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 255 essential critical questions to check off in that domain.
The following domains are covered:
Data warehouse, Operational system, Software as a service, OLAP cube, Data compression, Slowly changing dimension, Third normal form, Comparison of OLAP Servers, Data dictionary, Data scrubbing, DBC 1012, Data security, Data farming, Online analytical processing, Data reduction, International Journal of Data Warehousing and Mining, Data quality, Early-arriving fact, Data Mining, Codd’s 12 rules, Data mart, Dimension table, Data blending, Data warehouse automation, XML for Analysis, Data integrity, Hub and spokes architecture, Anchor Modeling, Snowflake schema, Accounting intelligence, National Diet Library, MultiDimensional eXpressions, Degenerate dimension, Extract, transform, load, Database normalization, Business intelligence software, Data wrangling, Surrogate key, Data warehouse, Relational database, Data analysis, Decision support, Sixth normal form, Data scraping, Executive information system, Data corruption, Metaphor Computer Systems, Master data management, Operational data store, Data warehouse appliance, Business intelligence, Information privacy, Database management system, Data cleansing, Entity-relationship model, Semantic warehousing, Predictive analytics, Data pre-processing, Business reporting, Extract transform load, Data loss, Data presentation architecture, Data structure, Business intelligence tools:
Data warehouse Critical Criteria:
Devise Data warehouse tasks and drive action.
– 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?
– What knowledge, skills and characteristics mark a good Data warehouse project manager?
– Is data warehouseing necessary for our business intelligence service?
– Is Data Warehouseing necessary for a business intelligence service?
– What is the difference between a database and data warehouse?
– What is the purpose of data warehouses and data marts?
– What are alternatives to building a data warehouse?
– Do we offer a good introduction to data warehouse?
– Data Warehouse versus Data Lake (Data Swamp)?
– Do you still need a data warehouse?
– Centralized data warehouse?
Operational system Critical Criteria:
Focus on Operational system governance and correct better engagement with Operational system results.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data warehouse services/products?
– Do Data warehouse rules make a reasonable demand on a users capabilities?
– Is the scope of Data warehouse defined?
Software as a service Critical Criteria:
Contribute to Software as a service management and finalize the present value of growth of Software as a service.
– Why are Service Level Agreements a dying breed in the software as a service industry?
– How will we insure seamless interoperability of Data warehouse moving forward?
– What are the business goals Data warehouse is aiming to achieve?
– What is our formula for success in Data warehouse ?
OLAP cube Critical Criteria:
Map OLAP cube goals and gather practices for scaling OLAP cube.
– How do senior leaders actions reflect a commitment to the organizations Data warehouse values?
– What vendors make products that address the Data warehouse needs?
– How do we Lead with Data warehouse in Mind?
Data compression Critical Criteria:
Dissect Data compression goals and create Data compression explanations for all managers.
– Will Data warehouse have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Can Management personnel recognize the monetary benefit of Data warehouse?
– What threat is Data warehouse addressing?
Slowly changing dimension Critical Criteria:
Recall Slowly changing dimension governance and triple focus on important concepts of Slowly changing dimension relationship management.
– What are all of our Data warehouse domains and what do they do?
– How do we manage Data warehouse Knowledge Management (KM)?
– Have all basic functions of Data warehouse been defined?
Third normal form Critical Criteria:
Face Third normal form results and cater for concise Third normal form education.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Data warehouse process?
– How do we go about Securing Data warehouse?
Comparison of OLAP Servers Critical Criteria:
Infer Comparison of OLAP Servers failures and achieve a single Comparison of OLAP Servers view and bringing data together.
– Who will be responsible for documenting the Data warehouse requirements in detail?
Data dictionary Critical Criteria:
Nurse Data dictionary results and get answers.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Data warehouse in a volatile global economy?
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data warehouse?
– How likely is the current Data warehouse plan to come in on schedule or on budget?
– What types of information should be included in the data dictionary?
– Is there a data dictionary?
Data scrubbing Critical Criteria:
Prioritize Data scrubbing quality and devote time assessing Data scrubbing and its risk.
– 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?
– What will be the consequences to the business (financial, reputation etc) if Data warehouse does not go ahead or fails to deliver the objectives?
DBC 1012 Critical Criteria:
Focus on DBC 1012 decisions and test out new things.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Data warehouse?
– Is there any existing Data warehouse governance structure?
– Does our organization need more Data warehouse education?
Data security Critical Criteria:
Experiment with Data security failures and innovate what needs to be done with Data security.
– 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?
– How do we ensure that implementations of Data warehouse products are done in a way that ensures safety?
– 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?
– How can you measure Data warehouse in a systematic way?
– Does it contain data security obligations?
– What is Data Security at Physical Layer?
– What is Data Security at Network Layer?
– How can the value of Data warehouse be defined?
– How will you manage data security?
Data farming Critical Criteria:
Check Data farming governance and improve Data farming service perception.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Data warehouse processes?
– What prevents me from making the changes I know will make me a more effective Data warehouse leader?
– How do we Identify specific Data warehouse investment and emerging trends?
Online analytical processing Critical Criteria:
Categorize Online analytical processing governance and cater for concise Online analytical processing education.
– what is the best design framework for Data warehouse organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– How will you know that the Data warehouse project has been successful?
Data reduction Critical Criteria:
Co-operate on Data reduction tasks and diversify disclosure of information – dealing with confidential Data reduction information.
– What other jobs or tasks affect the performance of the steps in the Data warehouse process?
– What are the Essentials of Internal Data warehouse Management?
– Which Data warehouse goals are the most important?
International Journal of Data Warehousing and Mining Critical Criteria:
Win new insights about International Journal of Data Warehousing and Mining projects and handle a jump-start course to International Journal of Data Warehousing and Mining.
– 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?
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data warehouse processes?
– Do we all define Data warehouse in the same way?
Data quality Critical Criteria:
Experiment with Data quality quality and grade techniques for implementing Data quality controls.
– 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?
– What are the known sources of errors in the administrative data (e.g. non-response, keying, coding errors)?
– What investigations/analyses have been conducted that reveal Data Quality characteristics?
– Does clear documentation of collection, aggregation and manipulation steps exist?
– Are key data-management staff identified with clearly assigned responsibilities?
– Which items are subject to revision either by editing or updating data values?
– Do we use controls throughout the data collection and management process?
– What is the proportion of duplicate records on the data file?
– Do you have a plan or procedure to collect and review data?
– Data rich enough to answer analysis/business question?
– Describe the overall aim of your policy and context?
– Which aspects of Data Quality are already strong?
– Are we Implementing enterprise-wide Data Quality?
– Verification: is the data complete and correct?
– What is the future of Data Quality management?
– What are you doing with all this data anyway?
– Are we Working with cloud applications?
– Is the frequency of review identified?
– How does the data enter the system?
– Do you train data collectors?
Early-arriving fact Critical Criteria:
Collaborate on Early-arriving fact quality and perfect Early-arriving fact conflict management.
– How do you determine the key elements that affect Data warehouse workforce satisfaction? how are these elements determined for different workforce groups and segments?
– How does the organization define, manage, and improve its Data warehouse processes?
– Will Data warehouse deliverables need to be tested and, if so, by whom?
Data Mining Critical Criteria:
Transcribe Data Mining management and finalize the present value of growth of Data Mining.
– 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?
– Think about the functions involved in your Data warehouse project. what processes flow from these functions?
– Who will be responsible for deciding whether Data warehouse goes ahead or not after the initial investigations?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– 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?
– What programs do we have to teach data mining?
Codd’s 12 rules Critical Criteria:
Survey Codd’s 12 rules management and perfect Codd’s 12 rules conflict management.
– What are the long-term Data warehouse goals?
Data mart Critical Criteria:
Accommodate Data mart management and spearhead techniques for implementing Data mart.
– At what point will vulnerability assessments be performed once Data warehouse is put into production (e.g., ongoing Risk Management after implementation)?
– What tools do you use once you have decided on a Data warehouse strategy and more importantly how do you choose?
– Do we have past Data warehouse Successes?
Dimension table Critical Criteria:
Examine Dimension table governance and find out.
– Do you monitor the effectiveness of your Data warehouse activities?
– How important is Data warehouse to the user organizations mission?
– How do we go about Comparing Data warehouse approaches/solutions?
Data blending Critical Criteria:
Rank Data blending risks and probe using an integrated framework to make sure Data blending is getting what it needs.
– What will drive Data warehouse change?
Data warehouse automation Critical Criteria:
Systematize Data warehouse automation leadership and question.
– Does Data warehouse appropriately measure and monitor risk?
– Are there Data warehouse Models?
XML for Analysis Critical Criteria:
Exchange ideas about XML for Analysis adoptions and know what your objective is.
– Who will be responsible for making the decisions to include or exclude requested changes once Data warehouse is underway?
– What potential environmental factors impact the Data warehouse effort?
Data integrity Critical Criteria:
X-ray Data integrity decisions and ask questions.
– Integrity/availability/confidentiality: How are data integrity, availability, and confidentiality maintained in the cloud?
– Can we do Data warehouse without complex (expensive) analysis?
– Can we rely on the Data Integrity?
– Data Integrity, Is it SAP created?
Hub and spokes architecture Critical Criteria:
Value Hub and spokes architecture risks and ask questions.
– Is there a Data warehouse Communication plan covering who needs to get what information when?
– How to deal with Data warehouse Changes?
Anchor Modeling Critical Criteria:
Weigh in on Anchor Modeling planning and visualize why should people listen to you regarding Anchor Modeling.
– Will new equipment/products be required to facilitate Data warehouse delivery for example is new software needed?
Snowflake schema Critical Criteria:
Merge Snowflake schema risks and grade techniques for implementing Snowflake schema controls.
– Who are the people involved in developing and implementing Data warehouse?
– What are the short and long-term Data warehouse goals?
Accounting intelligence Critical Criteria:
Huddle over Accounting intelligence results and mentor Accounting intelligence customer orientation.
– What is the total cost related to deploying Data warehouse, including any consulting or professional services?
National Diet Library Critical Criteria:
Talk about National Diet Library adoptions and interpret which customers can’t participate in National Diet Library because they lack skills.
– How is the value delivered by Data warehouse being measured?
MultiDimensional eXpressions Critical Criteria:
Cut a stake in MultiDimensional eXpressions results and budget the knowledge transfer for any interested in MultiDimensional eXpressions.
– Among the Data warehouse product and service cost to be estimated, which is considered hardest to estimate?
Degenerate dimension Critical Criteria:
Align Degenerate dimension leadership and summarize a clear Degenerate dimension focus.
Extract, transform, load Critical Criteria:
Set goals for Extract, transform, load projects and pioneer acquisition of Extract, transform, load systems.
– How can we incorporate support to ensure safe and effective use of Data warehouse into the services that we provide?
– Is Data warehouse dependent on the successful delivery of a current project?
– Are accountability and ownership for Data warehouse clearly defined?
Database normalization Critical Criteria:
Graph Database normalization leadership and get answers.
– What are the key elements of your Data warehouse performance improvement system, including your evaluation, organizational learning, and innovation processes?
– What are the disruptive Data warehouse technologies that enable our organization to radically change our business processes?
– Why should we adopt a Data warehouse framework?
Business intelligence software Critical Criteria:
Track Business intelligence software risks and research ways can we become the Business intelligence software company that would put us out of business.
– What tools and technologies are needed for a custom Data warehouse project?
Data wrangling Critical Criteria:
Check Data wrangling decisions and stake your claim.
– How do we keep improving Data warehouse?
Surrogate key Critical Criteria:
Unify Surrogate key outcomes and acquire concise Surrogate key education.
– How to Secure Data warehouse?
Data warehouse Critical Criteria:
Coach on Data warehouse tactics and catalog Data warehouse activities.
– Do those selected for the Data warehouse team have a good general understanding of what Data warehouse is all about?
– What business benefits will Data warehouse goals deliver if achieved?
Relational database Critical Criteria:
Concentrate on Relational database tasks and achieve a single Relational database view and bringing data together.
– 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?
– Have you identified your Data warehouse key performance indicators?
– Who sets the Data warehouse standards?
Data analysis Critical Criteria:
Consult on Data analysis engagements and look at the big picture.
– What are the Key enablers to make this Data warehouse move?
– What are some real time data analysis frameworks?
– How do we maintain Data warehouses Integrity?
Decision support Critical Criteria:
Guide Decision support tasks and define what do we need to start doing with Decision support.
– 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?
– A heuristic, a decision support system, or new practices to improve current project management?
– In a project to restructure Data warehouse outcomes, which stakeholders would you involve?
– How do I manage information (decision support) and operational (transactional) data?
– What are the access requirements for decision support data?
Sixth normal form Critical Criteria:
Gauge Sixth normal form governance and secure Sixth normal form creativity.
– What new services of functionality will be implemented next with Data warehouse ?
– Meeting the challenge: are missed Data warehouse opportunities costing us money?
Data scraping Critical Criteria:
Define Data scraping goals and use obstacles to break out of ruts.
– 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?
– Do the Data warehouse decisions we make today help people and the planet tomorrow?
Executive information system Critical Criteria:
Derive from Executive information system decisions and adopt an insight outlook.
– How do mission and objectives affect the Data warehouse processes of our organization?
– Is maximizing Data warehouse protection the same as minimizing Data warehouse loss?
Data corruption Critical Criteria:
Learn from Data corruption quality and explore and align the progress in Data corruption.
– Have the types of risks that may impact Data warehouse been identified and analyzed?
Metaphor Computer Systems Critical Criteria:
Apply Metaphor Computer Systems planning and devote time assessing Metaphor Computer Systems and its risk.
– Does Data warehouse systematically track and analyze outcomes for accountability and quality improvement?
– Is a Data warehouse Team Work effort in place?
Master data management Critical Criteria:
Conceptualize Master data management leadership and prioritize challenges of Master data management.
– 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 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?
Operational data store Critical Criteria:
Face Operational data store governance and finalize the present value of growth of Operational data store.
– What are the success criteria that will indicate that Data warehouse objectives have been met and the benefits delivered?
– In what ways are Data warehouse vendors and us interacting to ensure safe and effective use?
Data warehouse appliance Critical Criteria:
Interpolate Data warehouse appliance projects and modify and define the unique characteristics of interactive Data warehouse appliance projects.
Business intelligence Critical Criteria:
Match Business intelligence leadership and prioritize challenges of Business intelligence.
– If on-premise software is a must, a balance of choice and simplicity is essential. When specific users are viewing and interacting with analytics, can you use a named-user licensing model that offers accessibility without the need for hardware considerations?
– Research reveals that more than half of business intelligence projects hit a low degree of acceptance or fail. What factors influence the implementation negative or positive?
– Does the software let users work with the existing data infrastructure already in place, freeing your IT team from creating more cubes, universes, and standalone marts?
– Does the software allow users to bring in data from outside the company on-the-flylike demographics and market research to augment corporate data?
– When users are more fluid and guest access is a must, can you choose hardware-based licensing that is tailored to your exact configuration needs?
– Was your software written by your organization or acquired from a third party?
– What is your anticipated learning curve for Technical Administrators?
– Describe the process of data transformation required by your system?
– What are some best practices for managing business intelligence?
– What is your anticipated learning curve for Report Users?
– How stable is it across domains/geographies?
– What level of training would you recommend?
– Can your product map ad-hoc query results?
– Do you offer formal user training?
– Do you support video integration?
– What is your products direction?
– Types of data sources supported?
– How are you going to manage?
Information privacy Critical Criteria:
Use past Information privacy governance and don’t overlook the obvious.
Database management system Critical Criteria:
Apply Database management system decisions and gather Database management system models .
– Which customers cant participate in our Data warehouse domain because they lack skills, wealth, or convenient access to existing solutions?
– What database management systems have been implemented?
Data cleansing Critical Criteria:
Collaborate on Data cleansing adoptions and visualize why should people listen to you regarding Data cleansing.
– Is there an ongoing data cleansing procedure to look for rot (redundant, obsolete, trivial content)?
– Do we monitor the Data warehouse decisions made and fine tune them as they evolve?
– Who needs to know about Data warehouse ?
Entity-relationship model Critical Criteria:
Be clear about Entity-relationship model results and simulate teachings and consultations on quality process improvement of Entity-relationship model.
– Is Supporting Data warehouse documentation required?
Semantic warehousing Critical Criteria:
Brainstorm over Semantic warehousing visions and inform on and uncover unspoken needs and breakthrough Semantic warehousing results.
Predictive analytics Critical Criteria:
Weigh in on Predictive analytics leadership and devote time assessing Predictive analytics and its risk.
– What are direct examples that show predictive analytics to be highly reliable?
Data pre-processing Critical Criteria:
Look at Data pre-processing outcomes and ask what if.
– Who is the main stakeholder, with ultimate responsibility for driving Data warehouse forward?
Business reporting Critical Criteria:
Apply Business reporting results and slay a dragon.
– Think of your Data warehouse project. what are the main functions?
Extract transform load Critical Criteria:
Deduce Extract transform load tasks and devise Extract transform load key steps.
– Do several people in different organizational units assist with the Data warehouse process?
Data loss Critical Criteria:
Recall Data loss tactics and innovate what needs to be done with Data loss.
– You do not want to be informed of a data loss incident from the users themselves or from the data protection authority. Do you have technology that can detect breaches that have taken place; forensics available to investigate how the data was lost (or changed); and can you go back in time with full user logs and identify the incident to understand its scope and impact?
– Does the tool in use provide the ability for role-based administration for sub-administrators (e.g., administrators for a specific domain) to restrict access and visibility into system data and system changes (if applicable)?
– Does the tool we use provide the ability to send and receive secure email without browser plug ins or client software?
– 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?
– Does the tool we use allow the ability to add custom number templates (e.g., customer/client IDs)?
– Confidence -what is the data loss rate when the system is running at its required throughput?
– Are there encryption requirements, especially of off-line copies?
– Do we have the the ability to create multiple quarantine queues?
– Do all computers have up-to-date antivirus protection?
– Downtime and Data Loss: How much Can You Afford?
– Do you store a copy of backed up data off-site?
– Who is sending confidential information?
– What are your most offensive protocols?
– What do we hope to achieve with a DLP deployment?
– Do any copies need to be off-site?
– What is considered sensitive data?
– Who is the System Administrator?
– Why Data Loss Prevention?
– Why Bother With A DP SLA?
Data presentation architecture Critical Criteria:
Judge Data presentation architecture risks and oversee implementation of Data presentation architecture.
Data structure Critical Criteria:
Have a meeting on Data structure governance and report on the economics of relationships managing Data structure and constraints.
– 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?
Business intelligence tools Critical Criteria:
Audit Business intelligence tools strategies and intervene in Business intelligence tools processes and leadership.
– What are the top 3 things at the forefront of our Data warehouse agendas for the next 3 years?
– Why are Data warehouse skills important?
– Business Intelligence Tools?
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:
Data Warehouse Specialist Salaries – salary.com
HRSA Data Warehouse Home Page
Title Data Warehouse Analyst Jobs, Employment | Indeed.com
Software as a service External links:
Software as a Service – Banking Solutions | FinReach
Y2SaaS | Software as a Service
OLAP cube External links:
BI & OLAP Cubes – Jet Reports
Data Warehouse vs. OLAP Cube? – Stack Overflow
Analyze OLAP cube data with Excel | Microsoft Docs
Data compression External links:
Data compression (Book, 1976) [WorldCat.org]
SecureZIP | Enterprise Data Compression | PKWARE
Third normal form External links:
[PDF]Primary Key Version Third Normal Form
What is Third Normal Form (3NF)? – Definition from …
Normalisation 3NF: Understanding Third Normal Form – …
Comparison of OLAP Servers External links:
Comparison Of OLAP Servers – theinfolist.com
Comparison of OLAP Servers: Latest News & Videos, …
Data dictionary External links:
What is a Data Dictionary? – Definition from Techopedia
OpenAir Data Dictionary
16 Work with Data Dictionary – Oracle
Data security External links:
Data Security | Federal Trade Commission
Visitor & Access Management – TDS – Time Data Security
[PPT]Data Security – CITI – University of Massachusetts Amherst
Data farming External links:
[PDF]qsg data farming – Official DIBELS Home Page
SEED Center Hosts International Data Farming Workshop
Data Farming: How Big Data Is Revolutionizing Big Ag
Data reduction External links:
AuditorQC | Free Linearity and Daily QC Data Reduction
Data Reduction – Market Research
Data Reduction Registration Form – Verichem …
International Journal of Data Warehousing and Mining External links:
International Journal of Data Warehousing and Mining
Data quality External links:
A3-4-02: Data Quality and Integrity (10/24/2016) – Fannie Mae
Integrated Data Quality Solutions by helpIT systems
Early-arriving fact External links:
Early-arriving fact – Revolvy
Data Mining External links:
[USC04] 42 USC 2000ee-3: Federal agency data mining reporting
Data mining techniques (Book, 2002) [WorldCat.org]
Codd’s 12 rules External links:
Codd’s 12 Rules – Term Paper
Codd’s 12 rules – A Gentle Introduction to SQL – Google Sites
Data mart External links:
[PDF]Soil Data Mart – USDA
UNC Data Mart – University of North Carolina
[PDF]Finance Data Mart – Michigan Medicine
Dimension table External links:
Dimension Table – msdn.microsoft.com
Tube Dimension Table – Grating
Pipe Dimension Table – Grating
Data blending External links:
Data Blending – Tableau Software
Data blending is a process that is gaining attention among analysts and analytic companies due to the fact that it is a quick and straightforward method used to extract value from multiple data sources.
Data Blending | Impetus
Data warehouse automation External links:
biGENiUS – Data Warehouse Automation
XML for Analysis External links:
[PDF]XML for Analysis Specification
XML for Analysis (XMLA) – technet.microsoft.com
Data integrity External links:
Job Details: Data Integrity Specialist
Hub and spokes architecture External links:
Anchor Modeling External links:
Anchor Modeling – Posts | Facebook
Publications – Anchor Modeling
Snowflake schema External links:
Star & snowflake schema | Qlik Community
National Diet Library External links:
Free Data Service | National Diet Library
NDL Search – National Diet Library
MultiDimensional eXpressions External links:
Multidimensional Expressions (MDX) Reference
Degenerate dimension External links:
Degenerate Dimension – YouTube
Data Warehousing: What is degenerate dimension? – …
Extract, transform, load External links:
What is ETL (Extract, Transform, Load)? Webopedia Definition
What is ETL (Extract, Transform, Load)? Webopedia …
Database normalization External links:
[PDF]DATABASE NORMALIZATION – Center for …
Business intelligence software External links:
Business Intelligence Software | Solver
Business Intelligence Software | Products
[PDF]Business Intelligence Software – Open Systems Inc.
Data wrangling External links:
Pandas Cheat Sheet: Data Science and Data Wrangling in …
Surrogate key External links:
Difference between a primary key and a surrogate key
Surrogate key in SCD – Informatica Network
What is a surrogate key in a relational database? – Quora
Data warehouse External links:
Title Data Warehouse Analyst Jobs, Employment | Indeed.com
Cloud Data Warehouse | Snowflake
Relational database External links:
Tool for Relational Database – TablePlus
Relational Algebra | Relational Database | Relational Model
RDB: a Relational Database Management System
Data analysis External links:
Data Analysis Examples – IDRE Stats
Seven Bridges Genomics – The biomedical data analysis …
Decision support External links:
[PDF]International Journal of Decision Support Systems
Title:(director, Decision Support) jobs | Simply Hired
Sixth normal form External links:
Data scraping External links:
Automated data scraping from websites into Excel – YouTube
Automatic Data Scraping and Extraction Software – …
Executive information system External links:
[PDF]Transportation Executive Information System …
Harris Computer Systems (Executive Information System)
Data corruption External links:
Repair Logger Data Corruption – Zimbra :: Tech Center
Metaphor Computer Systems External links:
Metaphor Computer Systems
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.
Charles Irby | Metaphor Computer Systems Inc. | …
Ralph Kimball | Metaphor Computer Systems | …
Master data management External links:
MDM Tools: Master Data Management Products from Talend
Master Data Management (MDM) Support for Microsoft …
Healthcare Master Data Management
Operational data store External links:
Operational Data Store – YouTube
Operational Data Store – ODS – Gartner Tech Definitions
Data warehouse appliance External links:
[PDF]Teradata Data Warehouse Appliance 2690 – NDM
Business intelligence External links:
[PDF]Position Title: Business Intelligence Analyst – ttra
Information privacy External links:
Database management system External links:
10-7 Operating System, Database Management System, …
Petroleum Database Management System (PDMS)
Database Management System | Lucidea
Data cleansing External links:
[DOC]Without a data cleansing – University of Oklahoma
[DOC]Wave 1 – Data Cleansing Strategy – South Carolina
Entity-relationship model External links:
Introduction to the Entity-Relationship Model – YouTube
Course Notes for Comp 419 – The Entity-Relationship Model
Predictive analytics External links:
Predictive Analytics Solutions & Automated Big Data
Stategic Location Management & Predictive Analytics | …
Customer Analytics & Predictive Analytics Tools for …
Data pre-processing External links:
Module 1: Data Pre-processing – YouTube
Business reporting External links:
Business reporting. (Book, 1990) [WorldCat.org]
Extract transform load External links:
What is ETL (Extract Transform Load) Testing? – Quora
Data loss External links:
GTB Technologies – Enterprise Data Loss Prevention …
[PDF]Data Loss Prevention – WatchGuard
Data Loss and Data Backup Statistics?
Data structure External links:
C++ Data Structures – tutorialspoint.com
Data structures – C++ Tutorials
Business intelligence tools External links:
Compare Top Business Intelligence Tools – BI Software …