Home
Search results “Data quality issues in data mining”
Data Quality Concepts
 
06:56
Learn about data problems with multiple examples and the data QA process. The volume is low. Please click the Cc button to see subtitles in English. Next, view VBScript tutorials at https://www.youtube.com/watch?v=03BfHDJsFpk&index=1&list=PLc3SzDYhhiGXH8hEHtayRPdwAsddelkh6 Follow me on: Website: http://inderpsingh.blogspot.com/ Google+: https://plus.google.com/+InderPSingh Twitter: https://twitter.com/inder_p_singh
Introduction to Data Mining: Data Quality
 
02:00
In this Data Mining Fundamentals, we introduce the most overlooked step in data mining, Data Quality. Understanding your data quality problems is very important to creating robust models that will actually work in production. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M2X0 See what our past attendees are saying here: https://hubs.ly/H0f8M330 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 6629 Data Science Dojo
Resolve data quality issues: SAP Analytics Cloud (2018.5.1)
 
04:23
In this video tutorial, you'll resolve data quality issues in an imported dataset using Split, Change, Replace, and Concatenate functions.
Views: 1710 SAPAnalyticsTraining
Challenges and solutions to poor data quality
 
27:31
Adam Parkes, Senior Modeller at CH2M, discusses the challenges and solutions to poor data quality when carrying out 2D flood modelling. He discusses: - Freely available data sets (Open OS, LiDAR and others) – what, where and how - Evaluating the quality of existing data - Model sensitivity to data uncertainty - Model validation and calibration - Site observations – Recent experience from the December 2015 floods Originally delivered as a webinar on 15th March 2016.
Views: 767 Flood Modeller
How to Improve Your Data Quality
 
23:55
Presentation from Salesforce.org Higher Ed Summit 2018 by: Farrah Friedrich, University of St. Thomas, and Mike Walter, University of St. Thomas. Bad data frustrates users, and at the University of St Thomas we had several data problems in Salesforce such as duplicate records, inconsistent address formats, and name capitalization issues. Since these issues were a problem for our users we decided fixing this was a priority, so we implemented some native Salesforce features, installed a few AppExchange tools, and built a custom solution to improve our data quality. In this session we'll show you what we did to improve our data quality and share the solutions we used and lessons we learned so you can do this too.
Views: 334 Salesforce.org
Introduction to Data Mining: Data Noise
 
04:10
In this Data Mining Fundamentals tutorial, we discuss data noise that can overlap valid data and outliers. Noise can appear because of human inconsistency and labeling. We will provide you with several examples of data noise, and how data noise can be measured and recorded. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M3q0 See what our past attendees are saying here: https://hubs.ly/H0f8Llr0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 7163 Data Science Dojo
Top 5 Problems Caused by Poor Data Quality
 
03:22
In this episode of Blazent's Top 5, we describe the most common problems caused by poor data quality, according to a May 2016 survey published by 451 Research
Views: 718 Blazent
Privacy and Security Issues in Big Data and Data Mining
 
01:15:31
This talk will cover privacy-preserving data mining, an emerging research topic in data mining, whose basic idea is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Speaker: Vyas Krishnan, Ph.D. Associate Professor of Computer Science Saint Leo University
Your Data Is a Mess: Take Control of Data Quality Issues
 
01:00:51
Blast and Tealium webinar discussing data quality issues. Includes tips on why data quality issues happen, and how you can take back control to build trust in your brand and make confident data driven decisions.
NodeGraph explains Data Quality [Data Quality Management, Concepts & Issues]
 
06:31
What is data quality? What is data quality management? And what do the rest of all the data quality concepts mean? We'll fill you in! Tune in as Ellen explains what data quality is, what the benefits are, and how you can get started on improving the quality of your data. More specifically, this video will teach you about: - The six dimensions of data quality (i.e. completeness, uniqueness, timeliness, validity, accuracy, and consistency) - Data quality issues (i.e. the high lack of data quality across the globe) - She will then take you through some of the benefits of data quality management and will offer some tips on how to get started If you've made it this far, we recommend you continue reading on our website, where you will find lots more written content explaining data quality! To find out more about NodeGraph, your data quality platform for QlikView and Qlik Sense, head over to https://www.nodegraph.se
Views: 160 NodeGraph
Implementing Effective Data Quality
 
41:46
Data Quality webinar was origionally recorded June 26th, 2014. You will find more details at: http://datasourceconsulting.com/understanding-importance-data-quality/
Views: 4112 datasourcetv
Why data modeling saves time and money, and improves data quality
 
03:23
Learn why data modeling is essential to building robust supportable applications, and why Data Modeling Zone (www.DataModelingZone.com) is the place to sharpen your data modeling skills.
Views: 1453 Data Modeling
Data Quality Example
 
01:59
Example of the real-life problems that arise from data quality issues
Views: 30704 Timo Elliott
DATA QUALITY MINING
 
09:50
Data Quality Mining by TS SARMA
Views: 56 joshua udyagiri
8 Common Data Quality Challenges Addressed
 
02:57
Ensure that your data is fit for use in business processes.
Views: 390 Pitney Bowes
Data Cleaning Process Steps / Phases [Data Mining] Easiest Explanation Ever (Hindi)
 
04:26
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 13309 5 Minutes Engineering
Data Quality Concepts | Data Quality Tutorial | Data Warehousing Tutorial | Edureka
 
25:09
***** Data Warehousing & BI Training: https://www.edureka.co/data-warehousing-and-bi ***** Data quality assurance is the process of profiling the data to discover inconsistencies and other anomalies in the data, as well as performing data cleansing activities (e.g. removing outliers, missing data interpolation) to improve the data quality . These activities can be undertaken as part of data warehousing or as part of the database administration of an existing piece of applications software. Video covers the following topics : 1.Data Quality Concept 2Error Handling Concepts 3.ETL Summary 4.Data Extraction 5.Data Transform 6.Data Loading 7.What is Data warehouse? 8.Data warehouse Architecture 9.Why Data warehouse is used? Related Blogs: http://www.edureka.co/blog/a-brief-on-etl/?utm_source=youtube&utm_medium=referral&utm_campaign=data-quality-concept http://www.edureka.co/blog/architecture-of-a-data-warehouse/?utm_source=youtube&utm_medium=referral&utm_campaign=data-quality-concept Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Introduction to Dataware Housing’ have been covered in our course ‘Datawarehousing‘. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 24530 edureka!
7. Big Data Issues
 
04:40
An Introduction to Online Learning
Data Quality Matters - Tech Vision 2018 Trend
 
07:32
As more organizations push towards data driven decision making, integrity and quality of data become critical. See how a data intelligence practice can solve this.
Views: 15004 Accenture Technology
Introduction to Ten Steps To Data Quality
 
01:34
Simply put, information quality is providing the correct set of accurate information, at the correct time and place, to the correct people. However, ensuring quality information is far from simple. Whether you are just starting a project or are already in production, it is not unusual to find that data quality issues prevent organizations from realizing the full benefit of their investment in new business processes and systems. Join us to learn the Ten Steps to Quality Data and Trusted Information™ – a practical approach to creating, improving, and managing the quality of information critical to running your business, satisfying customers, and achieving company goals. If you working on real data quality-related projects that need real results, this is the seminar for you. What you learn here applies to all kinds of data and every type of organization – for-profit businesses of all sizes, education, government, healthcare, and nonprofit – because all depend on trusted information to succeed. This course is based on the extensive experience of the trainer/author/consultant and the book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™ (Morgan Kaufmann Publishers, 2008) by Danette McGilvray.
What is NodeGraph? [Data Quality, Automated Testing, Scalability & more!]
 
00:17
NodeGraph is your data quality platform for QlikView and Qlik Sense. Head over to our website and find out how you can improve your Qlik Data Quality today - https://www.nodegraph.se/ // New to NodeGraph? Welcome - we are a data quality platform for QlikView and Qlik Sense. So how does it work? With NodeGraph, you are able to visualize, analyze and understand your Qlik environment in an intuitive way. We allow you to visualize the data lineage behind your Qlik dashboard by displaying your Qlik Solution from data source to end-user application. NodeGraph further enhances your data quality capabilities through the use of automated testing - making it easier for you to set up and maintain data quality management in your QlikView and Qlik Sense solutions. All in all, NodeGraph enables you to: - Boost the data confidence throughout your organization - Embrace scalability - Ensure data consistency Basically, we show you what is going on behind the scenes of your Qlik Solution, allowing you to secure and maintain high data quality. Start improving your Qlik data quality today - https://www.nodegraph.se Follow us on Twitter - https://twitter.com/node_graph Find us on LinkedIn - https://www.linkedin.com/company/nodegraph/ As always, we would absolutely love to hear your thoughts and questions when it comes to data quality issues, data quality management, data quality concepts, etc. so please comment on this video to reach out to us! Have a fantastic day! Music: https://www.bensound.com/
Views: 1440 NodeGraph
How Data Quality Impacts Healthcare
 
00:54
Dr. Wilcox talks data quality and how properly documented care can impact the future of healthcare.
Views: 633 ImageTrend, Inc.
Data Quality
 
02:24
http://consensics.com data quality,email validation,data cleansing,company data, contacts, email verification If your business has Data Quality problems, then you're not alone. TDWI estimates that data quality issues cost businesses over $600B annually. It's a huge problem. One estimate says that it costs every business about 20% of their revenue. Everything that has been tried to solve these problems has one simple flaw: solutions that are limited to a view of the data inside a company can never truly know the truth. The only way to really solve data quality problems is to look outside the boundaries of your company and take a good hard look at reality.
Views: 114 consensics
Introduction to Data Mining: Document & Transaction Data
 
02:46
In this Data Mining Fundamentals video tutorial, we discuss another useful subcategory of record data, document data. We also discuss transaction data, which is record data where each record involves a set of items. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M0m0 See what our past attendees are saying here: https://hubs.ly/H0f8M0v0 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 7172 Data Science Dojo
New Python Tutorial: Diagnose data for cleaning
 
03:57
First video of our latest course by Daniel Chen: Cleaning Data in Python. Like and comment if you enjoyed the video! A vital component of data science involves acquiring raw data and getting it into a form ready for analysis. In fact, it is commonly said that data scientists spend 80% of their time cleaning and manipulating data, and only 20% of their time actually analyzing it. This course will equip you with all the skills you need to clean your data in Python, from learning how to diagnose your data for problems to dealing with missing values and outliers. At the end of the course, you'll apply all of the techniques you've learned to a case study in which you'll clean a real-world Gapminder dataset! So you've just got a brand new dataset and are itching to start exploring it. But where do you begin, and how can you be sure your dataset is clean? This chapter will introduce you to the world of data cleaning in Python! You'll learn how to explore your data with an eye for diagnosing issues such as outliers, missing values, and duplicate rows. Try the first chapter for free: https://www.datacamp.com/courses/cleaning-data-in-python
Views: 15018 DataCamp
Data-Ed Webinar: Data Quality Engineering
 
01:22:10
Organizations must realize what it means to utilize data quality management 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. Takeaways: Understanding foundational data quality concepts based on the DAMA DMBOK Utilizing data quality engineering in support of business strategy Data Quality guiding principles & best practices Steps for improving data quality at your organization
Views: 939 DATAVERSITY
Quality Measurement and Data Collection Special Issues– Part 1 of 2
 
01:21:29
Webinar 6 of the BHC Data Collection and Quality Reporting Webinar Series discusses continuous quality improvement, hybrid measure sampling, and other topics. Learn more at www.samhsa.gov/section-223.
Views: 345 SAMHSA
Data Quality: A Raising Data Warehousing Concern
 
03:51
Characteristics of Data Warehouse 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
Views: 248 Amin Chowdhury
Data-Ed Online: Data Quality Success Stories
 
01:31:05
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will demonstrate how chronic business challenges can often be attributed to the root problem of poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. Establishing this framework allows organizations to more efficiently identify business and data problems caused by structural issues versus practice-oriented defects; giving them the skillset to prevent these problems from re-occurring. Learning Objectives: - Understanding foundational data quality concepts based on the DAMA DMBOK - Utilizing data quality engineering in support of business strategy - Case Studies illustrating data quality success - Data quality guiding principles & best practices - Steps for improving data quality at your organization
Views: 149 DATAVERSITY
Introduction to Data Profiling
 
06:33
How to profile your data and identify data quality issues in it. Learn more here: https://pbi.bz/2DNpCvd
Views: 227 Pitney Bowes
Data Cleaning In Python (Practical Examples)
 
17:40
Data Cleaning In Python with Pandas In this tutorial we will see some practical issues we have when working with data,how to diagnose them and how to solve them. ==Tutorial and Data Set here== Github: https://goo.gl/erg89C Blog: https://goo.gl/6PJsdo Reference ====Common Data Cleaning Issues==== Reading File Inconsistent Column Names Missing Data Duplicates Inconsistent Data Types Outliers Noisy Data etc.
Views: 12355 J-Secur1ty
What is DATA QUALITY FIREWALL? What does DATA QUALITY FIREWALL mean? DATA QUALITY FIREWALL meaning
 
02:22
What is DATA QUALITY FIREWALL? What does DATA QUALITY FIREWALL mean? DATA QUALITY FIREWALL meaning - DATA QUALITY FIREWALL definition - DATA QUALITY FIREWALL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A data quality firewall is the use of software to protect a computer system from the entry of erroneous, duplicated or poor quality data. Gartner estimates that poor quality data causes failure in up to 50% of customer relationship management systems. Older technology required the tight integration of data quality software, whereas this can now be accomplished by loosely coupling technology in a service-oriented architecture. A data quality firewall guarantees database accuracy and consistency. This application ensures that only valid and high quality data enter the system, which means that it obliquely protects the database from damage; this is extremely important since database integrity and security are absolutely essential. A data quality firewall provides real time feedback information about the quality of the data submitted to the system. The main goal of a data quality process consists in capturing erroneous and invalid data, processing them and eliminating duplicates and, lastly, exporting valid data to the user without failing to store a back-up copy into the database. A data quality firewall acts similarly to a network security firewall. It enables packets to pass through specified ports by filtering out data that present quality issues and allowing the remaining, valid data to be stored in the database. In other words, the firewall sits between the data source and the database and works throughout the extraction, processing and loading of data. It is necessary that data streams be subject to accurate validity checks before they can be considered as being correct or trustworthy. Such checks are of a temporal, formal, logic and forecasting kind.
Views: 39 The Audiopedia
Data Mining Concepts & Techniques
 
04:01
Data Mining Concepts & Techniques, Motivation: Why data mining?, What is data mining?, Data Mining: On what kind of data?, Data mining functionality, Classification of data mining systems, Top-10 most popular data mining algorithms, Major issues in data mining, Overview of the course, Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data
Views: 1616 Computer Notes
Introduction to Data Mining & Concepts
 
02:41
Introduction to Data Mining & Concepts Scalability Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation Streaming Data Motivation: Why data mining?, What is data mining?, Data Mining: On what kind of data?, Data mining functionality, Classification of data mining systems, Top-10 most popular data mining algorithms, Major issues in data mining, Overview of the course,
Views: 103 Computer Notes
Data-Ed Online Webinar: Data Quality Success Stories
 
01:23:17
Organizations must realize what it means to utilize data quality management 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. Takeaways: - Understanding foundational data quality concepts based on the DAMA DMBOK - Utilizing data quality engineering in support of business strategy - Case Studies illustrating data quality success - Data Quality guiding principles & best practices - Steps for improving data quality at your organization
Views: 209 DATAVERSITY
Meeting the 3 Key Challenges of Big Data Projects
 
03:34
https://www.capgemini.com/insights-data Greg Hanson identifies these challenges as Completeness of data, Quality and Governance, and Operationalization, to make Big Data projects profitable, citing ‘The Big Data Payoff’, a joint research report produced by Informatica and Capgemini.
Views: 386 Capgemini
SystmOne Data Quality - Build Your Own Reports Showing Problem Records
 
07:33
Easily build your own report for records affected by key data quality issues related to patient demographic and referral data. The examples shown were created in MS Excel using a PivotTable linked to a blueFISH Explorer OLAP Cube and demonstrate how easy it is to create your own reports.
Views: 1795 blueFISHbi
Introduction to Data Mining: Graph & Ordered Data
 
04:12
In this Data Mining Fundamentals tutorial, we introduce graph data and ordered data, and discuss the different types of ordered data such as spatial-temporal and genomic data. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8LkV0 See what our past attendees are saying here: https://hubs.ly/H0f8M270 -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 6520 Data Science Dojo
Lonna Atkeson, "Data Quality, Professional Respondents and Discontinuous Survey"
 
55:04
Lonna Atkeson of the University of New Mexico presented a talk entitled "Data Quality, Professional Respondents and Discontinuous Survey: Issues of Engagement, Knowledge and Satisficing." International Methods Colloquium talk, November 6th 2015. http://www.methods-colloquium.com
Views: 105 Methods Colloquium
Top 5 Benefits of Good Data Quality
 
02:36
In this episode of Blazent's Top 5, we review the top benefits respondents reported in May 2016 survey by 451 Research on Enterprise Data Quality.
Views: 153 Blazent
The Path to Data Quality Excellence
 
03:09
Video Transcript In the era of big data and software as a service, we are witnessing a major industry transformation. In order to stay competitive, businesses have reduced the time it takes to deploy a new application from months to minutes. Though most applications have been webified and big data tools woven in, but the data quality tools that ensure that the data is genuine have not. This leaves developers little choice but to ignore data quality issues within their applications. At Service Objects, we believe that data validation is a critical step to reducing waste, identifying fraud, and maximizing operation efficiency. We believe that businesses require flexibility for on demand data quality, just as they have with software as a service, without the worry of service interruption. We believe that data quality tools like address validation, email validation, and phone verification should be run on a shared pool of resources and served on demand so they can be consumed by virtually any application, anytime, anywhere. In short, we believe that an integrated, software as a service, big data analytics, and real time data quality is the secret to unlocking the most powerful change in business operations that we've seen in a decade. For this to work, we believe that every contact record in your database should be as accurate, genuine, and up-to-date as possible. So how do we do this? Service Objects data validation uses a massive propietary database of verified, good records to compare and match with records in your database. Each record is cleansed, standardized, and enhanced with genuine, accurate information. Suspicious records can be marked for review, and bogus records can be marked for deletion. When integrated, our approach can work with virtually any existing application, without requiring new hardware. The only requirement in your application is IP connectivity. Why is this important? Because 98% of all companies say that data quality is one of their top concerns. We know that 28% of mailing addresses are undeliverable. 50% of emails collected at point of sale are not deliverable, and 30% of all leads generated online are bogus. For our customers, quality, completeness, and the accuracy of a contact data trump pure volume. We believe simply that every contact record in your database should be as accurate, genuine, and up-to-date as possible. And this is exactly what we've been doing for over 2,400 elite retailers, service organizations, enterprises, and government agencies nationwide. For more information about our industry leading, real time data validation services, please visit our website at serviceobjects.com.
Strategic Planning for Data Quality Blueprints
 
12:18
Charles Gaddy, Director of Global Sales & Alliances at Melissa, discusses ground-breaking strategy around data quality blueprints, innovative solutions for Fintech, AI, and future-proofing your business.
Views: 79 MelissaDataCorp