What is involved in Data Management and Integration
Find out what the related areas are that Data Management and 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 Management and Integration thinking-frame.
How far is your company on its Data Management and Integration journey?
Take this short survey to gauge your organization’s progress toward Data Management and 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 Management and Integration related domains to cover and 253 essential critical questions to check off in that domain.
The following domains are covered:
Data Management and Integration, Web service, Conjunctive query, Data pre-processing, Relational database, Extract, transform, load, Data blending, Query optimizer, Computer data storage, Materialized view, Enterprise integration, Data fusion, Invasive species, Data lake, Information silo, Edge data integration, Customer data integration, Virtual database, Data security, Wrapper pattern, Data cleansing, Three schema approach, Data loss, Data virtualization, Global warming, Research Data Alliance, Data warehouse, Data analysis, Data reduction, Object-relational mapping, Open Text, Integration Competency Center, Big data, Data curation, Ontology-based data integration, Data integration, Data scrubbing, First-order logic, Data corruption, Functional predicate, Web application, Innovative Medicines Initiative, Logic programming, Data modeling, Information server, Metadata standards, Data warehousing, Global As View, Service-oriented architecture, Semantic integration, Data architecture, Business semantics management, Data hub, Enterprise information integration, Database model, Data wrangling, Information privacy, Data farming, Local As View, National Science Foundation, Data integrity, Data quality, Data mapping, Schema matching:
Data Management and Integration Critical Criteria:
Investigate Data Management and Integration issues and devote time assessing Data Management and Integration and its risk.
– What vendors make products that address the Data Management and Integration needs?
– What are our Data Management and Integration Processes?
Web service Critical Criteria:
Merge Web service quality and research ways can we become the Web service company that would put us out of business.
– What are the success criteria that will indicate that Data Management and Integration objectives have been met and the benefits delivered?
– 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?
– Does our organization need more Data Management and Integration education?
– What is the best strategy going forward for data center disaster recovery?
– Amazon web services is which type of cloud computing distribution model?
– Have all basic functions of Data Management and Integration been defined?
Conjunctive query Critical Criteria:
Weigh in on Conjunctive query governance and report on setting up Conjunctive query without losing ground.
– What are internal and external Data Management and Integration relations?
– Is Data Management and Integration Required?
Data pre-processing Critical Criteria:
Talk about Data pre-processing issues and explain and analyze the challenges of Data pre-processing.
– How do we ensure that implementations of Data Management and Integration products are done in a way that ensures safety?
– Does Data Management and Integration systematically track and analyze outcomes for accountability and quality improvement?
– Do Data Management and Integration rules make a reasonable demand on a users capabilities?
Relational database Critical Criteria:
Mine Relational database strategies and finalize the present value of growth of Relational database.
– 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?
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data Management and Integration?
– What is Effective Data Management and Integration?
– Are there Data Management and Integration Models?
Extract, transform, load Critical Criteria:
Judge Extract, transform, load governance and know what your objective is.
– How do we go about Comparing Data Management and Integration approaches/solutions?
– Is a Data Management and Integration Team Work effort in place?
– Is the scope of Data Management and Integration defined?
Data blending Critical Criteria:
Judge Data blending outcomes and maintain Data blending for success.
– Do the Data Management and Integration decisions we make today help people and the planet tomorrow?
– Is Data Management and Integration dependent on the successful delivery of a current project?
Query optimizer Critical Criteria:
Dissect Query optimizer projects and check on ways to get started with Query optimizer.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Data Management and Integration process. ask yourself: are the records needed as inputs to the Data Management and Integration process available?
– How will you know that the Data Management and Integration project has been successful?
– Do we all define Data Management and Integration in the same way?
Computer data storage Critical Criteria:
Guide Computer data storage goals and secure Computer data storage creativity.
– What are the key elements of your Data Management and Integration performance improvement system, including your evaluation, organizational learning, and innovation processes?
– What are specific Data Management and Integration Rules to follow?
Materialized view Critical Criteria:
Reorganize Materialized view engagements and define what our big hairy audacious Materialized view goal is.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Data Management and Integration models, tools and techniques are necessary?
– Are there Data Management and Integration problems defined?
Enterprise integration Critical Criteria:
Have a round table over Enterprise integration visions and correct Enterprise integration management by competencies.
– What are our best practices for minimizing Data Management and Integration project risk, while demonstrating incremental value and quick wins throughout the Data Management and Integration project lifecycle?
– What are the top 3 things at the forefront of our Data Management and Integration agendas for the next 3 years?
– How is the value delivered by Data Management and Integration being measured?
Data fusion Critical Criteria:
Consult on Data fusion strategies and frame using storytelling to create more compelling Data fusion projects.
– Which customers cant participate in our Data Management and Integration domain because they lack skills, wealth, or convenient access to existing solutions?
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
– What are the barriers to increased Data Management and Integration production?
Invasive species Critical Criteria:
Model after Invasive species strategies and devote time assessing Invasive species and its risk.
– Does Data Management and 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?
– Who will be responsible for deciding whether Data Management and Integration goes ahead or not after the initial investigations?
– How do we Lead with Data Management and Integration in Mind?
Data lake Critical Criteria:
Revitalize Data lake strategies and handle a jump-start course to Data lake.
– Looking at hadoop big data in the rearview mirror, what would you have done differently after implementing a Data Lake?
– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?
– Do we need an enterprise data warehouse, a Data Lake, or both as part of our overall data architecture?
– Can the data be obtained at no cost, or is there a charge associated with access?
– How do we Improve Data Management and Integration service perception, and satisfaction?
– What data is being licensed, and how or where is it being made available?
– How would you arrive at the decomposition without such knowledge?
– What kinds of use are permitted/prohibited by the license?
– Big Data: what is different from large databases?
– Can I connect this data to data I already have?
– How strict to be with dimensional design?
– What are the values at the data points?
– Can we realistically store everything?
– Where are they commonly created?
– What processes touched my data?
– How is this data represented?
– Where did my data come from ?
– Where is the data located?
– What is geostatistics ?
– What method to use ?
Information silo Critical Criteria:
Huddle over Information silo planning and be persistent.
– Consider your own Data Management and Integration project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– How can you measure Data Management and Integration in a systematic way?
Edge data integration Critical Criteria:
Give examples of Edge data integration quality and finalize specific methods for Edge data integration acceptance.
– How can we improve Data Management and Integration?
Customer data integration Critical Criteria:
Air ideas re Customer data integration outcomes and mentor Customer data integration customer orientation.
– Meeting the challenge: are missed Data Management and Integration opportunities costing us money?
– How do we go about Securing Data Management and Integration?
Virtual database Critical Criteria:
Systematize Virtual database results and display thorough understanding of the Virtual database process.
– Is there a Data Management and Integration Communication plan covering who needs to get what information when?
– Is there any existing Data Management and Integration governance structure?
– Why is Data Management and Integration important for you now?
Data security Critical Criteria:
Match Data security management and finalize specific methods for Data security acceptance.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data Management and Integration processes?
– 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 record-keeping requirements of Data Management and Integration activities?
– 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?
Wrapper pattern Critical Criteria:
Cut a stake in Wrapper pattern engagements and drive action.
– Do several people in different organizational units assist with the Data Management and Integration process?
– What is the source of the strategies for Data Management and Integration strengthening and reform?
– Why should we adopt a Data Management and Integration framework?
Data cleansing Critical Criteria:
Have a meeting on Data cleansing issues and transcribe Data cleansing as tomorrows backbone for success.
– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?
– Do we monitor the Data Management and Integration decisions made and fine tune them as they evolve?
– How do we maintain Data Management and Integrations Integrity?
Three schema approach Critical Criteria:
Focus on Three schema approach issues and get answers.
– Is the Data Management and Integration organization completing tasks effectively and efficiently?
– What is the purpose of Data Management and Integration in relation to the mission?
Data loss Critical Criteria:
Guide Data loss engagements and budget the knowledge transfer for any interested in Data loss.
– You do not want to be informed of a data loss incident from the users themselves or from the data protection authority. Do you have technology that can detect breaches that have taken place; forensics available to investigate how the data was lost (or changed); and can you go back in time with full user logs and identify the incident to understand its scope and impact?
– 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?
– Will the Deployment be applied to all of the traffic of data in use, or in motion, or at rest?
– Should the deployment occur in high availability mode or should we configure in bypass mode?
– What are the best open source solutions for data loss prevention?
– What is the impact of the economy on executing our audit plans?
– What processes are in place to govern the informational flow?
– How will the setup of endpoints with the DLP manager occur?
– When was your last SWOT analysis for Internal Audit?
– Are all computer files backed up on a regular basis?
– Are there Data Dependencies or Consistency Groups?
– What Client Control Considerations were included?
– Downtime and Data Loss: How much Can You Afford?
– How do you contribute to the companies mission?
– Who has (or can have) access to my data?
– Who is sending confidential information?
– What are your most offensive protocols?
– Where can I store sensitive data?
– What about spot-checking instead?
Data virtualization Critical Criteria:
Derive from Data virtualization quality and gather Data virtualization models .
– What are the usability implications of Data Management and Integration actions?
– Do we have past Data Management and Integration Successes?
Global warming Critical Criteria:
Brainstorm over Global warming tasks and intervene in Global warming processes and leadership.
– How do senior leaders actions reflect a commitment to the organizations Data Management and Integration values?
– Are there any disadvantages to implementing Data Management and Integration? There might be some that are less obvious?
Research Data Alliance Critical Criteria:
Dissect Research Data Alliance issues and use obstacles to break out of ruts.
– What are all of our Data Management and Integration domains and what do they do?
Data warehouse Critical Criteria:
Facilitate Data warehouse quality and cater for concise Data warehouse education.
– 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 warehouseing necessary for our business intelligence service?
– 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?
Data analysis Critical Criteria:
Mix Data analysis projects and research ways can we become the Data analysis company that would put us out of business.
– In the case of a Data Management and Integration project, the criteria for the audit derive from implementation objectives. an audit of a Data Management and Integration project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Data Management and Integration project is implemented as planned, and is it working?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Does Data Management and Integration analysis isolate the fundamental causes of problems?
– Does Data Management and Integration appropriately measure and monitor risk?
– What are some real time data analysis frameworks?
Data reduction Critical Criteria:
Unify Data reduction tactics and track iterative Data reduction results.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Data Management and Integration. How do we gain traction?
– How can skill-level changes improve Data Management and Integration?
Object-relational mapping Critical Criteria:
Exchange ideas about Object-relational mapping risks and develop and take control of the Object-relational mapping initiative.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Data Management and Integration process?
– What prevents me from making the changes I know will make me a more effective Data Management and Integration leader?
– How much does Data Management and Integration help?
Open Text Critical Criteria:
Investigate Open Text projects and customize techniques for implementing Open Text controls.
– Does Data Management and Integration analysis show the relationships among important Data Management and Integration factors?
Integration Competency Center Critical Criteria:
Study Integration Competency Center results and get the big picture.
– Where do ideas that reach policy makers and planners as proposals for Data Management and Integration strengthening and reform actually originate?
Big data Critical Criteria:
Ventilate your thoughts about Big data tasks and describe which business rules are needed as Big data interface.
– If this nomination is completed on behalf of the customer, has that customer been made aware of this nomination in advance of this submission?
– How should we organize to capture the benefit of Big Data and move swiftly to higher maturity stages?
– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?
– What are the primary business drivers for our initiative. What business challenges do we face?
– Do you see areas in your domain or across domains where vendor lock-in is a potential risk?
– In which way does big data create, or is expected to create, value in the organization?
– How to identify relevant fragments of data easily from a multitude of data sources?
– How can the best Big Data solution be chosen based on use case requirements?
– What new Security and Privacy challenge arise from new Big Data solutions?
– Can good algorithms, models, heuristics overcome Data Quality problems?
– What are the new developments that are included in Big Data solutions?
– Does your organization have a strategy on big data or data analytics?
– Should we be required to inform individuals when we use their data?
– Is data-driven decision-making part of the organizations culture?
– How to visualize non-numeric data, e.g. text, icons, or images?
– What analytical tools do you consider particularly important?
– What is it that we don t know we don t know about the data?
– What are our tools for big data analytics?
– Why use expensive machines when cheap ones suffice?
Data curation Critical Criteria:
Steer Data curation strategies and integrate design thinking in Data curation innovation.
– Who sets the Data Management and Integration standards?
Ontology-based data integration Critical Criteria:
Investigate Ontology-based data integration engagements and differentiate in coordinating Ontology-based data integration.
– what is the best design framework for Data Management and Integration organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– What tools and technologies are needed for a custom Data Management and Integration project?
Data integration Critical Criteria:
Disseminate Data integration leadership and probe Data integration strategic alliances.
– What other jobs or tasks affect the performance of the steps in the Data Management and Integration process?
– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?
– Which Oracle Data Integration products are used in your solution?
Data scrubbing Critical Criteria:
Analyze Data scrubbing leadership and perfect Data scrubbing conflict management.
– To what extent does management recognize Data Management and Integration as a tool to increase the results?
– What are the Key enablers to make this Data Management and Integration move?
First-order logic Critical Criteria:
Graph First-order logic issues and ask what if.
– How important is Data Management and Integration to the user organizations mission?
– What are the long-term Data Management and Integration goals?
Data corruption Critical Criteria:
Co-operate on Data corruption quality and point out improvements in Data corruption.
– How would one define Data Management and Integration leadership?
Functional predicate Critical Criteria:
Transcribe Functional predicate issues and diversify disclosure of information – dealing with confidential Functional predicate information.
– Do those selected for the Data Management and Integration team have a good general understanding of what Data Management and Integration is all about?
– How do we make it meaningful in connecting Data Management and Integration with what users do day-to-day?
– Who needs to know about Data Management and Integration ?
Web application Critical Criteria:
Contribute to Web application failures and find answers.
– 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?
– How will we insure seamless interoperability of Data Management and Integration moving forward?
– Are my web application portfolios and databases ready to migrate to the Windows Azure platform?
– 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?
Innovative Medicines Initiative Critical Criteria:
Deduce Innovative Medicines Initiative adoptions and find answers.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data Management and Integration services/products?
– What are the disruptive Data Management and Integration technologies that enable our organization to radically change our business processes?
– Do you monitor the effectiveness of your Data Management and Integration activities?
Logic programming Critical Criteria:
Deduce Logic programming planning and display thorough understanding of the Logic programming process.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Data Management and Integration in a volatile global economy?
Data modeling Critical Criteria:
Incorporate Data modeling management and point out improvements in Data modeling.
– What about Data Management and Integration Analysis of results?
– How do we keep improving Data Management and Integration?
– What is our Data Management and Integration Strategy?
Information server Critical Criteria:
Face Information server tasks and get the big picture.
– At what point will vulnerability assessments be performed once Data Management and Integration is put into production (e.g., ongoing Risk Management after implementation)?
– Are we making progress? and are we making progress as Data Management and Integration leaders?
Metadata standards Critical Criteria:
Detail Metadata standards results and change contexts.
– Think about the functions involved in your Data Management and Integration project. what processes flow from these functions?
– Are the appropriate metadata standards including those for encoding and transmission of metadata information established?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data Management and Integration?
– Are there recognized Data Management and Integration problems?
– Which metadata standards will you use?
Data warehousing Critical Criteria:
Track Data warehousing failures and pay attention to the small things.
– What is the difference between Enterprise Information Management and Data Warehousing?
– What threat is Data Management and Integration addressing?
Global As View Critical Criteria:
Categorize Global As View failures and display thorough understanding of the Global As View process.
– Are assumptions made in Data Management and Integration stated explicitly?
Service-oriented architecture Critical Criteria:
Analyze Service-oriented architecture governance and intervene in Service-oriented architecture processes and leadership.
– What are your results for key measures or indicators of the accomplishment of your Data Management and Integration strategy and action plans, including building and strengthening core competencies?
– What management system can we use to leverage the Data Management and Integration experience, ideas, and concerns of the people closest to the work to be done?
Semantic integration Critical Criteria:
Understand Semantic integration strategies and secure Semantic integration creativity.
– Will Data Management and Integration have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– How will you measure your Data Management and Integration effectiveness?
Data architecture Critical Criteria:
Group Data architecture adoptions and customize techniques for implementing Data architecture controls.
– Can we add value to the current Data Management and Integration decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– How can we incorporate support to ensure safe and effective use of Data Management and Integration into the services that we provide?
– Does your bi software work well with both centralized and decentralized data architectures and vendors?
Business semantics management Critical Criteria:
Focus on Business semantics management leadership and summarize a clear Business semantics management focus.
– How do we Identify specific Data Management and Integration investment and emerging trends?
– What are the short and long-term Data Management and Integration goals?
– Is Supporting Data Management and Integration documentation required?
Data hub Critical Criteria:
Meet over Data hub failures and interpret which customers can’t participate in Data hub because they lack skills.
– What will drive Data Management and Integration change?
Enterprise information integration Critical Criteria:
Contribute to Enterprise information integration engagements and innovate what needs to be done with Enterprise information integration.
Database model Critical Criteria:
Devise Database model risks and devise Database model key steps.
– In what ways are Data Management and Integration vendors and us interacting to ensure safe and effective use?
– How to Secure Data Management and Integration?
Data wrangling Critical Criteria:
Use past Data wrangling results and catalog Data wrangling activities.
– How do we manage Data Management and Integration Knowledge Management (KM)?
Information privacy Critical Criteria:
Add value to Information privacy projects and oversee Information privacy management by competencies.
Data farming Critical Criteria:
Powwow over Data farming outcomes and define what do we need to start doing with Data farming.
Local As View Critical Criteria:
Think about Local As View results and achieve a single Local As View view and bringing data together.
– Can we do Data Management and Integration without complex (expensive) analysis?
National Science Foundation Critical Criteria:
Unify National Science Foundation decisions and proactively manage National Science Foundation risks.
Data integrity Critical Criteria:
Be responsible for Data integrity leadership and catalog Data integrity activities.
– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?
– When a Data Management and Integration manager recognizes a problem, what options are available?
– How to deal with Data Management and Integration Changes?
– Data Integrity, Is it SAP created?
– Can we rely on the Data Integrity?
Data quality Critical Criteria:
Pilot Data quality issues and intervene in Data quality processes and leadership.
– Do we conduct regular data quality audits to ensure that our strategies for enforcing quality control are up-to-date and that any corrective measures undertaken in the past have been successful in improving Data Quality?
– Which audit findings of the Data Management and reporting system warrant recommendation notes and changes to the design in order to improve Data Quality?
– Are data sufficiently precise to present a fair picture of performance and enable management decision-making at the appropriate levels?
– What issues should you consider when determining whether existing data may possibly serve as a source of information?
– At all levels at which data are aggregated, are procedures in place to reconcile discrepancies in reports?
– Do we double check that the data collected follows the plans and procedures for data collection?
– Does clear documentation of collection, aggregation and manipulation steps exist?
– Is data recorded with sufficient precision/detail to measure relevant indicators?
– Which items are subject to revision either by editing or updating data values?
– Accuracy: does the data accurately represent reality or a verifiable source?
– What criteria should be used to assess the performance of the system?
– What is the proportion of missing values for each field?
– Describe the overall aim of your policy and context?
– Are people involved in the development identified?
– What research is relevant to Data Quality?
– What about attribute completeness?
– Do you train data collectors?
– Is data flagged correctly?
– Is the system acceptable?
Data mapping Critical Criteria:
Cut a stake in Data mapping failures and integrate design thinking in Data mapping innovation.
Schema matching Critical Criteria:
Be responsible for Schema matching management and perfect Schema matching conflict management.
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data Management and 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:
Web service External links:
HOW TO: Write a Simple Web Service by Using Visual C# .NET
Free BIN/IIN Lookup Web Service – binlist.net
HOW TO: Write a Simple Web Service by Using Visual C# .NET
Conjunctive query External links:
Conjunctive query containment revisited – ScienceDirect
Relational database External links:
Cloud SQL – MySQL & PostgreSQL Relational Database …
How to Design Relational Database with ERD? – Visual …
Relational Database Terms Flashcards | Quizlet
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
Data blending External links:
What Is Data Blending? – Datawatch Corporation
What Is Data Blending, and Which Tools Make It Easier?
What Is Data Blending? – Datawatch Corporation
Query optimizer External links:
The Query Optimizer – Oracle
Computer data storage External links:
computer data storage service – TheBlaze
Computer Data Storage Options – Ferris State University
Materialized view External links:
How to refresh materialized view in oracle – Stack Overflow
Indexed View (or) Materialized View | SQL Programmers
Force a materialized view refresh – Burleson Oracle Consulting
Enterprise integration External links:
Office of Enterprise Integration (OEI)
Enterprise Integration – Jacksonville, FL – Inc.com
Enterprise Integration Group | Good IVR. It’s what we do.
Data fusion External links:
Global Data Fusion, a Background Screening Company
[PDF]Data Fusion Centers – Esri
Invasive species External links:
Invasive Species Species Profiles & Reporting Information
Invasive Species List and Scorecards for California
Invasive Species – Wisconsin DNR
Data lake External links:
SMG Data Lake
What is data lake? – Definition from WhatIs.com
How to Design a Successful Data Lake – Knowledgent
Information silo External links:
Jan 29, 2003 · Definition An information silo , or a group of such silos, is an insular management system in which one information system or …
http://Wednesday 2/7 Midday Web Weather – kxii.com
What is an Information Silo (IT Silo)? Webopedia Definition
Information Silo – investopedia.com
Edge data integration External links:
Edge Data Integration – etltools.net
Customer data integration External links:
Customer Data Integration – Just another Tamr Inc. Sites site
Customer Data Integration | CDI | MuleSoft
Customer Data Integration and Master Data Management
Virtual database External links:
Chain Store Guide Virtual Database Tour
Virtual Databases · Teiid
Virtual Database System – DELPHIX CORP.
Data security External links:
Data Security from Multiple Levels of Protection | H&R Block®
FedEx Data Security Upgrade
[PDF]CPHS Data Security Requirements – CA OSHPD
Wrapper pattern External links:
Ravelry: U.AN – The Woolly Wrapper pattern by Barbara Lawler
Modern Wrapper Pattern – Churchmouse Yarns & Teas
Data cleansing External links:
IMA Ltd. | MRO Material Master Data Cleansing and …
Data cleansing – SlideShare
Experian | Data Cleansing | Data View
Three schema approach External links:
Three schema approach – revolvy.com
http://www.revolvy.com/topic/Three schema approach&item_type=topic
Data loss External links:
Data Loss Prevention & Protection | Symantec
Data virtualization External links:
What is data virtualization? – Definition from WhatIs.com
Data Virtualization Technology | Actifio
What is Data Virtualization and Why Does It Matter?
Research Data Alliance External links:
Research Data Alliance Europe – Home | Facebook
Data Type Registries: A Research Data Alliance Working …
Data warehouse External links:
EZ Data Warehouse
Data Warehouse Specialist Salaries – Salary.com
[PDF]Data Warehouse – Utility’s Smart Grid Clearinghouse
http://smartgrid.epri.com/UseCases/DW – Utility DOE SG Clearhouse_ph2add.pdf
Data analysis External links:
Regional Data Warehouse/Data Analysis Site
Data Analysis – Illinois State Board of Education
How to Write a Data Analysis | Bizfluent
Data reduction External links:
Data Reduction – Market Research
AuditorQC | Free Linearity and Daily QC Data Reduction
LISA data reduction | JILA Science
Open Text External links:
OTEX – Open Text Corp Stock quote – CNNMoney.com
Open Text Media Manager
Open Text – OTEX – Stock Price & News | The Motley Fool
Integration Competency Center External links:
The Role of the Integration Competency Center – Gartner
Big data External links:
Loudr: Big Data for Music Rights
Databricks – Making Big Data Simple
Take 5 Media Group – Build an audience using big data
Data curation External links:
What is data curation? – Definition from WhatIs.com
Data Curation – The Hyve – Extract, Transform & Load
Data curation (Book, 2017) [WorldCat.org]
Data integration External links:
Data Integration – Technology
First-order logic External links:
What is first-order logic? – Definition from WhatIs.com
[PDF]First-Order Logic (FOL) Constant symbols aka. …
Data corruption External links:
Data corruption – UFOpaedia
Web application External links:
ABIMM WEB Application – ess.pirates.com
INSPI² Web Application – Safeguard Properties
Web Application Definition – Computer
Logic programming External links:
Logic programming (eBook, 1991) [WorldCat.org]
[PDF]Logic Programming – imd.solutions
Logic programming (Book, 1991) [WorldCat.org]
Data modeling External links:
What is Data Modeling? Webopedia Definition
Data Modeling | IT Pro
Data Modeling Foundations Flashcards | Quizlet
Information server External links:
Internet information server (Book, 1996) [WorldCat.org]
Internet Information server (VHS tape, 1997) [WorldCat.org]
How to obtain versions of Internet Information Server (IIS)
Metadata standards External links:
current operational and proposed metadata standards
Metadata Standards – Study Periods
List of Metadata Standards | Digital Curation Centre
Data warehousing External links:
Data warehousing (Book, 2001) [WorldCat.org]
Data Warehousing – Concepts – Tutorials Point
Data Warehousing Dummies – AbeBooks
Global As View External links:
GAV abbreviation stands for Global as View – allacronyms.com
Service-oriented architecture External links:
Understanding Service-Oriented Architecture
Messaging Patterns in Service-Oriented Architecture, Part 1
Service-Oriented Architecture Summary | Accenture
Semantic integration External links:
Semantic Integration · GitHub
What is the definition of semantic integration? – Quora
Data architecture External links:
Certica Solutions: K-12 Cloud Platform and Data Architecture
Business semantics management External links:
Business semantics management – update.revolvy.com
https://update.revolvy.com/topic/Business semantics management
business semantics management | pieter de leenheer
Business semantics management: A case study for …
Data hub External links:
GS1 US Data Hub | Product
Welcome – the Indiana Data Hub
Front Page | Data Hub – New Jersey Child Welfare Data Hub
Enterprise information integration External links:
OSF Uses: Enterprise Information Integration
Enterprise Information Integration – Semantic Arts
Database model External links:
Relational Database Model | Database Management | …
DR Database Model – Application:MM
Data wrangling External links:
What Is Data Wrangling? – Datawatch Corporation
Data Wrangling Tools & Software | Trifacta
Big Data: Data Wrangling – Old Dominion University
Information privacy External links:
Health Information Privacy | HHS.gov
Data farming External links:
SEED Center Hosts International Data Farming Workshop
[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
National Science Foundation External links:
NSF – National Science Foundation
National Science Foundation – Visit Alexandria VA
Data integrity External links:
[PDF]IMPROVING TITLE I DATA INTEGRITY FOR …
Data Integrity Jobs – Apply Now | CareerBuilder
Data Integrity Services SM – Experian
Data quality External links:
A3-4-02: Data Quality and Integrity (10/24/2016) – Fannie Mae
CWS Data Quality Portal
CRMfusion Salesforce Data Quality Software Applications
Data mapping External links:
What is Data Mapping? – Definition from Techopedia
Local Government Data Mapping and Integration with …
LEA Data Mapping
Schema matching External links:
[PDF]A survey of approaches to automatic schema matching
[PDF]Schema Matching using Machine Learning – UMass …