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 273 essential critical questions to check off in that domain.
The following domains are covered:
Data warehouse, Data reduction, Customer relationship management, Decision support, Data transformation, Base transceiver station, Data pre-processing, OLAP cube, Relational database, Data curation, Fact table, Data extraction, Computer data storage, Data Mining Extensions, Business intelligence tools, Data editing, Executive information system, Dimensional modeling, Transaction data, Data scraping, Slowly changing dimension, Market research, Data quality, Data warehouse automation, Business intelligence, Data redundancy, Data fusion, Data model, Data presentation architecture, General Mills, Decision support system, VDM Verlag, Codd’s 12 rules, Data integrity, Online transaction processing, Data integration, Database normalization, Master data management, Online analytical processing, Early-arriving fact, Business intelligence software, Snowflake schema, Database management system, Data compression, Comparison of OLAP Servers, Data analysis, DBC 1012, Sperry Univac, Data warehouse appliance, Data structure, Enterprise resource planning, Software as a service, Pattern recognition, Data Mining, Hub and spokes architecture, International Journal of Data Warehousing and Mining, Data corruption, Data wrangling, Data loss, Metaphor Computer Systems, Entity-relationship model, Legacy system:
Data warehouse Critical Criteria:
Huddle over Data warehouse risks and get going.
– 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?
– Who are the people involved in developing and implementing Data warehouse?
– 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?
– Data Warehouse versus Data Lake (Data Swamp)?
– Do you still need a data warehouse?
– Centralized data warehouse?
– How much does Data warehouse help?
Data reduction Critical Criteria:
Facilitate Data reduction issues and suggest using storytelling to create more compelling Data reduction projects.
– Why is it important to have senior management support for a Data warehouse project?
– Who will be responsible for documenting the Data warehouse requirements in detail?
– Have all basic functions of Data warehouse been defined?
Customer relationship management Critical Criteria:
Test Customer relationship management projects and look for lots of ideas.
– Can visitors/customers easily find all relevant information about your products (e.g., prices, options, technical specifications, quantities, shipping information, order status) on your website?
– In CRM we keep record of email addresses and phone numbers of our customers employees. Will we now need to ask for explicit permission to store them?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data warehouse?
– How long (on average) between a potential issue being posted online and being flagged to the client?
– How do customer relationship management systems provide value for businesses?
– When shipping a product, do you send tracking information to the customer?
– What do you consider a short call and what is the threshold in seconds?
– Do you have a mechanism to collect visitor/customer information?
– what is Different Between B2C B2B Customer Experience Management?
– How is the emergence of new CRM solutions offered factored in?
– The performance measurement revolution: why now and what next?
– Is it easy for your visitors or customers to contact you?
– Do you offer value to visitors coming to your website?
– How do we currently collect and process our data?
– How can CRM be a source of competitive advantage?
– Have you developed any proprietary metrics?
– What benefits do you want out of CRM?
– What happens to reports?
– What do they buy?
Decision support Critical Criteria:
Group Decision support governance and ask questions.
– 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?
– When a Data warehouse manager recognizes a problem, what options are available?
– What vendors make products that address the Data warehouse needs?
– What are the access requirements for decision support data?
Data transformation Critical Criteria:
Deliberate Data transformation governance and frame using storytelling to create more compelling Data transformation projects.
– How likely is the current Data warehouse plan to come in on schedule or on budget?
– Are we making progress? and are we making progress as Data warehouse leaders?
– Describe the process of data transformation required by your system?
– What is the process of data transformation required by your system?
Base transceiver station Critical Criteria:
Map Base transceiver station issues and ask questions.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data warehouse processes?
– In a project to restructure Data warehouse outcomes, which stakeholders would you involve?
– Who needs to know about Data warehouse ?
Data pre-processing Critical Criteria:
Own Data pre-processing strategies and finalize specific methods for Data pre-processing acceptance.
– 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?
– Meeting the challenge: are missed Data warehouse opportunities costing us money?
– Why should we adopt a Data warehouse framework?
OLAP cube Critical Criteria:
Infer OLAP cube management and report on setting up OLAP cube without losing ground.
– How do we know that any Data warehouse analysis is complete and comprehensive?
– What will drive Data warehouse change?
Relational database Critical Criteria:
Review Relational database planning and gather practices for scaling 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?
– Will Data warehouse deliverables need to be tested and, if so, by whom?
– Why is Data warehouse important for you now?
Data curation Critical Criteria:
Scrutinze Data curation planning and interpret which customers can’t participate in Data curation because they lack skills.
– Think of your Data warehouse project. what are the main functions?
– How do we manage Data warehouse Knowledge Management (KM)?
– Are there recognized Data warehouse problems?
Fact table Critical Criteria:
Powwow over Fact table quality and summarize a clear Fact table focus.
– Is maximizing Data warehouse protection the same as minimizing Data warehouse loss?
– What are specific Data warehouse Rules to follow?
– Are we Assessing Data warehouse and Risk?
Data extraction Critical Criteria:
Test Data extraction failures and find out.
– Who will be responsible for making the decisions to include or exclude requested changes once Data warehouse is underway?
– How can data extraction from dashboards be automated?
– Is the scope of Data warehouse defined?
– What is Effective Data warehouse?
Computer data storage Critical Criteria:
Judge Computer data storage strategies and remodel and develop an effective Computer data storage strategy.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Data warehouse models, tools and techniques are necessary?
– Risk factors: what are the characteristics of Data warehouse that make it risky?
– How would one define Data warehouse leadership?
Data Mining Extensions Critical Criteria:
Look at Data Mining Extensions leadership and intervene in Data Mining Extensions processes and leadership.
– How do we ensure that implementations of Data warehouse products are done in a way that ensures safety?
– Does our organization need more Data warehouse education?
– What are the short and long-term Data warehouse goals?
Business intelligence tools Critical Criteria:
Administer Business intelligence tools management and interpret which customers can’t participate in Business intelligence tools because they lack skills.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Data warehouse. How do we gain traction?
– What tools do you use once you have decided on a Data warehouse strategy and more importantly how do you choose?
– Business Intelligence Tools?
Data editing Critical Criteria:
Differentiate Data editing risks and clarify ways to gain access to competitive Data editing services.
– Can Management personnel recognize the monetary benefit of Data warehouse?
– What are all of our Data warehouse domains and what do they do?
– What is our Data warehouse Strategy?
Executive information system Critical Criteria:
Exchange ideas about Executive information system visions and maintain Executive information system for success.
– Does Data warehouse create potential expectations in other areas that need to be recognized and considered?
Dimensional modeling Critical Criteria:
Deliberate Dimensional modeling engagements and assess what counts with Dimensional modeling that we are not counting.
– Who is the main stakeholder, with ultimate responsibility for driving Data warehouse forward?
Transaction data Critical Criteria:
Wrangle Transaction data quality and define what do we need to start doing with Transaction data.
– At what point will vulnerability assessments be performed once Data warehouse is put into production (e.g., ongoing Risk Management after implementation)?
Data scraping Critical Criteria:
Investigate Data scraping engagements and improve Data scraping service perception.
– 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?
– Does Data warehouse appropriately measure and monitor risk?
Slowly changing dimension Critical Criteria:
Graph Slowly changing dimension outcomes and create Slowly changing dimension explanations for all managers.
– Does Data warehouse systematically track and analyze outcomes for accountability and quality improvement?
– How can the value of Data warehouse be defined?
Market research Critical Criteria:
Prioritize Market research leadership and assess and formulate effective operational and Market research strategies.
– Does the software allow users to bring in data from outside the company on-the-flylike demographics and market research to augment corporate data?
– Do Data warehouse rules make a reasonable demand on a users capabilities?
– What are internal and external Data warehouse relations?
Data quality Critical Criteria:
Map Data quality management and gather Data quality models .
– Were changes made during the file extract period to how the data are processed, such as changes to mode of data collection, changes to instructions for completing the application form, changes to the edit, changes to classification codes, or changes to the query system used to retrieve the data?
– Review availability, completeness and timeliness of reports from all service delivery points. how many reports should there have been from all service delivery points?
– Has the program/project clearly documented (in writing) what is reported to who, and how and when reporting is required?
– How do you express quality with regard to making a decision from a statistical hypothesis test?
– Are there established qualitative and quantitative measures of metaData Quality?
– Which items are subject to revision either by editing or updating data values?
– What are the data quality requirements required by the business user?
– What criteria should be used to assess the performance of the system?
– Has management performed regular Data Quality assessments?
– What is the typical reimbursement for sharing your data?
– Does data meet the specifications you assumed?
– Completeness: is all necessary data present?
– Data Quality: how good is your data?
– Where is the Domain Expertise?
– Do you train data collectors?
– Who sets public standards ?
– Where do you clean data?
– Are the results stable?
– Are the data complete?
– Are records complete?
Data warehouse automation Critical Criteria:
Have a meeting on Data warehouse automation engagements and define what our big hairy audacious Data warehouse automation goal is.
– Will Data warehouse have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– What role does communication play in the success or failure of a Data warehouse project?
– Is Data warehouse Realistic, or are you setting yourself up for failure?
Business intelligence Critical Criteria:
Grade Business intelligence adoptions and achieve a single Business intelligence view and bringing data together.
– As we develop increasing numbers of predictive models, then we have to figure out how do you pick the targets, how do you optimize the models?
– Does the software provide fast query performance, either via its own fast in-memory software or by directly connecting to fast data stores?
– What are the approaches to handle RTB related data 100 GB aggregated for business intelligence?
– What statistics should one be familiar with for business intelligence and web analytics?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– What is the difference between Enterprise Information Management and Data Warehousing?
– What are typical responsibilities of someone in the role of Business Analyst?
– What specialized bi knowledge does your business have that can be leveraged?
– Who prioritizes, conducts and monitors business intelligence projects?
– What are the key skills a Business Intelligence Analyst should have?
– No single business unit responsible for enterprise data?
– Does your software integrate with active directory?
– To create parallel systems or custom workflows?
– What is the future of BI Score cards KPI etc?
– Can your product map ad-hoc query results?
– What is required to present video images?
– Make or buy BI Business Intelligence?
– Using dashboard functions?
Data redundancy Critical Criteria:
Steer Data redundancy adoptions and overcome Data redundancy skills and management ineffectiveness.
– Do we monitor the Data warehouse decisions made and fine tune them as they evolve?
– How important is Data warehouse to the user organizations mission?
Data fusion Critical Criteria:
Scan Data fusion failures and reinforce and communicate particularly sensitive Data fusion decisions.
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
– What are your most important goals for the strategic Data warehouse objectives?
– What business benefits will Data warehouse goals deliver if achieved?
Data model Critical Criteria:
Discuss Data model results and work towards be a leading Data model expert.
– What is the total cost related to deploying Data warehouse, including any consulting or professional services?
– 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?
– Does the Data warehouse task fit the clients priorities?
– Physical data model available?
– Logical data model available?
Data presentation architecture Critical Criteria:
Understand Data presentation architecture issues and research ways can we become the Data presentation architecture company that would put us out of business.
– For your Data warehouse project, identify and describe the business environment. is there more than one layer to the business environment?
– Do those selected for the Data warehouse team have a good general understanding of what Data warehouse is all about?
– Does Data warehouse analysis isolate the fundamental causes of problems?
General Mills Critical Criteria:
Win new insights about General Mills tasks and clarify ways to gain access to competitive General Mills services.
– How will you know that the Data warehouse project has been successful?
Decision support system Critical Criteria:
Judge Decision support system goals and find answers.
– Who sets the Data warehouse standards?
VDM Verlag Critical Criteria:
Nurse VDM Verlag visions and mentor VDM Verlag customer orientation.
– Does Data warehouse analysis show the relationships among important Data warehouse factors?
Codd’s 12 rules Critical Criteria:
Deduce Codd’s 12 rules planning and visualize why should people listen to you regarding Codd’s 12 rules.
Data integrity Critical Criteria:
Start Data integrity projects and test out new things.
– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?
– What other jobs or tasks affect the performance of the steps in the Data warehouse process?
– What knowledge, skills and characteristics mark a good Data warehouse project manager?
– What is the purpose of Data warehouse in relation to the mission?
– Data Integrity, Is it SAP created?
– Can we rely on the Data Integrity?
Online transaction processing Critical Criteria:
Accommodate Online transaction processing issues and explain and analyze the challenges of Online transaction processing.
– 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 our Data warehouse Processes?
Data integration Critical Criteria:
Map Data integration decisions and pay attention to the small things.
– Among the Data warehouse product and service cost to be estimated, which is considered hardest to estimate?
– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?
– What are the record-keeping requirements of Data warehouse activities?
– Which Oracle Data Integration products are used in your solution?
Database normalization Critical Criteria:
Mine Database normalization governance and devote time assessing Database normalization and its risk.
– How do mission and objectives affect the Data warehouse processes of our organization?
Master data management Critical Criteria:
Adapt Master data management tactics and perfect Master data management conflict management.
– 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?
– What tools and technologies are needed for a custom Data warehouse project?
– 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?
Online analytical processing Critical Criteria:
Mix Online analytical processing visions and report on the economics of relationships managing Online analytical processing and constraints.
– Think about the kind of project structure that would be appropriate for your Data warehouse project. should it be formal and complex, or can it be less formal and relatively simple?
– How do we keep improving Data warehouse?
Early-arriving fact Critical Criteria:
Understand Early-arriving fact outcomes and get out your magnifying glass.
– What are the barriers to increased Data warehouse production?
Business intelligence software Critical Criteria:
Do a round table on Business intelligence software risks and plan concise Business intelligence software education.
– Are there Data warehouse problems defined?
Snowflake schema Critical Criteria:
Probe Snowflake schema leadership and assess what counts with Snowflake schema that we are not counting.
– Is there a Data warehouse Communication plan covering who needs to get what information when?
– Is there any existing Data warehouse governance structure?
Database management system Critical Criteria:
Closely inspect Database management system planning and arbitrate Database management system techniques that enhance teamwork and productivity.
– Can we add value to the current Data warehouse decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– What database management systems have been implemented?
– How to Secure Data warehouse?
Data compression Critical Criteria:
Probe Data compression governance and get going.
Comparison of OLAP Servers Critical Criteria:
Investigate Comparison of OLAP Servers goals and do something to it.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Data warehouse process?
– Who will be responsible for deciding whether Data warehouse goes ahead or not after the initial investigations?
– How can skill-level changes improve Data warehouse?
Data analysis Critical Criteria:
Cut a stake in Data analysis failures and probe using an integrated framework to make sure Data analysis is getting what it needs.
– 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 is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– What are some real time data analysis frameworks?
DBC 1012 Critical Criteria:
Infer DBC 1012 strategies and diversify by understanding risks and leveraging DBC 1012.
Sperry Univac Critical Criteria:
Talk about Sperry Univac failures and look at the big picture.
– How do senior leaders actions reflect a commitment to the organizations Data warehouse values?
Data warehouse appliance Critical Criteria:
Rank Data warehouse appliance adoptions and define what do we need to start doing with Data warehouse appliance.
– How can you negotiate Data warehouse successfully with a stubborn boss, an irate client, or a deceitful coworker?
– In what ways are Data warehouse vendors and us interacting to ensure safe and effective use?
Data structure Critical Criteria:
Analyze Data structure failures and pay attention to the small things.
– 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?
– What are the business goals Data warehouse is aiming to achieve?
– What threat is Data warehouse addressing?
Enterprise resource planning Critical Criteria:
Contribute to Enterprise resource planning tasks and catalog Enterprise resource planning activities.
– Do several people in different organizational units assist with the Data warehouse process?
Software as a service Critical Criteria:
Discuss Software as a service adoptions and pay attention to the small things.
– 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?
– Why are Service Level Agreements a dying breed in the software as a service industry?
– Are accountability and ownership for Data warehouse clearly defined?
Pattern recognition Critical Criteria:
Set goals for Pattern recognition outcomes and forecast involvement of future Pattern recognition projects in development.
– Is Supporting Data warehouse documentation required?
Data Mining Critical Criteria:
Detail Data Mining leadership and reinforce and communicate particularly sensitive Data Mining decisions.
– 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 are the top 3 things at the forefront of our Data warehouse agendas for the next 3 years?
– 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?
– How do we Improve Data warehouse service perception, and satisfaction?
– What programs do we have to teach data mining?
Hub and spokes architecture Critical Criteria:
Confer over Hub and spokes architecture results and transcribe Hub and spokes architecture as tomorrows backbone for success.
– What are the key elements of your Data warehouse performance improvement system, including your evaluation, organizational learning, and innovation processes?
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data warehouse processes?
International Journal of Data Warehousing and Mining Critical Criteria:
Have a session on International Journal of Data Warehousing and Mining management and ask questions.
– What are the success criteria that will indicate that Data warehouse objectives have been met and the benefits delivered?
Data corruption Critical Criteria:
Revitalize Data corruption issues and document what potential Data corruption megatrends could make our business model obsolete.
– What prevents me from making the changes I know will make me a more effective Data warehouse leader?
Data wrangling Critical Criteria:
Mix Data wrangling issues and inform on and uncover unspoken needs and breakthrough Data wrangling results.
Data loss Critical Criteria:
X-ray Data loss goals and interpret which customers can’t participate in Data loss because they lack skills.
– The goal of a disaster recovery plan is to minimize the costs resulting from losses of, or damages to, the resources or capabilities of your IT facilities. The success of any disaster recovery plan depends a great deal on being able to determine the risks associated with data loss. What is the impact to our business if the data is lost?
– Are we doing adequate due diligence before contracting with third party providers -particularly in regards to involving audit departments prior to contractual commitments?
– Is website access and maintenance information accessible by the ED and at least one other person (e.g., Board Chair)?
– What types of controls and associated technologies are considered essential to auditing third party processing?
– Does the Executive Director and at least one other person (e.g., Board Chair) have access to all passwords?
– Does the tool we use provide the ability to print an easy-to-read policy summary for audit purposes?
– What is a standard data flow, and what should be the source and destination of the identified data?
– Is the use of CCM destined to become an important and requisite audit methodology best practice?
– Confidence -what is the data loss rate when the system is running at its required throughput?
– What are the risks associated with third party processing that are of most concern?
– Where does your sensitive data reside, both internally and with third parties?
– How has the economy impacted how we determine ongoing vendor viability?
– Are there effective automation solutions available to help with this?
– What are the best open source solutions for data loss prevention?
– How can hashes help prevent data loss from DoS or DDoS attacks?
– What processes are in place to govern the informational flow?
– What is your most important data?
Metaphor Computer Systems Critical Criteria:
Apply Metaphor Computer Systems issues and finalize specific methods for Metaphor Computer Systems acceptance.
– How do we go about Securing Data warehouse?
Entity-relationship model Critical Criteria:
Reason over Entity-relationship model risks and acquire concise Entity-relationship model education.
Legacy system Critical Criteria:
Frame Legacy system management and stake your claim.
– If the path forward waits until a new generation of devices essentially replaces an old generation of devices which could be somewhere between 5 and 15 years, what does the path forward look like for the legacy devices and their software maintenance?
– Recognizing that most equipment lasts a decade or more, what cyber security, compatibility and integration issues affect legacy equipment and merit attention?
– How will you handle the legal implications? What are the challenges associated with Data Quality or with working alongside legacy systems?
– What are the existing tasks, methods and techniques to enable migration of legacy on-premise software to the cloud?
– If a component c is dynamically replaced, what are the potential effects of the replacement related to c?
– If a change is made to a component c, what other components might be affected by c?
– What are the relationships with other business processes and are these necessary?
– How do you inventory and assess business processes as part of an ERP evaluation?
– What are the main practical motivations behind legacy migrations to the cloud?
– Should there be a complete replacement of legacy mainframes and applications?
– Does the software system satisfy the expectations of the user?
– What is the allocation time for each resource?
– What is the complexity of the output produced?
– Are all Stakeholders supporting this vision?
– Do other systems depend on it for data?
– When and where is the management done?
– Addressing existing Legacy Software?
– What do our customers need or want?
– Is the software system effective?
– What is to be Maintained?
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 | http://theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Data warehouse External links:
Urology at UCLA | Urology Data warehouse
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 reduction External links:
What is DATA REDUCTION – Science Dictionary
OEC – Data Reduction Techniques – Online Ethics
What is DATA REDUCTION? definition of DATA …
Customer relationship management External links:
Customer Relationship Management | CRM Software – Vtiger
Agile CRM – Customer Relationship Management
Decision support External links:
STATdx | Diagnostic Decision Support for Radiology
Data transformation External links:
MSIGHTS | Marketing Data Transformation and Reporting
Base transceiver station External links:
base transceiver station Jobs – Monster.com
Base Transceiver Station (BTS) – Techopedia.com
Base Transceiver Station (BTS) – Gartner IT Glossary
OLAP cube External links:
How to Create OLAP Cube in Analysis Services: 9 Steps
What is OLAP cube? – Definition from WhatIs.com
Data Warehouse vs. OLAP Cube | Solver Blog
Relational database External links:
How to Design Relational Database with ERD? – Visual …
Cloud SQL – MySQL & PostgreSQL Relational Database …
Relational Database Concepts – YouTube
Data curation External links:
Data curation (Book, 2017) [WorldCat.org]
What is data curation? – Definition from WhatIs.com
Title: Data Curation APIs – arXiv
Fact table External links:
Factless Fact Table | Learn about Factless Fact Table
Fact table – Oracle FAQ
Multiple Fact Tables – Common Dimensions |Tableau …
Data extraction External links:
Data Extraction – iMacros
NeXtraction – Intelligent Data Extraction
Computer data storage External links:
computer data storage service – TheBlaze
Computer Data Storage Options – Ferris State University
Data Mining Extensions External links:
Data Mining Extensions (DMX) Reference | Microsoft Docs
Data Mining Extensions (DMX) Reference
Data Mining Extensions (DMX) Operator Reference
Business intelligence tools External links:
Top Business Intelligence Tools – 2017 Reviews – BI
Rebiz | Business Intelligence Tools
Top Business Intelligence Tools – 2018 Reviews & Pricing
Data editing External links:
Statistical data editing (Book, 1994) [WorldCat.org]
Data Editing – NaturalPoint Product Documentation Ver 2.0
Data Editing – NaturalPoint Product Documentation Ver 1.10
Executive information system External links:
[PDF]Transportation Executive Information System (TEIS) …
Harris Computer Systems (Executive Information System)
[PDF]Implementing an Executive Information System: Seven …
Dimensional modeling External links:
Dimensional Modeling: Banding – Benny Austin
[PDF]Dimensional Modeling 101 – Purdue University
Dimensional Modeling – ZenTut
Transaction data External links:
Medical Transaction Data Reporting FAQ | WCIRB California
SEC.gov | Short Sale Volume and Transaction Data
Open Market Operations: Transaction Data – FEDERAL …
Data scraping External links:
Automated data scraping from websites into Excel – YouTube
Data Scraping | Alex’s Web Scraping Service
Agenty – Cloud Hosted Data Scraping Tool
Slowly changing dimension External links:
SSIS Slowly Changing Dimension Type 2 – Tutorial Gateway
Market research External links:
The Arcview Group | Cannabis Investment & Market Research
[PDF]MARKET RESEARCH – www.acquisition.gov
Market Overview & Stock Market Research | Scottrade
Data quality External links:
CWS Data Quality Portal
[PDF]Data Quality Report – arb.ca.gov
CRMfusion Salesforce Data Quality Software Applications
Data warehouse automation External links:
Data Warehouse Automation Events | WhereScape | …
biGENiUS – Data Warehouse Automation
Data Warehouse Automation | Magnitude Software
Business intelligence External links:
SQL Server Business Intelligence | Microsoft
EnsembleIQ | The premier business intelligence resource
Data redundancy External links:
What is Data Redundancy? – Definition from Techopedia
Data Redundancy – Intro to Hadoop and MapReduce – YouTube
Data fusion External links:
Data Fusion Solutions
Global Data Fusion, a Background Screening Company
Data Fusion & Analysis Tools – xcmdfe.xcmdata.org
Data model External links:
Analysis Data Model (ADaM) | CDISC
General Mills External links:
Login – General Mills Inc. – Hewitt
General Mills Convenience & Foodservice
Save with 22 General Mills coupons and sales for February, 2018. Today’s top offer: $1.00 Off. Coupon Sherpa, #1 in coupons.
Decision support system External links:
[PDF]Global Decision Support System (GDSS) 1 of 5
Maintenance Decision Support System – Iteris
Minol – Decision Support System – Occupancy Factor
VDM Verlag External links:
Victoria Strauss – VDM Verlag Dr. Mueller – SFWA
Codd’s 12 rules External links:
Codd’s 12 Rules for DBMS – W3schools
Codd’s 12 rules – w3resource
DBMS Codd’s 12 Rules – tutorialspoint.com
Data integrity External links:
Data Integrity Jobs – Apply Now | CareerBuilder
Data Integrity Specialist Jobs, Employment | Indeed.com
Data Integrity Jobs, Employment | Indeed.com
Online transaction processing External links:
Online Transaction Processing – Gartner IT Glossary
What is OLTP (online transaction processing)? – …
eCash.com Online transaction processing – Wefunder
Database normalization External links:
Description of the database normalization basics
Database Normalization Essays – ManyEssays.com
Master data management External links:
Best Master Data Management (MDM) Software in 2018 | G2 …
Master Data Management | IBM Analytics
Online analytical processing External links:
Working with Online Analytical Processing (OLAP)
Business intelligence software External links:
Business Intelligence Software | Solver
Business Intelligence Software Explained – Webopedia
Mortgage Business Intelligence Software :: Motivity Solutions
Snowflake schema External links:
Snowflake Schema – ZenTut
Database management system External links:
ChurchSuite – Church Database Management System
Database Management System (DBMS) – Techopedia.com
Relational Database Management System (RDBMS) – …
Data compression External links:
The Data Compression Guide – sites.google.com
SecureZIP | Enterprise Data Compression | PKWARE
Data compression (Book, 1976) [WorldCat.org]
Comparison of OLAP Servers External links:
COMPARISON OF OLAP SERVERS – The Economic Times
Comparison of OLAP Servers – revolvy.com
https://www.revolvy.com/topic/Comparison of OLAP Servers
Data analysis External links:
Data Analysis – Illinois State Board of Education
Seven Bridges Genomics – The biomedical data analysis …
Sperry Univac External links:
Sperry Univac presents Ken Jennings EPCOT 1982 – YouTube
SPERRY UNIVAC ROSEVILLE MN [WorldCat Identities]
http://www.worldcat.org/identities/nc-sperry univac roseville mn
The Connector People – SPERRY UNIVAC
Data warehouse appliance External links:
Monitoring Pack for Microsoft Data Warehouse Appliance
Data structure External links:
Data structures – C++ Tutorials
Enterprise resource planning External links:
Enterprise resource planning Flashcards | Quizlet
What is ERP (Enterprise resource planning)? – NetSuite.com
[PDF]Navy Enterprise Resource Planning (ERP) – DOT&E
Software as a service External links:
Enterprise Gamification Software as a Service Platform
FinTech Software as a Service for AP, AR & HR | DataServ
What is SaaS? Software as a Service | Microsoft Azure
Pattern recognition External links:
Cup And Handle Pattern Recognition And Chart Analysis
Dora’s Ballet Adventure Game: Pattern Recognition – Nick Jr.
Tradable Patterns – Trade Better with Pattern Recognition
Data Mining External links:
Data mining | computer science | Britannica.com
Data Mining Extensions (DMX) Reference | Microsoft Docs
What is Data Mining in Healthcare?
Hub and spokes architecture External links:
Hub and spokes architecture – WOW.com
International Journal of Data Warehousing and Mining External links:
International Journal of Data Warehousing and Mining h-Index
International Journal of Data Warehousing and Mining
Data corruption External links:
Data corruption – UFOpaedia
Repair Logger Data Corruption – Zimbra :: Tech Center
Data wrangling External links:
Big Data: Data Wrangling – Old Dominion University
Data Wrangling Tools & Software | Trifacta
What Is Data Wrangling? – Datawatch Corporation
Data loss External links:
Data Loss Prevention & Protection | Symantec
Technical Overview of DLP (data loss prevention) in Exchange
Metaphor Computer Systems External links:
Metaphor Computer Systems Slide Show – John Weeks
Metaphor Computer Systems
http://Metaphor Computer Systems was a Xerox PARC spin-off that created an advanced workstation, database gateway, a unique graphical office interface, and software applications that communicate. The Metaphor machine was one of the first commercial workstations to offer a complete hardware/software package and a GUI. Although the company achieved some commercial success, it never achieved the fame of either the Apple Macintosh or Microsoft Windows.
PCC – Michael Trigoboff – Metaphor Computer Systems
Entity-relationship model External links:
[PDF]Chapter 2: Entity-Relationship Model
Legacy system External links:
Legacy System – Spectrum—Payroll | TLM | HR | Benefits