Big Data and Data Standardization at LinkedInAlexis Baird
From a talk I gave to a group of Connecticut College students in November of 2012. This looks at some of the challenges of dealing with huge amounts of member-inputted data as well as techniques used to solve these challenges and product applications of that member-inputted data.
-Discover information throughout the enterprise on network file shares, SharePoint, Office 365, and cloud file sharing services.
-Migrate, copy, or sync files to and from multiple platforms to enable a more organized and secure enterprise without impacting your end users' productivity.
-Improve the value of your data by removing redundant, obsolete, and trivial data from your enterprise.
The Data Cleansing Process - A Roadmap to Material Master Data QualityI.M.A. Ltd.
The document outlines a 12-step process for cleansing material master data to improve data quality. The steps include pre-cleanse evaluation, establishing data standards, reviewing test data, mining and standardizing existing data, enhancing data, identifying and consolidating duplicates, addressing incomplete records, quality control review, formatting data, final delivery and upload, and implementing post-cleanse governance. The overall goal is to assess current data quality, apply standard rules and formats, research and validate data, and maintain quality going forward.
This document provides information about data cleansing services offered by Gary Ng at Success Manager. It details two data cleansing software products from Data Ladder called Data Match 2010 and Data Cleaner Pro priced at $899 and $299 respectively. It also lists online and on-site training services priced at $50 and $100 per hour respectively. Additional information and demos of the Data Ladder products can be found online. It also mentions a parts list management software from BuyPLM that can help add new parts with strong control. Contact information is provided to ask any questions.
Brief Introduction to the 12 Steps of Evaluation Data CleaningJennifer Morrow
The document outlines 12 steps for cleaning evaluation data to ensure it is accurate, complete, high-quality, reliable, unbiased, and valid. The steps include creating a data codebook and analysis plan, performing frequency analyses to check for errors, modifying variables, assessing normality and missing data, and testing assumptions before final analyses. Following these steps can help produce credible, generalizable conclusions and avoid statistical issues.
Talk given at the 2015 Fall Regional in Oshkosh WI.
"An Approach to Address Parsing and Data Standardization"
Abstract:
Maintaining fully parsed address elements in your database can be one of the most beneficial steps toward
achieving quality and consistency in addressing. Parsed address elements also serve a preparatory step in
modeling an address toward NG9-1-1 supporting formats such as the FGDC address standard. In this talk,
we’ll take a look at the approach we’ve used for parsing site addresses for the V1 Statewide Parcel Map, the
role regular expressions played in this approach, and will unveil a suite of (free) ArcPy tools that can help you
parse addresses, standardize field values, and achieve other tasks.
Presenters:
Codie See
David Vogel
Big Data and Data Standardization at LinkedInAlexis Baird
From a talk I gave to a group of Connecticut College students in November of 2012. This looks at some of the challenges of dealing with huge amounts of member-inputted data as well as techniques used to solve these challenges and product applications of that member-inputted data.
-Discover information throughout the enterprise on network file shares, SharePoint, Office 365, and cloud file sharing services.
-Migrate, copy, or sync files to and from multiple platforms to enable a more organized and secure enterprise without impacting your end users' productivity.
-Improve the value of your data by removing redundant, obsolete, and trivial data from your enterprise.
The Data Cleansing Process - A Roadmap to Material Master Data QualityI.M.A. Ltd.
The document outlines a 12-step process for cleansing material master data to improve data quality. The steps include pre-cleanse evaluation, establishing data standards, reviewing test data, mining and standardizing existing data, enhancing data, identifying and consolidating duplicates, addressing incomplete records, quality control review, formatting data, final delivery and upload, and implementing post-cleanse governance. The overall goal is to assess current data quality, apply standard rules and formats, research and validate data, and maintain quality going forward.
This document provides information about data cleansing services offered by Gary Ng at Success Manager. It details two data cleansing software products from Data Ladder called Data Match 2010 and Data Cleaner Pro priced at $899 and $299 respectively. It also lists online and on-site training services priced at $50 and $100 per hour respectively. Additional information and demos of the Data Ladder products can be found online. It also mentions a parts list management software from BuyPLM that can help add new parts with strong control. Contact information is provided to ask any questions.
Brief Introduction to the 12 Steps of Evaluation Data CleaningJennifer Morrow
The document outlines 12 steps for cleaning evaluation data to ensure it is accurate, complete, high-quality, reliable, unbiased, and valid. The steps include creating a data codebook and analysis plan, performing frequency analyses to check for errors, modifying variables, assessing normality and missing data, and testing assumptions before final analyses. Following these steps can help produce credible, generalizable conclusions and avoid statistical issues.
Talk given at the 2015 Fall Regional in Oshkosh WI.
"An Approach to Address Parsing and Data Standardization"
Abstract:
Maintaining fully parsed address elements in your database can be one of the most beneficial steps toward
achieving quality and consistency in addressing. Parsed address elements also serve a preparatory step in
modeling an address toward NG9-1-1 supporting formats such as the FGDC address standard. In this talk,
we’ll take a look at the approach we’ve used for parsing site addresses for the V1 Statewide Parcel Map, the
role regular expressions played in this approach, and will unveil a suite of (free) ArcPy tools that can help you
parse addresses, standardize field values, and achieve other tasks.
Presenters:
Codie See
David Vogel
The presentation provided an overview of SAP's ASAP 8 methodology framework for SAP projects and services. Key points included:
1) ASAP 8 is SAP's prescriptive methodology for delivering projects and services across SAP's delivery models, including Rapid Deployment Solutions, Assemble to Order, and traditional design-based projects.
2) Over 200 SAP preconfigured services are based on ASAP 8's work breakdown structure to provide consistency.
3) Updates in ASAP 8 include improvements to quality gates, guidance for agile and design thinking approaches, and changes to the work stream structure.
4) ASAP 8 can be accessed on SAP's service marketplace and supports a
This document discusses data cleansing and provides steps in the data cleansing process. It defines data cleansing as detecting and correcting inaccurate or corrupt records in a database. The key steps described are parsing, correcting, standardizing, matching, and consolidating data. The goal of data cleansing is to clean data within and between databases to make information consistent and suitable for effective decision making. Metadata should document rules and data quality should be built into new systems through regular cleansing schedules.
The document discusses the implementation of SAP BI at Coca-Cola Co. Ltd. It describes the company's organizational structure and legacy systems challenges. It then outlines the SAP BI components implemented, including SAP SEM for planning and SAP Co for financial reporting. The system architecture integrated these components with a central data warehouse. Benefits included faster, more accurate decision-making. A case study of Coca-Cola Hellinic showed end-to-end process improvements and a platform for future growth after implementing SAP.
Coca-Cola Hellenic, one of the largest Coca-Cola bottlers worldwide, has started a three year long project to substitute all legacy systems with a SAP implementation called Wave 2, in order to maximize efficiencies in use of resources and apply common best practices and polices accross the group.
Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Best practice strategies to clean up and maintain your database with Hether G...Blackbaud Pacific
In this webinar Hether Ghelf, Blackbaud Pacific’s Senior Consultant & Project Manager, discusses a best practice approach to database cleaning and continued maintenance.
Cleansing your data can have an immediate impact on your business by increasing retention and response rates, decreasing the volume of mail returned from post, and ensuring mail is reaching your organisation’s constituents.
View the recording here: https://www.blackbaud.com.au/notforprofit-events/webinars/past
case study on ERP success(cadbury) and failure(hershey's)Chitrangada Roy
Cadbury implemented SAP ERP successfully, reducing costs through integrated systems. However, initial rollout caused excess inventory as production was not properly coordinated. Hershey rushed its ERP implementation in 2.5 years instead of 4, sacrificing testing. This caused order fulfillment issues, lost sales of $150M, and a 25% inventory increase, showing risks of compressed schedules. Both show ERP can integrate operations but must be carefully planned to avoid disruptions.
The document discusses SAP's AcceleratedSAP methodology for implementing SAP solutions. It describes the five phases of the methodology - Project Preparation, Business Blueprint, Realization, Final Preparation, and Go Live & Support. Each phase has specific goals and deliverables aimed at successfully implementing SAP and achieving business benefits. The methodology provides structure, guidance, and tools to help projects be on time, on budget and deliver business goals.
The document discusses techniques for deduplicating customer data including address matching, name matching, fuzzy matching, and using business rules. It covers matching techniques, processing deduplication, timing of deduplication, involving different teams, and having a mindset of continuous improvement. The goal is to increase data quality, compress data, and make the best effort to determine which records are duplicates while recognizing it may not be 100% accurate.
Poor data quality costs businesses significantly through wasted labor, lost productivity and direct financial losses. Rapid changes in circumstances like addresses, phone numbers, jobs and relationships mean that master data changes at a rate of around 2% per month. Siebel's data management solution and new data quality products from Oracle help address these issues through comprehensive data profiling, cleansing, matching and enrichment capabilities. Regular data stewardship is important for ongoing monitoring and governance to maintain high quality master data.
Are you ready to meet constantly evolving AML regulation requirements?
Don’t let lack of good data quality hinder your programs efficiency and effectiveness. Your KYC, AML and Sanction Screening systems are plagued by the poor customer DATA QUALITY they are working with.
Whether you want to improve detection, boost productivity, or mitigate risk, building a future-proof and accurate entity resolution all starts with your data.
Join us to find out how to, fix customer data quality, resolve entities with accuracy and precision to satisfy new regulations. Discover how to enhance compliance in KYC, AML, fraud prevention and sanction screening with a comprehensive view of customers, transactions and relationships based on high-quality data monitoring- according to your own KPIs.
Email Appending is one of the most taboo subjects in the digital marketing world. If done correctly, the potential ROI is ENORMOUS! But take a shortcut or miss one step in the process and you'll derail your entire email marketing program.
Join us for an in-depth look at the email appending process, how to get it right, and the benefits of doing so. We'll take a look at 4 leading brands that append responsibly and reap the game-changing benefits. We'll also provide some real-life examples of appending project gone wrong.
This can't-miss session will teach you everything you always wanted to know about email appending but were too afraid to ask.
eCommerce Trends and Strategic Planning for 2024PushON Ltd
Loqate are part of the enormously successful GBG group. They do interesting things with data which means that they can provide genuine insight into eCommerce. Alex Bryan, Customer Success Manager for Loqate will prep you for 2024
This document discusses the importance of data quality and data governance. It states that poor data quality can lead to wrong decisions, bad reputation, and wasted money. It then provides examples of different dimensions of data quality like accuracy, completeness, currency, and uniqueness. It also discusses methods and tools for ensuring data quality, such as validation, data merging, and minimizing human errors. Finally, it defines data governance as a set of policies and standards to maintain data quality and provides examples of data governance team missions and a sample data quality scorecard.
Creating Service Strategy For Your Organization Complete DeckSlideTeam
Create an unbeatable service for the customers with our exhaustively covered Service Strategy PowerPoint presentation. The service sector is one of the biggest sectors of the economy. Service industry includes services such as information technology, banking, hospitality, travel, transportation, healthcare, finance, insurance, retail, e commerce, marketing, sales, and many more. Service providers need to provide the most service value to customers to succeed in the competitive market. Service companies also need to keep improving their service quality to attract customers. Learn the dimensions of service quality and ways to increase service value for superiority. Also learn the ways to acquire competitive advantage in the market and perform better than other services. Access our fully customizable services templates to make a service strategy that yields you rich dividends. Check out whether you need to introduce a new service or upgrade your existing services depending on market conditions, customer needs, and your current business performance. Creating a service plan requires a methodological approach that captures market data and customer data. Learn all about implementing service strategy in your organization with this powerful presentation. https://bit.ly/3cbISR4
Creating Service Strategy For Your Organization Complete DeckSlideTeam
Create an unbeatable service for the customers with our exhaustively covered Service Strategy PowerPoint presentation. The service sector is one of the biggest sectors of the economy. Service industry includes services such as information technology, banking, hospitality, travel, transportation, healthcare, finance, insurance, retail, e commerce, marketing, sales, and many more. Service providers need to provide the most service value to customers to succeed in the competitive market. Service companies also need to keep improving their service quality to attract customers. Learn the dimensions of service quality and ways to increase service value for superiority. Also learn the ways to acquire competitive advantage in the market and perform better than other services. Access our fully customizable services templates to make a service strategy that yields you rich dividends. Check out whether you need to introduce a new service or upgrade your existing services depending on market conditions, customer needs, and your current business performance. Creating a service plan requires a methodological approach that captures market data and customer data. Learn all about implementing service strategy in your organization with this powerful presentation. https://bit.ly/3cbISR4
How RingCentral Optimized Account-Based Insights and Buyer Intelligence To Ra...G3 Communications
Access the full webcast here: https://dg-r.co/2L5QdrM
Data fuels every marketer’s strategy, but not all data is created equal. To be successful today, marketers need to be able to effectively analyze, optimize and maintain data accuracy as the first steps to gaining true insight. This is particularly true for account-based programs, where visibility into key decision makers is imperative. As the saying goes, “Garbage in, garbage out.” If the data going into your CRM is incomplete, incorrect or simply not the right data, any programs that rely on that data will deliver disappointing results.
During this webinar, David Cowings, Chief Marketing Data Scientist at RingCentral, and Chris Lynde, CEO of SaleScout Data Solutions, will share the steps needed to optimize buyer contact data and sales intelligence, with real-life insights from RingCentral’s demand gen data optimization process.
Big Data - it's the big buzz. But is it dead on arrival?
In this presentation Daragh O Brien looks at the history of information management, the challenges of data quality and governance, and the implications for big data...
The document discusses common issues with information and communication technology (ICT) projects, including frequent failures and cost overruns. It analyzes findings from a study of over 100 companies that found 68% of large ICT projects facing issues and 50% exceeding budgets and timelines. Common causes identified include unrealistic expectations from sales, inflexible processes and staff, poor management, and failure to properly define requirements or involve key users and stakeholders. The document proposes approaches like mapping current systems and flows, critical path analysis, listening to organizational needs, and modular development to help address these issues.
Optimizing address capture is something that is often overlooked from a user and business perspective. But clean address capture can enhance the user experience, not only while entering an address, but also during the whole customer journey. At the same time, it can also help an organization to successfully recognize, market to, and extract value from, its customer base. In this webinar, we'll take a look at:
• How to get data right from the start with only seven keystrokes and avoid bad data
• Enhancing the user experience with seven keystrokes
• Cost benefits of improved address capture in the call center
• Case studies covering better customer targeting and improved logistics
• Address capture from a global perspective
The presentation provided an overview of SAP's ASAP 8 methodology framework for SAP projects and services. Key points included:
1) ASAP 8 is SAP's prescriptive methodology for delivering projects and services across SAP's delivery models, including Rapid Deployment Solutions, Assemble to Order, and traditional design-based projects.
2) Over 200 SAP preconfigured services are based on ASAP 8's work breakdown structure to provide consistency.
3) Updates in ASAP 8 include improvements to quality gates, guidance for agile and design thinking approaches, and changes to the work stream structure.
4) ASAP 8 can be accessed on SAP's service marketplace and supports a
This document discusses data cleansing and provides steps in the data cleansing process. It defines data cleansing as detecting and correcting inaccurate or corrupt records in a database. The key steps described are parsing, correcting, standardizing, matching, and consolidating data. The goal of data cleansing is to clean data within and between databases to make information consistent and suitable for effective decision making. Metadata should document rules and data quality should be built into new systems through regular cleansing schedules.
The document discusses the implementation of SAP BI at Coca-Cola Co. Ltd. It describes the company's organizational structure and legacy systems challenges. It then outlines the SAP BI components implemented, including SAP SEM for planning and SAP Co for financial reporting. The system architecture integrated these components with a central data warehouse. Benefits included faster, more accurate decision-making. A case study of Coca-Cola Hellinic showed end-to-end process improvements and a platform for future growth after implementing SAP.
Coca-Cola Hellenic, one of the largest Coca-Cola bottlers worldwide, has started a three year long project to substitute all legacy systems with a SAP implementation called Wave 2, in order to maximize efficiencies in use of resources and apply common best practices and polices accross the group.
Description of four techniques for Data Cleaning:
1.DWCLEANER Framework
2.Data Mining Techniques include Association Rule and Functional Dependecies
,...
Best practice strategies to clean up and maintain your database with Hether G...Blackbaud Pacific
In this webinar Hether Ghelf, Blackbaud Pacific’s Senior Consultant & Project Manager, discusses a best practice approach to database cleaning and continued maintenance.
Cleansing your data can have an immediate impact on your business by increasing retention and response rates, decreasing the volume of mail returned from post, and ensuring mail is reaching your organisation’s constituents.
View the recording here: https://www.blackbaud.com.au/notforprofit-events/webinars/past
case study on ERP success(cadbury) and failure(hershey's)Chitrangada Roy
Cadbury implemented SAP ERP successfully, reducing costs through integrated systems. However, initial rollout caused excess inventory as production was not properly coordinated. Hershey rushed its ERP implementation in 2.5 years instead of 4, sacrificing testing. This caused order fulfillment issues, lost sales of $150M, and a 25% inventory increase, showing risks of compressed schedules. Both show ERP can integrate operations but must be carefully planned to avoid disruptions.
The document discusses SAP's AcceleratedSAP methodology for implementing SAP solutions. It describes the five phases of the methodology - Project Preparation, Business Blueprint, Realization, Final Preparation, and Go Live & Support. Each phase has specific goals and deliverables aimed at successfully implementing SAP and achieving business benefits. The methodology provides structure, guidance, and tools to help projects be on time, on budget and deliver business goals.
The document discusses techniques for deduplicating customer data including address matching, name matching, fuzzy matching, and using business rules. It covers matching techniques, processing deduplication, timing of deduplication, involving different teams, and having a mindset of continuous improvement. The goal is to increase data quality, compress data, and make the best effort to determine which records are duplicates while recognizing it may not be 100% accurate.
Poor data quality costs businesses significantly through wasted labor, lost productivity and direct financial losses. Rapid changes in circumstances like addresses, phone numbers, jobs and relationships mean that master data changes at a rate of around 2% per month. Siebel's data management solution and new data quality products from Oracle help address these issues through comprehensive data profiling, cleansing, matching and enrichment capabilities. Regular data stewardship is important for ongoing monitoring and governance to maintain high quality master data.
Are you ready to meet constantly evolving AML regulation requirements?
Don’t let lack of good data quality hinder your programs efficiency and effectiveness. Your KYC, AML and Sanction Screening systems are plagued by the poor customer DATA QUALITY they are working with.
Whether you want to improve detection, boost productivity, or mitigate risk, building a future-proof and accurate entity resolution all starts with your data.
Join us to find out how to, fix customer data quality, resolve entities with accuracy and precision to satisfy new regulations. Discover how to enhance compliance in KYC, AML, fraud prevention and sanction screening with a comprehensive view of customers, transactions and relationships based on high-quality data monitoring- according to your own KPIs.
Email Appending is one of the most taboo subjects in the digital marketing world. If done correctly, the potential ROI is ENORMOUS! But take a shortcut or miss one step in the process and you'll derail your entire email marketing program.
Join us for an in-depth look at the email appending process, how to get it right, and the benefits of doing so. We'll take a look at 4 leading brands that append responsibly and reap the game-changing benefits. We'll also provide some real-life examples of appending project gone wrong.
This can't-miss session will teach you everything you always wanted to know about email appending but were too afraid to ask.
eCommerce Trends and Strategic Planning for 2024PushON Ltd
Loqate are part of the enormously successful GBG group. They do interesting things with data which means that they can provide genuine insight into eCommerce. Alex Bryan, Customer Success Manager for Loqate will prep you for 2024
This document discusses the importance of data quality and data governance. It states that poor data quality can lead to wrong decisions, bad reputation, and wasted money. It then provides examples of different dimensions of data quality like accuracy, completeness, currency, and uniqueness. It also discusses methods and tools for ensuring data quality, such as validation, data merging, and minimizing human errors. Finally, it defines data governance as a set of policies and standards to maintain data quality and provides examples of data governance team missions and a sample data quality scorecard.
Creating Service Strategy For Your Organization Complete DeckSlideTeam
Create an unbeatable service for the customers with our exhaustively covered Service Strategy PowerPoint presentation. The service sector is one of the biggest sectors of the economy. Service industry includes services such as information technology, banking, hospitality, travel, transportation, healthcare, finance, insurance, retail, e commerce, marketing, sales, and many more. Service providers need to provide the most service value to customers to succeed in the competitive market. Service companies also need to keep improving their service quality to attract customers. Learn the dimensions of service quality and ways to increase service value for superiority. Also learn the ways to acquire competitive advantage in the market and perform better than other services. Access our fully customizable services templates to make a service strategy that yields you rich dividends. Check out whether you need to introduce a new service or upgrade your existing services depending on market conditions, customer needs, and your current business performance. Creating a service plan requires a methodological approach that captures market data and customer data. Learn all about implementing service strategy in your organization with this powerful presentation. https://bit.ly/3cbISR4
Creating Service Strategy For Your Organization Complete DeckSlideTeam
Create an unbeatable service for the customers with our exhaustively covered Service Strategy PowerPoint presentation. The service sector is one of the biggest sectors of the economy. Service industry includes services such as information technology, banking, hospitality, travel, transportation, healthcare, finance, insurance, retail, e commerce, marketing, sales, and many more. Service providers need to provide the most service value to customers to succeed in the competitive market. Service companies also need to keep improving their service quality to attract customers. Learn the dimensions of service quality and ways to increase service value for superiority. Also learn the ways to acquire competitive advantage in the market and perform better than other services. Access our fully customizable services templates to make a service strategy that yields you rich dividends. Check out whether you need to introduce a new service or upgrade your existing services depending on market conditions, customer needs, and your current business performance. Creating a service plan requires a methodological approach that captures market data and customer data. Learn all about implementing service strategy in your organization with this powerful presentation. https://bit.ly/3cbISR4
How RingCentral Optimized Account-Based Insights and Buyer Intelligence To Ra...G3 Communications
Access the full webcast here: https://dg-r.co/2L5QdrM
Data fuels every marketer’s strategy, but not all data is created equal. To be successful today, marketers need to be able to effectively analyze, optimize and maintain data accuracy as the first steps to gaining true insight. This is particularly true for account-based programs, where visibility into key decision makers is imperative. As the saying goes, “Garbage in, garbage out.” If the data going into your CRM is incomplete, incorrect or simply not the right data, any programs that rely on that data will deliver disappointing results.
During this webinar, David Cowings, Chief Marketing Data Scientist at RingCentral, and Chris Lynde, CEO of SaleScout Data Solutions, will share the steps needed to optimize buyer contact data and sales intelligence, with real-life insights from RingCentral’s demand gen data optimization process.
Big Data - it's the big buzz. But is it dead on arrival?
In this presentation Daragh O Brien looks at the history of information management, the challenges of data quality and governance, and the implications for big data...
The document discusses common issues with information and communication technology (ICT) projects, including frequent failures and cost overruns. It analyzes findings from a study of over 100 companies that found 68% of large ICT projects facing issues and 50% exceeding budgets and timelines. Common causes identified include unrealistic expectations from sales, inflexible processes and staff, poor management, and failure to properly define requirements or involve key users and stakeholders. The document proposes approaches like mapping current systems and flows, critical path analysis, listening to organizational needs, and modular development to help address these issues.
Optimizing address capture is something that is often overlooked from a user and business perspective. But clean address capture can enhance the user experience, not only while entering an address, but also during the whole customer journey. At the same time, it can also help an organization to successfully recognize, market to, and extract value from, its customer base. In this webinar, we'll take a look at:
• How to get data right from the start with only seven keystrokes and avoid bad data
• Enhancing the user experience with seven keystrokes
• Cost benefits of improved address capture in the call center
• Case studies covering better customer targeting and improved logistics
• Address capture from a global perspective
GDPR for Things - ThingsCon Amsterdam 2017Saskia Videler
This document provides an overview of key concepts regarding the GDPR (General Data Protection Regulation) and best practices for compliance. It discusses the GDPR requirements around privacy, data protection principles, and data subject rights. It also covers topics like data flow mapping, privacy policies, consent, and data minimization. The document emphasizes the importance of understanding what personal data is, knowing what data you collect and how it flows, allowing users to access and delete their data, and fixing privacy policies to be clear and understandable for users.
This document summarizes a webinar series from CRM360 on improving customer data quality. The series is presented by Ticomix, SugarCRM, D&B, Nurture Marketing and Bristol Strategy Group. The first part focuses on "Dirty Data Done Dirt Cheap" and discusses how poor customer data quality in CRMs can cost companies millions per year. Common data quality issues are outlined along with how accurate customer data can help sales, marketing and customer support. The webinar promotes D&B's data services for improving customer data quality and providing enriched customer profiles. Future webinars will focus on nurturing customers, optimizing sales pipelines, and leveraging CRM for success.
WhatCounts & FreshAddress break down 8 actionable, no-cost strategies to collect the maximum quantity of email addresses while maintaining database quality. We'll show you how to put these tips into action as well as how to effectively utilize third-party services to support your email efforts.
Want your bank to trust you? You need a credit score. Want your customers to ...YeurDreamin'
The document discusses how the Post Office in the UK uses Salesforce platforms like the Partner Community, Sales Cloud, and Marketing Cloud to integrate data from its 11,500 branches and manage customer relationships. It emphasizes the importance of trusting data processes and users by outlining how the Post Office validates data entry, manages duplicates, and measures data quality with a "Trust Score" to ensure clean customer records. Maintaining trusted and high-quality data is important for campaign performance, lead conversion, customer retention, and making effective business decisions.
The Accountant Entrepreneur — Doug SleeterSleeter Group
The Accountant Entrepreneur — Evolve Your Business Model For Maximum Growth
Moving your practice —and your clients —to the cloud is about more than technology. It's a fundamental shift in your business model, a combination of broadening your abilities with today's technology and redesigning your business to maximize growth.
There are compelling new business models for accountants who want to take charge of their future. This presentation presents a roadmap to help you tackle new services for new clients, how to select the right vertical market focus, and how to drive success by driving improvements in business processes with your clients.
Nonprofit data migration: You can't take it all with you WebinarThird Sector Labs
This document summarizes a webinar about the challenges of migrating donor data from an old customer relationship management (CRM) system to a new one. It discusses how data degrades over time through various causes like lack of standards, technology issues, and changes in people's lives. When moving to a new CRM, organizations cannot simply transfer all their old data and must apply data governance standards to prioritize what gets migrated. The success of a data migration should be measured by how well the new system supports planned activities, not just by the percentage of old data transferred. Archives of the old data and ongoing data management are also recommended.
Your CRM data is a perishable asset when left on its own, it’s value will diminish unless you look after it well. Improve data quality to boost your marketing ROI. Cleanse, update and validate your CRM databases to fill in missing information. Ensure all of your data is accurate and consistent regardless of the data source to establish more meaningful customers’ interactions. Explore CRMIT's Data Management Services
Launch Your Streaming Platforms in MinutesRoshan Dwivedi
The claim of launching a streaming platform in minutes might be a bit of an exaggeration, but there are services that can significantly streamline the process. Here's a breakdown:
Pros of Speedy Streaming Platform Launch Services:
No coding required: These services often use drag-and-drop interfaces or pre-built templates, eliminating the need for programming knowledge.
Faster setup: Compared to building from scratch, these platforms can get you up and running much quicker.
All-in-one solutions: Many services offer features like content management systems (CMS), video players, and monetization tools, reducing the need for multiple integrations.
Things to Consider:
Limited customization: These platforms may offer less flexibility in design and functionality compared to custom-built solutions.
Scalability: As your audience grows, you might need to upgrade to a more robust platform or encounter limitations with the "quick launch" option.
Features: Carefully evaluate which features are included and if they meet your specific needs (e.g., live streaming, subscription options).
Examples of Services for Launching Streaming Platforms:
Muvi [muvi com]
Uscreen [usencreen tv]
Alternatives to Consider:
Existing Streaming platforms: Platforms like YouTube or Twitch might be suitable for basic streaming needs, though monetization options might be limited.
Custom Development: While more time-consuming, custom development offers the most control and flexibility for your platform.
Overall, launching a streaming platform in minutes might not be entirely realistic, but these services can significantly speed up the process compared to building from scratch. Carefully consider your needs and budget when choosing the best option for you.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Utilocate offers a comprehensive solution for locate ticket management by automating and streamlining the entire process. By integrating with Geospatial Information Systems (GIS), it provides accurate mapping and visualization of utility locations, enhancing decision-making and reducing the risk of errors. The system's advanced data analytics tools help identify trends, predict potential issues, and optimize resource allocation, making the locate ticket management process smarter and more efficient. Additionally, automated ticket management ensures consistency and reduces human error, while real-time notifications keep all relevant personnel informed and ready to respond promptly.
The system's ability to streamline workflows and automate ticket routing significantly reduces the time taken to process each ticket, making the process faster and more efficient. Mobile access allows field technicians to update ticket information on the go, ensuring that the latest information is always available and accelerating the locate process. Overall, Utilocate not only enhances the efficiency and accuracy of locate ticket management but also improves safety by minimizing the risk of utility damage through precise and timely locates.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Why Mobile App Regression Testing is Critical for Sustained Success_ A Detail...kalichargn70th171
A dynamic process unfolds in the intricate realm of software development, dedicated to crafting and sustaining products that effortlessly address user needs. Amidst vital stages like market analysis and requirement assessments, the heart of software development lies in the meticulous creation and upkeep of source code. Code alterations are inherent, challenging code quality, particularly under stringent deadlines.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
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#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
OpenMetadata Community Meeting - 5th June 2024OpenMetadata
The OpenMetadata Community Meeting was held on June 5th, 2024. In this meeting, we discussed about the data quality capabilities that are integrated with the Incident Manager, providing a complete solution to handle your data observability needs. Watch the end-to-end demo of the data quality features.
* How to run your own data quality framework
* What is the performance impact of running data quality frameworks
* How to run the test cases in your own ETL pipelines
* How the Incident Manager is integrated
* Get notified with alerts when test cases fail
Watch the meeting recording here - https://www.youtube.com/watch?v=UbNOje0kf6E
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
1. Data, how to get it clean
and keep it clean?
The best way to make money is to stop wasting it!
2. Agenda:
Who are DQ
Setting the scene
Acceptable Quality
Data Defects
Get it Clean
Keep it Clean
Q&A via web chat
Close
3. Setting the scene…
Who are
we ?
What do
we do ?
How do
we do it
?
What’s in
it for our
clients ?
4. UK B2C Data – annual rates of change…
UK Population is 63.23 M
UK Households 26.4 M
• Over 3.25 M (5.1%) people move house
• 0.584 M (0.9%) people pass away
• 0.813 M (1.3%) Births
• 0.290 M (0.5%) Marry
• 0.130 M (0.2%) Divorce
• 0.500 M (1.9%) Changes by Royal Mail
• 0.250 M (1.4%) people sign up to MPS
½ life of B2C data 1 to 1.2 years
5. UK B2B Data – annual rates of change…
4.934 M trading businesses in the UK
• 3.10 M (62.8%) sole proprietorships
• 0.43 M (8.8%) partnerships
• 1.40 M (28.4%) limited companies
• 0.60 M (12.2%) dormant businesses
5.7 M company or individual details changes:
• 1 moves every 6 Minutes
• 1 fails every 4 minutes
On average a person changes jobs 11 times
during their career
Over 1.1 M (22.3%) businesses are registered with the CTPS
2.43 M employees of UK businesses:
• 99.9% of businesses employ less than 250 staff
• 99.2% of businesses employ less than 50 people who employ 59% of total
staff
@ 24% p.a. ½ life attrition = 3 years
@ 35% p.a. ½ life attrition = 2 years
6. Data decay – the impacts…
Financial:
• £220 M per-annum wasted on inaccurate mailings
• £95 M per-annum wasted by companies mailing people who have moved addresses
• It costs more to mail a moved or deceased individual than to suppress them
• Increase response rates – the same return with less mail
Brand:
• Duplicates and incorrect details cause a negative perception
• Mailing deceased individuals or bereaved families causes significant distress
• Mailing someone who no longer lives at an address does not impress
Compliance:
• Best practice – comply with Direct Marketing Association guidelines
• Calling a consumer who has registered their objection to receiving direct marketing phone calls is illegal
• Mailing a consumer who has registered their objection to receiving direct mail is bad management, contravenes the
DMA Code of Practice and could be illegal
Environment
• Protect the environment – help cut down on wasteful mailing
8. The Data Quality Delusion
Everyone
understand the
importance of
data quality
Everyone agrees
data quality is
important
Everyone cares
about data
quality
Everyone knows
what actions to
take to improve
data quality
10. Open Area
Known to others and
known to self
Blind Area
Known to others not
known to self
Hidden Area
Not known to others
and known to self
Unknown Area
Unknown to others
and unknown to self
Johari
Window
Johari Window - You don‟t know what you
don‟t know...
Self
Others
Expand the Open Area
ReduceBlindArea
Reduce the Hidden Area ?
Johari
Window
11. Acceptable levels of data quality?
All data has some level of
quality, the question is at
what level is it
unacceptable?
How does
anyone
know?
Who‟s
responsible?
How much
is low
quality data
actually
costing?
Unacceptable
Acceptable
12. All data has some level of quality, the
question is at what level is it unacceptable.
Temp
< 37°C
Hyperthermia
Temp
= 37°C
Normal
Temp
> 37°C
Abnormal
Temp
> 37.8°C
Get help
13. How can we end up with bad data?
A Boy's name
beginning
with the
letter J:
"Gerald.."
A word
beginning
with Z:
"Xylophone.."
A part of the
body
beginning
with N:
"Knee..“
A mode of
transport
that you can
walk in: "Your
shoes.."
14. Getting your data clean and keeping it clean
Identify, correct, prevent
15. Get it Clean the basics
About “CURING” data defects
Batch process automation
Mass defect identification
• Mastering & Merging
• Manual review
Time consuming
More costly than prevention
16. Keep it Clean the basics
Prevention better than cure
Ongoing process
• People
• Process
• Technology
Costs of prevention many times
lower than cure!
17. Waging war on error…
Findingdefects
Definingstandards
Correctingdata
Preventingerror
Monitoringdefects
Referencedata
Internaldata
18. Boolean Logic & Dates
DD/MM/YY v MM/DD/YY
•10/10/09 = 10/10/09
•99/99/99 was accepted as a
valid date structure yet it‟s
clearly wrong
Is it European
format
DD/MM/YYYY or US
format
MM/DD/YYYY?
Precision
•DD/MM/YY or
DD/MM/YYYY
OK to
Mail =
Y
Not OK
to Mail
= Y
OK to
Mail =
N
Not OK
to Mail
= N
19. Numbers in Text and Shared Numbers
Systems
Contain:
• 0‟s and/or O‟s
• 1‟s and/or I‟s
• Tel numbers with
9 x 000 000 000
Same product
– different
numbers in 2
systems
• Same Part number 99 000 1111
• 99 000 1111 = 1 days cold ration pack
• 99 000 1111 = Radio valves
• Leasing Agreement numbers
• ID Counters shared across systems
• SKU‟s
• Tank & Aircraft Parts
20. Misinterpretation & Standards
M = Male in one
system and
Married in
another
S = Single in
one system
and
Separated in
another
Gender
•9 variants in
the gender
field of a hotel
project
Padhraic, Pádraig or Páraic
Lane, LN, Ln, Road, Rd, Rd. etc.
MI or Michigan
US or USA or United States
GB or UK or United Kingdom
Mr. or Mister
Hants or Hampshire
21. Dislocation, misfielding
Address A Address B
123 Arcasia Avenue 123 Arcasia Ave
Fareham
Hampshire Fareham
PO16 8XT Hants
PO16 8XT
Person A Person B
Martin
P Martin P
Doyle Doyle
02392 988303 +1 312-253-7873
+1 312-253-7873 02392 988303
22. Anomalies & Congruence
eMail does
not tally with
name parts
Currency does
not tally with
location
Goods
shipped
before order
Values not in
application
pick lists
(metadata)
Default
values used
Notes (memo)
fields used
without
validation
rules
23. DQ Studio – identifying and fixing
• Product demonstration by:
• Martin Kerr
• How to connect, identify and
correct defects…
24. DQ Studio
Classify
•Is the data in your database what you think it
is?
Compare
•How similar is value A to value B in % similarity
Format
•Email
•I.P.
•Postcode
•Telephone
•URL
Generate:
•phonetic tokens
•pattern tokens
Transform data
•13 Categories
•5 Spoken Languages
Validate
•Email
•I.P. Address
•Postal code
•Telephone
•URL
25. DQ Studio
Derive:
• Job Title
• Role
• Level
• Gender
• Male, female, unknown
• Telephone
• Country
• Location
• Number Type
Parse:
• Email
• I.P. Address
• Telephone
Verify
• Locations (240 Countries)
• Phones
• Businesses
• Contacts
27. Matching – What is it?
• Identification and
management of records
which:
• Are the same
• Might be the same
• Are not the same
•PAF Batch
•PAF Lookup
•No Way
•Gone Away
•Passed Away
•Append
•Table v Table•Table v Itself
Dedupe
X-
Match
X-Ref
API
X-Ref
Data
28. How is it done?
Black White
Manually
•Internally
•External Bureau service
Automatically •Software
Using black and
white magic...
•Black = Matches
•White = Non Matches
•Grey = Ambiguous
Carefully to
avoid:
•Too many matches
•Too few matches
•Errors in matches
29. The grey areas - When is a match a match?
Bob = Bobby = Rob= Robert
= Robby= Roberto?
Thomson = Thompson =
Tomson = Thomson?
Xerox = Zerocks? PO16 8XT = P0I6 8XT?
+44 (0) 2392 988303 =
O2392 9883O3?
10TH Feb 2009 = 10/02/09
= 02/10/2009?
Hants = Hampshire =
Hamps?
martin.doyle@dqglobal.com
=
doyle.martin@dqglobal.com
30. Grey to Black or Grey to White
• Transformations (Synonyms)
• Phonetics
• String comparisons
• Intelligence
• Rules
• Spelling
• Typo‟s
• Logic
• Experience
• Lookups
31. Mastering Perfection & merging?
Problems:
• Which data survives?
• Which data gets re-assigned?
• Which data gets stored?
• Which data gets thrown away
Solutions:
• Define the record master
• Define the field merge rules
• Use technology to automate
processes
• Humanise exceptions
35. Cleaning up your business systems:
Back-up your data
Define pick lists
Ensure legacy data conforms to picklists
Delete any temporary fields set-up for test and still in the
production system
Delete or archive old data
Identify contacts with no email and/or no telephone #
Identify and correct contacts with bogus phone numbers
Identify records whose email bounces
Identify businesses without contacts
Archive linked documents which are „n‟ years old,
however, take care with legal including: invoices and
contracts
User admin – delete any users who no longer access
systems
Review any prospects, suspects or opportunities not
properly closed i.e. > „n‟ weeks from opening
36. Actions to consider…
Change attitudes to “ABC” thinking
Think prevention not cure
Apply DQ processes
Verify, Format & Validate
Suppress records
Merge duplicates
Append missing data for segmentation
Govern and Comply
Measure & Manage
Get a CXO sponsor
Prune & Consolidate & Remove competition
Common dictionary of terms
Define customer value, and lifetime?
37. In conclusion…
Identify
•recognise there is
a problem?
Qualify
•gather evidence,
what, when,
where and how
large is the
problem?
Quantify
•what‟s
specifically doing
the damage?
Accept
•acknowledge the
scale of the task?
Define
•the goals and
what will be
measured?
Perform
•carry out the
tasks agreed in
the order or
significance
38. Questions…
• Build a better business based on trusted
data…
• Contact DQ Global
• www.DQGlobal.com
• Talk to a consultant
• sales@DQGlobal.com
• +44 2392 988303 (Europe)
• +1 314-253-7873(North America)
Editor's Notes
Its inherently true that at some level everyone understand the importance of data qualityGenerally, everyone agrees data quality is importantNot true that everyone cares about data qualityCertainly not true that everyone knows what actions to take to improve data quality
Idea is to maximise the Open Area so that we all know as much as possible...This is why data profiling is critical to success in DQ Projects, if you dont know where you are you can’t plot a journey to where you’re going.
Without some means of measurement - how does anyone know?Without governance how does anyone know who’s responsible?Without Measurement and Governance and an understanding of the downstream impacts of data quality, how does any business know how much low quality data is actually costing?
MDIt doesn’t matter what the room temperature is, its always room temperature – Stephen Wright.In our scenario, if this scale related to body temperature, then too cold hyperthermia could be an issue, too hot and feverish then all sorts of complications are possible.
Answers from a game show... Called Family Fortunes... Where the hard of thinking gave these answers which I thought were applicable to our context.A Boy's name beginning with the letter J: "Gerald.."A word beginning with Z: "Xylophone.."A part of the body beginning with N: "Knee..“And now you know why SoundEX does not work well as a matching algorithm...A mode of transport that you can walk in: "Your shoes..“ - That’s what happens with free text fields in databases – no validation!