233 In-Depth Data Integration Questions for Professionals

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 233 essential critical questions to check off in that domain.

The following domains are covered:

Data Integration, Core data integration, Logic programming, Service-oriented architecture, National Science Foundation, Semantic integration, Edge data integration, Open Text, Object-relational mapping, Three schema approach, Information privacy, Innovative Medicines Initiative, Data mediation, Data fusion, Data wrangling, Schema matching, Conjunctive query, Enterprise integration, Enterprise application integration, Data quality, Enterprise architecture framework, Information explosion, Extract, transform, load, Integration Consortium, Logical schema, Information integration, Materialized view, Ontology-based data integration, First-order logic, Virtual database, Metadata standards, Data lake, Data blending, Alon Y. Halevy, Data curation, Business semantics management, Information silo, Invasive species, Web service, Wrapper pattern, Data mapping, Customer data integration, Data mining, Master data management, Data farming, Local As View, Data Integration, Resource depletion, Integration Competency Center, Data editing, Data virtualization, Data integrity, Big data, Data warehouse, Data pre-processing, Data security, Data reduction, Information server, Data warehousing, Database model, Global As View:

Data Integration Critical Criteria:

Check Data Integration risks and assess what counts with Data Integration that we are not counting.

– What other jobs or tasks affect the performance of the steps in the Data Integration process?

– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?

– How do we measure improved Data Integration service perception, and satisfaction?

– Can Management personnel recognize the monetary benefit of Data Integration?

– Which Oracle Data Integration products are used in your solution?

Core data integration Critical Criteria:

Probe Core data integration quality and overcome Core data integration skills and management ineffectiveness.

– What will be the consequences to the business (financial, reputation etc) if Data Integration does not go ahead or fails to deliver the objectives?

– When a Data Integration manager recognizes a problem, what options are available?

Logic programming Critical Criteria:

Chat re Logic programming engagements and pioneer acquisition of Logic programming systems.

– What is the source of the strategies for Data Integration strengthening and reform?

– Think of your Data Integration project. what are the main functions?

Service-oriented architecture Critical Criteria:

Huddle over Service-oriented architecture engagements and finalize the present value of growth of Service-oriented architecture.

– What other organizational variables, such as reward systems or communication systems, affect the performance of this Data Integration process?

– Risk factors: what are the characteristics of Data Integration that make it risky?

– How can you measure Data Integration in a systematic way?

National Science Foundation Critical Criteria:

Discourse National Science Foundation tactics and pioneer acquisition of National Science Foundation systems.

– Which individuals, teams or departments will be involved in Data Integration?

Semantic integration Critical Criteria:

Scan Semantic integration decisions and suggest using storytelling to create more compelling Semantic integration projects.

– How do you determine the key elements that affect Data Integration workforce satisfaction? how are these elements determined for different workforce groups and segments?

– What about Data Integration Analysis of results?

Edge data integration Critical Criteria:

Accommodate Edge data integration tasks and get answers.

– Think about the functions involved in your Data Integration project. what processes flow from these functions?

– How do mission and objectives affect the Data Integration processes of our organization?

– How to Secure Data Integration?

Open Text Critical Criteria:

Illustrate Open Text management and interpret which customers can’t participate in Open Text because they lack skills.

– Is Data Integration Realistic, or are you setting yourself up for failure?

– What potential environmental factors impact the Data Integration effort?

Object-relational mapping Critical Criteria:

Grasp Object-relational mapping risks and test out new things.

– Can we add value to the current Data Integration decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

– What are your most important goals for the strategic Data Integration objectives?

– Have the types of risks that may impact Data Integration been identified and analyzed?

Three schema approach Critical Criteria:

Start Three schema approach tasks and devote time assessing Three schema approach and its risk.

– Do we monitor the Data Integration decisions made and fine tune them as they evolve?

– How important is Data Integration to the user organizations mission?

– Who sets the Data Integration standards?

Information privacy Critical Criteria:

Generalize Information privacy management and optimize Information privacy leadership as a key to advancement.

– Among the Data Integration product and service cost to be estimated, which is considered hardest to estimate?

– How can you negotiate Data Integration successfully with a stubborn boss, an irate client, or a deceitful coworker?

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data Integration?

Innovative Medicines Initiative Critical Criteria:

Study Innovative Medicines Initiative failures and find the ideas you already have.

– Does the Data Integration task fit the clients priorities?

– How to deal with Data Integration Changes?

Data mediation Critical Criteria:

Detail Data mediation decisions and track iterative Data mediation results.

Data fusion Critical Criteria:

Study Data fusion leadership and gather practices for scaling Data fusion.

– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?

– Who will be responsible for deciding whether Data Integration goes ahead or not after the initial investigations?

– In a project to restructure Data Integration outcomes, which stakeholders would you involve?

– Are assumptions made in Data Integration stated explicitly?

Data wrangling Critical Criteria:

Recall Data wrangling decisions and define Data wrangling competency-based leadership.

– How do we go about Comparing Data Integration approaches/solutions?

– Why should we adopt a Data Integration framework?

Schema matching Critical Criteria:

Examine Schema matching adoptions and perfect Schema matching conflict management.

– Will Data Integration deliverables need to be tested and, if so, by whom?

– Are there Data Integration problems defined?

Conjunctive query Critical Criteria:

Understand Conjunctive query quality and revise understanding of Conjunctive query architectures.

– What are our best practices for minimizing Data Integration project risk, while demonstrating incremental value and quick wins throughout the Data Integration project lifecycle?

– How much does Data Integration help?

Enterprise integration Critical Criteria:

Extrapolate Enterprise integration projects and forecast involvement of future Enterprise integration projects in development.

– What is the purpose of Data Integration in relation to the mission?

– Do we all define Data Integration in the same way?

Enterprise application integration Critical Criteria:

Revitalize Enterprise application integration issues and achieve a single Enterprise application integration view and bringing data together.

– What are the implications of cloud computing to enterprise application integration?

– Meeting the challenge: are missed Data Integration opportunities costing us money?

– Do Data Integration rules make a reasonable demand on a users capabilities?

Data quality Critical Criteria:

Be clear about Data quality issues and point out Data quality tensions in leadership.

– Information on verification or evidence for the value and accuracy how can I check the value or have a confidence in it?

– What percentage of eligibles are not included on the data file or what percentage of those mandated are not compliant?

– Are there clearly defined and followed procedures to periodically verify source data?

– Are key data-management staff identified with clearly assigned responsibilities?

– What criteria should be used to assess the performance of the system?

– Does the data clearly and adequately represent the intended result?

– Program goals are key – what do you want to do with the data?

– Which aspects of Data Quality are already strong?

– Does data meet the specifications you assumed?

– What are you doing with all this data anyway?

– Timeliness: is data available when needed?

– What research is relevant to Data Quality?

– Scan individual records are there gaps?

– Data Quality: how good is your data?

– How does the data enter the system?

– Why is Data Quality necessary?

– Can we interpret the data?

– Where do you clean data?

– Are records complete?

Enterprise architecture framework Critical Criteria:

Explore Enterprise architecture framework planning and pay attention to the small things.

– What are internal and external Data Integration relations?

– How do we go about Securing Data Integration?

Information explosion Critical Criteria:

Exchange ideas about Information explosion tactics and be persistent.

– Does Data Integration analysis isolate the fundamental causes of problems?

– Are there recognized Data Integration problems?

Extract, transform, load Critical Criteria:

Define Extract, transform, load management and question.

– Is there a Data Integration Communication plan covering who needs to get what information when?

– What will drive Data Integration change?

– What are current Data Integration Paradigms?

Integration Consortium Critical Criteria:

Be responsible for Integration Consortium planning and ask what if.

– Think about the kind of project structure that would be appropriate for your Data Integration project. should it be formal and complex, or can it be less formal and relatively simple?

– Are we making progress? and are we making progress as Data Integration leaders?

– How can we improve Data Integration?

Logical schema Critical Criteria:

Weigh in on Logical schema visions and check on ways to get started with Logical schema.

– Consider your own Data Integration project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?

Information integration Critical Criteria:

Ventilate your thoughts about Information integration adoptions and display thorough understanding of the Information integration process.

– 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?

– Are we Assessing Data Integration and Risk?

Materialized view Critical Criteria:

Trace Materialized view planning and spearhead techniques for implementing Materialized view.

– 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?

– Have you identified your Data Integration key performance indicators?

Ontology-based data integration Critical Criteria:

Mix Ontology-based data integration risks and modify and define the unique characteristics of interactive Ontology-based data integration projects.

– What are the Essentials of Internal Data Integration Management?

First-order logic Critical Criteria:

Explore First-order logic tasks and separate what are the business goals First-order logic is aiming to achieve.

– 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?

– Do several people in different organizational units assist with the Data Integration process?

– What threat is Data Integration addressing?

Virtual database Critical Criteria:

Analyze Virtual database adoptions and ask questions.

– Who is the main stakeholder, with ultimate responsibility for driving Data Integration forward?

– How will you measure your Data Integration effectiveness?

Metadata standards Critical Criteria:

Guard Metadata standards failures and look for lots of ideas.

– Are the appropriate metadata standards including those for encoding and transmission of metadata information established?

– What business benefits will Data Integration goals deliver if achieved?

– Which metadata standards will you use?

Data lake Critical Criteria:

Apply Data lake risks and oversee Data lake requirements.

– Looking at hadoop big data in the rearview mirror, what would you have done differently after implementing a 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?

– What kinds of use are permitted/prohibited by the license?

– Can I connect this data to data I already have?

– What are the values at the data points?

– Where are they commonly created?

– What processes touched my data?

– How is this data represented?

– Where did my data come from ?

– Why analysis inside a DBMS?

– Where is the data located?

– What Is Data Governance ?

– Is Big data different?

– How old is this data?

– MapReduce: forgotten?

Data blending Critical Criteria:

Deduce Data blending quality and clarify ways to gain access to competitive Data blending services.

– What tools do you use once you have decided on a Data Integration strategy and more importantly how do you choose?

– Why is it important to have senior management support for a Data Integration project?

– How do we keep improving Data Integration?

Alon Y. Halevy Critical Criteria:

Investigate Alon Y. Halevy visions and simulate teachings and consultations on quality process improvement of Alon Y. Halevy.

– How will we insure seamless interoperability of Data Integration moving forward?

– Is a Data Integration Team Work effort in place?

Data curation Critical Criteria:

Model after Data curation projects and modify and define the unique characteristics of interactive Data curation projects.

– How do we manage Data Integration Knowledge Management (KM)?

– Does our organization need more Data Integration education?

– How do we maintain Data Integrations Integrity?

Business semantics management Critical Criteria:

Interpolate Business semantics management tasks and improve Business semantics management service perception.

Information silo Critical Criteria:

Have a meeting on Information silo adoptions and integrate design thinking in Information silo innovation.

– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data Integration?

– What are the record-keeping requirements of Data Integration activities?

– What are the business goals Data Integration is aiming to achieve?

Invasive species Critical Criteria:

Categorize Invasive species tactics and diversify by understanding risks and leveraging Invasive species.

– What new services of functionality will be implemented next with Data Integration ?

– How can skill-level changes improve Data Integration?

Web service Critical Criteria:

Differentiate Web service risks and find the essential reading for Web service researchers.

– 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?

Wrapper pattern Critical Criteria:

Discourse Wrapper pattern visions and devise Wrapper pattern key steps.

– Does Data Integration analysis show the relationships among important Data Integration factors?

– What are the usability implications of Data Integration actions?

Data mapping Critical Criteria:

Confer re Data mapping goals and get going.

– 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?

Customer data integration Critical Criteria:

Collaborate on Customer data integration issues and define what do we need to start doing with Customer data integration.

– Will Data Integration have an impact on current business continuity, disaster recovery processes and/or infrastructure?

Data mining Critical Criteria:

Group Data mining tasks and plan concise Data mining education.

– 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 vendors make products that address the Data Integration needs?

– Does Data Integration appropriately measure and monitor risk?

– What programs do we have to teach data mining?

Master data management Critical Criteria:

Weigh in on Master data management goals and look at it backwards.

– What are some of the master data management architecture patterns?

– Why should we use or invest in a Master Data Management product?

– Who will provide the final approval of Data Integration deliverables?

– What Is Master Data Management?

Data farming Critical Criteria:

Give examples of Data farming results and adjust implementation of Data farming.

– Who are the people involved in developing and implementing Data Integration?

– How does the organization define, manage, and improve its Data Integration processes?

Local As View Critical Criteria:

Collaborate on Local As View decisions and change contexts.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data Integration processes?

– What is Effective Data Integration?

Data Integration Critical Criteria:

Merge Data Integration failures and define what our big hairy audacious Data Integration goal is.

– Will new equipment/products be required to facilitate Data Integration delivery for example is new software needed?

Resource depletion Critical Criteria:

Air ideas re Resource depletion governance and display thorough understanding of the Resource depletion process.

– What tools and technologies are needed for a custom Data Integration project?

– How do we Improve Data Integration service perception, and satisfaction?

– How is the value delivered by Data Integration being measured?

Integration Competency Center Critical Criteria:

Gauge Integration Competency Center visions and gather Integration Competency Center models .

– How would one define Data Integration leadership?

Data editing Critical Criteria:

Deliberate Data editing visions and look at it backwards.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data Integration services/products?

Data virtualization Critical Criteria:

Group Data virtualization projects and stake your claim.

– Are accountability and ownership for Data Integration clearly defined?

Data integrity Critical Criteria:

Discourse Data integrity tasks and observe effective Data integrity.

– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?

– How likely is the current Data Integration plan to come in on schedule or on budget?

– Data Integrity, Is it SAP created?

– Can we rely on the Data Integrity?

Big data Critical Criteria:

Give examples of Big data quality and don’t overlook the obvious.

– While a move from Oracles MySQL may be necessary because of its inability to handle key big data use cases, why should that move involve a switch to Apache Cassandra and DataStax Enterprise?

– Do you see the need to support the development and implementation of technical solutions that are enhancing data protection by design and by default?

– Is the software compatible with new database formats for raw, unstructured, and semi-structured big data?

– What rules and regulations should exist about combining data about individuals into a central repository?

– Wheres the evidence that using big data intelligently will improve business performance?

– Do we understand public perception of transportation service delivery at any given time?

– In which way does big data create, or is expected to create, value in the organization?

– How can the best Big Data solution be chosen based on use case requirements?

– At which levels do you see the need for standardisation actions?

– How do we track the provenance of the derived data/information?

– How much data is really relevant to the problem solution?

– Even when we have a lot of data, do we understand it?

– Which Oracle applications are used in your project?

– From which country is your organization from?

– What if the data cannot fit on your computer?

– What is the cost of partitioning/balancing?

– Wait, DevOps does not apply to Big Data?

– So how are managers using big data?

– How do I get to there from here?

Data warehouse Critical Criteria:

Powwow over Data warehouse issues and probe using an integrated framework to make sure Data warehouse is getting what it needs.

– What tier data server has been identified for the storage of decision support data contained in a data warehouse?

– Do we need an enterprise data warehouse, a Data Lake, or both as part of our overall data architecture?

– What prevents me from making the changes I know will make me a more effective Data Integration leader?

– What does a typical data warehouse and business intelligence organizational structure look like?

– Does big data threaten the traditional data warehouse business intelligence model stack?

– Is Data Integration dependent on the successful delivery of a current project?

– Is Data Warehouseing necessary for a business intelligence service?

– What is the difference between a database and data warehouse?

– What is the purpose of data warehouses and data marts?

– What are alternatives to building a data warehouse?

– Do we offer a good introduction to data warehouse?

– Data Warehouse versus Data Lake (Data Swamp)?

– Do you still need a data warehouse?

– Centralized data warehouse?

Data pre-processing Critical Criteria:

Match Data pre-processing risks and check on ways to get started with Data pre-processing.

– What are the Key enablers to make this Data Integration move?

Data security Critical Criteria:

Dissect Data security management and adopt an insight outlook.

– Does the cloud solution offer equal or greater data security capabilities than those provided by your organizations data center?

– What are the success criteria that will indicate that Data Integration objectives have been met and the benefits delivered?

– 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?

Data reduction Critical Criteria:

Start Data reduction management and prioritize challenges of Data reduction.

– What are the key elements of your Data Integration performance improvement system, including your evaluation, organizational learning, and innovation processes?

Information server Critical Criteria:

Conceptualize Information server tactics and define what our big hairy audacious Information server goal is.

– Does Data Integration create potential expectations in other areas that need to be recognized and considered?

– What is our Data Integration Strategy?

Data warehousing Critical Criteria:

Examine Data warehousing management and shift your focus.

– What is the difference between Enterprise Information Management and Data Warehousing?

– Have all basic functions of Data Integration been defined?

Database model Critical Criteria:

Bootstrap Database model visions and give examples utilizing a core of simple Database model skills.

– Which customers cant participate in our Data Integration domain because they lack skills, wealth, or convenient access to existing solutions?

Global As View Critical Criteria:

Learn from Global As View quality and find out.


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.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Core data integration External links:

Core Data Integration Project

Logic programming External links:

Logic programming (eBook, 1991) [WorldCat.org]

EX-LOGIC Programming – Biamp Systems

[PDF]Chapter 2: Basic Ladder Logic Programming – …

Service-oriented architecture External links:

Service-Oriented Architecture Summary | Accenture

Microservices vs. Service-Oriented Architecture – NGINX

Understanding Service-Oriented Architecture

National Science Foundation External links:

NSF Remote Access | NSF – National Science Foundation

National Science Foundation Mathematical Sciences …

Semantic integration External links:

Semantic Integration · GitHub

Semantic Integration

What is the definition of semantic integration? – Quora

Edge data integration External links:

Edge Data Integration – etltools.net

Open Text External links:

Open Text – OTEX – Stock Price & News | The Motley Fool

Open Text Media Manager

OTEX – Open Text Corp Stock quote – CNNMoney.com

Three schema approach External links:

Three schema approach – revolvy.com
http://www.revolvy.com/topic/Three schema approach&item_type=topic

Information privacy External links:

Information Privacy | Citizens Bank

Your Health Information Privacy Rights (HIPAA) – WebMD

Innovative Medicines Initiative External links:


Data mediation External links:

What is Data Mediation | IGI Global

Data Mediation Platform – TRACT – GoTransverse

[PDF]Ontology Driven Data Mediation in Web Services

Data fusion External links:

Data fusion : concepts and ideas (eBook, 2012) [WorldCat.org]

Global Data Fusion, a Background Screening Company

[PDF]Data Fusion Centers – Esri

Data wrangling External links:

Data Wrangling Tools & Software | Trifacta

Big Data: Data Wrangling – Old Dominion University

Schema matching External links:

[PDF]Schema Matching using Machine Learning – UMass …

[PDF]A survey of approaches to automatic schema matching

Conjunctive query External links:

Conjunctive query containment revisited – ScienceDirect

Triggerware Creating a Conjunctive Query – YouTube

Enterprise integration External links:

Enterprise Integration – Jacksonville, FL – Inc.com

Office of Enterprise Integration (OEI)

Enterprise application integration External links:

Enterprise Application Integration and Migration | SmartIMS

Enterprise Application Integration Service | AVASOFT

InSync – Enterprise Application Integration Platform

Data quality External links:

[PDF]Data Quality Report – arb.ca.gov

A3-4-02: Data Quality and Integrity (10/24/2016) – Fannie Mae

CRMfusion Salesforce Data Quality Software Applications

Enterprise architecture framework External links:

Enterprise Architecture Framework – EA Masterclass

Information explosion External links:

The Information explosion. (Film, 1967) [WorldCat.org]

The information explosion. (Book, 1971) [WorldCat.org]

[PDF]The Information Explosion: A (Very) Brief History

Extract, transform, load External links:

ETL (Extract, transform, load) Salary | PayScale
http://www.payscale.com › United States › Skill/Specialty

What is ETL (Extract, Transform, Load)? Webopedia Definition

Integration Consortium External links:

Integration Consortium Inc – GuideStar Profile

Logical schema External links:

Setup Logical Schema ODI – Data Enthusiast

Information integration External links:

[1708.02967v2] Information Integration In Large Brain …

[PPT]Information Integration – Subbarao Kambhampati

Materialized view External links:

How to refresh materialized view in oracle – Stack Overflow

Materialized Views in Oracle — DatabaseJournal.com

Materialized view
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.

First-order logic External links:

[PDF]First-Order Logic (FOL) Constant symbols aka. …

What is first-order logic? – Definition from WhatIs.com

Virtual database External links:

First steps – Creating a Virtual Database

Physical vs virtual database server? | BMC Communities

Virtual Databases · Teiid

Metadata standards External links:

current operational and proposed metadata standards

Metadata Standards – Study Periods

[PDF]Metadata Standards and Metadata Registries

Data lake External links:

Data Lake | Microsoft Azure

SMG Data Lake

How to Design a Successful Data Lake – Knowledgent

Data blending External links:

[PDF]7 Steps to Successful Data Blending for Excel

Data Blending and Data Integration – Tableau Software

Data Blending – 2 Minute Demo from Alteryx – YouTube

Alon Y. Halevy External links:

The Infinite Emotions Of Coffee By Alon Y. Halevy

Alon Y. Halevy is the author of The Infinite Emotions of Coffee (3.89 avg rating, 9 ratings, 2 reviews, published 2011)

Alon Y. Halevy – ACM author profile page

Data curation External links:

Data curation (Book, 2017) [WorldCat.org]

What is data curation? – Definition from WhatIs.com

SPEC Kit 354: Data Curation (May 2017) – publications.arl.org

Business semantics management External links:

business semantics management | pieter de leenheer

Information silo External links:

What is an Information Silo (IT Silo)? Webopedia Definition

Information silo – Revolvy
https://www.revolvy.com/topic/Information silo&item_type=topic

Information Silo – investopedia.com

Invasive species External links:

Invasive Species Top Priorities – Invasive Species Council

Invasive Species List and Scorecards for California

Invasive Species – Wisconsin DNR

Web service External links:

HOW TO: Write a Simple Web Service by Using Visual C# .NET

Amazon.com – Marketplace Web Service

Getting Started · Building a RESTful Web Service

Wrapper pattern External links:

Ravelry: Modern Wrapper pattern by Churchmouse Yarns …

Modern Wrapper Pattern – Churchmouse Yarns & Teas

Data mapping External links:

Intuitive Data Mapping Software | illustreets

Data Mapping Automation | Products | OneTrust

Local Government Data Mapping and Integration with …

Customer data integration External links:

Customer Data Integration | CDI | MuleSoft

Customer Data Integration – Just another Tamr Inc. Sites site

Customer Data Integration and Master Data Management

Data mining External links:

Data Mining on the Florida Department of Corrections Website

Data mining | computer science | Britannica.com

UT Data Mining

Master data management External links:

Best Master Data Management (MDM) Software in 2018 | G2 …

Master Data Management | IBM Analytics

Data farming External links:

[PDF]qsg data farming – Official DIBELS Home Page

T10: Data Farming – OCEANS’16 MTS/IEEE Monterey

Local As View External links:

LAV abbreviation stands for Local As View – All Acronyms

Resource depletion External links:

What does Resource depletion mean? – Definitions.net
http://www.definitions.net/definition/Resource depletion

Resource Depletion Essay – 949 Words – StudyMode

Resource Depletion | HuffPost

Integration Competency Center External links:

The Role of the Integration Competency Center – Gartner

Data editing External links:

Statistical data editing (Book, 1994) [WorldCat.org]

Data Editing – NaturalPoint Product Documentation Ver 1.10

Data Editing – NaturalPoint Product Documentation Ver 2.0

Data virtualization External links:

Data Virtualization – Informatica

Data Virtualization Technology | Actifio

What is Data Virtualization and Why Does It Matter?

Data integrity External links:

Data Integrity Services SM – Experian

Data Integrity Jobs – Apply Now | CareerBuilder

Data Integrity Jobs, Employment | Indeed.com

Big data External links:

Loudr: Big Data for Music Rights

ZestFinance.com: Machine Learning & Big Data Underwriting

Take 5 Media Group – Build an audience using big data

Data warehouse External links:

HRSA Data Warehouse Home Page

Title 2 Data Warehouse – Data.gov

[PDF]Data Warehouse – Utility’s Smart Grid Clearinghouse
http://smartgrid.epri.com/UseCases/DW – Utility DOE SG Clearhouse_ph2add.pdf

Data security External links:

Data Security from Multiple Levels of Protection | H&R Block®

What is data security – answers.com

[PDF]CPHS Data Security Requirements – CA OSHPD

Data reduction External links:

AuditorQC | Free Linearity and Daily QC Data Reduction

LISA data reduction | JILA Science

OEC – Data Reduction Techniques – Online Ethics

Information server External links:

Internet Information Server

Geographic Information Server | WVDEP GIS Server

[PPT]IBM Information Server

Data warehousing External links:

Data Warehousing Dummies – AbeBooks

HEDW – Higher Education Data Warehousing Forum

Data warehousing (Book, 2009) [WorldCat.org]

Database model External links:

DR Database Model – Application:ADM – meditech.com

What is a Database Model | Lucidchart

Relational Database Model | Database Management | …