What is involved in Data Integration
Find out what the related areas are that Data Integration connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Data Integration thinking-frame.
How far is your company on its Data Integration journey?
Take this short survey to gauge your organization’s progress toward Data Integration leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.
To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.
Start the Checklist
Below you will find a quick checklist designed to help you think about which Data Integration related domains to cover and 244 essential critical questions to check off in that domain.
The following domains are covered:
Data Integration, Virtual database, Ontology-based data integration, Database model, Data compression, Computer data storage, Three schema approach, Data pre-processing, Information server, Open Text, Data blending, Resource depletion, Wrapper pattern, Data virtualization, Service-oriented architecture, Data Integration, Data architecture, Web application, Data modeling, First-order logic, Data cleansing, Data editing, Data loss, European Bioinformatics Institute, Data quality, Innovative Medicines Initiative, Enterprise architecture framework, Data wrangling, Data mediation, Semantic integration, Data curation, Data reduction, Web service, Data security, Enterprise integration, Data mining, Object-relational mapping, Information integration, Data hub, Relational database, Invasive species, Schema matching, Data analysis, Data scrubbing, Query optimizer, Data corruption, Local As View, Materialized view, Metadata standards, National Science Foundation, Data integrity, Data fusion, Data lake, Data farming, Extract, transform, load, Information explosion, Research Data Alliance, Data validation, Data mapping, Information silo, Conjunctive query, Master data management, Alon Y. Halevy, Integration Consortium:
Data Integration Critical Criteria:
Shape Data Integration failures and point out improvements in Data Integration.
– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?
– What business benefits will Data Integration goals deliver if achieved?
– What are the short and long-term Data Integration goals?
– Which Oracle Data Integration products are used in your solution?
Virtual database Critical Criteria:
Analyze Virtual database failures and be persistent.
– Will Data Integration have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– What are your most important goals for the strategic Data Integration objectives?
Ontology-based data integration Critical Criteria:
Study Ontology-based data integration strategies and revise understanding of Ontology-based data integration architectures.
– Do we monitor the Data Integration decisions made and fine tune them as they evolve?
– Are there recognized Data Integration problems?
Database model Critical Criteria:
Frame Database model goals and innovate what needs to be done with Database model.
– What will be the consequences to the business (financial, reputation etc) if Data Integration does not go ahead or fails to deliver the objectives?
– Risk factors: what are the characteristics of Data Integration that make it risky?
– How will we insure seamless interoperability of Data Integration moving forward?
Data compression Critical Criteria:
Have a meeting on Data compression tasks and give examples utilizing a core of simple Data compression skills.
– Think about the people you identified for your Data Integration project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?
– How do we ensure that implementations of Data Integration products are done in a way that ensures safety?
– How is the value delivered by Data Integration being measured?
Computer data storage Critical Criteria:
Understand Computer data storage failures and look at it backwards.
– Do Data Integration rules make a reasonable demand on a users capabilities?
– Is there any existing Data Integration governance structure?
Three schema approach Critical Criteria:
Discuss Three schema approach planning and explain and analyze the challenges of Three schema approach.
– To what extent does management recognize Data Integration as a tool to increase the results?
– When a Data Integration manager recognizes a problem, what options are available?
– What are the barriers to increased Data Integration production?
Data pre-processing Critical Criteria:
Pilot Data pre-processing engagements and arbitrate Data pre-processing techniques that enhance teamwork and productivity.
– Does our organization need more Data Integration education?
– What threat is Data Integration addressing?
Information server Critical Criteria:
Transcribe Information server engagements and gather Information server models .
– Does Data Integration include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?
– How do we Improve Data Integration service perception, and satisfaction?
Open Text Critical Criteria:
Have a session on Open Text projects and describe which business rules are needed as Open Text interface.
– Where do ideas that reach policy makers and planners as proposals for Data Integration strengthening and reform actually originate?
– What new services of functionality will be implemented next with Data Integration ?
– How do we manage Data Integration Knowledge Management (KM)?
Data blending Critical Criteria:
Look at Data blending issues and correct Data blending management by competencies.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Data Integration process?
– What are the long-term Data Integration goals?
– Are there Data Integration problems defined?
Resource depletion Critical Criteria:
Check Resource depletion tasks and find answers.
– Does Data Integration analysis show the relationships among important Data Integration factors?
– What potential environmental factors impact the Data Integration effort?
– What about Data Integration Analysis of results?
Wrapper pattern Critical Criteria:
Wrangle Wrapper pattern outcomes and attract Wrapper pattern skills.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Data Integration. How do we gain traction?
– Do those selected for the Data Integration team have a good general understanding of what Data Integration is all about?
Data virtualization Critical Criteria:
Systematize Data virtualization strategies and budget the knowledge transfer for any interested in Data virtualization.
– Are we making progress? and are we making progress as Data Integration leaders?
– What are the record-keeping requirements of Data Integration activities?
Service-oriented architecture Critical Criteria:
Do a round table on Service-oriented architecture failures and use obstacles to break out of ruts.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Data Integration process. ask yourself: are the records needed as inputs to the Data Integration process available?
– Are there any easy-to-implement alternatives to Data Integration? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– How likely is the current Data Integration plan to come in on schedule or on budget?
Data Integration Critical Criteria:
Pay attention to Data Integration issues and separate what are the business goals Data Integration is aiming to achieve.
– What tools do you use once you have decided on a Data Integration strategy and more importantly how do you choose?
– Which Data Integration goals are the most important?
Data architecture Critical Criteria:
Detail Data architecture strategies and maintain Data architecture for success.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Data Integration in a volatile global economy?
– Can we describe the data architecture and relationship between key variables. for example, are data stored in a spreadsheet with one row for each person/entity, a relational database, or some other format?
– Do we need an enterprise data warehouse, a Data Lake, or both as part of our overall data architecture?
– Does your bi software work well with both centralized and decentralized data architectures and vendors?
– Is Data Integration Realistic, or are you setting yourself up for failure?
– What is Effective Data Integration?
Web application Critical Criteria:
Canvass Web application leadership and attract Web application skills.
– I keep a record of names; surnames and emails of individuals in a web application. Do these data come under the competence of GDPR? And do both the operator of the web application and I need to treat them that way?
– Are my web application portfolios and databases ready to migrate to the Windows Azure platform?
– Are there any disadvantages to implementing Data Integration? There might be some that are less obvious?
– How do we measure improved Data Integration service perception, and satisfaction?
– Who Is Responsible for Web Application Security in the Cloud?
– How do you approach building a large web application?
– How does IT exploit a Web Application?
Data modeling Critical Criteria:
Map Data modeling decisions and catalog what business benefits will Data modeling goals deliver if achieved.
– Will new equipment/products be required to facilitate Data Integration delivery for example is new software needed?
– How do we know that any Data Integration analysis is complete and comprehensive?
– Can we do Data Integration without complex (expensive) analysis?
First-order logic Critical Criteria:
See the value of First-order logic outcomes and define what our big hairy audacious First-order logic goal is.
– Why are Data Integration skills important?
Data cleansing Critical Criteria:
Chat re Data cleansing decisions and pay attention to the small things.
– What prevents me from making the changes I know will make me a more effective Data Integration leader?
– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?
– Why is it important to have senior management support for a Data Integration project?
– Which individuals, teams or departments will be involved in Data Integration?
Data editing Critical Criteria:
Rank Data editing decisions and differentiate in coordinating Data editing.
– How can the value of Data Integration be defined?
– How can we improve Data Integration?
Data loss Critical Criteria:
Examine Data loss adoptions and work towards be a leading Data loss expert.
– Does the tool we use have the ability to integrate with Enterprise Active Directory Servers to determine users and build user, role, and business unit policies?
– Does the tool we use have a quarantine that includes the ability to redact and/or highlight sensitive information?
– Does the tool we use allow the ability to add custom number templates (e.g., customer/client IDs)?
– Confidence -what is the data loss rate when the system is running at its required throughput?
– Does our tool have the ability to integrate with Digital Rights Management Client & Server?
– Does the tool we use support the ability to configure user content management alerts?
– What are the risks associated with third party processing that are of most concern?
– What is the impact of the economy on executing our audit plans?
– What processes are in place to govern the informational flow?
– Do all computers have up-to-date antivirus protection?
– Are all computer files backed up on a regular basis?
– What Client Control Considerations were included?
– Downtime and Data Loss: How much Can You Afford?
– How do you contribute to the companies mission?
– What does off-site mean in your organization?
– Where can I store sensitive data?
– Why Data Loss Prevention?
– Why Bother With A DP SLA?
– What Causes Data Loss?
European Bioinformatics Institute Critical Criteria:
Accommodate European Bioinformatics Institute failures and integrate design thinking in European Bioinformatics Institute innovation.
– What role does communication play in the success or failure of a Data Integration project?
Data quality Critical Criteria:
Understand Data quality decisions and use obstacles to break out of ruts.
– How is source data collected (paper questionnaire, computer assisted person interview, computer assisted telephone interview, web data collection form)?
– Validation: does data meet analytic and sample specific requirements (usually done by a qa officer or external party)?
– Are clearly written instructions available on how to use the reporting tools/forms related to people reached/served?
– Integrity: is the structure of data and relationships among entities and attributes maintained consistently?
– Which quality elements and parameters do you test and what types of methods do you use to evaluate quality?
– Are Data Quality challenges identified and are mechanisms in place for addressing them?
– Is data reported correctly (transcription, conversion, units of measurement, etc.)?
– Do we use controls throughout the data collection and management process?
– Can good algorithms, models, heuristics overcome Data Quality problems?
– What features do you need most in Data Quality software?
– Do you clearly document your data collection methods?
– Completeness: is all necessary data present?
– What research is relevant to Data Quality?
– How do you determine the quality of data?
– How does the data enter the system?
– What makes up a good record?
– Who sets public standards ?
– Is data flagged correctly?
– Are records complete?
Innovative Medicines Initiative Critical Criteria:
Huddle over Innovative Medicines Initiative governance and report on developing an effective Innovative Medicines Initiative strategy.
– Have the types of risks that may impact Data Integration been identified and analyzed?
– Is a Data Integration Team Work effort in place?
Enterprise architecture framework Critical Criteria:
Disseminate Enterprise architecture framework leadership and inform on and uncover unspoken needs and breakthrough Enterprise architecture framework results.
– What are the disruptive Data Integration technologies that enable our organization to radically change our business processes?
– In a project to restructure Data Integration outcomes, which stakeholders would you involve?
Data wrangling Critical Criteria:
Gauge Data wrangling outcomes and learn.
– In what ways are Data Integration vendors and us interacting to ensure safe and effective use?
– What are our Data Integration Processes?
Data mediation Critical Criteria:
Derive from Data mediation visions and acquire concise Data mediation education.
– Does Data Integration systematically track and analyze outcomes for accountability and quality improvement?
– What are specific Data Integration Rules to follow?
Semantic integration Critical Criteria:
Guard Semantic integration results and visualize why should people listen to you regarding Semantic integration.
– What is the source of the strategies for Data Integration strengthening and reform?
– Who are the people involved in developing and implementing Data Integration?
Data curation Critical Criteria:
Examine Data curation governance and find the ideas you already have.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data Integration processes?
Data reduction Critical Criteria:
Paraphrase Data reduction engagements and get going.
– Think about the functions involved in your Data Integration project. what processes flow from these functions?
– Does Data Integration create potential expectations in other areas that need to be recognized and considered?
Web service Critical Criteria:
Confer over Web service adoptions and arbitrate Web service techniques that enhance teamwork and productivity.
– Expose its policy engine via web services for use by third-party systems (e.g. provisioning, help desk solutions)?
– How does this standard provide users the ability to access applications and services through web services?
– What is the best strategy going forward for data center disaster recovery?
– Amazon web services is which type of cloud computing distribution model?
– Will Data Integration deliverables need to be tested and, if so, by whom?
Data security Critical Criteria:
Concentrate on Data security adoptions and define what our big hairy audacious Data security goal is.
– What are your current levels and trends in key measures or indicators of Data Integration product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– Does the cloud solution offer equal or greater data security capabilities than those provided by your organizations data center?
– What are the minimum data security requirements for a database containing personal financial transaction records?
– Do these concerns about data security negate the value of storage-as-a-service in the cloud?
– What are the challenges related to cloud computing data security?
– So, what should you do to mitigate these risks to data security?
– Does it contain data security obligations?
– What is Data Security at Physical Layer?
– What is Data Security at Network Layer?
– How will you manage data security?
Enterprise integration Critical Criteria:
Audit Enterprise integration governance and suggest using storytelling to create more compelling Enterprise integration projects.
– How do we go about Securing Data Integration?
Data mining Critical Criteria:
Think carefully about Data mining strategies and inform on and uncover unspoken needs and breakthrough Data mining results.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data Integration processes?
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data Integration services/products?
– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?
– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– What is the difference between business intelligence business analytics and data mining?
– Is business intelligence set to play a key role in the future of Human Resources?
– What programs do we have to teach data mining?
Object-relational mapping Critical Criteria:
Generalize Object-relational mapping planning and pay attention to the small things.
Information integration Critical Criteria:
Frame Information integration management and point out improvements in Information integration.
– What other jobs or tasks affect the performance of the steps in the Data Integration process?
– Why is Data Integration important for you now?
Data hub Critical Criteria:
Administer Data hub decisions and tour deciding if Data hub progress is made.
– Can we add value to the current Data Integration decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– How can you measure Data Integration in a systematic way?
Relational database Critical Criteria:
Design Relational database strategies and don’t overlook the obvious.
Invasive species Critical Criteria:
Probe Invasive species tasks and secure Invasive species creativity.
– What are our best practices for minimizing Data Integration project risk, while demonstrating incremental value and quick wins throughout the Data Integration project lifecycle?
– Who is the main stakeholder, with ultimate responsibility for driving Data Integration forward?
– What are the Key enablers to make this Data Integration move?
Schema matching Critical Criteria:
Pilot Schema matching strategies and question.
Data analysis Critical Criteria:
Brainstorm over Data analysis projects and use obstacles to break out of ruts.
– What are some real time data analysis frameworks?
– Have all basic functions of Data Integration been defined?
– How much does Data Integration help?
Data scrubbing Critical Criteria:
Incorporate Data scrubbing decisions and find out.
Query optimizer Critical Criteria:
Communicate about Query optimizer quality and integrate design thinking in Query optimizer innovation.
– Which customers cant participate in our Data Integration domain because they lack skills, wealth, or convenient access to existing solutions?
– Think of your Data Integration project. what are the main functions?
Data corruption Critical Criteria:
Discuss Data corruption leadership and intervene in Data corruption processes and leadership.
Local As View Critical Criteria:
Huddle over Local As View risks and revise understanding of Local As View architectures.
– Do several people in different organizational units assist with the Data Integration process?
– What sources do you use to gather information for a Data Integration study?
Materialized view Critical Criteria:
Graph Materialized view governance and modify and define the unique characteristics of interactive Materialized view projects.
– Do the Data Integration decisions we make today help people and the planet tomorrow?
Metadata standards Critical Criteria:
Tête-à-tête about Metadata standards outcomes and frame using storytelling to create more compelling Metadata standards projects.
– Are the appropriate metadata standards including those for encoding and transmission of metadata information established?
– Which metadata standards will you use?
– How do we maintain Data Integrations Integrity?
National Science Foundation Critical Criteria:
Extrapolate National Science Foundation engagements and find answers.
– Who will be responsible for deciding whether Data Integration goes ahead or not after the initial investigations?
– Is Supporting Data Integration documentation required?
– Do we have past Data Integration Successes?
Data integrity Critical Criteria:
Give examples of Data integrity goals and don’t overlook the obvious.
– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?
– How would one define Data Integration leadership?
– Can we rely on the Data Integrity?
– Data Integrity, Is it SAP created?
Data fusion Critical Criteria:
Scrutinze Data fusion quality and forecast involvement of future Data fusion projects in development.
– What are the key elements of your Data Integration performance improvement system, including your evaluation, organizational learning, and innovation processes?
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
Data lake Critical Criteria:
Explore Data lake projects and finalize the present value of growth of Data lake.
– Do we address the daunting challenge of Big Data: how to make an easy use of highly diverse data and provide knowledge?
– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?
– Can the data be obtained at no cost, or is there a charge associated with access?
– What data is being licensed, and how or where is it being made available?
– Did it get exported, when, where how will it be used (organizational)?
– What are internal and external Data Integration relations?
– Data Warehouse versus Data Lake (Data Swamp)?
– How strict to be with dimensional design?
– What are the values at the data points?
– Can we realistically store everything?
– How do we Lead with Data Integration in Mind?
– What is Regulatory Compliance ?
– How is this data represented?
– Where did my data come from ?
– Why analysis inside a DBMS?
– What is the environment?
– What is geostatistics ?
– How old is this data?
– What method to use ?
Data farming Critical Criteria:
Scrutinze Data farming decisions and prioritize challenges of Data farming.
– How does the organization define, manage, and improve its Data Integration processes?
– What are the usability implications of Data Integration actions?
Extract, transform, load Critical Criteria:
Disseminate Extract, transform, load decisions and test out new things.
– Is the Data Integration organization completing tasks effectively and efficiently?
– How can skill-level changes improve Data Integration?
Information explosion Critical Criteria:
Pay attention to Information explosion risks and prioritize challenges of Information explosion.
– What are your results for key measures or indicators of the accomplishment of your Data Integration strategy and action plans, including building and strengthening core competencies?
Research Data Alliance Critical Criteria:
Powwow over Research Data Alliance projects and secure Research Data Alliance creativity.
– What is our Data Integration Strategy?
Data validation Critical Criteria:
Match Data validation leadership and look at it backwards.
– How can we incorporate support to ensure safe and effective use of Data Integration into the services that we provide?
– What will drive Data Integration change?
– How to deal with Data Integration Changes?
Data mapping Critical Criteria:
Reconstruct Data mapping management and probe the present value of growth of Data mapping.
– How do you determine the key elements that affect Data Integration workforce satisfaction? how are these elements determined for different workforce groups and segments?
Information silo Critical Criteria:
Participate in Information silo strategies and budget for Information silo challenges.
– For your Data Integration project, identify and describe the business environment. is there more than one layer to the business environment?
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data Integration?
– Who sets the Data Integration standards?
Conjunctive query Critical Criteria:
Check Conjunctive query strategies and point out improvements in Conjunctive query.
– How do we make it meaningful in connecting Data Integration with what users do day-to-day?
Master data management Critical Criteria:
Group Master data management decisions and gather Master data management models .
– What knowledge, skills and characteristics mark a good Data Integration project manager?
– Who will be responsible for documenting the Data Integration requirements in detail?
– What are some of the master data management architecture patterns?
– Why should we use or invest in a Master Data Management product?
– What Is Master Data Management?
Alon Y. Halevy Critical Criteria:
Be responsible for Alon Y. Halevy quality and oversee Alon Y. Halevy requirements.
– Can Management personnel recognize the monetary benefit of Data Integration?
Integration Consortium Critical Criteria:
Air ideas re Integration Consortium quality and define Integration Consortium competency-based leadership.
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data Integration Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | http://theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Data Integration External links:
Cloud Data Integration – boomi.com
http://Ad · boomi.com/Cloud/Integration
Data Integration Challenges? – go.globalscape.com
http://Ad · go.globalscape.com/data/integration
Customer Data Integration – Just another Tamr Inc. Sites site
Virtual database External links:
SQL Virtual Database
Virtual Databases · Teiid – JBoss Developer
Ontology-based data integration External links:
Ontology-Based Data Integration Methods: A Framework …
Database model External links:
Database Model – TimeSnapper
Data compression External links:
PKZIP | Data Compression | PKWARE
Data compression (Book, 2004) [WorldCat.org]
Data compression (Book, 1976) [WorldCat.org]
Data pre-processing External links:
Information server External links:
Internet Information Server 4.0 – support.microsoft.com
[PPT]IBM Information Server – IBM – United States
[PDF]IBM Information Server Administration Guide
Open Text External links:
Open Text Livelink ECM™ eDOCS Collaboration
OTEX : Summary for Open Text Corporation – Yahoo Finance
Open Text – OTEX – Stock Price & News | The Motley Fool
Data blending External links:
Data Blending | Alteryx
Ad-hoc reporting, data analysis and data blending software
Data blending is a process that is gaining attention among analysts and analytic companies due to the fact that it is a quick and straightforward method used to extract value from multiple data sources.
Resource depletion External links:
Resource Depletion Essay – 949 Words – StudyMode
Category:Resource Depletion – OWASP
Data virtualization External links:
What is Data Virtualization and Why Does It Matter?
Market Guide for Data Virtualization – Gartner
Data Integration External links:
Cloud Data Integration – A Gartner iPaaS Leader
http://Ad · boomi.com/Cloud/Integration
Customer Data Integration – Just another Tamr Inc. Sites site
Data Integration Service. Simplified. · Xplenty
Data architecture External links:
Data Architecture Services | Data Intensity
DATAVERSITY – Data Architecture Summit 2017
Certica Solutions: K-12 Cloud Platform and Data Architecture
Web application External links:
ArcGIS Web Application
InspectAll: Web Application
ABIMM WEB Application
Data modeling External links:
[PDF]Course Title: Data Modeling for Business Analysts
The Difference Between Data Analysis and Data Modeling
Data modeling (Book, 1995) [WorldCat.org]
Data cleansing External links:
[DOC]Without a data cleansing – University of Oklahoma
Data Cleansing Solution – Salesforce.com
IMA Ltd. | MRO Material Master Data Cleansing and …
Data editing External links:
Statistical data editing (Book, 1994) [WorldCat.org]
Data Editing – NaturalPoint Product Documentation Ver 1.10
[PDF]Overview of Data Editing Procedures in Surveys
Data loss External links:
Data Loss and Data Backup Statistics?
A1Logic – Data Loss Prevention
European Bioinformatics Institute External links:
Research | European Bioinformatics Institute
European Bioinformatics Institute (EMBL-EBI) – Home | Facebook
European Bioinformatics Institute Drives Innovation | …
Data quality External links:
ISO Data Quality – NOAA Environmental Data Management …
Data quality (Book, 2001) [WorldCat.org]
Data wrangling External links:
Data Wrangling with MongoDB Online Course | Udacity
Big Data: Data Wrangling – Old Dominion University
Data Wrangling in R – Lynda.com
Data mediation External links:
Data Mediation Platform – TRACT – GoTransverse
Data Mediation – CA Support Online
Semantic integration External links:
2 answers: The definition of semantic integration – Quora
[PDF]Once is Enough: N400 Indexes Semantic Integration …
Data curation External links:
What is data curation? – Definition from WhatIs.com
Data curation (Book, 2017) [WorldCat.org]
SPEC Kit 354: Data Curation (May 2017) – publications.arl.org
Data reduction External links:
AuditorQC | Free Linearity and Daily QC Data Reduction
LISA data reduction | JILA Science
Data Reduction – Market Research
Web service External links:
Amazon.com – Marketplace Web Service
kumo cloud™ Mobile App and Web Service for HVAC Control
MSU Police Web Service
Data security External links:
Data Security – WSU Technology Knowledge Base
Data Security – OWASP
Data Security | Federal Trade Commission
Enterprise integration External links:
OEI Leadership – Office of Enterprise Integration (OEI)
Enterprise Integration – Jacksonville, FL – Inc.com
Office of Enterprise Integration (OEI)
Data mining External links:
Data Mining (Book, 2014) [WorldCat.org]
data aggregation in data mining ppt
Data Mining (eBook, 2016) [WorldCat.org]
Object-relational mapping External links:
“Object-Relational Mapping as a Persistence Mechanism …
Information integration External links:
CiteSeerX — Information Integration
[PPT]Information Integration – Subbarao Kambhampati
Data hub External links:
[PDF]DHIS Data Hub (Fact Sheet) – Centers for Disease …
SF Housing Data Hub
Data Hub for all the World’s Airports – World Airport Codes
Relational database External links:
Tool for Relational Database – TablePlus
Introduction to Relational Databases — DatabaseJournal.com
Relational Database Design – (Third Edition) – ScienceDirect
Invasive species External links:
Invasive Species List and Scorecards for California
Invasive Species – Invasive Species
Oregon Invasive Species Council
Schema matching External links:
ERIC – A Semantic Analysis of XML Schema Matching for …
[PDF]Schema Matching using Machine Learning
Data analysis External links:
AnswerMiner – Data analysis made easy
Seven Bridges Genomics – The biomedical data analysis …
LZ Retailytics – Must-Have Retail Data Analysis Platform
Query optimizer External links:
Enabling The New Greenplum Query Optimizer | PivotalGuru
11 The Query Optimizer – Oracle
Data corruption External links:
Data corruption – UFOpaedia
Data corruption when multiple users perform read and …
Data corruption – Infogalactic: the planetary knowledge core
Materialized view External links:
http://In computing, a materialized view is a database object that contains the results of a query. For example, it may be a local copy of data located remotely, or may be a subset of the rows and/or columns of a table or join result, or may be a summary using an aggregate function.
Oracle Disable Materialized View Refresh – Stack Overflow
Materialized Views in Oracle — DatabaseJournal.com
Metadata standards External links:
List of Metadata Standards | Digital Curation Centre
List of Metadata Standards | Digital Curation Centre
Metadata Standards – CollectiveAccess Documentation
National Science Foundation External links:
NSF – National Science Foundation
Data integrity External links:
Data Integrity Jobs, Employment | Indeed.com
Data Integrity Specialist Jobs, Employment | Indeed.com
Data Integrity Jobs – Apply Now | CareerBuilder
Data fusion External links:
Data fusion : concepts and ideas (eBook, 2012) …
Global Data Fusion, a Background Screening Company
Data Fusion Solutions
Data lake External links:
Healthcare Analytics & Data Lake Solutions – Optum
How TD Bank Made Its Data Lake More Usable – datanami.com
How to Design a Successful Data Lake – Knowledgent
Data farming External links:
[PDF]qsg data farming – Official DIBELS Home Page
Extract, transform, load External links:
What is ETL (Extract, Transform, Load)? Webopedia Definition
Information explosion External links:
The information explosion. (Book, 1971) [WorldCat.org]
The World’s Information Explosion – The Numbers – WSJ
The Information explosion. (Film, 1967) [WorldCat.org]
Research Data Alliance External links:
The Research Data Alliance Magazine – issuu
research data alliance | News & Events
Data validation External links:
Excel Data Validation Messages for Users – Contextures Inc.
Excel Drop Down Lists – Data Validation
Data Validation in Excel – EASY Excel Tutorial
Data mapping External links:
Intuitive Data Mapping Software | illustreets
What is Data Mapping? – Bridging the Gap
PrivacyPerfect – Data mapping software supporting …
Information silo External links:
Information Silo – Investopedia
Conjunctive query External links:
“Conjunctive Query Containment with Respect to View …
Master data management External links:
Master Data Management – Comprehensive Data Governance
http://Ad · marketing.boomi.com/Boomi/MDM-Solution
Master Data Management – Comprehensive Data Governance
http://Ad · marketing.boomi.com/Boomi/MDM-Solution
Best Master Data Management (MDM) Software – G2 Crowd
Alon Y. Halevy External links:
Alon Y. Halevy (Author of The Infinite Emotions of Coffee)
Alon Y. Halevy – Revolvy
https://www.revolvy.com/topic/Alon Y. Halevy&item_type=topic
Lifeboat Foundation Bios: Dr. Alon Y. Halevy