Dirty Data - SparkPlugs
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This document discusses data management strategies. It begins by noting two common data problems organizations face: not knowing there is a data issue, and knowing there is an issue but not knowing how to address it. It then discusses how dirty data can negatively impact revenue by up to 30% and presents a simple formula for fixing data issues. The document outlines three types of data management: data validation, data standardization, and data appending. It then discusses implementing these three types of management in inbound data, inline data, and outbound data to maximize a CRM investment and revenue. Examples and tools to help with data management are also referenced.
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Simplilearn's Data Scientist Master’s Program will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, R Studio, Data Visualization, Regression models, Hadoop, Spark, PROC SQL, SAS Macros, Statistical procedures, tools and analytics, and many more. The courseware also covers a capstone project which encompasses all the key aspects from data extraction, cleaning, visualisation to model building and tuning. These skills will help you prepare for the role of a Data Scientist.
Who should take this course?
The data science role requires the perfect amalgam of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue the Data Scientist Master’s Program, including:
IT professionals
Analytics Managers
Business Analysts
Banking and Finance professionals
Marketing Managers
Supply Chain Network Managers
Those new to the data analytics domain
Students in UG/ PG Analytics Programs
Learn more at https://www.simplilearn.com/big-data-and-analytics/senior-data-scientist-masters-program-training
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Bound Tech is a top institute that provides hands-on Tableau training taught by experienced trainers using real-world scenarios and examples. The training covers fundamental concepts, advanced concepts, and job-oriented skills over 50-60 hours. Students learn how to rapidly analyze data, create dashboards and reports, and share analytics using features of Tableau. The course also provides skills needed for roles like business analyst, data scientist, and Tableau developer.
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Lead & opportunity stages, definitions & next steps sample
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What are Parameters in Tableau | Tableau Tutorial | Edureka
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Big Data Matching - How to Find Two Similar Needles in a Really Big Haystack
When consolidating multiple sources of information from across your organization, how do you find the records that relate to the same customer, the same company or the same product? This is the challenge faced by many businesses today when putting a data lake to work. The problem is made far worse when different systems may not have the same contact entered the same way. Is Bob Smith the same as Robert Smith? How about Dr. Robert L. Smith - is he the same person? What about Syncsort, Inc and Sinksort Corp.? Are those the same company? One must compare each individual record to every other record in the dataset with some very sophisticated matching algorithms to determine who is who, and you may have to compare the data multiple times in multiple ways to resolve each entity.
Just to add to the difficulty, let’s say your organization has very large volumes of records in your data lake - you don’t have to compare a thousand records to a thousand other records multiple times - you must compare a million to a million, or 100 million to 100 million. This kind of compute intensive comparison can bring even a powerful cluster to its knees.
This is a problem Syncsort customers must solve, and we have developed some very powerful and intelligent software to tackle it.
View this presentation as we discuss the challenges of entity resolution at scale, how Syncsort’s Trillium data quality software line has tackled them successfully in production clusters and see a demonstration of this software in action.
Data Quality
The document discusses the importance of data quality and having a data strategy. It notes that poor quality data can lead to skewed analysis, improper campaign targeting, and wasted resources. It also outlines steps for improving data quality such as data audits, profiling data sources, data cleansing, and establishing business rules for data management. Maintaining high quality data requires both internal processes and leveraging external data services and is a key part of building data as a strategic asset for the business.
InfoDataSphere Presentation
InfoDataSphere is a growth enablement partner that offers various data and marketing services. They have over 7 years of experience in the industry and work with top data vendors and Fortune 100 companies. Their services include data management, email campaigns, appending missing contact details, and more. They aim to help clients complete their business growth cycle and maximize their data potential.
InfoDataSphere Presentation
InfoDataSphere is a data management and marketing partner that offers services such as data compilation, cleansing, and appending. They maintain a database of over 62 million business contacts across 83 countries. Their data management process involves verifying, standardizing, and continuously updating contact information from various sources to provide clients with accurate lists and leads ready to be marketed to.
Make more confident business decisions with data you can trust
Key business initiatives revolve around transforming customer experiences, applying AI/ML to proven business use cases and increase efficiency, leveraging the power of location to drive new business insights, ensuring the business is competitive, secure, and compliant.
Each of these data-driven business initiatives heavily depends on the integration, governance, accuracy, consistency and enrichment of data to deliver the maximum benefits to the organisations who want to reach a competitive position in the marketplace in the shortest time.
Data Integrity - a journey to increase trust in data, accelerate data-driven transformation and produce better business results - is the key to transform your company into a successful data-driven one.
Discover more about the Precisely Data Integrity in this webcast.
How Data.com Drives Data Quality
Dun & Bradstreet collects data from over 30,000 global sources to maintain records on over 233 million companies worldwide. They use a proprietary process called DUNSRight that involves global data collection, entity matching, assigning D-U-N-S numbers, determining corporate linkages, and using predictive indicators to ensure high data quality. Data.com also sources data from its community of over 2 million members who contribute and update contact records, as well as validating data through technological processes. Both Data.com and Dun & Bradstreet have dedicated teams that continuously monitor data quality and make improvements to their methods.
Building a Data Quality Program from Scratch
The document outlines steps for building a data quality program from scratch, including defining data quality, identifying factors that impact quality, best practices, common causes of poor quality data, benefits of high quality data, and who is responsible. It then provides recommendations for getting started with a proof of concept, expanding to full projects, profiling data, analyzing and fixing issues, monitoring, and celebrating wins.
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The document discusses how poor data quality can negatively impact marketing ROI and provides strategies for addressing data quality issues. It suggests enhancing CRM data through data cleaning and integration with external sources to improve customer targeting, segmentation, and campaign analysis. Regular data maintenance is important to reduce costs from data issues and maximize returns from sales and marketing databases.
Data Marketing Show One Source Final
The document discusses how poor data quality can negatively impact marketing ROI and provides strategies for addressing data quality issues. It suggests enhancing CRM data through data cleaning and integration with external sources to improve customer targeting, segmentation, and overall marketing performance. Specific strategies proposed include balancing data collection with quality, standardizing data entry, and designating responsibility for data management.
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With recent studies indicating that 80% of AI and machine learning projects are failing due to data quality related issues, it’s critical to think holistically about this fact. This is not a simple topic – issues in data quality can occur throughout from starting the project through to model implementation and usage.
View this webinar on-demand, where we start with four foundational data steps to get our AI and ML projects grounded and underway, specifically:
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Data quality and bi
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Pioneer marketers services brochure
Pioneer Marketers is a global data and marketing outsourcing company with over 1,500 employees across multiple global delivery centers. They provide a wide range of marketing services including data management, digital marketing, lead generation, and customer relationship management. Their capabilities allow clients to penetrate new markets, increase lead volumes and conversions, and improve marketing efficiencies.
RingLead Applications Overview
RingLead creates easy-to-use yet powerful applications that will keep each record in your CRM and/or marketing automation database normalized, unique and complete.
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- Data Normalization
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From Compliance to Customer 360: Winning with Data Quality & Data Governance
Winning football teams will dominate opponents both defensively and offensively. Similarly, the most successful businesses will best utilize enterprise data for effective “defense” (e.g., compliance, such as GDPR and CCAR) as well as “offense” (increased customer engagement and revenue).
View our on-demand webcast and discover how integrated data quality and data governance tools help you confidently achieve regulatory compliance, as well as revenue-building initiatives requiring a 360-degree view of your customers.
Data management experts Ian Rowlands, Product Marketing Manager of ASG and Harald Smith, Director, Product Management of Trillium Software discusses how Trillium Software for data quality, integrated with ASG’s Enterprise Data Intelligence solution, helps you pinpoint where data quality impacts your business, ensuring your enterprise data can be trusted to drive regulatory compliance as well as better business decisions.
View this on-demand webcast to learn how to:
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• Gain insight into where data quality gaps may exist, which may impact regulatory compliance and customer engagement initiatives
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How to Overcome Your Data Quality Superstitions
Are you fearful that trying to fix your company’s data quality issues will result in 7 years of bad luck? Too often, data quality superstitions lead to paralysis by analysis. You don't need a rabbit's foot to make progress with your data management strategy, you just have to separate fact from fable.
Donato Diorio and Michael Farrington, two experts in CRM and marketing automation technology, dispel several common data superstitions providing tangible and actionable advice to ensure “good luck” for all who rely on your data.
Shannon duffy of salesforce clean datea and more sales - small biz summit 2012
The document contains a safe harbor statement for salesforce.com noting that any forward-looking statements could differ materially from actual results due to risks and uncertainties. It lists risks including developing new functionality, possible fluctuations in operating results, interruptions in web hosting, outcomes of litigation, risks associated with possible mergers and acquisitions, limited operating history, and utilization by larger enterprise customers. The statement says customers should make purchase decisions based on currently available features.
Into dq ed wrazen
Trillium Software provides a solution called Trillium Refine that allows users to gain insights from big data more quickly and easily. Trillium Refine provides self-service data preparation, integration, and quality capabilities. It allows users to discover, cleanse, normalize, and transform data from various sources for analysis. This overcomes challenges with traditional ETL approaches that are slow, costly and require IT expertise. Trillium Refine provides benefits like faster analytics, improved marketing through better customer insights, and increased efficiency.
Sound Data Quality for CRM
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.
White paper
Avail our Data Appending Services to ensure that your Data Management services are up-to-date with the latest information. We help you identify obsolete and missing data with most recent, relevant information to gain an edge over your competitors.
Moreover, our Data Cleansing Services promptly and accurately append the missing information in the database like email addresses, telephone numbers, fax numbers, titles etc and keeps your target lists error-free.
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Dirty Data - SparkPlugs
- 2. Introduction Josh Martin Senior Project Manager Sure Shot Media 972.202.9910 josh@sureshotmedia.com www.sureshotmedia.com @SSMM_JoshMartin • Eloqua Certified Partner • Offer full support for consulting, design and development projects • Developers of SparkPlugs
- 3. 2 Common Problems 1. Not knowing there’s a data problem
- 4. 2 Common Problems 1. Not 2. Knowing there is a data problem, but not knowing what to do about it.
- 5. The True Cost of Dirty Data An organization with a strong commitment to data quality can produce nearly 70 percent more revenue than a company with only average data- quality procedures. - Sirius Decisions
- 7. How Do We Fix It?
- 8. Building an Effective Data Strategy The ways you need to manage data + the places you need to manage data
- 9. 3 Types of Data Management Data Validation making sure data is correct and/or formatted properly Data Standardization making sure all data matches your standard values Data Appending adding new records or filling in missing info on existing ones
- 10. Data Validation Making sure data is complete and/or formatted properly E-mail addresses are correct, valid and standardized ZIP CodesTM contain at least five digits Incorrect or bogus data is eliminated Phone numbers are correct, valid and in the proper format
- 11. Data Standardization Making sure all data matches your standard values State and country fields comply with International Standardization Organization (ISO) formats Industries match the industry list Annual revenue ranges match the list Names are in lowercase and begin with a capital letter
- 12. Data Appending Adding new records or filling in missing info on existing ones Missing or incomplete company information B2B and B2C e-mail addresses New contacts or accounts Demographic information
- 13. 3 Places to Implement Data Mgmt. Inbound Data reduce the amount of bad inbound data on forms and lists, while enhancing new data added to your database Inline Data continually clean and enhance your database through intuitive programs and triggers Outbound Data validate data before it is sent to other systems
- 14. Inbound Data Reduce the amount of bad inbound data on forms and lists, while enhancing new data added to your database Verifying e-mail addresses on forms Verifying phone and address information Scrubbing data on uploaded lists
- 15. Inline Data Continually clean and enhance your database through intuitive programs and triggers Data quality programs Standard and custom Cloud Connectors Salesforce.com triggers
- 16. Outbound Data Validate data before it is sent to other systems CRM integration Marketing data warehouses List and data exports
- 17. Examples
- 18. Tools for Data mgmt. and more…. Apps to Help You Maximize Your Eloqua Investment - Data, Events, Channels, Content - www.sparkplugsdata.com