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 287 essential critical questions to check off in that domain.
The following domains are covered:
Data warehouse, Customer relationship management, Codd’s 12 rules, Extract, transform, load, Business process, Market research, Data extraction, Data element, Data editing, Data dictionary, Data vault modeling, VDM Verlag, Snowflake schema, Database management system, Semantic warehousing, Data integration, Data scraping, Metaphor Computer Systems, Data loss, Data validation, MultiDimensional eXpressions, Business intelligence tools, Online transaction processing, Anchor Modeling, XML for Analysis, Data scrubbing, Data warehouse, Dimensional modeling, Online analytical processing, Business intelligence, Data reduction, Data warehouse appliance, Data wrangling, Data fusion, Surrogate key, Dimension table, National Diet Library, Pattern recognition, Data compression, Computer data storage, Comparison of OLAP Servers, Operational data store, Sperry Univac, Hub and spokes architecture, Fact table, Enterprise resource planning, Extract transform load, Data pre-processing, Early-arriving fact, Data presentation architecture, Data cleansing, Decision support, DBC 1012, Data mart, Business reporting, Third normal form, Entity-relationship model, Data quality, Legacy system, Data structure, Decision support system, International Journal of Data Warehousing and Mining, Accounting intelligence, Master data management, General Mills, OLAP cube:
Data warehouse Critical Criteria:
Pilot Data warehouse issues and describe the risks of Data warehouse sustainability.
– For your Data warehouse project, identify and describe the business environment. is there more than one layer to the business environment?
– What tier data server has been identified for the storage of decision support data contained in a data warehouse?
– 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?
– What potential environmental factors impact the Data warehouse effort?
– 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 are alternatives to building a data warehouse?
– Do we offer a good introduction to data warehouse?
– Data Warehouse versus Data Lake (Data Swamp)?
– Do you still need a data warehouse?
– Centralized data warehouse?
Customer relationship management Critical Criteria:
Have a session on Customer relationship management engagements and perfect Customer relationship management conflict management.
– 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?
– Can the Exchange define how First Call Resolution will be calculated, and how a resolvable call is distinguished from a nonresolvable call?
– Why would potential clients outsource their business to us if they can perform the same level of Customer Service in house?
– What is the value of integrating social intelligence listening and engagement into the CRM your business is using?
– How long (on average) between a potential issue being posted online and being flagged to the client?
– You may want to forge more relationships with affluent customers, but do they want them with you?
– Do you have a mechanism in place to quickly respond to visitor/customer inquiries and orders?
– What is your approach to server analytics and community analytics for program measurement?
– What are the basic activities of customer life-cycle management?
– What are the standard hours for phone, email and chat support?
– Can you identify your customers when they visit your website?
– How is a typical client engagement with your firm structured?
– What steps do we use in rolling out customer selfservice?
– Do we adhere to best practices interface design?
– Can your customers interact with each other?
– How long should e-mail messages be stored?
– What s the Best Way to Outsource CRM?
– Can customers place orders online?
– How many open tickets are there?
– Can metadata be loaded?
Codd’s 12 rules Critical Criteria:
Accommodate Codd’s 12 rules adoptions and probe Codd’s 12 rules strategic alliances.
– How do your measurements capture actionable Data warehouse information for use in exceeding your customers expectations and securing your customers engagement?
– Why is it important to have senior management support for a Data warehouse project?
– How does the organization define, manage, and improve its Data warehouse processes?
Extract, transform, load Critical Criteria:
Mix Extract, transform, load adoptions and prioritize challenges of Extract, transform, load.
– What are the key elements of your Data warehouse performance improvement system, including your evaluation, organizational learning, and innovation processes?
– Think about the functions involved in your Data warehouse project. what processes flow from these functions?
– Why are Data warehouse skills important?
Business process Critical Criteria:
Transcribe Business process failures and report on setting up Business process without losing ground.
– Do we identify maximum allowable downtime for critical business functions, acceptable levels of data loss and backlogged transactions, RTOs, RPOs, recovery of the critical path (i.e., business processes or systems that should receive the highest priority), and the costs associated with downtime? Are the approved thresholds appropriate?
– To what extent will this product open up for subsequent add-on products, e.g. business process outsourcing services built on top of a program-as-a-service offering?
– 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?
– When conducting a business process reengineering study, what should we look for when trying to identify business processes to change?
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data warehouse services/products?
– What are the disruptive Data warehouse technologies that enable our organization to radically change our business processes?
– Do you design data protection and privacy requirements into the development of your business processes and new systems?
– Do the functional areas need business process integration (e.g., order entl. billing, or Customer Service)?
– If we process purchase orders; what is the desired business process around supporting purchase orders?
– 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 will business process and behavioral change be managed?
– Does Data warehouse appropriately measure and monitor risk?
Market research Critical Criteria:
Accelerate Market research outcomes and look at it backwards.
– Does the software allow users to bring in data from outside the company on-the-flylike demographics and market research to augment corporate data?
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Data warehouse process?
– What are the long-term Data warehouse goals?
Data extraction Critical Criteria:
Confer over Data extraction issues and mentor Data extraction customer orientation.
– 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?
– Do several people in different organizational units assist with the Data warehouse process?
– How can data extraction from dashboards be automated?
– Are there Data warehouse Models?
Data element Critical Criteria:
Own Data element engagements and get the big picture.
– 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?
– At what organizational level is it appropriate to have a new data element?
– How can the data element influence practice, policy, or research?
– Can Management personnel recognize the monetary benefit of Data warehouse?
– At what level is it appropriate to maintain a new data element?
– Can the data element be clearly and commonly defined?
– What are the Essentials of Internal Data warehouse Management?
– What are the usability implications of Data warehouse actions?
Data editing Critical Criteria:
Discourse Data editing outcomes and use obstacles to break out of ruts.
– What prevents me from making the changes I know will make me a more effective Data warehouse leader?
– Have the types of risks that may impact Data warehouse been identified and analyzed?
– Is Data warehouse Realistic, or are you setting yourself up for failure?
Data dictionary Critical Criteria:
Discuss Data dictionary strategies and find out.
– What types of information should be included in the data dictionary?
– What is our formula for success in Data warehouse ?
– Is there a data dictionary?
Data vault modeling Critical Criteria:
Illustrate Data vault modeling quality and grade techniques for implementing Data vault modeling controls.
– 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?
– What sources do you use to gather information for a Data warehouse study?
VDM Verlag Critical Criteria:
Nurse VDM Verlag management and find out what it really means.
– How can we incorporate support to ensure safe and effective use of Data warehouse into the services that we provide?
– How will you measure your Data warehouse effectiveness?
Snowflake schema Critical Criteria:
Pilot Snowflake schema engagements and define what do we need to start doing with Snowflake schema.
– Are there any disadvantages to implementing Data warehouse? There might be some that are less obvious?
Database management system Critical Criteria:
Contribute to Database management system adoptions and modify and define the unique characteristics of interactive Database management system projects.
– What business benefits will Data warehouse goals deliver if achieved?
– What database management systems have been implemented?
– How do we keep improving Data warehouse?
Semantic warehousing Critical Criteria:
Analyze Semantic warehousing outcomes and get out your magnifying glass.
– Risk factors: what are the characteristics of Data warehouse that make it risky?
Data integration Critical Criteria:
Prioritize Data integration strategies and finalize specific methods for Data integration acceptance.
– Does Data warehouse systematically track and analyze outcomes for accountability and quality improvement?
– In which area(s) do data integration and BI, as part of Fusion Middleware, help our IT infrastructure?
– How do we Identify specific Data warehouse investment and emerging trends?
– Which Oracle Data Integration products are used in your solution?
Data scraping Critical Criteria:
Troubleshoot Data scraping strategies and check on ways to get started with Data scraping.
– What are your most important goals for the strategic Data warehouse objectives?
Metaphor Computer Systems Critical Criteria:
Graph Metaphor Computer Systems results and acquire concise Metaphor Computer Systems education.
– How do we know that any Data warehouse analysis is complete and comprehensive?
– How do we Improve Data warehouse service perception, and satisfaction?
Data loss Critical Criteria:
Use past Data loss visions and find out.
– Does management recognize that there is an increased motivation for fraud and data crimes, concurrent with expectations on audit departments to recognize such activities despite reduced budgets?
– Are we doing adequate due diligence before contracting with third party providers -particularly in regards to involving audit departments prior to contractual commitments?
– Could you lose your service when an investigation into data loss of another customer starts to affect your privacy and data?
– Is website access and maintenance information accessible by the ED and at least one other person (e.g., Board Chair)?
– Does the tool we use provide the ability for mobile devices to access critical portions of the management interface?
– Does the tool we use provide the ability to print an easy-to-read policy summary for audit purposes?
– Are we protecting our data properly at rest if an attacker compromises our applications or systems?
– Are audit plans and programs being modified / created to address data loss prevention?
– Does the tool we use support the ability to configure user content management alerts?
– How do we maintaining integrity between communication ports and firewalls?
– Do we have the the ability to create multiple quarantine queues?
– What processes are in place to govern the informational flow?
– What are all the egress points present in the network?
– Do all computers have up-to-date antivirus protection?
– Do all computers have up-to-date anti-spam protection?
– What is the retention period of the data?
– What do we hope to achieve with a DLP deployment?
– Do any copies need to be off-site?
– Who is the System Administrator?
– What Causes Data Loss?
Data validation Critical Criteria:
Survey Data validation issues and slay a dragon.
– Does Data warehouse analysis isolate the fundamental causes of problems?
– Which individuals, teams or departments will be involved in Data warehouse?
– What threat is Data warehouse addressing?
MultiDimensional eXpressions Critical Criteria:
Audit MultiDimensional eXpressions decisions and report on the economics of relationships managing MultiDimensional eXpressions and constraints.
– How do senior leaders actions reflect a commitment to the organizations Data warehouse values?
– Are assumptions made in Data warehouse stated explicitly?
– Does our organization need more Data warehouse education?
Business intelligence tools Critical Criteria:
Inquire about Business intelligence tools strategies and observe effective Business intelligence tools.
– How do you determine the key elements that affect Data warehouse workforce satisfaction? how are these elements determined for different workforce groups and segments?
– Who is the main stakeholder, with ultimate responsibility for driving Data warehouse forward?
– What are current Data warehouse Paradigms?
– Business Intelligence Tools?
Online transaction processing Critical Criteria:
Grade Online transaction processing planning and give examples utilizing a core of simple Online transaction processing skills.
– Can we do Data warehouse without complex (expensive) analysis?
– Will Data warehouse deliverables need to be tested and, if so, by whom?
Anchor Modeling Critical Criteria:
Steer Anchor Modeling management and change contexts.
– How likely is the current Data warehouse plan to come in on schedule or on budget?
– Are accountability and ownership for Data warehouse clearly defined?
XML for Analysis Critical Criteria:
Extrapolate XML for Analysis management and get going.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data warehouse processes?
– What are the Key enablers to make this Data warehouse move?
Data scrubbing Critical Criteria:
Analyze Data scrubbing results and use obstacles to break out of ruts.
– What tools do you use once you have decided on a Data warehouse strategy and more importantly how do you choose?
Data warehouse Critical Criteria:
Transcribe Data warehouse goals and know what your objective is.
– What are the success criteria that will indicate that Data warehouse objectives have been met and the benefits delivered?
– Do we need an enterprise data warehouse, a Data Lake, or both as part of our overall data architecture?
– Does Data warehouse create potential expectations in other areas that need to be recognized and considered?
– What is the purpose of data warehouses and data marts?
Dimensional modeling Critical Criteria:
Refer to Dimensional modeling decisions and finalize the present value of growth of Dimensional modeling.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data warehouse processes?
– Meeting the challenge: are missed Data warehouse opportunities costing us money?
– Are we making progress? and are we making progress as Data warehouse leaders?
Online analytical processing Critical Criteria:
Study Online analytical processing projects and attract Online analytical processing skills.
– 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?
– Is maximizing Data warehouse protection the same as minimizing Data warehouse loss?
– Is a Data warehouse Team Work effort in place?
Business intelligence Critical Criteria:
Detail Business intelligence planning and adopt an insight outlook.
– Does a BI business intelligence CoE center of excellence approach to support and enhancements benefit our organization and save cost?
– Can you easily add users and features to quickly scale and customize to your organizations specific needs?
– Can you filter, drill down, or add entirely new data to your visualization with mobile editing?
– What should recruiters look for in a business intelligence professional?
– What social media dashboards are available and how do they compare?
– Which other Oracle Business Intelligence products are used in your solution?
– Number of data sources that can be simultaneously accessed?
– What are the main full web business intelligence solutions?
– No single business unit responsible for enterprise data?
– What are the most efficient ways to create the models?
– Is your software easy for IT to manage and upgrade?
– What programs do we have to teach data mining?
– Will your product work from a mobile device?
– What is required to present video images?
– What is your expect product life cycle?
– What is your annual maintenance?
Data reduction Critical Criteria:
Revitalize Data reduction strategies and grade techniques for implementing Data reduction controls.
– Will Data warehouse have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– How is the value delivered by Data warehouse being measured?
Data warehouse appliance Critical Criteria:
Transcribe Data warehouse appliance tactics and simulate teachings and consultations on quality process improvement of Data warehouse appliance.
– What are our best practices for minimizing Data warehouse project risk, while demonstrating incremental value and quick wins throughout the Data warehouse project lifecycle?
– How do we go about Securing Data warehouse?
Data wrangling Critical Criteria:
Brainstorm over Data wrangling decisions and optimize Data wrangling leadership as a key to advancement.
– Do the Data warehouse decisions we make today help people and the planet tomorrow?
– Is the Data warehouse organization completing tasks effectively and efficiently?
Data fusion Critical Criteria:
Investigate Data fusion goals and point out improvements in Data fusion.
– What new requirements emerge in terms of information processing/management to make physical and virtual world data fusion possible?
– Why is Data warehouse important for you now?
Surrogate key Critical Criteria:
Be responsible for Surrogate key tasks and point out Surrogate key tensions in leadership.
– Who needs to know about Data warehouse ?
Dimension table Critical Criteria:
Learn from Dimension table leadership and maintain Dimension table for success.
– What will drive Data warehouse change?
– How can the value of Data warehouse be defined?
National Diet Library Critical Criteria:
Illustrate National Diet Library adoptions and stake your claim.
– Do we have past Data warehouse Successes?
Pattern recognition Critical Criteria:
Familiarize yourself with Pattern recognition strategies and triple focus on important concepts of Pattern recognition relationship management.
– What will be the consequences to the business (financial, reputation etc) if Data warehouse does not go ahead or fails to deliver the objectives?
– Who sets the Data warehouse standards?
Data compression Critical Criteria:
Debate over Data compression leadership and shift your focus.
– How can you measure Data warehouse in a systematic way?
Computer data storage Critical Criteria:
Incorporate Computer data storage engagements and use obstacles to break out of ruts.
– Do we monitor the Data warehouse decisions made and fine tune them as they evolve?
– Is Data warehouse Required?
Comparison of OLAP Servers Critical Criteria:
Pilot Comparison of OLAP Servers goals and spearhead techniques for implementing Comparison of OLAP Servers.
– Is Data warehouse dependent on the successful delivery of a current project?
Operational data store Critical Criteria:
Be clear about Operational data store quality and check on ways to get started with Operational data store.
– How do we go about Comparing Data warehouse approaches/solutions?
Sperry Univac Critical Criteria:
Investigate Sperry Univac tactics and explore and align the progress in Sperry Univac.
– What is our Data warehouse Strategy?
Hub and spokes architecture Critical Criteria:
Deduce Hub and spokes architecture planning and budget the knowledge transfer for any interested in Hub and spokes architecture.
– How can skill-level changes improve Data warehouse?
– What are our Data warehouse Processes?
Fact table Critical Criteria:
Graph Fact table tactics and budget the knowledge transfer for any interested in Fact table.
– What are all of our Data warehouse domains and what do they do?
Enterprise resource planning Critical Criteria:
Start Enterprise resource planning engagements and report on setting up Enterprise resource planning without losing ground.
– What is the purpose of Data warehouse in relation to the mission?
Extract transform load Critical Criteria:
Powwow over Extract transform load risks and acquire concise Extract transform load education.
Data pre-processing Critical Criteria:
Transcribe Data pre-processing goals and gather Data pre-processing models .
– What are our needs in relation to Data warehouse skills, labor, equipment, and markets?
– What are internal and external Data warehouse relations?
Early-arriving fact Critical Criteria:
Communicate about Early-arriving fact management and finalize specific methods for Early-arriving fact acceptance.
– Do Data warehouse rules make a reasonable demand on a users capabilities?
Data presentation architecture Critical Criteria:
Tête-à-tête about Data presentation architecture issues and innovate what needs to be done with Data presentation architecture.
Data cleansing Critical Criteria:
Mix Data cleansing decisions and do something to it.
– 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?
– Do those selected for the Data warehouse team have a good general understanding of what Data warehouse is all about?
– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?
– How do mission and objectives affect the Data warehouse processes of our organization?
Decision support Critical Criteria:
Deliberate over Decision support planning and overcome Decision support skills and management ineffectiveness.
– 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?
– Think of your Data warehouse project. what are the main functions?
– What are the access requirements for decision support data?
DBC 1012 Critical Criteria:
Canvass DBC 1012 visions and check on ways to get started with DBC 1012.
– What about Data warehouse Analysis of results?
Data mart Critical Criteria:
Read up on Data mart governance and find answers.
Business reporting Critical Criteria:
Dissect Business reporting engagements and give examples utilizing a core of simple Business reporting skills.
– Which Data warehouse goals are the most important?
Third normal form Critical Criteria:
Align Third normal form issues and create Third normal form explanations for all managers.
– When a Data warehouse manager recognizes a problem, what options are available?
Entity-relationship model Critical Criteria:
Map Entity-relationship model goals and differentiate in coordinating Entity-relationship model.
– 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?
– Are there recognized Data warehouse problems?
Data quality Critical Criteria:
Pilot Data quality quality and work towards be a leading Data quality expert.
– 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?
– Which audit findings of the Data Management and reporting system warrant recommendation notes and changes to the design in order to improve Data Quality?
– 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?
– How can statistical hypothesis testing lead me to make an incorrect conclusion or decision?
– What are some of the different sources of error (variability) in my collected data?
– Is data recorded with sufficient precision/detail to measure relevant indicators?
– What types of decision errors could I make in a statistical hypothesis test?
– Is information on the physical properties of the media required?
– Can good algorithms, models, heuristics overcome Data Quality problems?
– Program goals are key – what do you want to do with the data?
– What is the proportion of missing values for each field?
– Data rich enough to answer analysis/business question?
– Establishing an end-to-end data governance process?
– What are you doing with all this data anyway?
– What about attribute completeness?
– Are the attributes independent?
– Why is Data Quality necessary?
– How can we improve Data warehouse?
– Where to clean?
Legacy system Critical Criteria:
Closely inspect Legacy system tactics and define what do we need to start doing with Legacy system.
– The process of conducting a data migration involves access to both the legacy source and the target source. The target source must be configured according to requirements. If youre using a contractor and provided that the contractor is under strict confidentiality, do you permit the contractor to house copies of your source data during the implementation?
– Are the migration costs associated with the migration to the selected alternative included in thew new system/application investment, the legacy investment, or in a separate migration investment?
– Many organizations maintain their current legacy systems, believing in the old saying, If it is not broken, why fix it. Do we know where in our organization this thinking makes sense?
– What actions must be taken in order to successfully integrate numerous systems, including legacy data from earlier systems that are desired for analysis?
– If a new system is expected to interface/Integrate with any one of your current 3rd party/ Legacy system then what is the interface/integration required?
– Are there technological or capacity constraints from the legacy systems that would mandate or preclude real-time integration with the existing systems?
– If a component c is dynamically replaced, what are the potential effects of the replacement related to c?
– Is there an expectation that the new solutions will be run in parallel with legacy solutions?
– Do different parts of the organization use different processes for the same function?
– Should there be a complete replacement of legacy mainframes and applications?
– Does the software system satisfy the expectations of the user?
– What are the costs of the resources used in the process?
– What is the best definition of System of Systems?
– What are the resources involved in the process?
– What is the complexity of the output produced?
– How will we procure services and technology?
– Is the software system productive?
– How does it all fit together?
– How is the management done?
Data structure Critical Criteria:
Substantiate Data structure decisions and drive action.
– Can we add value to the current Data warehouse decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– Among the Data warehouse product and service cost to be estimated, which is considered hardest to estimate?
– 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?
Decision support system Critical Criteria:
Add value to Decision support system visions and improve Decision support system service perception.
– Is there any existing Data warehouse governance structure?
International Journal of Data Warehousing and Mining Critical Criteria:
Survey International Journal of Data Warehousing and Mining leadership and find the essential reading for International Journal of Data Warehousing and Mining researchers.
– 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?
Accounting intelligence Critical Criteria:
Focus on Accounting intelligence goals and pay attention to the small things.
– Who will provide the final approval of Data warehouse deliverables?
Master data management Critical Criteria:
Consult on Master data management engagements and test out new things.
– How do we ensure that implementations of Data warehouse products are done in a way that ensures safety?
– How do we measure improved Data warehouse service perception, and satisfaction?
– 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?
General Mills Critical Criteria:
Inquire about General Mills tasks and adjust implementation of General Mills.
OLAP cube Critical Criteria:
Discourse OLAP cube tasks and revise understanding of OLAP cube architectures.
– How do we Lead with Data warehouse in Mind?
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
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:
Enterprise Data Warehouse | IT@UMN
Cloud Data Warehouse | Snowflake
Customer relationship management External links:
Oracle – Siebel Customer Relationship Management
1workforce – Customer Relationship Management …
Oracle – Siebel Customer Relationship Management
Codd’s 12 rules External links:
Codd’s 12 Rules for Relational Database Management – …
Codd’s 12 Rules – Database Answers Home Page
Codd’s 12 rules – A Gentle Introduction to SQL – Google Sites
Extract, transform, load External links:
ETL (Extract, transform, load) Salary | PayScale
www.payscale.com › United States › Skill/Specialty
What is ETL (Extract, Transform, Load)? Webopedia Definition
Business process External links:
[PDF]Business Process Guide Position Management : …
Canon Business Process Services
Business Process Management Jobs – CareerBuilder
Market research External links:
Online Q+Q Market Research Technologies | FocusVision
The Arcview Group | Cannabis Investment & Market Research
Market Overview & Stock Market Research | Scottrade
Data extraction External links:
[PDF]Data extraction Presentation – PBworks
TeamBeam – Meta-Data Extraction from Scientific Literature
NeXtraction – Intelligent Data Extraction
Data element External links:
[PDF]Data Element – Form Version 2017v01
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
Data dictionary External links:
What is a Data Dictionary? – Definition from Techopedia
[XLS]Data Dictionary – Product Database
What is a Data Dictionary? – Bridging the Gap
Data vault modeling External links:
[PDF]Data Vault Modeling – NYOUG
Data Vault Modeling and Snowflake | Snowflake
VDM Verlag External links:
Victoria Strauss – VDM Verlag Dr. Mueller – SFWA
Snowflake schema External links:
Star schema or snowflake schema or flat file? | Qlik …
Star & snowflake schema | Qlik Community
Star Schema vs Snowflake Schema performance – Stack Overflow
Database management system External links:
Kuder Administrative Database Management System – …
10-7 Operating System, Database Management System, …
Petroleum Database Management System (PDMS)
Data integration External links:
IBM Data Integration – IBM Analytics
PPT – Data integration PowerPoint Presentation – ID:4073218
Metaphor Computer Systems External links:
PCC – Michael Trigoboff – Metaphor Computer Systems
Computer electronics enclosure – Metaphor Computer Systems
Metaphor Computer Systems Slide Show – John Weeks
Data loss External links:
3 Common Types of Data Loss — and How to Prevent Them
Data Loss Prevention & Protection | Symantec
Data validation External links:
Excel Drop Down Lists – Data Validation
Data Validation in Excel – EASY Excel Tutorial
Description and examples of data validation in Excel
Business intelligence tools External links:
DataBay Resources | Business Intelligence Tools
Business Intelligence Tools for Small Companies – A …
Business Intelligence Tools for Developers, DBAs & …
Online transaction processing External links:
What is Online Transaction Processing (OLTP)? – …
Online Transaction Processing, Oracle Databases, Big …
Anchor Modeling External links:
Home | Anchor Modeling Academy
Anchor Modeling (@anchormodeling) | Twitter
Anchor Modeling – Home | Facebook
XML for Analysis External links:
XML for Analysis – Simba Technologies
[PDF]XML for Analysis Specification
XML for Analysis (XMLA) Reference | Microsoft Docs
Data warehouse External links:
Cloud Data Warehouse | Snowflake
Title Data Warehouse Analyst Jobs, Employment | Indeed.com
Dimensional modeling External links:
[PDF]Basics of Dimensional Modeling – University of …
[PDF]Dimensional Modeling 101 – Purdue University – …
Three-dimensional modeling of wave-induced residual …
Online analytical processing External links:
Working with Online Analytical Processing (OLAP)
Business intelligence External links:
List of Business Intelligence Skills – The Balance
Data reduction External links:
AuditorQC | Free Linearity and Daily QC Data Reduction
LISA data reduction | JILA Science
[1506.08864] Data Reduction with the MIKE Spectrometer
Data warehouse appliance External links:
Monitoring Pack for Microsoft Data Warehouse Appliance
Data wrangling External links:
Big Data: Data Wrangling – Old Dominion University
Data fusion External links:
Global Data Fusion, a Background Screening Company
Data fusion : concepts and ideas (eBook, 2012) …
[PDF]Data Fusion Centers – Esri: GIS Mapping Software, …
Surrogate key External links:
INSERT ALL INTO and Sequence.nextval for a Surrogate Key
What is a surrogate key in a relational database? – Quora
Dimension table External links:
Dimension Table – msdn.microsoft.com
What is dimension table? – Definition from WhatIs.com
Tube Dimension Table – McNichols Company – Grating
National Diet Library External links:
Online Gallery | National Diet Library
Free Data Service | National Diet Library
National Diet Library law. (Book, 1961) [WorldCat.org]
Pattern recognition External links:
Pattern recognition – Encyclopedia of Mathematics
Pattern Recognition. (eBook, 2008) [WorldCat.org]
Tradable Patterns – Trade Better with Pattern Recognition
Data compression External links:
CiteSeerX — Data Compression
Data compression (Book, 2004) [WorldCat.org]
Data compression (Book, 1976) [WorldCat.org]
Computer data storage External links:
ELSYM5 Manual | Computer Data Storage | Materials
Comparison of OLAP Servers External links:
COMPARISON OF OLAP SERVERS – The Economic Times
Comparison of OLAP Servers – topics.revolvy.com
topics.revolvy.com/topic/Comparison of OLAP Servers
Operational data store External links:
Operational Data Store (ODS) Defined | James Serra’s Blog
Are Operational Data Store Still Leveraged – Stack Overflow
Sperry Univac External links:
1978 Sperry Univac Computer System – YouTube
Sperry Univac – Bristol, Tennessee – Local Business | …
Sperry UNIVAC: Collectibles | eBay
Hub and spokes architecture External links:
Fact table External links:
Factless Fact Table – Wisdomschema
Fact table – Oracle FAQ
FACT table | Qlik Community
Enterprise resource planning External links:
What is ERP (Enterprise resource planning)? – NetSuite.com
MDConnect | Enterprise Resource Planning (ERP) system
Enterprise Resource Planning System | Hill International
Extract transform load External links:
Early-arriving fact External links:
Early-arriving fact – Revolvy
Data presentation architecture External links:
Data cleansing External links:
Data cleansing – SlideShare
Data Cleansing Services | Database Cleaning | Data …
Decision support External links:
Planning and Decision Support
STATdx | Diagnostic Decision Support for Radiology
What is Decision Support System | InTechOpen
Data mart External links:
UNC Data Mart – University of North Carolina
[PDF]Institutional Research Data Mart: Instructor Guide …
MPR Data Mart
Business reporting External links:
Title Business Reporting Analyst Jobs, Employment | Indeed.com
Historically Underutilized Business Reporting
Business reporting. (Book, 1990) [WorldCat.org]
Third normal form External links:
What is Third Normal Form (3NF)? – Definition from Techopedia
Database – Third Normal Form (3NF) – Tutorials Point
What is Third Normal Form (3NF)? – Definition from …
Entity-relationship model External links:
Data quality External links:
ISO Data Quality – NOAA Environmental Data Management …
Legacy system External links:
Legacy System Catalog – Implant Direct
Data structure External links:
Data structures – C++ Tutorials
C++ Data Structures – tutorialspoint.com
Decision support system External links:
North Carolina Accounting System Decision Support System
What is Decision Support System | InTechOpen
CureMatch Decision Support System For Oncologist to …
International Journal of Data Warehousing and Mining External links:
International Journal of Data Warehousing and Mining …
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining h …
Accounting intelligence External links:
World Accounting Intelligence – Home | Facebook
General Mills External links:
General Mills Convenience & Foodservice
Login – General Mills Inc. – Hewitt
General Mills: 2016 flour recall consumer Iinformation
OLAP cube External links:
Analyze OLAP cube data with Excel | Microsoft Docs
Data Warehouse vs. OLAP Cube? – Stack Overflow
How to Create OLAP Cube in Analysis Services: 9 Steps