOGY, FACULTY TECHNOLOGY POLICY AND MANAGEMENT (TPM)
✔ASSISTANT PROFESSOR AT THE FACULTY OF COMPUTING, UNIVERSITY OF LATVIA
✔RESEARCHER IN THE INNOVATION LABORATORY, FACULTY OF COMPUTING, UNIVERSITY OF LATVIA
✔IT-EXPERT AT THE LATVIAN BIOMEDICAL RESEARCH AND STUDY CENTRE, BBMRI-ERIC LV NATIONAL NODE
✔ADVISOR FOR THE INSTITUTE FOR SOCIAL AND POLITICAL STUDIES, UNIVERSITY OF LATVIA
✔DATA SECURITY SOLUTIONS, LATVIA
MOST RECENT EXPERIENCE
PAST EXPERIENCE

3. https://www.linkedin.com/posts/georgefirican_data-dataquality-datamanagement-activity-7001229524768108544-v-ne/?originalSubdomain=mv

6. DATA … DATA ARE EVERYWHERE
Sources: Premium Vector | Artificial intelligence logo, icon. vector symbol ai, deep learning blockchain neural network concept. machine learning, artificial intelligence, ai. (freepik.com), Top 10 Successful Data Science Companies in 2023 - Learn | Hevo (hevodata.com),
How to Use Business Intelligence (BI) to Improve Organizational Alignment | Wyn Enterprise (grapecity.com), Machine learning logo - Wi6Labs, Business Intelligence Icon Gráfico por aimagenarium · Creative Fabrica, Open Data – GEOAFRICA,
https://www.gartner.com/en/articles/4-emerging-technologies-you-need-to-know-about?utm_medium=social&utm_source=linkedin&utm_campaign=SM_GB_YOY_GTR_SOC_SF1_SM-SWG&utm_content=&sf267111387=1

7. DATA … DATA ARE EVERYWHERE
M-Files on Twitter: "Data is the New Oil – Especially in Oil and Gas! https://t.co/zFlrvQqlMs https://t.co/qE3Q4aLNQy" / Twitter

8. DATA QUALITY - WHAT, WHY, HOW, 10 BEST PRACTICES & MORE - Enterprise Master Data Management • Profisee

9. https://dataladder.com/the-impact-of-poor-data-quality-risks-challenges-and-solutions/

10. https://twitter.com/bright_data/status/1346443370718240768

11. 🤨 "Data is the new oil."​ | LinkedIn

12. Data is the New Oil - HubMeta

13. Data is the New Oil - HubMeta
NOT REALLY

14. “DATA IS THE NEW OIL” WHY IT IS NOT?
BUT!
✓
Source: Here's Why Data Is Not The New Oil (forbes.com), Image sources: Oil well – Wikipedia, How do we get oil and gas out of the ground? (world-petroleum.org), Customized Silos For Effective Storage of Food | Nextech Solutions (nextechagrisolutions.com)
DATA, LIKE OIL is a source of power,
and those, who control them,
are establishing themselves as «masters of the universe»,
just as oil barons did 100 years ago

15. effectively infinitely durable and reusable
treating like oil –storing in siloes, has little benefit & reduces its usefulness
a finite resource
can be replicated indefinitely & moved around the world at
the speed of light, at low cost, through fiber optic networks
OIL
requires huge amounts of resources to be
transported to where it is needed
when used, its energy being lost as heat or light, or
permanently converted into another form (e.g., plastic)
becomes more useful the more it is used - once
processed, data often reveals further applications
as the world’s oil reserves dwindle, extracting
it becomes increasingly difficult and expensive
becoming increasingly available as computer
technology advances
data mining doesn’t intrinsically involve damage to the
environment & exploitation of finite natural resources
*apart from the electricity used to run the system
oil drilling involve causing damage to the natural
environment and exploitation of finite natural resources
“DATA IS THE NEW OIL” WHY IT IS NOT?
✘
Source: Here's Why Data Is Not The New Oil (forbes.com), Image sources: Oil well – Wikipedia, How do we get oil and gas out of the ground? (world-petroleum.org), Customized Silos For Effective Storage of Food | Nextech Solutions (nextechagrisolutions.com)
DATA
✘
✘
✘
✘

16. IF WE THINK ABOUT DATA AS A POWER SOURCE OR FUEL,
IT WOULD MAKE MORE SENSE TO COMPARE THEM WITH
RENEWABLE SOURCES LIKE THE
SUN, WIND AND TIDES”
-B. Marr, Forbes
Here's Why Data Is Not The New Oil (forbes.com)
Letter from the Editor: Here comes the sun (medicalnewstoday.com), A healthy wind | MIT News | Massachusetts Institute of Technology, Tidal phenomenon: high and low tides | Ponant Magazine

17. AMONG OTHER “NUANCES”,
DATA QUALITY IS USE-CASE DEPENDENT AND DYNAMIC IN NATURE
“ABSOLUTE DATA QUALITY”
DATA QUALITY LEVEL AT WHICH THE DATA WOULD SATISFY
ALL POSSIBLE USE CASES - IS IMPOSSIBLE TO ACHIEVE,
BUT IT IS A GOAL TO BE PURSUED

19. Def. 1: FITNESS-FOR-USE
Def. 2: FITNESS-FOR-PURPOSE
Def. 3: FREE OF ERRORS

20. Def. 1: FITNESS-FOR-USE
Def. 2: FITNESS-FOR-PURPOSE
Def. 3: FREE OF ERRORS
UTILITY*
WARRANTY*
=
=
According to ITIL® 4: the framework for the management of IT-enabled service

21. ISO def.: THE DEGREE TO WHICH
DATA SATISFIES THE REQUIREMENTS
OF ITS INTENDED PURPOSE
ISO/IEC 25012

22. IN SIMPLER TERMS… THINK OF WINE…
INTRINSIC - flavor type & intensity
EXTRINSIC - brand, packaging…
Based on ISO 19157,
Langstaff, S. A. (2010). Sensory quality control in the wine industry.
Lacagnina, C., David, R., Nikiforova, A., Kuusniemi, M. E., Cappiello, C., Biehlmaier, O., Wright, L., Schubert, C., Bertino, A., Thiemann, H., & Dennis, R. (2023). Towards a data quality framework for

24. NOT ONLY ABOUT WHAT, BUT
ALSO ABOUT HOW?
IT IS A PROCESS

25. NOT ONLY ABOUT WHAT, BUT
ALSO ABOUT HOW?
IT IS A PROCESS –
DATA QUALITY MANAGEMENT PROCESS

27. DEFINE
MEASURE
ANALYSE
IMPROVE TDQM
DATA QUALITY MANAGEMENT PROCESS
TOTAL DATA QUALITY MANAGEMENT LIFCYCLE (BY MIT)
DEFINE: IDENTIFY RELEVANT DQ DIMENSIONS
MEASURE: PRODUCE DQ METRICS
ANALYSE: IDENTIFY ROOT CAUSES FOR DQ PROBLEMS AND
DETERMINE THE IMPACT OF POOR DQ
IMPROVE: IDENTIFY AND EMPLOY TECHNIQUES FOR
IMPROVING DQ

28. •Lacagnina, C., David, R., Nikiforova, A., Kuusniemi, M. E., Cappiello, C., Biehlmaier, O., Wright, L.,
Schubert, C., Bertino, A., Thiemann, H., & Dennis, R. (2023). Towards a data quality framework
for EOSC. Zenodo. https://doi.org/10.5281/zenodo.7515816

29. Source: https://healthinstitute.illinois.edu/connect/news/berd-tips-dimensions-of-data-quality
AVAILABILITY
INTERNAL CONSISTENCY
EXTERNAL CONSISTENCY
ACCESSIBILITY
COMPREHENSIVENESS
INTEGRITY
SEMANTIC ACCURACY
SYNTACTIC ACCURACY
RELEVANCE
BELIEVABILITY
TRUSTWORTHINESS
UNAMBIGUITY
DQ DIMENSIONS
CURRENCY
VOLATILITY
EASE OF UNDERSTANDING
CREDIBILITY
PORTABILITY
RESPONSIVENESS
OBJECTIVITY
REPUTATION
RELIABILITY
AND MANY MORE…

30. Relevance
Availability
Internal consistency
External consistency
Accessibility
Comprehensiveness
Believability
Integrity
Trustworthiness
Semantic accuracy
Unambiguity
Syntactic accuracy
Source: https://healthinstitute.illinois.edu/connect/news/berd-tips-dimensions-of-data-quality
THERE ARE MORE THAN 100 DATA QUALITY DIMENSIONS

31. IS THERE ANY COMMONLY ACCEPTED DQ DIMENSION
CLASSIFICATION?
https://iso25000.com/index.php/en/iso-25000-standards/iso-25012/136-iso-iec-2012
ISO 25012
SOFTWARE ENGINEERING — SOFTWARE
PRODUCT QUALITY REQUIREMENTS
AND EVALUATION (SQUARE) — DATA
QUALITY MODEL

32. DIMENSIONS VARY IN DEFINITION AND SCOPE
ONE AND THE SAME NOTION CAN REFER TO DIFFERENT DIMENSIONS
ONE AND THE SAME DIMENSION CAN HAVE
DIFFERENT NOTIONS [IN DIFFERENT SOURCES]
DATA QUALITY RULES ARE THEN DEFINED
FOR EACH DIMENSION
METRICS ARE THEN SELECTED FOR THEM

33. SIMPLER
USER-ORIENTED
APPROACH
BASED ON USER DEFINED DATA
QUALITY REQUIREMENTS

34. ✓ STANDARDIZATION, NORMALIZATION AND PARSING
✓ MATCHING / DEDUPLICATION AND MERGING
✓ DATA CLEANSING
✓ VALIDATION
✓ DATA PROFILING / AUDITING
✓ SOME A FEW OF THEM SUPPORT (SEMI-)AUTOMATED DQ RULE RECOGNITION
BASED ON METADATA, BUILT-IN RULES, OR MACHINE LEARNING
DQ TOOLS FOR (SEMI-)AUTOMATED DQM

36. SO FAR…
DEFINITION USER TIME
DIMENSION
PROCESS PURPOSE

37. SO FAR…
DEFINITION USER TIME
DIMENSION
PROCESS PURPOSE
WHAT ELSE?

38. DATA OBJECT
DATASET
DATABASE DATA REPOSITORY INFORMATION SYSTEM
SOFTWARE
NO ONE-SIZE-FITS-ALL

39. DATA OBJECT
DATASET
DATABASE DATA REPOSITORY INFORMATION SYSTEM
SOFTWARE
DATA OWNER
KNOWN
THIRD-PARTY
NO ONE-SIZE-FITS-ALL

40. DATA OBJECT
DATASET
DATABASE DATA REPOSITORY INFORMATION SYSTEM
SOFTWARE
DATA STRUCTURE
NO ONE-SIZE-FITS-ALL
STRUCTURED DATA UNSTRUCTURED DATA
SEMI-STRUCTURED DATA
Image sources: https://monkeylearn.com/blog/semi-structured-data/, https://www.pngitem.com/middle/ioJTTbR_organization-structure-icon-png-download-structures-icon-png/

41. DATA OBJECT
DATASET
DATABASE DATA REPOSITORY INFORMATION SYSTEM
SOFTWARE
DATA WAREHOUSE DATA LAKE
Maybe even something else?
NO ONE-SIZE-FITS-ALL

42. DATA OBJECT
DATASET
DATABASE DATA REPOSITORY INFORMATION SYSTEM
SOFTWARE
Running Analytics on the Data Lake - The Databricks Blog
NO ONE-SIZE-FITS-ALL

43. Image source: https://www.grazitti.com/blog/data-lake-vs-data-warehouse-which-one-should-you-go-for/, https://www.qubole.com/data-lakes-vs-data-warehouses-the-co-existence-argument/
SCHEMA ON READ
SCHEMA ON WRITE
“SINGLE SOURCE
OF TRUTH”

44. Implementing a Data Lake or Data Warehouse Architecture for Business Intelligence? | by Lan Chu | Towards Data Science
NB: EXTRACT-TRANSFORM-LOAD
IS NOT DQM!!!

45. https://www.slideteam.net/data-lake-it-avoid-data-swamp-in-a-data-lake.html
HOW TO AVOID DATA SWAMP?

47. Image source: The abstracted future of data engineering | by Justin Gage | Datalogue | Medium
OR HOW TO AVOID GIGO*?
*“GARBAGE IN, GARBAGE OUT”

48. DATA LAKE FOR BI
BUSINESS DATA LAKE
https://www.capgemini.com/wp-content/uploads/2017/07/pivotal_data_lake_vs_traditional_bi_20140805.pdf

49. DATA LAKE
+
DATA WRANGLING
[an asset, not a silver bullet]
✔
Source: https://monkeylearn.com/blog/data-wrangling/, https://www.altair.com/what-is-data-wrangling/ , https://pediaa.com/what-is-the-difference-between-data-wrangling-and-data-cleaning

50. Image source: https://www.google.com/url?sa=i&url=https%3A%2F%2Ftwitter.com%2Frokar9%2Fstatus%2F1452339921629302784&psig=AOvVaw2IUSKtgUWxeaplk56f7CoK&ust=1668004535620000&source=images&cd=vfe&ved=0CA4QjhxqFwoTCJDHwbjnnvsCFQAAAAAdAAAAABAM

51. THE DATA WRANGLING PROCESS TO PREPARE DATA AND INTEGRATE IT INTO IS
DEPENDING ON THE IS AND THE DESIRED OR REQUIRED TARGET QUALITY*, INDIVIDUAL STEPS
SHOULD BE CARRIED OUT SEVERAL TIMES ➔ !!! DATA WRANGLING IS A CONTINUOUS PROCESS
!!! THAT REPEATS ITSELF REPEATEDLY AT REGULAR INTERVALS.
Information
System
Azeroual, O., Schöpfel, J., Ivanovic, D., & Nikiforova, A. (2022). Combining data lake and
data wrangling for ensuring data quality in CRIS. Procedia Computer Science, 211, 3-16.

52. DATA LAKE VS DATA WAREHOUSE
HOW TO TAKE
THE ADVANTAGES OF BOTH?

53. DATA LAKE VS DATA WAREHOUSE
HOW TO TAKE
THE ADVANTAGES OF BOTH?
DATA LAKEHOUSE

54. DATA LAKEHOUSE IS SEEN AS A COMBINATION OF DATA WAREHOUSING WORKLOADS & DATA LAKE ECONOMICS
Running Analytics on the Data Lake - The Databricks Blog

55. Running Analytics on the Data Lake - The Databricks Blog, Build a Lake House Architecture on AWS | AWS Big Data Blog (amazon.com), The Data Lakehouse, the Data Warehouse and a Modern Data platform architecture - Microsoft Community Hub

56. DATA OBJECT
DATASET
DATABASE DATA REPOSITORY INFORMATION SYSTEM
SOFTWARE
Running Analytics on the Data Lake - The Databricks Blog

57. DATA QUALITY-AWARE SOFTWARE
DEVELOPMENT
&
DATA QUALITY MODEL-BASED TESTING

58. THINK DATA QUALITY FIRST!!! OR TOWARDS DATA
QUALITY BY DESIGN
Guerra-García, C., Nikiforova, A., Jiménez, S., Perez-Gonzalez, H. G., Ramírez-Torres, M., & Ontañon-
García, L. (2023). ISO/IEC 25012-based methodology for managing data quality requirements in the
development of information systems: Towards Data Quality by Design. Data & Knowledge
Engineering, 145,
DAQUAVORD - A METHODOLOGY FOR PROJECT MANAGEMENT OF DATA QUALITY REQUIREMENTS
SPECIFICATION - AIMED AT ELICITING DQ REQUIREMENTS ARISING FROM DIFFERENT USERS’ VIEWPOINTS
THESE DQ REQUIREMENTS SERVE AS DATA QUALITY SOFTWARE REQUIREMENT AT THE TIME
OF THE DEVELOPMENT OF SOFTWARE THAT TAKES DATA QUALITY INTO ACCOUNT BY
DEFAULT.
IS BASED ON THE VIEWPOINT-ORIENTED REQUIREMENTS DEFINITION (VORD) METHOD, AND
THE LATEST AND MOST GENERALLY ACCEPTED ISO/IEC 25012 STANDARD.

59. DATA ARTIFACT
WHAT DQM APPROACH DEPENDS ON?
DEFINITION USER
TIME
DIMENSION
PROCESS PURPOSE

61. MUSK’S TOP PRIORITY: TO IMPROVE THE
PRODUCT…
Q: HOW DOES ONE ENSURE THE RELIABILITY OF DATA
AND DECISIONS MADE BASED ON SAID DATA?
THE ANSWER LIES NOT IN MANAGING THE DATA ALONE,
BUT ALSO THE INFORMATION AROUND AND ABOUT DATA
ACQUISITION, TRANSFORMATIONS AND VISUALIZATION
TO PROVIDE A BETTER UNDERSTANDING AND SUPPORT
DECISION MAKERS
https://www.gqindia.com/get-smart/content/5-things-elon-musk-did-to-become-one-of-the-richest-men-in-the-world

62. https://www.gqindia.com/get-smart/content/5-things-elon-musk-did-to-become-one-of-the-richest-men-in-the-world
MUSK’S TOP PRIORITY: TO IMPROVE THE
PRODUCT…
Q: HOW DOES ONE ENSURE THE RELIABILITY OF DATA
AND DECISIONS MADE BASED ON SAID DATA?
THE ANSWER LIES NOT IN MANAGING THE DATA ALONE,
BUT ALSO THE INFORMATION AROUND AND ABOUT DATA
ACQUISITION, TRANSFORMATIONS AND VISUALIZATION
TO PROVIDE A BETTER UNDERSTANDING AND SUPPORT
DECISION MAKERS
BY FOCUSING ON SUSTAINABLE DATA, CLEAR
DATA GOVERNANCE
AND STRONG DATA MANAGEMENT

63. https://www.softcrylic.com/blogs/data-catalogs-in-data-governance/

64. https://www.gqindia.com/get-smart/content/5-things-elon-musk-did-to-become-one-of-the-richest-men-in-the-world
DATA GOVERNANCE IS THE ANSWER
https://www.edq.com/blog/data-quality-vs-data-governance/
Azeroual O., Nikiforova A., Sha K. (2023) Overlooked Aspects of Data Governance:
Workflow Framework For Enterprise Data Deduplication

65. https://www.gqindia.com/get-smart/content/5-things-elon-musk-did-to-become-one-of-the-richest-men-in-the-world
DATA GOVERNANCE IS THE ANSWER
https://www.edq.com/blog/data-quality-vs-data-governance/

67. https://www.gqindia.com/get-smart/content/5-things-elon-musk-did-to-become-one-of-the-richest-men-in-the-world
DATA QUALITY MANAGEMENT IS A CONTINUOUS PROCESS

68. https://www.gqindia.com/get-smart/content/5-things-elon-musk-did-to-become-one-of-the-richest-men-in-the-world
THINK DATA QUALITY FIRST!
“1-10-100” RULE
1$ SPENT ON PREVENTION
SAVES 10$ ON APPRAISAL AND
100$ ON FAILURE COSTS
https://twitter.com/bright_data/status/1346443370718240768

69. https://www.gqindia.com/get-smart/content/5-things-elon-musk-did-to-become-one-of-the-richest-men-in-the-world
DEVELOP DATA QUALITY MANAGEMENT AND
GOVERNANCE STRATEGIES
MANTAIN DQM & DQG STRATEGIES
DEFINE
MEASURE
ANALYSE
IMPROVE

71. +
=
https://starwars.fandom.com/wiki/Destruction_of_Despayre, https://www.linkedin.com/posts/georgefirican_data-dataquality-datamanagement-activity-7001229524768108544-v-ne/?originalSubdomain=mv, History in Objects: Death Star Plans Datacard • Lucasfilm, Video Analysis of an Exploding Death Star | WIRED

72. +
=
https://starwars.fandom.com/wiki/Destruction_of_Despayre, https://www.linkedin.com/posts/georgefirican_data-dataquality-datamanagement-activity-7001229524768108544-v-ne/?originalSubdomain=mv, History in Objects: Death Star Plans Datacard • Lucasfilm, Video Analysis of an Exploding Death Star | WIRED

73. For more information, see ResearchGate,
anastasijanikiforova.com
For questions or any queries, contact me via
Nikiforova.Anastasija@gmail.com,