This document provides an overview of Data Quality Services (DQS) matching and Master Data Services (MDS). It discusses record matching, data issues that affect matching, the DQS matching process, and key components like the matching policy and knowledge base. It also introduces MDS and its configuration tools.
Data mining Course
Chapter 2: Data preparation and processing
Introduction
Domain Expert
Goal identification and Data Understanding
Data Cleaning
Missing values
Noisy Data
Inconsistent Data
Data Integration
Data Transformation
Data Reduction
Feature Selection
Sampling
Discretization
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This first session in a series of six ‘Packed Lunch’ webinars provides an overview of Data Virtualization technology, its applications and how it is adding business value to organizations around the world.
More information and FREE registrations to this webinar: http://goo.gl/z7mq2S
Landing page for the entire Packed Lunch webinar series: http://goo.gl/NATMHw
Attend & get unique insights into:
What Data Virtualization is and what sets it apart from traditional integration tools
How it both complements and leverages existing enterprise architectures
The Denodo Data Virtualization platform and its capabilities
Outlier analysis,Chapter-12, Data Mining: Concepts and TechniquesAshikur Rahman
This slide is prepared for a course of Dept. of CSE, Islamic Univresity of Technology (IUT).
Course: CSE 4739- Data Mining
This topic is based on:
Data Mining: Concepts and Techniques
Book by Jiawei Han
Chapter 12
Data mining Course
Chapter 2: Data preparation and processing
Introduction
Domain Expert
Goal identification and Data Understanding
Data Cleaning
Missing values
Noisy Data
Inconsistent Data
Data Integration
Data Transformation
Data Reduction
Feature Selection
Sampling
Discretization
Introduction to Data Virtualization (session 1 from Packed Lunch Webinar Series)Denodo
This first session in a series of six ‘Packed Lunch’ webinars provides an overview of Data Virtualization technology, its applications and how it is adding business value to organizations around the world.
More information and FREE registrations to this webinar: http://goo.gl/z7mq2S
Landing page for the entire Packed Lunch webinar series: http://goo.gl/NATMHw
Attend & get unique insights into:
What Data Virtualization is and what sets it apart from traditional integration tools
How it both complements and leverages existing enterprise architectures
The Denodo Data Virtualization platform and its capabilities
Outlier analysis,Chapter-12, Data Mining: Concepts and TechniquesAshikur Rahman
This slide is prepared for a course of Dept. of CSE, Islamic Univresity of Technology (IUT).
Course: CSE 4739- Data Mining
This topic is based on:
Data Mining: Concepts and Techniques
Book by Jiawei Han
Chapter 12
Data-Ed Online: Approaching Data QualityDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Learning Objectives:
Help you understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBoK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
06. Transformation Logic Template (Source to Target)Alan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of transmission of data between one data storage location to another. (a.k.a. Source to Target mapping)
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Master Data Services is bundled with SQL Server 2012 to help resolve many of the Master Data Management issues that companies are faced with when integrating data. In this session, James will show an overview of Master Data Services 2012, including the out of the box Web UI, the highly developed Excel Add-in, and how to get started with loading MDS with your data.
Microsoft master data services mds overviewEugene Zozulya
Master data management (MDM) is a technology discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.
Master data management tools can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data.
Microsoft Master Data Services (MDS) is the SQL Server solution for master data management. Master data management (MDM) describes the efforts made by an organization to discover and define non-transactional lists of data, with the goal of compiling maintainable master lists. An MDM project generally includes an evaluation and restructuring of internal business processes along with the implementation of MDM technology. The result of a successful MDM solution is reliable, centralized data that can be analyzed, resulting in better business decisions.
Other Master Data Services features include hierarchies, granular security, transactions, data versioning, and business rules.
Master Data Services includes the following components and tools:
- Master Data Services Configuration Manager, a tool you use to create and configure Master Data Services databases and web applications.
- Master Data Manager, a web application you use to perform administrative tasks (like creating a model or business rule), and that users access to update data.
- MDSModelDeploy.exe, a tool you use to create packages of your model objects and data so you can deploy them to other environments.
- Master Data Services web service, which developers can use to extend or develop custom solutions for Master Data Services.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
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Tutustuminen data-analytiikan ja big datan maailmaan. Valikoitua sisältöä Edutech Data ja analytiikka liiketoiminnan kehittämisessä koulutuspäivästä. Kouluttajina Pasi Hellsten & Jari Jussila. @EdutechTUT #Data4BizTraining
Introduction to Master Data Services in SQL Server 2012Stéphane Fréchette
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Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
chris.bradley@dmadvisors.co.uk
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
Data-Ed Online: Approaching Data QualityDATAVERSITY
Good data is like good water: best served fresh, and ideally well-filtered. Data Management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, the delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues. Organizations must realize what it means to utilize Data Quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor Data Quality. Showing how Data Quality should be engineered provides a useful framework in which to develop an effective approach. This, in turn, allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Learning Objectives:
Help you understand foundational Data Quality concepts based on the DAMA Guide to Data Management Book of Knowledge (DAMA DMBoK), as well as guiding principles, best practices, and steps for improving Data Quality at your organization
Demonstrate how chronic business challenges for organizations are often rooted in poor Data Quality
Share case studies illustrating the hallmarks and benefits of Data Quality success
06. Transformation Logic Template (Source to Target)Alan D. Duncan
This document template defines an outline structure for the clear and unambiguous definition of transmission of data between one data storage location to another. (a.k.a. Source to Target mapping)
Introduction to Microsoft’s Master Data Services (MDS)James Serra
Master Data Services is bundled with SQL Server 2012 to help resolve many of the Master Data Management issues that companies are faced with when integrating data. In this session, James will show an overview of Master Data Services 2012, including the out of the box Web UI, the highly developed Excel Add-in, and how to get started with loading MDS with your data.
Microsoft master data services mds overviewEugene Zozulya
Master data management (MDM) is a technology discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.
Master data management tools can be used to support master data management by removing duplicates, standardizing data (mass maintaining), and incorporating rules to eliminate incorrect data from entering the system in order to create an authoritative source of master data.
Microsoft Master Data Services (MDS) is the SQL Server solution for master data management. Master data management (MDM) describes the efforts made by an organization to discover and define non-transactional lists of data, with the goal of compiling maintainable master lists. An MDM project generally includes an evaluation and restructuring of internal business processes along with the implementation of MDM technology. The result of a successful MDM solution is reliable, centralized data that can be analyzed, resulting in better business decisions.
Other Master Data Services features include hierarchies, granular security, transactions, data versioning, and business rules.
Master Data Services includes the following components and tools:
- Master Data Services Configuration Manager, a tool you use to create and configure Master Data Services databases and web applications.
- Master Data Manager, a web application you use to perform administrative tasks (like creating a model or business rule), and that users access to update data.
- MDSModelDeploy.exe, a tool you use to create packages of your model objects and data so you can deploy them to other environments.
- Master Data Services web service, which developers can use to extend or develop custom solutions for Master Data Services.
This is a slide deck that was assembled as a result of months of Project work at a Global Multinational. Collaboration with some incredibly smart people resulted in content that I wish I had come across prior to having to have assembled this.
Tutustuminen data-analytiikan ja big datan maailmaanJari Jussila
Tutustuminen data-analytiikan ja big datan maailmaan. Valikoitua sisältöä Edutech Data ja analytiikka liiketoiminnan kehittämisessä koulutuspäivästä. Kouluttajina Pasi Hellsten & Jari Jussila. @EdutechTUT #Data4BizTraining
Introduction to Master Data Services in SQL Server 2012Stéphane Fréchette
What is Master Data Services? Why is it important? - Will discuss Master Data Services capabilities, it's underlying architecture. Will demo creating a model, using SQL Server 2012 MDS add-in for Microsoft Excel, creating hierarchies, business rules and exposing/integrating data with other interfaces (Data Warehouse)
Agile Data Science is a lean methodology that is adopted from Agile Software Development. At the core it centers around people, interactions, and building minimally viable products to ship fast and often to solicit customer feedback. In this presentation, I describe how this work was done in the past with examples. Get started today with our help by visiting http://www.alpinenow.com
How to identify the correct Master Data subject areas & tooling for your MDM...Christopher Bradley
1. What are the different Master Data Management (MDM) architectures?
2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative?
3. A reference architecture for MDM.
4. Selection criteria for MDM tooling.
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Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
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We can help you!
Learn how RTTS’ Data Validation Assessment provides:
- an evaluation of your current data validation process
- recommendations on how to improve your process and
- a proposal for successful implementation
This slide deck addresses the following issues:
- How do I find out if I have bad data?
- How do I ensure I am testing the proper data permutations?
- How much of my data needs to be validated and automated?
- Which critical data endpoints need to be tested?
- How do I test data in my cloud environments?
And much more!
For more information, visit:
https://www.rttsweb.com/services/solutions/data-validation-assessment
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Benefits of a data warehouse
Designing of Data Warehouse
Extract, Transform, Load (ETL)
Data Quality
Classification Of Data Quality Issues
Causes Of Data Quality
Impact of Data Quality Issues
Cost of Poor Data Quality
Confidence and Satisfaction-based impacts
Impact on Productivity
Risk and Compliance impacts
Why Data Quality Influences?
Causes of Data Quality Problems
How to deal: Missing Data
Data Corruption
Data: Out of Range error
Techniques of Data Quality Control
Data warehousing security
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Performance management
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Fine-tune operations
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How to Think ?
What happened?
What is happening?
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What will happen?
What do I want to happen?
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When consolidating multiple sources of information from across your organization, how do you find the records that relate to the same customer, the same company or the same product? This is the challenge faced by many businesses today when putting a data lake to work. The problem is made far worse when different systems may not have the same contact entered the same way. Is Bob Smith the same as Robert Smith? How about Dr. Robert L. Smith - is he the same person? What about Syncsort, Inc and Sinksort Corp.? Are those the same company? One must compare each individual record to every other record in the dataset with some very sophisticated matching algorithms to determine who is who, and you may have to compare the data multiple times in multiple ways to resolve each entity.
Just to add to the difficulty, let’s say your organization has very large volumes of records in your data lake - you don’t have to compare a thousand records to a thousand other records multiple times - you must compare a million to a million, or 100 million to 100 million. This kind of compute intensive comparison can bring even a powerful cluster to its knees.
This is a problem Syncsort customers must solve, and we have developed some very powerful and intelligent software to tackle it.
View this presentation as we discuss the challenges of entity resolution at scale, how Syncsort’s Trillium data quality software line has tackled them successfully in production clusters and see a demonstration of this software in action.
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
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Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
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f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Getting started with Amazon Bedrock Studio and Control Tower
Dqs mds-matching 15042015
1. DQS/MDS INTRO’S
&
DQS MATCHING
Microsoft
SQL Server 2012
SQL Server 2014
Neil Hambly
SQL Server
Evangelist /
Practice Lead
PASS
London
Chapter Leader
Melissa Data MVP
PASS
Virtual Chapter
“Professional
Development”
Leader
Contributing
Author
2. Agenda
Matching Project
What is record matching?
Data Issues
DQS Matching Process
DQS Data Matching Principles
Matching Policy
DQS Intro
MDS Intro
3. Data Cleansing:
Modifications, removal, correcting data or incomplete,
either computer-assisted or interactively.
Matching:
Identification of duplicates in a rules-based process,
perform de-duplication., verifying data quality using
reference data provider. Use reference data services from
Azure Marketplace providers
Profiling:
Analysis of data for insight into its data quality , domain
management, matching, and data cleansing processes.
Profiling is a powerful tool in a DQS data quality solution.
Monitoring:
Determine the state of data quality activities. Validate
data quality solution is doing what it was designed to do.
Knowledge Base:
DQS is a knowledge-driven solution , analyzing data using
knowledge built with DQS. Create data quality processes
to enhance the knowledge of data , continuously
improving data quality
4. • Create a Matching Policy
• Data Quality Matching
• Match Similar Data
5. Master Data Services Configuration Manager
Tool to create and configure Master Data Services
databases and web applications.
Master Data Manager
Web application for performing administrative tasks
(creating a model or business rule), and that users
access to modify master data.
MDSModelDeploy.exe
Tool to create packages of your model objects and
data, for deploying to other environments.
Master Data Services web service
Developers can use to develop custom solutions for
Master Data Services.
Master Data Services Add-in for Excel
Manage data and create new entities and attributes.
10. Record matching is the task of identifying
records that match the same real world
entity.
11. The Cost of Duplicate Data
…a few examples…
Direct marketing communications are doubled up unnecessarily.
Product shipments and customer-site based services could be sent
to the wrong address due to an incorrect duplicate record being
used.
Your sales reporting may be inaccurate due to an over-
inflated number of customers.
Inaccurate sales analysis due to sales being split between multiple
records that represent the same customer, resulting in an
undervaluing of some key customers.
12. Where do Duplicate Records come from?
Poorly designed software No verification of existing records upon entry
Formatting &
abbreviations
"Doctor Robert Smith" Vs. "Dr. Bob Smith".
Data validation Human errors can creep into the system when fields’
input is not validated
Company merging and
acquisitions
Merging systems may result in duplicates in the merged
data.
Change of attributes The same person may appear to not exist in the
database if some of the attributes were changed
(e.g., address, name etc.)
13. …Data Issues…
There are different ways to represent the same person or address in a database:
Data is ‘fuzzy’ in nature (spelling mistakes, abbreviations etc.).
14. How Data Issues Affects Matching?
Matching Results
Matching Results Reasoning
The Data
17. Identifies exact and approximate matches, enabling
removal of duplicate data.
Enables creating a matching policy interactively using a
computer-assisted process.
Ensures that values that are equivalent, but were entered
in a different format or style, are in fact rendered
uniform.
18.
19. A matching policy is prepared in the knowledge base.
A matching policy consists of matching rules that
assess how well one record matches to another.
Specify in the rule whether records’ values have to be
an exact match, similar, or prerequisite.
Train your policy by running and tuning each rule
separately.
20. Identify the attributes in your data that are most
significant for matching.
Create domains/composite domains based on your data
structure.
Define matching rules.
Birth Date Gender
Composite Domain Full Name
F. Name M. Name L. Name Email Phone
Composite Domain Full Address
Street City State Country
21. Similarity, select Similar if field values can be similar. Select Exact if field values must
be identical.
Weight, determines the contribution of each domain in the rule to the overall
matching score for two records.
Prerequisite validates whether field values return a 100% match; else the records are
not considered a match.
Minimum matching score is the threshold at or above which two records are
considered to be a match.
22. Domains of type ‘Date’, ‘Integer’ or ‘Decimal’ can be matched using the
‘Similar’ property by assigning a tolerance either in percentage or integer.
Field values that fall within the defined tolerance are considered a match.
23. Uniqueness Usage Description Domains
Low • Define as Prerequisite
• Define with lower weights
Provides discriminatory
information
Gender, City, State
High • Define as Similar or Exact
• Define with higher weights
Provides highly identifiable
information and is highly
discriminatory
Names (First, Last,
Company),
Address Line 1
Completeness Usage Description
Low Do not use or define with low weight High level of missing values
High Include for matching if the column
provides highly identifiable
information
Low level of missing values
24. • The Matching Results tab displays statistics for the current and
previous run of a matching rule.
• Restore the previous rule.
27. The DQS matching system uses the knowledge accumulated in the
knowledge base to propose matching candidates. This knowledge
includes:
Synonyms, Syntax Errors and their Leading Value (by domain)
Domain Values and their synonyms and syntax errors are used
by the matching system to find identical or similar records.
Term-Based Relations (TBR)
TBR improves consistency of data attributes values by
transforming data values to a single form using user-defined
term relations. In matching, TBRs are only applied in-memory
for boosting matching accuracy.
Nulls and Equivalents (“Unknown”, “99999”…)
Manage values that represent missing data by linking to the
‘DQS_Null’ value to assure that they are considered as a
match.
28. String 1 String 2 Similarity Score Character
Before After
175 CLEARBROOK ROAD P.O. BOX 535 175 CLEARBROOK ROAD P.O.BOX 535 0.92 1.00 .
1834 E. 42ND STREET 1834 E. 42ND. ST. 0.695 0.857 .
1721 DE KALB AVE, NE 1721 DE KALB AVE NE 0.88 1.00 ,
14538 S. GARFIELD AVE., BLDG. 1-B 14538 S GARFIELD AVE BLDG 1B 0.676 0.944 , . -
#704, SJ Technoville BD, 60-19 704 SJ Technoville BD 60 19 0.65 1.00 # , -
Example:
29.
30. Export - export both matching results (clusters) and survivors
(unique records).
A Matching project is performed in three steps:
Mapping - map source columns to domains.
Matching - run matching and view the results; it includes additional
functionality such as:
• Reject records
• Filter results by ‘Matched’ & ‘Unmatched’ and by matching
score.
• Display clusters in two different methods (overlapping and
non- overlapping )
31. In Overlapping clusters a record may appear more than once in various clustered
results. This structure may be harder to read since the same record exists in multiple
clusters.
In Non-Overlapping clusters, the system unifies clusters containing the same
record. This structure is easier to read as you won't repeat the same observation
twice.
Overlapping Clusters
(A~B) , (B~C)
Non-Overlapping Cluster
(A~B~C)
33. Check the Rejected box to move the records out of the proposed cluster upon
moving to the next page in the activity. Unlike the Cleansing Data Project where
records move between tabs instantly, the rejected records are not removed from
the clusters on the user interface.
DQS Client User Interface
Exported Matching Results
34. Matching and Survivorship results can be exported to a SQL table,
Excel or CSV file for further analysis or consumption.
35. in a matching rule
In a Matching Rule Minimum matching score parameter