Top 284 Integrated Clinical Business Enterprise Data Warehouse Free Questions to Collect the Right answers

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 284 essential critical questions to check off in that domain.

The following domains are covered:

Data warehouse, Sixth normal form, Sperry Univac, Data extraction, Data security, Data model, Master data management, Data loading, Dimension table, Online analytical processing, Data cleansing, Data structure, Data warehouse appliance, OLAP cube, Data Mining Extensions, Entity-relationship model, VDM Verlag, Anchor Modeling, Market research, Enterprise resource planning, International Journal of Data Warehousing and Mining, Decision support system, Data transformation, Fact table, Metaphor Computer Systems, Data corruption, Data integration, Data element, Data analysis, Operational system, Data curation, Degenerate dimension, Data quality, Predictive analytics, Business intelligence software, Data compression, Data reduction, Data loss, Semantic warehousing, Pattern recognition, Early-arriving fact, Data warehouse, Hub and spokes architecture, Extract, transform, load, General Mills, Business intelligence tools, Business process, Data warehouse automation, Data fusion, National Diet Library, Executive information system, Data storage, Data dictionary, Business intelligence, Data presentation architecture, Operational data store, Column-oriented DBMS, Dimensional modeling, Surrogate key, Sperry Corporation, Data blending, Relational database, Data scraping, Database management system:

Data warehouse Critical Criteria:

Interpolate Data warehouse failures and visualize why should people listen to you regarding Data warehouse.

– 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?

– Is data warehouseing necessary for our business intelligence service?

– Is Data Warehouseing necessary for a business intelligence service?

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

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

– What are alternatives to building a data warehouse?

– Do we offer a good introduction to data warehouse?

– Data Warehouse versus Data Lake (Data Swamp)?

– Why is Data warehouse important for you now?

– Do you still need a data warehouse?

– How do we keep improving Data warehouse?

Sixth normal form Critical Criteria:

Focus on Sixth normal form quality and revise understanding of Sixth normal form architectures.

– Who needs to know about Data warehouse ?

– What are specific Data warehouse Rules to follow?

– What threat is Data warehouse addressing?

Sperry Univac Critical Criteria:

Define Sperry Univac tactics and find answers.

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

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

– How will you measure your Data warehouse effectiveness?

Data extraction Critical Criteria:

Inquire about Data extraction quality and correct better engagement with Data extraction results.

– How can data extraction from dashboards be automated?

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

– Are there Data warehouse Models?

Data security Critical Criteria:

Have a session on Data security issues and cater for concise Data security education.

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

– What are the minimum data security requirements for a database containing personal financial transaction records?

– Do these concerns about data security negate the value of storage-as-a-service in the cloud?

– What are the challenges related to cloud computing data security?

– So, what should you do to mitigate these risks to data security?

– What are the barriers to increased Data warehouse production?

– Does it contain data security obligations?

– What is Data Security at Physical Layer?

– What is Data Security at Network Layer?

– How will you manage data security?

Data model Critical Criteria:

Systematize Data model visions and balance specific methods for improving Data model results.

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

– 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?

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

– What about Data warehouse Analysis of results?

– Physical data model available?

– Logical data model available?

Master data management Critical Criteria:

Coach on Master data management tactics and oversee Master data management requirements.

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

– 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?

Data loading Critical Criteria:

Have a meeting on Data loading planning and create a map for yourself.

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

– What are the long-term Data warehouse goals?

– How can the value of Data warehouse be defined?

Dimension table Critical Criteria:

Face Dimension table governance and budget the knowledge transfer for any interested in Dimension table.

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

Online analytical processing Critical Criteria:

Understand Online analytical processing outcomes and modify and define the unique characteristics of interactive Online analytical processing projects.

– 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?

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

– Why are Data warehouse skills important?

Data cleansing Critical Criteria:

Set goals for Data cleansing governance and suggest using storytelling to create more compelling Data cleansing projects.

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

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

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

– What are all of our Data warehouse domains and what do they do?

Data structure Critical Criteria:

Do a round table on Data structure tasks and probe using an integrated framework to make sure Data structure is getting what it needs.

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

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

– 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?

Data warehouse appliance Critical Criteria:

Be clear about Data warehouse appliance risks and find out.

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

– How would one define Data warehouse leadership?

– Is the scope of Data warehouse defined?

OLAP cube Critical Criteria:

Shape OLAP cube issues and slay a dragon.

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

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

– Does our organization need more Data warehouse education?

Data Mining Extensions Critical Criteria:

Scrutinze Data Mining Extensions quality and attract Data Mining Extensions 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?

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

Entity-relationship model Critical Criteria:

Frame Entity-relationship model goals and define Entity-relationship model competency-based leadership.

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

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

– What is our formula for success in Data warehouse ?

VDM Verlag Critical Criteria:

Differentiate VDM Verlag goals and explain and analyze the challenges of VDM Verlag.

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

– Have you identified your Data warehouse key performance indicators?

– What are the Essentials of Internal Data warehouse Management?

Anchor Modeling Critical Criteria:

Illustrate Anchor Modeling governance and optimize Anchor Modeling leadership as a key to advancement.

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

Market research Critical Criteria:

Discuss Market research strategies and explain and analyze the challenges of Market research.

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

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

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

Enterprise resource planning Critical Criteria:

Meet over Enterprise resource planning governance and probe using an integrated framework to make sure Enterprise resource planning is getting what it needs.

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

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

– Do we have past Data warehouse Successes?

International Journal of Data Warehousing and Mining Critical Criteria:

Map International Journal of Data Warehousing and Mining governance and oversee implementation of International Journal of Data Warehousing and Mining.

– 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?

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

Decision support system Critical Criteria:

Jump start Decision support system engagements and get out your magnifying glass.

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

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

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

– What is Effective Data warehouse?

Data transformation Critical Criteria:

Frame Data transformation planning and budget for Data transformation challenges.

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

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

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

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

– How do we Identify specific Data warehouse investment and emerging trends?

Fact table Critical Criteria:

Focus on Fact table tactics and change contexts.

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

– What are current Data warehouse Paradigms?

Metaphor Computer Systems Critical Criteria:

Unify Metaphor Computer Systems visions and secure Metaphor Computer Systems creativity.

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

– Are there recognized Data warehouse problems?

Data corruption Critical Criteria:

Use past Data corruption leadership and acquire concise Data corruption education.

Data integration Critical Criteria:

Merge Data integration outcomes and report on developing an effective Data integration strategy.

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

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

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

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

– Are there Data warehouse problems defined?

Data element Critical Criteria:

Recall Data element decisions and perfect Data element conflict management.

– 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?

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

– 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?

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

– Can the data element be clearly and commonly defined?

– How can skill-level changes improve Data warehouse?

Data analysis Critical Criteria:

X-ray Data analysis adoptions and handle a jump-start course to Data analysis.

– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

– What are some real time data analysis frameworks?

Operational system Critical Criteria:

Illustrate Operational system decisions and modify and define the unique characteristics of interactive Operational system projects.

– How do we maintain Data warehouses Integrity?

Data curation Critical Criteria:

Start Data curation outcomes and handle a jump-start course to Data curation.

– 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?

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

Degenerate dimension Critical Criteria:

Cut a stake in Degenerate dimension adoptions and diversify by understanding risks and leveraging Degenerate dimension.

– How do your measurements capture actionable Data warehouse information for use in exceeding your customers expectations and securing your customers engagement?

Data quality Critical Criteria:

Study Data quality goals and find the ideas you already have.

– Do we conduct regular data quality audits to ensure that our strategies for enforcing quality control are up-to-date and that any corrective measures undertaken in the past have been successful in improving Data Quality?

– Are data timely enough to influence management decision-making (i.e., in terms of frequency and currency)?

– Do we double check that the data collected follows the plans and procedures for data collection?

– Does the reported data contain enough information to represent performance measure activities?

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

– Perform inventory (expensive), or use proxy (track complaints). audit samples?

– What kinds of practical constraints on collecting data should you identify?

– Accessibility: is the data easily accessible, understandable, and usable?

– Do you define jargon and other terminology used in data collection tools?

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

– How does big data impact Data Quality and governance best practices?

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

– Think through your sampling design what are you trying to show?

– How can you control the probability of making decision errors?

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

– What is the proportion of missing values for each field?

– Establishing an end-to-end data governance process?

– What do we mean by Data Quality ?

– How to handle censored data?

– Can we interpret the data?

Predictive analytics Critical Criteria:

Exchange ideas about Predictive analytics engagements and attract Predictive analytics skills.

– How do senior leaders actions reflect a commitment to the organizations Data warehouse values?

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

– What will drive Data warehouse change?

Business intelligence software Critical Criteria:

Guard Business intelligence software management and look at it backwards.

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

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

Data compression Critical Criteria:

Graph Data compression issues and stake your claim.

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

– Does Data warehouse appropriately measure and monitor risk?

Data reduction Critical Criteria:

Cut a stake in Data reduction tactics and balance specific methods for improving Data reduction results.

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

– Who sets the Data warehouse standards?

Data loss Critical Criteria:

Deliberate Data loss failures and remodel and develop an effective Data loss strategy.

– You do not want to be informed of a data loss incident from the users themselves or from the data protection authority. Do you have technology that can detect breaches that have taken place; forensics available to investigate how the data was lost (or changed); and can you go back in time with full user logs and identify the incident to understand its scope and impact?

– Does the tool in use have the ability to integrate with Active Directory or sync directory on a scheduled basis, or do look-ups within a multi-domain forest in the sub-100-millisecond range?

– Does the tool in use allow the ability to search for registered data (e.g., database data) or specific files by name, hash marks, or watermarks, and to detect partial-file-content matches?

– Does the tool in use allow the ability to use Smart number identifiers (e.g., the ability to recognize that 999 99 9999 is not a valid Social Security number)?

– Do you have a policy in place to deal with data being lost or stolen (e.g., who needs to be notified, what steps need to be taken to mitigate damages)?

– Does the Executive Director and at least one other person (e.g., Board Chair) have access to all passwords?

– Are we protecting our data properly at rest if an attacker compromises our applications or systems?

– Does the tool we use support the ability to configure user content management alerts?

– Do handovers take place in a quiet room off the main ENT (ear nose throat) ?

– Are there encryption requirements, especially of off-line copies?

– What are the best open source solutions for data loss prevention?

– How will the setup of endpoints with the DLP manager occur?

– Are all computer files backed up on a regular basis?

– Verbal handover reports: what skills are needed?

– How will we know our systems have been hacked?

– What is the retention period of the data?

– What do we hope to achieve with a DLP deployment?

– Are Incident response plans documented?

– What are your most offensive protocols?

– Are all computers password protected?

Semantic warehousing Critical Criteria:

Have a meeting on Semantic warehousing engagements and get out your magnifying glass.

– What potential environmental factors impact the Data warehouse effort?

– Have all basic functions of Data warehouse been defined?

Pattern recognition Critical Criteria:

Grade Pattern recognition quality and explore and align the progress in Pattern recognition.

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

Early-arriving fact Critical Criteria:

Guard Early-arriving fact outcomes and define what our big hairy audacious Early-arriving fact goal is.

– 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 vendors make products that address the Data warehouse needs?

Data warehouse Critical Criteria:

Categorize Data warehouse results and test out new things.

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

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

– Centralized data warehouse?

Hub and spokes architecture Critical Criteria:

Interpolate Hub and spokes architecture results and check on ways to get started with Hub and spokes architecture.

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

Extract, transform, load Critical Criteria:

Align Extract, transform, load planning and visualize why should people listen to you regarding Extract, transform, load.

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

General Mills Critical Criteria:

Wrangle General Mills failures and explore and align the progress in General Mills.

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

Business intelligence tools Critical Criteria:

Inquire about Business intelligence tools projects and innovate what needs to be done with Business intelligence tools.

– Think about the people you identified for your Data warehouse project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?

– Business Intelligence Tools?

Business process Critical Criteria:

Administer Business process decisions and separate what are the business goals Business process is aiming to achieve.

– 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?

– 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?

– Have senior executives clearly identified and explained concerns regarding Customer Service issues and other change drivers, and emphasized that major improvements are imperative?

– 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?

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

– What are the disruptive Data warehouse technologies that enable our organization to radically change our business processes?

– What finance, procurement and Human Resources business processes should be included in the scope of a erp solution?

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

– How do clients contact client services with any questions about business processes?

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

– 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?

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

– How will business process and behavioral change be managed?

Data warehouse automation Critical Criteria:

Deliberate Data warehouse automation failures and question.

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

Data fusion Critical Criteria:

Trace Data fusion decisions and spearhead techniques for implementing Data fusion.

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

– Where do ideas that reach policy makers and planners as proposals for Data warehouse strengthening and reform actually originate?

National Diet Library Critical Criteria:

Check National Diet Library tactics and get out your magnifying glass.

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

Executive information system Critical Criteria:

Scan Executive information system outcomes and gather practices for scaling Executive information system.

– Does the Data warehouse task fit the clients priorities?

Data storage Critical Criteria:

Examine Data storage leadership and define Data storage competency-based leadership.

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

– What procedures does your intended long-term data storage facility have in place for preservation and backup?

– What are the data storage and the application logic locations?

– Is Data warehouse Required?

Data dictionary Critical Criteria:

Think about Data dictionary projects and drive action.

– 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?

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

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

– Is there a data dictionary?

Business intelligence Critical Criteria:

Define Business intelligence issues and develop and take control of the Business intelligence initiative.

– What are the potential areas of conflict that can arise between organizations IT and marketing functions around the deployment and use of business intelligence and data analytics software services and what is the best way to resolve them?

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

– Does the software provide fast query performance, either via its own fast in-memory software or by directly connecting to fast data stores?

– Are NoSQL databases used primarily for applications or are they used in Business Intelligence use cases as well?

– Does your bi solution require weeks of training before new users can analyze data and publish dashboards?

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

– Do we have trusted vendors to guide us through the process of adopting business intelligence systems?

– What strategies will we pursue to ensure the success of the business intelligence competency center?

– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?

– What is the future scope for combination of Business Intelligence and Cloud Computing?

– Does creating or modifying reports or dashboards require a reporting team?

– Who prioritizes, conducts and monitors business intelligence projects?

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

– Number of data sources that can be simultaneously accessed?

– How are business intelligence applications delivered?

– What is your expect product life cycle?

– Why do we need business intelligence?

– Do you offer formal user training?

Data presentation architecture Critical Criteria:

See the value of Data presentation architecture goals and explain and analyze the challenges of Data presentation architecture.

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

Operational data store Critical Criteria:

Review Operational data store outcomes and modify and define the unique characteristics of interactive Operational data store projects.

– What is our Data warehouse Strategy?

Column-oriented DBMS Critical Criteria:

Recall Column-oriented DBMS leadership and simulate teachings and consultations on quality process improvement of Column-oriented DBMS.

Dimensional modeling Critical Criteria:

Transcribe Dimensional modeling engagements and adjust implementation of Dimensional modeling.

Surrogate key Critical Criteria:

Participate in Surrogate key tactics and slay a dragon.

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

Sperry Corporation Critical Criteria:

Troubleshoot Sperry Corporation strategies and reinforce and communicate particularly sensitive Sperry Corporation decisions.

Data blending Critical Criteria:

Gauge Data blending issues and adjust implementation of Data blending.

– Are assumptions made in Data warehouse stated explicitly?

– What are the usability implications of Data warehouse actions?

Relational database Critical Criteria:

Graph Relational database strategies and transcribe Relational database as tomorrows backbone for success.

– 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?

Data scraping Critical Criteria:

Own Data scraping issues and look for lots of ideas.

– What are internal and external Data warehouse relations?

Database management system Critical Criteria:

Chat re Database management system decisions and report on developing an effective Database management system strategy.

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

– What database management systems have been implemented?


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 |

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 warehouse External links:

[PDF]Data Warehouse – Utility’s Smart Grid Clearinghouse – Utility DOE SG Clearhouse_ph2add.pdf

Enterprise Data Warehouse | IT@UMN

HRSA Data Warehouse Home Page

Sixth normal form External links:

Sixth normal form – Google Groups

6NF abbreviation stands for Sixth normal form – All Acronyms

On the Sixth Normal Form – Anchor Modeling

Sperry Univac External links:

1978 Sperry Univac Computer System – YouTube


Introducing Sperry Univac System 80 Computer (rare …

Data extraction External links:

Data Extraction – iMacros

NeXtraction – Intelligent Data Extraction

Data extraction from audio and text – Google Cloud Platform

Data security External links:


What is Data Security? – Definition from Techopedia

Account Data Security at Fidelity

Data model External links:

Analysis Data Model (ADaM) | CDISC

Data Warehouse data model | Microsoft Docs

Cloud Firestore Data Model | Firebase

Master data management External links:

Best Master Data Management (MDM) Software – G2 Crowd

MDM Platform | Master Data Management Platform | Profisee

Infor Master Data Management

Data loading External links:

What is Data Loading? – Definition from Techopedia

Dimension table External links:

What is dimension table? – Definition from

[PDF]Bearing Units Dimension Table

Tube Dimension Table – Grating

Online analytical processing External links:

Working with Online Analytical Processing (OLAP)

Data cleansing External links:

[DOC]Without a data cleansing – University of Oklahoma

Data Cleansing Solution –

Data structure External links:

Data structures – C++ Tutorials

C++ Data Structures

Data warehouse appliance External links:

Data Warehouse Appliance – McKnight Consulting Group

OLAP cube External links:

Analyze OLAP cube data with Excel | Microsoft Docs

Data Warehouse vs. OLAP Cube? – Stack Overflow

Data Warehouse vs. OLAP Cube | Solver Blog

Data Mining Extensions External links:

Data Mining Extensions (DMX) Reference | Microsoft Docs

Data Mining Extensions (DMX) Reference

DMX abbreviation stands for Data Mining Extensions

Entity-relationship model External links:

The Entity-Relationship Model

[PDF]Chapter 2: Entity-Relationship Model

Enhanced Entity-Relationship Model

VDM Verlag External links:

Victoria Strauss – VDM Verlag Dr. Mueller – SFWA

VDM Verlag Dr Müller (@VdmVerlag_de) | Twitter

Anchor Modeling External links:

Anchor Modeling – Home | Facebook

Home | Anchor Modeling Academy

Publications – Anchor Modeling

Enterprise resource planning External links:

What is ERP (Enterprise resource planning)? –

What is ERP | ERP Systems | Enterprise Resource Planning

ERP (Enterprise Resource Planning) System, ERP …

International Journal of Data Warehousing and Mining External links:

International Journal of Data Warehousing and Mining h …

International Journal of Data Warehousing and Mining – …

International Journal of Data Warehousing and Mining

Decision support system External links:

Maintenance Decision Support System – Iteris

Decision Support System – DSS – Investopedia

Fact table External links:

Factless fact table | James Serra’s Blog

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

Factless Fact Table – Wisdomschema

Metaphor Computer Systems External links:

Metaphor Computer Systems | Crunchbase

Metaphor Computer Systems Slide Show – John Weeks

PCC – Michael Trigoboff – Metaphor Computer Systems

Data corruption External links:

Data corruption – UFOpaedia

Data integration External links:

Data Integration – Kettle | Hitachi Vantara Community

Cloud Data Integration & File Sync | Layer2 Cloud Connector

Data element External links:

[PDF]Data Element Description – National Center for …


[PDF]Alphabetical Data Element List

Data analysis External links:

Equity in Athletics Data Analysis – US Department of …

Data Analysis – Illinois State Board of Education

[PDF]General Position Information PB034 – Data Analysis …

Operational system External links:

[PDF]Operational System 463L Pallets and Nets

Data curation External links:

Data curation (Book, 2017) []

[PPT]Materials Data Curation System – NIST

What is data curation? – Definition from

Degenerate dimension External links:

What does Degenerate Dimension mean? – Business …

Data Warehousing: What is degenerate dimension? – …

Degenerate Dimension – YouTube

Data quality External links:

CRMfusion Salesforce Data Quality Software Applications

Free Address Lookup Tool – Experian Data Quality

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

Predictive analytics External links:

Predictive Analytics Software, Social Listening | NewBrand

What is predictive analytics? – Definition from

Predictive Analytics Solutions for Global Industry | Uptake

Business intelligence software External links:

Business Intelligence Software | Solver

Business Intelligence Software – ERP & Project …

Business Intelligence Software Explained – Webopedia…

Data compression External links:

Data compression (Book, 2004) []

PKZIP | Data Compression | PKWARE

The Data Compression Guide –

Data reduction External links:

What Is Data Reduction? – wiseGEEK

What is DATA REDUCTION – Science Dictionary

AuditorQC | Free Linearity and Daily QC Data Reduction

Data loss External links:

How to Use Data Loss Prevention in Office 365 | SherWeb

Data Loss Prevention & Protection | Symantec

Data Loss and Data Recovery Infographic – EaseUS

Pattern recognition External links:

Pattern Recognition – MATLAB & Simulink – MathWorks

Tradable Patterns – Trade Better with Pattern Recognition

Mike the Knight Potion Practice: Pattern Recognition

Data warehouse External links:

[PDF]Data Warehouse – Utility’s Smart Grid Clearinghouse – Utility DOE SG Clearhouse_ph2add.pdf

Condition Categories – Chronic Conditions Data Warehouse

Title Data Warehouse Analyst Jobs, Employment |

Hub and spokes architecture External links:

Hub and spokes architecture –

Extract, transform, load External links:

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

General Mills External links:

Login – General Mills Inc. – Hewitt

General Mills – Home | Facebook

General Mills – Minneapolis, MN

Business intelligence tools External links:

Why Use Business Intelligence Tools? 10 Strategic Benefits

Product Overview: Business Intelligence Tools | PitchBook

Business Intelligence Tools & Software | Square

Business process External links:

Infosys BPM – Business Process Management | BPM …

What Is a Business Process? (with picture) – wiseGEEK

Business Process Outsourcing | BPO | DATAMARK, Inc.

Data warehouse automation External links:

Data Warehouse Automation Done Right Panoply | Panoply

Data Warehouse Automation | Attunity

Data Warehouse Automation | Magnitude Software

Data fusion External links:

Data Fusion Analysis For Test Validation System

Global Data Fusion, a Background Screening Company

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

National Diet Library External links:

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

Floor Plan | National Diet Library

National Diet Library | library, Tokyo, Japan |

Executive information system External links:



[PDF]Transportation Executive Information System …

Data storage External links:

Optimized Enterprise Data Storage and Protection | Leonovus

Cloud Storage – Online Data Storage | Google Cloud Platform

Estimate Request Units and Data Storage – DocumentDB

Data dictionary External links:

What is a Data Dictionary? – Bridging the Gap

Creating Metadata and a Data Dictionary |

NTDS 2018 Data Dictionary

Business intelligence External links:

Business Intelligence | Microsoft

Business Intelligence Tools & Software | Square

Business Intelligence Software – ERP & Project …

Operational data store External links:

Operational Data Store (ODS) Defined | James Serra’s Blog

Operational Data Store – ODS – Gartner Tech Definitions

Column-oriented DBMS External links:

ClickHouse — open source distributed column-oriented DBMS

CiteSeerX — C-Store: A Column-oriented DBMS

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

Dimensional modeling External links:

[PDF]Dimensional Modeling 101 – Purdue University

[PDF]Dimensional Modeling: In a Business Intelligence …

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

Surrogate key External links:

Surrogate Key vs. Natural Key | IT Pro

What is a surrogate key in a relational database? – Quora

What is surrogate key ? Where we use it explain with examples?

Sperry Corporation External links:

[PDF]An IT Legacy Paper A Brief History of Sperry Corporation


Taiwan SPERRY CORPORATION Home page | …

Data blending External links:

What is Data Blending? | Trifacta

data blending | Drawing with Numbers

What Is Data Blending, and Which Tools Make It Easier?

Relational database External links:

How to Design Relational Database with ERD?

Amazon Aurora – Relational Database Built for the Cloud – …

Amazon Relational Database Service (RDS) – AWS

Data scraping External links:

Agenty – Cloud Hosted Data Scraping Tool

Automated data scraping from websites into Excel – YouTube

Database management system External links:

Relational Database Management System | MariaDB Products

Database Management System (DBMS) –

Petroleum Database Management System (PDMS)