Data Cleaning Process
•
1 like•7,486 views
Get to know how we clean your data
Report
Share
Report
Share
Download to read offline
Recommended
Data Cleaning Techniques
Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Data Quality
In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
Data Governance Takes a Village (So Why is Everyone Hiding?)
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
Review of Data Management Maturity Models
Review existing data management maturity models to identify core set of characteristics of an effective data maturity model:
DMBOK (Data Management Book of Knowledge) from DAMA (Data Management Association)
MIKE2.0 (Method for an Integrated Knowledge Environment) Information Maturity Model (IMM)
IBM Data Governance Council Maturity Model
Enterprise Data Management Council Data Management Maturity Model
Data Quality Strategies
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 a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body 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
Data Quality as a prerequisite for you business success: when should I start ...
Data Quality as a prerequisite for you business success: when should I start ...Anastasija Nikiforova
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.Data Cleansing
The old adage, "You are what you eat", also applies to machine learning and data science. The models and insights gained from analyzing data are only as good as the input data. To understand where data preparation falls in an analytics solution, the Extract, Transform, and Load (ETL) process is covered.
Following an overview of the necessity behind data preparation, various cleansing techniques are demonstrated. These data issues and techniques are exemplified, using real situations, with before and after snapshots of data and the code snippets that perform the cleansing.
These slides were presented at Penn State's Nittany Watson Challenge Immersion event on January 19-20, 2017.
Recommended
Data Cleaning Techniques
Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Data Quality
In this lecture we discuss data quality and data quality in Linked Data. This 50 minute lecture was given to masters student at Trinity College Dublin (Ireland), and had the following contents:
1) Defining Quality
2) Defining Data Quality - What, Why, Costs
3) Identifying problems early - using a simple semantic publishing process as an example
4) Assessing Linked (big) Data quality
5) Quality of LOD cloud datasets
References can be found at the end of the slides
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 (CC-BY-SA-40) International License.
Data Governance Takes a Village (So Why is Everyone Hiding?)
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
Review of Data Management Maturity Models
Review existing data management maturity models to identify core set of characteristics of an effective data maturity model:
DMBOK (Data Management Book of Knowledge) from DAMA (Data Management Association)
MIKE2.0 (Method for an Integrated Knowledge Environment) Information Maturity Model (IMM)
IBM Data Governance Council Maturity Model
Enterprise Data Management Council Data Management Maturity Model
Data Quality Strategies
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 a high quality. Determining how Data Quality should be engineered provides a useful framework for utilizing Data Quality management effectively in support of business strategy, which in turns allows for speedy identification of business problems, delineation between structural and practice-oriented defects in Data Management, and proactive prevention of future issues.
Over the course of this webinar, we will:
Help you understand foundational Data Quality concepts based on “The DAMA Guide to the Data Management Body 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
Data Quality as a prerequisite for you business success: when should I start ...
Data Quality as a prerequisite for you business success: when should I start ...Anastasija Nikiforova
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.Data Cleansing
The old adage, "You are what you eat", also applies to machine learning and data science. The models and insights gained from analyzing data are only as good as the input data. To understand where data preparation falls in an analytics solution, the Extract, Transform, and Load (ETL) process is covered.
Following an overview of the necessity behind data preparation, various cleansing techniques are demonstrated. These data issues and techniques are exemplified, using real situations, with before and after snapshots of data and the code snippets that perform the cleansing.
These slides were presented at Penn State's Nittany Watson Challenge Immersion event on January 19-20, 2017.
Data Quality Best Practices
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Introduction to Data Governance
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Data-Ed Slides: Best Practices in Data Stewardship (Technical)
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
Introduction to data pre-processing and cleaning
Brief Introduction to data pre-processing and cleaning
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Data Governance
TDWI Presentation explaining how Enterprise Data Governance can be utilized to support Business Strategy.
Geek Sync | Data Architecture and Data Governance: A Powerful Data Management...
You can watch the replay for this Geek Sync webcast, Data Architecture and Data Governance: A Powerful Data Management Duo, on the IDERA Resource Center, http://ow.ly/95yL50A4rZg.
Batman and Robin. Han Solo and Chewbacca. Mario and Luigi. Just like these famous pairings, so it is for data architecture and data governance — they’re aligned to support each other in a variety of ways. Like data governance, data architecture as a practice can be leveraged to identify and enforce standards within the systems landscape to support business objectives. And data architecture certainly benefits from sound business oversight and stakeholder influences inherent in a successful data governance program.
Join Kelle O’Neal to learn about the critical aspects of aligning data architecture and data governance, with a specific focus on:
-Why aligning data architecture and data governance is important
-The key intersections of people, processes and technology between data architecture and data governance
-How data architecture and data governance work together to enforce standards
-The capabilities that data governance can apply to data architecture without interfering
-How your project and development methodologies can help drive alignment
Enterprise Architecture vs. Data Architecture
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
DataEd Webinar: Reference & Master Data Management - Unlocking Business Value
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions—its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning Objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
Data Quality for Non-Data People
Most companies do not think of data when they start out, let alone the quality of that data. With the proliferation of data and the usages of that data, organizations are compelled to focus more and more on data and their quality.
Join Kasu Sista of The Wisdom Chain to understand how to think about, implement, and maintain data quality.
You will learn about:
What do data people think about?
How do you get them to listen to what you want?
Business processes and data life span
Impact of data capture and data quality on down stream business processes
Data quality metrics and how to define them and use them
Practical metadata and data governance
What are the takeaways from the session?
How to talk to your data people
Understanding the importance of capturing data in the right way
Understanding the importance of quality metrics and bench marks
Understanding of operationalizing data quality processes
Measuring Data Quality Return on Investment
Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy.
Data Profiling: The First Step to Big Data Quality
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
How to Build & Sustain a Data Governance Operating Model
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Architecture Best Practices for Advanced Analytics
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Data Modeling, Data Governance, & Data Quality
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Catalog as the Platform for Data Intelligence
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
Data management
This presentation provides a few key tips for effective data management: how to plan ahead, how to organize data, how to preserve data, and how to market.
Data-Ed: Best Practices with the Data Management Maturity Model
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
NLP Data Cleansing Based on Linguistic Ontology Constraints
Slides for the following paper: NLP Data Cleansing Based on Linguistic Ontology Constraints
Abstract: Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is – compared to other domains, such as biology – a late Linked Data adopter. However, it has seen a
steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the lemon and NIF vocabularies in 277 test cases and point out common quality issues.
More Related Content
What's hot
Data Quality Best Practices
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Introduction to Data Governance
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Data-Ed Slides: Best Practices in Data Stewardship (Technical)
In order to find value in your organization's data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is: captured right the first time, validated through automated means, and integrated into business processes. Whether its data profiling or in depth root cause analysis, data stewards can be counted on to ensure the organization's mission critical data is reliable. In this webinar we will approach this framework, and punctuate important facets of a data steward’s role.
Learning Objectives:
- Understand the business need for a data governance framework
- Learn why embedded data quality principles are an important part of system/process design
- Identify opportunities to help drive your organization to a data driven culture
Introduction to data pre-processing and cleaning
Brief Introduction to data pre-processing and cleaning
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
Data Governance
TDWI Presentation explaining how Enterprise Data Governance can be utilized to support Business Strategy.
Geek Sync | Data Architecture and Data Governance: A Powerful Data Management...
You can watch the replay for this Geek Sync webcast, Data Architecture and Data Governance: A Powerful Data Management Duo, on the IDERA Resource Center, http://ow.ly/95yL50A4rZg.
Batman and Robin. Han Solo and Chewbacca. Mario and Luigi. Just like these famous pairings, so it is for data architecture and data governance — they’re aligned to support each other in a variety of ways. Like data governance, data architecture as a practice can be leveraged to identify and enforce standards within the systems landscape to support business objectives. And data architecture certainly benefits from sound business oversight and stakeholder influences inherent in a successful data governance program.
Join Kelle O’Neal to learn about the critical aspects of aligning data architecture and data governance, with a specific focus on:
-Why aligning data architecture and data governance is important
-The key intersections of people, processes and technology between data architecture and data governance
-How data architecture and data governance work together to enforce standards
-The capabilities that data governance can apply to data architecture without interfering
-How your project and development methodologies can help drive alignment
Enterprise Architecture vs. Data Architecture
Enterprise Architecture (EA) provides a visual blueprint of the organization, and shows key interrelationships between data, process, applications, and more. By abstracting these assets in a graphical view, it’s possible to see key interrelationships, particularly as they relate to data and its business impact across the organization. Join us for a discussion on how Data Architecture is a key component of an overall Enterprise Architecture for enhanced business value and success.
DataEd Webinar: Reference & Master Data Management - Unlocking Business Value
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions—its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning Objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
Data Quality for Non-Data People
Most companies do not think of data when they start out, let alone the quality of that data. With the proliferation of data and the usages of that data, organizations are compelled to focus more and more on data and their quality.
Join Kasu Sista of The Wisdom Chain to understand how to think about, implement, and maintain data quality.
You will learn about:
What do data people think about?
How do you get them to listen to what you want?
Business processes and data life span
Impact of data capture and data quality on down stream business processes
Data quality metrics and how to define them and use them
Practical metadata and data governance
What are the takeaways from the session?
How to talk to your data people
Understanding the importance of capturing data in the right way
Understanding the importance of quality metrics and bench marks
Understanding of operationalizing data quality processes
Measuring Data Quality Return on Investment
Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy.
Data Profiling: The First Step to Big Data Quality
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
How to Build & Sustain a Data Governance Operating Model
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Data Architecture Best Practices for Advanced Analytics
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Data Modeling, Data Governance, & Data Quality
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Catalog as the Platform for Data Intelligence
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
Data management
This presentation provides a few key tips for effective data management: how to plan ahead, how to organize data, how to preserve data, and how to market.
What's hot (20)
Data-Ed Slides: Best Practices in Data Stewardship (Technical)
Data-Ed Slides: Best Practices in Data Stewardship (Technical)
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...
Geek Sync | Data Architecture and Data Governance: A Powerful Data Management...
Geek Sync | Data Architecture and Data Governance: A Powerful Data Management...
DataEd Webinar: Reference & Master Data Management - Unlocking Business Value
DataEd Webinar: Reference & Master Data Management - Unlocking Business Value
Data Profiling: The First Step to Big Data Quality
Data Profiling: The First Step to Big Data Quality
How to Build & Sustain a Data Governance Operating Model
How to Build & Sustain a Data Governance Operating Model
Data Architecture Best Practices for Advanced Analytics
Data Architecture Best Practices for Advanced Analytics
Data Catalog as the Platform for Data Intelligence
Data Catalog as the Platform for Data Intelligence
Viewers also liked
Data-Ed: Best Practices with the Data Management Maturity Model
The Data Management Maturity (DMM) model is a framework for the evaluation and assessment of an organization's data management capabilities. The model allows an organization to evaluate its current state data management capabilities, discover gaps to remediate, and strengths to leverage. The assessment method reveals priorities, business needs, and a clear, rapid path for process improvements. This webinar will describe the DMM, its evolution, and illustrate its use as a roadmap guiding organizational data management improvements.
NLP Data Cleansing Based on Linguistic Ontology Constraints
Slides for the following paper: NLP Data Cleansing Based on Linguistic Ontology Constraints
Abstract: Linked Data comprises of an unprecedented volume of structured data on the Web and is adopted from an increasing number of domains. However, the varying quality of published data forms a barrier for further adoption, especially for Linked Data consumers. In this paper, we extend a previously developed methodology of Linked Data quality assessment, which is inspired by test-driven software development. Specifically, we enrich it with ontological support and different levels of result reporting and describe how the method is applied in the Natural Language Processing (NLP) area. NLP is – compared to other domains, such as biology – a late Linked Data adopter. However, it has seen a
steep rise of activity in the creation of data and ontologies. NLP data quality assessment has become an important need for NLP datasets. In our study, we analysed 11 datasets using the lemon and NIF vocabularies in 277 test cases and point out common quality issues.
Best Practices: Data Admin & Data Management
I built this presentation for Informatica World in 2006. It is all about Data Administration, Data Quality and Data Management. It is NOT about the Informatica product. This presentation was a hit, with standing room only full of about 150 people. The content is still useful and applicable today. If you want to use my material, please put (C) Dan Linstedt, all rights reserved, http://LearnDataVault.com
Applying Data Quality Best Practices at Big Data Scale
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Scaling Big Data Cleansing
Data cleansing approaches have usually focused on detecting and fixing errors with little attention to big data scaling. This presents a serious impediment since identifying and repairing dirty data often involves processing huge input datasets, handling sophisticated error discovery approaches and managing huge arbitrary errors. With large datasets, error detection becomes overly expensive and complicated especially when considering user-defined functions. Furthermore, a distinctive algorithm is desired to optimize sophisticated error discovery, that requires inequality joins, rather than naïvely parallelizing them. Also, when repairing large errors, their skewed distribution may obstruct effective error repairs. In this dissertation, I present solutions to overcome the above three problems in scaling data cleansing.
Data Cleansing introduction (for BigClean Prague 2011)
Presentation from the BigClean event in spring 2011 in Prague. Briefly introduces to data quality, cleansing and shows some examples from existing open data/open government projects.
Viewers also liked (8)
Data-Ed: Best Practices with the Data Management Maturity Model
Data-Ed: Best Practices with the Data Management Maturity Model
NLP Data Cleansing Based on Linguistic Ontology Constraints
NLP Data Cleansing Based on Linguistic Ontology Constraints
Applying Data Quality Best Practices at Big Data Scale
Applying Data Quality Best Practices at Big Data Scale
Data Cleansing introduction (for BigClean Prague 2011)
Data Cleansing introduction (for BigClean Prague 2011)
More from InfoCheckPoint
Why EMAIL MARKETING is STILL the Preferred Digital Marketing Tactic
Get to know Why EMAIL MARKETING is STILL the preferred Digital Marketing Tactic . Stats that make you gasp!
Data Quality - The Cleansing Process
One way to ensure ROI on data investment is with a quality database. Get to know how we keep your data lean, green and clean.
Infographic on Data Updation and Quality
Get to know all about data and how we keep data lean, clean and green!
Sales Case Study
Get to know how to increase response rates and sales close rates through this case study.
Develop a Data Quality Scorecard
Measure, monitor, manage and maximize ROI on your data investment. Utilize a data quality scorecard!
Turn Up the Volume on Telemarketing ROI
Integrate telemarketing with your overall marketing approach. By using inbound marketing you will be able to nurture every lead and avoid prospects resisting your phone calls. Get to know how to boost telemarketing ROI with data intelligence and qualified leads.
Connect With High Value B2B Customers
Info CheckPoint provides a marketing database that includes exclusive profiles relevant to your business. We have data experts who maintain accurate databases and with an advanced search platform, you can gain access to data instantly.
Data Verification
Gain an overview of data verification and validation, the methods and techniques used to keep data clean as well as new business practices in the industry that help in maintaining data quality and preventing data decay.
Adopt new approaches of “Think Blue” and “Think Green”, in order to create a pollution free virtual environment.
Check out more - http://www.infocheckpoint.com/Images/pdf/Expand-your-Enterprise-Exponentially-whitepaper.pdf
More from InfoCheckPoint (9)
Why EMAIL MARKETING is STILL the Preferred Digital Marketing Tactic
Why EMAIL MARKETING is STILL the Preferred Digital Marketing Tactic
Access Firmographic Details of 6+ Million Comapnies
Access Firmographic Details of 6+ Million Comapnies
Recently uploaded
falcon-invoice-discounting-a-premier-platform-for-investors-in-india
Falcon stands out as a top-tier P2P Invoice Discounting platform in India, bridging esteemed blue-chip companies and eager investors. Our goal is to transform the investment landscape in India by establishing a comprehensive destination for borrowers and investors with diverse profiles and needs, all while minimizing risk. What sets Falcon apart is the elimination of intermediaries such as commercial banks and depository institutions, allowing investors to enjoy higher yields.
Cracking the Workplace Discipline Code Main.pptx
Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
Although discipline is not a one-size-fits-all approach, it can help create a work environment that encourages personal growth and accountability rather than solely relying on punitive measures.
In this deck, you will learn the significance of workplace discipline for organisational success. You’ll also learn
• Four (4) workplace discipline methods you should consider
• The best and most practical approach to implementing workplace discipline.
• Three (3) key tips to maintain a disciplined workplace.
Unveiling the Secrets How Does Generative AI Work.pdf
At its core, generative artificial intelligence relies on the concept of generative models, which serve as engines that churn out entirely new data resembling their training data. It is like a sculptor who has studied so many forms found in nature and then uses this knowledge to create sculptures from his imagination that have never been seen before anywhere else. If taken to cyberspace, gans work almost the same way.
Business Valuation Principles for Entrepreneurs
This insightful presentation is designed to equip entrepreneurs with the essential knowledge and tools needed to accurately value their businesses. Understanding business valuation is crucial for making informed decisions, whether you're seeking investment, planning to sell, or simply want to gauge your company's worth.
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdf
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
Pitch Deck Teardown: RAW Dating App's $3M Angel deck
Pitch Deck Teardown: RAW Dating App's $3M Angel deck
What are the main advantages of using HR recruiter services.pdf
HR recruiter services offer top talents to companies according to their specific needs. They handle all recruitment tasks from job posting to onboarding and help companies concentrate on their business growth. With their expertise and years of experience, they streamline the hiring process and save time and resources for the company.
5 Things You Need To Know Before Hiring a Videographer
Dive into this presentation to discover the 5 things you need to know before hiring a videographer in Toronto.
Accpac to QuickBooks Conversion Navigating the Transition with Online Account...
This article provides a comprehensive guide on how to
effectively manage the convert Accpac to QuickBooks , with a particular focus on utilizing online accounting services to streamline the process.
Lookback Analysis
Explore our most comprehensive guide on lookback analysis at SafePaaS, covering access governance and how it can transform modern ERP audits. Browse now!
Role of Remote Sensing and Monitoring in Mining
Remote sensing and monitoring are changing the mining industry for the better. These are providing innovative solutions to long-standing challenges. Those related to exploration, extraction, and overall environmental management by mining technology companies Odisha. These technologies make use of satellite imaging, aerial photography and sensors to collect data that might be inaccessible or from hazardous locations. With the use of this technology, mining operations are becoming increasingly efficient. Let us gain more insight into the key aspects associated with remote sensing and monitoring when it comes to mining.
Digital Transformation in PLM - WHAT and HOW - for distribution.pdf
Presentation describing the Digital Transformation for PLM in the context of a Dassault Systemes portfolio.
Global Interconnection Group Joint Venture[960] (1).pdf
Global Interconnection Group Joint Venture[960] (1).pdf
Affordable Stationery Printing Services in Jaipur | Navpack n Print
Looking for professional printing services in Jaipur? Navpack n Print offers high-quality and affordable stationery printing for all your business needs. Stand out with custom stationery designs and fast turnaround times. Contact us today for a quote!
Discover the innovative and creative projects that highlight my journey throu...
Discover the innovative and creative projects that highlight my journey through Full Sail University. Below, you’ll find a collection of my work showcasing my skills and expertise in digital marketing, event planning, and media production.
Meas_Dylan_DMBS_PB1_2024-05XX_Revised.pdf
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...
Marvin neemt je in deze presentatie mee in de voordelen van non-endemic advertising op retail media netwerken. Hij brengt ook de uitdagingen in beeld die de markt op dit moment heeft op het gebied van retail media voor niet-leveranciers.
Retail media wordt gezien als het nieuwe advertising-medium en ook mediabureaus richten massaal retail media-afdelingen op. Merken die niet in de betreffende winkel liggen staan ook nog niet in de rij om op de retail media netwerken te adverteren. Marvin belicht de uitdagingen die er zijn om echt aansluiting te vinden op die markt van non-endemic advertising.
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
Recently uploaded (20)
falcon-invoice-discounting-a-premier-platform-for-investors-in-india
falcon-invoice-discounting-a-premier-platform-for-investors-in-india
Unveiling the Secrets How Does Generative AI Work.pdf
Unveiling the Secrets How Does Generative AI Work.pdf
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdf
Search Disrupted Google’s Leaked Documents Rock the SEO World.pdf
Pitch Deck Teardown: RAW Dating App's $3M Angel deck
Pitch Deck Teardown: RAW Dating App's $3M Angel deck
What are the main advantages of using HR recruiter services.pdf
What are the main advantages of using HR recruiter services.pdf
5 Things You Need To Know Before Hiring a Videographer
5 Things You Need To Know Before Hiring a Videographer
Accpac to QuickBooks Conversion Navigating the Transition with Online Account...
Accpac to QuickBooks Conversion Navigating the Transition with Online Account...
Digital Transformation in PLM - WHAT and HOW - for distribution.pdf
Digital Transformation in PLM - WHAT and HOW - for distribution.pdf
Global Interconnection Group Joint Venture[960] (1).pdf
Global Interconnection Group Joint Venture[960] (1).pdf
Affordable Stationery Printing Services in Jaipur | Navpack n Print
Affordable Stationery Printing Services in Jaipur | Navpack n Print
Discover the innovative and creative projects that highlight my journey throu...
Discover the innovative and creative projects that highlight my journey throu...
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...
RMD24 | Debunking the non-endemic revenue myth Marvin Vacquier Droop | First ...
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...