What is involved in Data warehouse

Find out what the related areas are that Data warehouse 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 warehouse thinking-frame.

How far is your company on its Integrated Clinical Business Enterprise Data Warehouse journey?

Take this short survey to gauge your organization’s progress toward Integrated Clinical Business Enterprise Data Warehouse 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 warehouse related domains to cover and 283 essential critical questions to check off in that domain.

The following domains are covered:

Data warehouse, Data integrity, Data dictionary, Data transformation, Database management system, Operational data store, Comparison of OLAP Servers, MultiDimensional eXpressions, Data element, Pattern recognition, National Diet Library, Data redundancy, Column-oriented DBMS, Business process, Snowflake schema, Data integration, Entity-relationship model, Operational system, DBC 1012, Business intelligence software, XML for Analysis, Customer relationship management, Data wrangling, VDM Verlag, International Journal of Data Warehousing and Mining, Slowly changing dimension, Semantic warehousing, Data quality, Predictive analytics, Software as a service, Market research, Business intelligence, Data loading, Codd’s 12 rules, Accounting intelligence, Third normal form, Data model, Data compression, Online transaction processing, Legacy system, Data scrubbing, Online analytical processing, Data editing, Data structure, Data pre-processing, Data farming, Hub and spokes architecture, Data Mining Extensions, Data extraction, Dimensional modeling, Fact table, Data presentation architecture, Data corruption, Executive information system, Data cleansing, Degenerate dimension, Data vault modeling, Decision support, Extract transform load, Database normalization, Early-arriving fact, Business intelligence tools, Data fusion, Master data management:

Data warehouse Critical Criteria:

Define Data warehouse engagements and learn.

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

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

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

– 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 the Essentials of Internal Data warehouse Management?

– What are alternatives to building a data warehouse?

– Do we offer a good introduction to data warehouse?

– Data Warehouse versus Data Lake (Data Swamp)?

– Are there recognized Data warehouse problems?

– Do you still need a data warehouse?

– Centralized data warehouse?

Data integrity Critical Criteria:

Mix Data integrity projects and define what do we need to start doing with Data integrity.

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

– Is the Data warehouse organization completing tasks effectively and efficiently?

– Who sets the Data warehouse standards?

– What about Data warehouse Analysis of results?

– Can we rely on the Data Integrity?

– Data Integrity, Is it SAP created?

Data dictionary Critical Criteria:

Set goals for Data dictionary visions and ask questions.

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

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

– What types of information should be included in the data dictionary?

– What threat is Data warehouse addressing?

– Is there a data dictionary?

Data transformation Critical Criteria:

Experiment with Data transformation governance and correct Data transformation management by competencies.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Data warehouse process. ask yourself: are the records needed as inputs to the Data warehouse process available?

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

– Describe the process of data transformation required by your system?

– What is the process of data transformation required by your system?

Database management system Critical Criteria:

Check Database management system outcomes and drive action.

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

– What database management systems have been implemented?

– Have all basic functions of Data warehouse been defined?

Operational data store Critical Criteria:

Test Operational data store strategies and change contexts.

– What are your current levels and trends in key measures or indicators of Data warehouse 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?

– What role does communication play in the success or failure of a Data warehouse project?

– How can we improve Data warehouse?

Comparison of OLAP Servers Critical Criteria:

Test Comparison of OLAP Servers leadership and forecast involvement of future Comparison of OLAP Servers projects in development.

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

– In what ways are Data warehouse vendors and us interacting to ensure safe and effective use?

– What sources do you use to gather information for a Data warehouse study?

MultiDimensional eXpressions Critical Criteria:

Discourse MultiDimensional eXpressions management and slay a dragon.

– What are your results for key measures or indicators of the accomplishment of your Data warehouse strategy and action plans, including building and strengthening core competencies?

– Does our organization need more Data warehouse education?

Data element Critical Criteria:

Dissect Data element planning and drive action.

– Is there an existing data element or combination of data elements that can answer the same question that the proposed new data element is meant to address?

– Is collecting this data element the most efficient way to influence practice, policy, or research?

– Is collecting this data element the most efficient way to influence practice policy, or research?

– Who can provide us with information about our current data systems and data elements?

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

– At what organizational level is it appropriate to have a new data element?

– How can the data element influence practice, policy, or research?

– At what level is it appropriate to maintain a new data element?

– Can the data element be clearly and commonly defined?

– What are our Data warehouse Processes?

Pattern recognition Critical Criteria:

See the value of Pattern recognition management and budget the knowledge transfer for any interested in Pattern recognition.

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

– What are internal and external Data warehouse relations?

National Diet Library Critical Criteria:

Reason over National Diet Library strategies and describe the risks of National Diet Library sustainability.

– What are the top 3 things at the forefront of our Data warehouse agendas for the next 3 years?

– What are the long-term Data warehouse goals?

– How to Secure Data warehouse?

Data redundancy Critical Criteria:

Trace Data redundancy leadership and prioritize challenges of Data redundancy.

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

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

– Are we Assessing Data warehouse and Risk?

Column-oriented DBMS Critical Criteria:

Trace Column-oriented DBMS projects and adjust implementation of Column-oriented DBMS.

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

– Do the Data warehouse decisions we make today help people and the planet tomorrow?

– How can skill-level changes improve Data warehouse?

Business process Critical Criteria:

Transcribe Business process tactics and overcome Business process skills and management ineffectiveness.

– Have the segments, goals and performance objectives been translated into an actionable and realistic target business and information architecture expressed within business functions, business processes, and information requirements?

– What is the importance of knowing the key performance indicators KPIs for a business process when trying to implement a business intelligence system?

– Are interruptions to business activities counteracted and critical business processes protected from the effects of major failures or disasters?

– Has business process Cybersecurity has been included in continuity of operations plans for areas such as customer data, billing, etc.?

– Do you design data protection and privacy requirements into the development of your business processes and new systems?

– If we process purchase orders; what is the desired business process around supporting purchase orders?

– To satisfy customers and stakeholders, which internal business process must we excel in?

– If we accept checks what is the desired business process around supporting checks?

– What are the relationships with other business processes and are these necessary?

– Will existing staff require re-training, for example, to learn new business processes?

– What would Eligible entity be asked to do to facilitate your normal business process?

– Do changes in business processes fall under the scope of change management?

– What business process supports the entry and validation of the data?

– How do we improve business processes and how do we deliver on that?

– What core business processes drive our industry and channel today?

– On what basis would you decide to redesign a business process?

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

– What/how are business processes defined?

– What is the business process?

Snowflake schema Critical Criteria:

Exchange ideas about Snowflake schema visions and cater for concise Snowflake schema education.

– How to deal with Data warehouse Changes?

Data integration Critical Criteria:

Learn from Data integration results and catalog what business benefits will Data integration goals deliver if achieved.

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

– Is there any existing Data warehouse governance structure?

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

– How will you measure your Data warehouse effectiveness?

Entity-relationship model Critical Criteria:

Prioritize Entity-relationship model decisions and describe which business rules are needed as Entity-relationship model interface.

– Are there any easy-to-implement alternatives to Data warehouse? Sometimes other solutions are available that do not require the cost implications of a full-blown project?

– How will you know that the Data warehouse project has been successful?

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

Operational system Critical Criteria:

Focus on Operational system tactics and tour deciding if Operational system progress is made.

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

– What vendors make products that address the Data warehouse needs?

DBC 1012 Critical Criteria:

Investigate DBC 1012 goals and work towards be a leading DBC 1012 expert.

– What is our formula for success in Data warehouse ?

Business intelligence software Critical Criteria:

Read up on Business intelligence software planning and work towards be a leading Business intelligence software expert.

– what is the best design framework for Data warehouse organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

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

XML for Analysis Critical Criteria:

Group XML for Analysis adoptions and explore and align the progress in XML for Analysis.

– What are the usability implications of Data warehouse actions?

Customer relationship management Critical Criteria:

Dissect Customer relationship management quality and revise understanding of Customer relationship management architectures.

– How do we deal with the most important challenge of a crm program: how do we get a single version of the customer truth?

– Does the current system allow for service cases to be opened in the CRM directly from the exchange site?

– Is there an iphone app for mobile scrm or customer relationship management?

– When shipping a product, do you send tracking information to the customer?

– What is the potential value of increasing the loyalty of our customers?

– Is there a pattern to our clients buying habits (e.g., seasonal)?

– How do you measure progress and evaluate training effectiveness?

– What is our core business and how will it evolve in the future?

– What is your process for gathering business requirements?

– Is there an incentive for visitors/customers to register?

– Do we adhere to best practices interface design?

– What Type of Information May be Released?

– Is the metadata cache size acceptable?

– How many cases have been resolved?

– Can customers place orders online?

– How much e-mail should be routed?

– Are we better off going outside?

– What is the client software?

– What happens to workflows?

– Who Are Our Customers?

Data wrangling Critical Criteria:

Guard Data wrangling issues and assess what counts with Data wrangling that we are not counting.

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

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

VDM Verlag Critical Criteria:

Infer VDM Verlag goals and diversify disclosure of information – dealing with confidential VDM Verlag information.

– What management system can we use to leverage the Data warehouse experience, ideas, and concerns of the people closest to the work to be done?

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

– Are there Data warehouse Models?

International Journal of Data Warehousing and Mining Critical Criteria:

Be clear about International Journal of Data Warehousing and Mining outcomes and display thorough understanding of the International Journal of Data Warehousing and Mining process.

– To what extent does management recognize Data warehouse as a tool to increase the results?

– Who will be responsible for documenting the Data warehouse requirements in detail?

Slowly changing dimension Critical Criteria:

Huddle over Slowly changing dimension governance and clarify ways to gain access to competitive Slowly changing dimension services.

– Have you identified your Data warehouse key performance indicators?

– How do we Lead with Data warehouse in Mind?

Semantic warehousing Critical Criteria:

Air ideas re Semantic warehousing strategies and attract Semantic warehousing skills.

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

– Are assumptions made in Data warehouse stated explicitly?

Data quality Critical Criteria:

Use past Data quality issues and point out Data quality tensions in leadership.

– Which audit findings of the Data Management and reporting system warrant recommendation notes and changes to the design in order to improve Data Quality?

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

– What issues should you consider when determining whether existing data may possibly serve as a source of information?

– Integrity: is the structure of data and relationships among entities and attributes maintained consistently?

– What investigations/analyses have been conducted that reveal Data Quality characteristics?

– Does clear documentation of collection, aggregation, and manipulation steps exist?

– Does clear documentation of collection, aggregation and manipulation steps exist?

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

– Do you clearly document your data collection methods?

– What is the future of Data Quality management?

– Do the uploaded files fit an expected pattern?

– Data Quality: how good is your data?

– Is the review date identified?

– Is the information accurate?

– Is data flagged correctly?

– Can we interpret the data?

– How much does Data warehouse help?

– Where to clean?

Predictive analytics Critical Criteria:

Do a round table on Predictive analytics issues and describe the risks of Predictive analytics sustainability.

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

– What are direct examples that show predictive analytics to be highly reliable?

Software as a service Critical Criteria:

Infer Software as a service risks and devote time assessing Software as a service and its risk.

– Why are Service Level Agreements a dying breed in the software as a service industry?

– Do we have past Data warehouse Successes?

Market research Critical Criteria:

Align Market research leadership and drive action.

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

– Does the software allow users to bring in data from outside the company on-the-flylike demographics and market research to augment corporate data?

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

Business intelligence Critical Criteria:

Generalize Business intelligence risks and ask questions.

– Does your mobile solution allow you to interact with desktop-authored dashboards using touchscreen gestures like taps, flicks, and pinches?

– What information can be provided in regards to a sites usage and business intelligence usage within the intranet environment?

– How should a complicated business setup their business intelligence and analysis to make decisions best?

– Does your bi software work well with both centralized and decentralized data architectures and vendors?

– Can you filter, drill down, or add entirely new data to your visualization with mobile editing?

– What specialized bi knowledge does your business have that can be leveraged?

– Is Business Intelligence a more natural fit within Finance or IT?

– What else does the data tell us that we never thought to ask?

– What are the main full web business intelligence solutions?

– What are the top trends in the business intelligence space?

– What are typical data-mining applications?

– What is required to present video images?

– Describe any training materials offered?

– Is your BI software easy to understand?

– What is your expect product life cycle?

– Make or buy BI Business Intelligence?

– Types of data sources supported?

Data loading Critical Criteria:

Disseminate Data loading decisions and find the ideas you already have.

– What will drive Data warehouse change?

– Are there Data warehouse problems defined?

Codd’s 12 rules Critical Criteria:

Value Codd’s 12 rules outcomes and don’t overlook the obvious.

– For your Data warehouse project, identify and describe the business environment. is there more than one layer to the business environment?

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

Accounting intelligence Critical Criteria:

Trace Accounting intelligence engagements and be persistent.

– How can we incorporate support to ensure safe and effective use of Data warehouse into the services that we provide?

Third normal form Critical Criteria:

Exchange ideas about Third normal form outcomes and handle a jump-start course to Third normal form.

– In the case of a Data warehouse project, the criteria for the audit derive from implementation objectives. an audit of a Data warehouse project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Data warehouse project is implemented as planned, and is it working?

Data model Critical Criteria:

Have a round table over Data model quality and grade techniques for implementing Data model controls.

– Do those selected for the Data warehouse team have a good general understanding of what Data warehouse is all about?

– What are the data model, data definitions, structure, and hosting options of purchased applications (COTS)?

– What is the physical data model definition (derived from logical data models) used to design the database?

– Is a Data warehouse Team Work effort in place?

– Physical data model available?

– Logical data model available?

Data compression Critical Criteria:

Refer to Data compression failures and use obstacles to break out of ruts.

– How do we keep improving Data warehouse?

Online transaction processing Critical Criteria:

Deduce Online transaction processing governance and track iterative Online transaction processing results.

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

– Which Data warehouse goals are the most important?

Legacy system Critical Criteria:

Exchange ideas about Legacy system failures and observe effective Legacy system.

– Many times we look for new applications to meet our business needs – requirements are defined, software is evaluated, software selected, software configuration begins – Ready Set Go! More like Ready, Set, but what about the data?

– At risk with respect to legacy modernization; are you too dependent on expensive skill sets or technologies that cannot respond quickly to changes in the marketplace?

– Is the system capable, or can it be made capable, of being interoperable and integrated with other systems?

– Data feeds are often derived from application programs or legacy data sources. what does it mean?

– What are some strategies for integrating legacy equipment into a robust, modernized grid?

– The need for high-quality software is glaring. But what constitutes software quality?

– How do you inventory and assess business processes as part of an ERP evaluation?

– Should there be a complete replacement of legacy mainframes and applications?

– Will the selected alternative replace a legacy system in-part or in-whole?

– How significant is the value that the legacy system provides the business?

– Do we make sure that we Modernize our Legacy Systems AND Cut Costs?

– What is the impact on the system of removing a certain component?

– How do new technologies relate to the legacy systems?

– What is the complexity of the output produced?

– Do other systems depend on it for data?

– Who or what is doing the managing?

– How will we operate in the future?

– Risk – What are the project risks?

– Is the software system reliable?

– What are the outputs produced?

Data scrubbing Critical Criteria:

Read up on Data scrubbing management and improve Data scrubbing service perception.

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

Online analytical processing Critical Criteria:

Substantiate Online analytical processing projects and catalog what business benefits will Online analytical processing goals deliver if achieved.

– Do you monitor the effectiveness of your Data warehouse activities?

Data editing Critical Criteria:

Consult on Data editing failures and arbitrate Data editing techniques that enhance teamwork and productivity.

– Who needs to know about Data warehouse ?

Data structure Critical Criteria:

Consult on Data structure adoptions and triple focus on important concepts of Data structure relationship management.

– What if the needle in the haystack happens to be a complex data structure?

– Is the process repeatable as we change algorithms and data structures?

Data pre-processing Critical Criteria:

Apply Data pre-processing tactics and correct Data pre-processing management by competencies.

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

Data farming Critical Criteria:

Examine Data farming governance and display thorough understanding of the Data farming process.

– What are the short and long-term Data warehouse goals?

– How do we maintain Data warehouses Integrity?

Hub and spokes architecture Critical Criteria:

Consult on Hub and spokes architecture risks and reinforce and communicate particularly sensitive Hub and spokes architecture decisions.

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

– Does Data warehouse appropriately measure and monitor risk?

Data Mining Extensions Critical Criteria:

Have a round table over Data Mining Extensions results and question.

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

Data extraction Critical Criteria:

Face Data extraction engagements and summarize a clear Data extraction focus.

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

– How can data extraction from dashboards be automated?

– Is Supporting Data warehouse documentation required?

Dimensional modeling Critical Criteria:

Design Dimensional modeling goals and gather practices for scaling Dimensional modeling.

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

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

Fact table Critical Criteria:

Be clear about Fact table visions and innovate what needs to be done with Fact table.

Data presentation architecture Critical Criteria:

Analyze Data presentation architecture strategies and probe using an integrated framework to make sure Data presentation architecture is getting what it needs.

– Why is Data warehouse important for you now?

Data corruption Critical Criteria:

Graph Data corruption leadership and raise human resource and employment practices for Data corruption.

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

Executive information system Critical Criteria:

Survey Executive information system visions and get answers.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Data warehouse in a volatile global economy?

Data cleansing Critical Criteria:

Familiarize yourself with Data cleansing planning and look at the big picture.

– Does Data warehouse 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?

– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?

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

– Are there any disadvantages to implementing Data warehouse? There might be some that are less obvious?

Degenerate dimension Critical Criteria:

Chat re Degenerate dimension management and overcome Degenerate dimension skills and management ineffectiveness.

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

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

– Is maximizing Data warehouse protection the same as minimizing Data warehouse loss?

Data vault modeling Critical Criteria:

Have a meeting on Data vault modeling results and achieve a single Data vault modeling view and bringing data together.

Decision support Critical Criteria:

Graph Decision support goals and devise Decision support key steps.

– A heuristic, a decision support system, or new practices to improve current project management?

– How do I manage information (decision support) and operational (transactional) data?

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

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

– What are the access requirements for decision support data?

Extract transform load Critical Criteria:

Collaborate on Extract transform load visions and differentiate in coordinating Extract transform load.

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

Database normalization Critical Criteria:

Explore Database normalization leadership and get going.

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

– How do we ensure that implementations of Data warehouse products are done in a way that ensures safety?

– How would one define Data warehouse leadership?

Early-arriving fact Critical Criteria:

Exchange ideas about Early-arriving fact risks and develop and take control of the Early-arriving fact initiative.

Business intelligence tools Critical Criteria:

Dissect Business intelligence tools goals and reinforce and communicate particularly sensitive Business intelligence tools decisions.

– Business Intelligence Tools?

Data fusion Critical Criteria:

Analyze Data fusion issues and clarify ways to gain access to competitive Data fusion services.

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

Master data management Critical Criteria:

X-ray Master data management leadership and know what your objective is.

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

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

– 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?

– What is Effective Data warehouse?


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Integrated Clinical Business Enterprise Data Warehouse Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]


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:

Data integrity External links:

Data Integrity Specialist Jobs, Employment | Indeed.com


[PDF]Data Integrity and Compliance With CGMP Guidance …

Data dictionary External links:

What is a Data Dictionary? – Definition from Techopedia

[PDF]LANDFIRE Mean Fire Return Interval Data Dictionary …

OpenAir Data Dictionary

Data transformation External links:

Data transformation | FileMaker Community

[PDF]Data transformation and normality – Evaluation

Data Transformation Studio

Database management system External links:

10-7 Operating System, Database Management System, …

Relational Database Management System | MariaDB Products

Database Management System | Lucidea

Operational data store External links:

Operational Data Store – YouTube

Operational Data Store – ODS – Gartner Tech Definitions

ECATS OPERATIONAL DATA STORE – ncpublicschools.org

Comparison of OLAP Servers External links:

Comparison of OLAP Servers – revolvy.com
www.revolvy.com/topic/Comparison of OLAP Servers

Comparison of OLAP Servers: Latest News & Videos, …

Comparison of OLAP Servers – Revolvy
topics.revolvy.com/topic/Comparison of OLAP Servers

MultiDimensional eXpressions External links:

Multidimensional Expressions (MDX) Reference

Data element External links:

Relationships Among Ana Recognized Data Element Sets …

NDUS Data Element Dictionary

Data Element Catalog (DEC) – National Institutes of Health

Pattern recognition External links:

Pattern Recognition – Official Site

Pattern Recognition — Alexander Whitley

Pattern Recognition – IMDb

National Diet Library External links:

National Diet Library law. (Book, 1961) [WorldCat.org]

Online Gallery | National Diet Library

National Diet Library | library, Tokyo, Japan | Britannica.com

Data redundancy External links:

Security & Data Redundancy – help.mycase.com

Column-oriented DBMS External links:

Paper Review: C-Store: A column-oriented DBMS

Column-oriented DBMS |THWACK

[PDF]C-Store: A Column-oriented DBMS – MIT Database …

Business process External links:

What is business process? – Definition from WhatIs.com

Business Process Improvement Jobs – Monster.com

Business Process Analyst Jobs, Employment | Indeed.com

Snowflake schema External links:

Star & snowflake schema | Qlik Community

Data integration External links:

Best Cloud Data Integration Software in 2017 | G2 Crowd

Data Integration Coordinator | Associated Grant Makers

KingswaySoft – Data Integration Solutions

Entity-relationship model External links:

Entity-Relationship Model

Course Notes for Comp 419 – The Entity-Relationship Model

Introduction to the Entity-Relationship Model – YouTube

Operational system External links:

[PDF]Operational System 463L Pallets and Nets

Functional Context Training in an Operational System. – …

Business intelligence software External links:

[PDF]Business Intelligence Software – Open Systems Inc.

SAP Crystal Reports | Business Intelligence Software

solverglobal.com – Business Intelligence Software | Solver

XML for Analysis External links:

XML for Analysis (XMLA) – technet.microsoft.com

[PDF]XML for Analysis Specification

Customer relationship management External links:

1workforce – Customer Relationship Management …

Customer Relationship Management Login – NOVAtime

Salesnet CRM Solutions | Customer Relationship Management

Data wrangling External links:

Big Data: Data Wrangling – Old Dominion University

Data Wrangling in R – Lynda.com

Data Wrangling with MongoDB Online Course | Udacity

International Journal of Data Warehousing and Mining External links:

International Journal of Data Warehousing and Mining

International Journal of Data Warehousing and Mining

Slowly changing dimension External links:

SSIS Slowly Changing Dimension Type 2 – Tutorial Gateway

SSIS- Slowly Changing Dimension (SCD) Tutorial

Data quality External links:

Webbula – The Data Quality Experts

Data quality (Book, 2001) [WorldCat.org]

Predictive analytics External links:

Predictive Analytics Software, Social Listening | NewBrand

Predictive Analytics for Healthcare | Forecast Health

Best Predictive Analytics Software in 2017 | G2 Crowd

Software as a service External links:

What is Software as a Service (SaaS) – Salesforce.com

What is SaaS? Software as a Service | Microsoft Azure

Software as a Service – Banking Solutions | FinReach

Market research External links:

Market Research Future: Industry Analysis Report, …

Market Overview & Stock Market Research | Scottrade

1Q | Instant Mobile Marketing / Market Research

Business intelligence External links:

business intelligence jobs | Dice.com

[PDF]Position Title: Business Intelligence Analyst – ttra

List of Business Intelligence Skills – The Balance

Data loading External links:

The Data Loading Performance Guide

Codd’s 12 rules External links:

Codd’s 12 Rules for Relational Database Management – …

Codd’s 12 Rules – Database Answers Home Page June04th

Codd’s 12 Rules – RDBMS Basics – RDBMS for Begineers – …

Accounting intelligence External links:

World Accounting Intelligence – Home | Facebook

Third normal form External links:

What is Third Normal Form (3NF)? – Definition from …

Normalisation 3NF: Understanding Third Normal Form – …

Converting Databases to Third Normal Form (3NF)

Data model External links:

COT – Data Model

IPLD – The data model of the content-addressable web

Data Warehouse data model | Microsoft Docs

Data compression External links:

The Data Compression Guide – sites.google.com

Data compression (Book, 2004) [WorldCat.org]

Data compression (Book, 1976) [WorldCat.org]

Online transaction processing External links:

eCash.com Online transaction processing – Wefunder

Online Transaction Processing – Gartner IT Glossary

Slow SQL Online Transaction Processing performance …

Legacy system External links:

Legacy System Catalog – Implant Direct

Wally’s Legacy System Report – Dilbert Comic Strip on …

[PDF]Legacy Systems Costing You

Online analytical processing External links:

Working with Online Analytical Processing (OLAP)

Oracle Online Analytical Processing (OLAP)

Data editing External links:

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

Data Editing – NaturalPoint Product Documentation Ver 1.10

Data structure External links:

Data structures – C++ Tutorials

data structure – Wiktionary

C++ Data Structures – tutorialspoint.com

Data farming External links:

[PDF]qsg data farming – Official DIBELS Home Page

Hub and spokes architecture External links:

Hub and spokes architecture – WOW.com

Hub and spokes architecture – WOW.com

Data Mining Extensions External links:

Data Mining Extensions (DMX) Reference | Microsoft Docs

Data Mining Extensions (DMX) Reference

Data Mining Extensions (DMX) Operator Reference

Data extraction External links:

NeXtraction – Intelligent Data Extraction

Data Extraction – iMacros

TeamBeam – Meta-Data Extraction from Scientific Literature

Dimensional modeling External links:

Three-Dimensional Modeling | 3-D model | COST of …

“Two-Dimensional Modeling of AP/HTPB Utilizing a …

HEC-RAS: Two-Dimensional Modeling – WEST Consultants

Fact table External links:

what is dimension table and what is fact table. – Informatica

Multiple Fact Tables – Common Dimensions |Tableau …

Factless Fact Table | Learn about Factless Fact Table

Data corruption External links:

Data corruption – UFOpaedia

How to Recover from Outlook Data Corruption: 6 Steps

SQL Server data corruption when a memory range is …

Executive information system External links:

Harris Computer Systems (Executive Information System)

[PDF]Transportation Executive Information System …

Best Executive Information System Software – G2 Crowd

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 …

Degenerate dimension External links:

Degenerate Dimension – YouTube

Data Warehousing: What is degenerate dimension? – …

Data vault modeling External links:

[PDF]Data Vault Modeling – NYOUG

Data Vault Modeling and Snowflake | Snowflake

Decision support External links:

Decision Support Manager Salaries – Salary.com

SAR Decision Support Tool – TriWest Healthcare Alliance

Extract transform load External links:

RapidMiner Extract Transform Load Transforming …

Database normalization External links:


Description of the database normalization basics

Database Normalization Essays – ManyEssays.com

Early-arriving fact External links:

Early-arriving fact – Revolvy
broom02.revolvy.com/topic/Early-arriving fact

Business intelligence tools External links:

DataBay Resources | Business Intelligence Tools

Big Data and Marketing Analytics | Business Intelligence Tools

Business Intelligence Tools for Small Companies – A …

Data fusion External links:

Data fusion : concepts and ideas (eBook, 2012) …

Data Fusion Solutions

[PDF]Data Fusion Centers – esri.com

Master data management External links:

Best Master Data Management (MDM) Software – G2 Crowd

Categories: Documents