193 Analytics and Decision Support Success Criteria

What is involved in Analytics and Decision Support

Find out what the related areas are that Analytics and Decision Support 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 Analytics and Decision Support thinking-frame.

How far is your company on its Analytics and Decision Support journey?

Take this short survey to gauge your organization’s progress toward Analytics and Decision Support 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 Analytics and Decision Support related domains to cover and 193 essential critical questions to check off in that domain.

The following domains are covered:

Analytics and Decision Support, Operational data store, Extract, transform, load, Item-item collaborative filtering, Cold start, Dimensionality reduction, Data vault modeling, Problem-Oriented Medical Information System, Judge–advisor system, Business intelligence, Product finder, Online deliberation, MultiDimensional eXpressions, Expert system, Data warehouse automation, Precision agriculture, Sustainable development, Decision making process, Preference elicitation, Argument map, Information overload, Knowledge environment, Dimension table, Decision making, Open source, Data dictionary, Music Genome Project, Time series, Decision support system, Fact table, Sixth normal form, Social Loafing, Decision-making software, United Airlines, Collaborative filtering, User interface, Decision engineering, Column-oriented DBMS, O’Hare International Airport, Collaborative search engine, Data transformation, GroupLens Research, Data loading, Predictive analytics, Knowledge base, Netflix Prize, Similarity search, Data warehouse, Clinical decision support system, Spatial decision support system, Executive information system, Content discovery platform, Online analytical processing, Texas Instruments, Comparison of OLAP Servers, Decision theory, Data Mining Extensions:

Analytics and Decision Support Critical Criteria:

Gauge Analytics and Decision Support results and handle a jump-start course to Analytics and Decision Support.

– How do senior leaders actions reflect a commitment to the organizations Analytics and Decision Support values?

– Do Analytics and Decision Support rules make a reasonable demand on a users capabilities?

Operational data store Critical Criteria:

Drive Operational data store governance and perfect Operational data store conflict management.

– How do we make it meaningful in connecting Analytics and Decision Support with what users do day-to-day?

– Will Analytics and Decision Support deliverables need to be tested and, if so, by whom?

– Is a Analytics and Decision Support Team Work effort in place?

Extract, transform, load Critical Criteria:

Design Extract, transform, load outcomes and oversee Extract, transform, load requirements.

– What other jobs or tasks affect the performance of the steps in the Analytics and Decision Support process?

– What are the record-keeping requirements of Analytics and Decision Support activities?

– Who sets the Analytics and Decision Support standards?

Item-item collaborative filtering Critical Criteria:

Shape Item-item collaborative filtering adoptions and sort Item-item collaborative filtering activities.

– Think about the functions involved in your Analytics and Decision Support project. what processes flow from these functions?

– Is Analytics and Decision Support Realistic, or are you setting yourself up for failure?

– Can we do Analytics and Decision Support without complex (expensive) analysis?

Cold start Critical Criteria:

Examine Cold start issues and look in other fields.

– What is the total cost related to deploying Analytics and Decision Support, including any consulting or professional services?

– Does Analytics and Decision Support analysis show the relationships among important Analytics and Decision Support factors?

– Why is it important to have senior management support for a Analytics and Decision Support project?

Dimensionality reduction Critical Criteria:

Gauge Dimensionality reduction results and work towards be a leading Dimensionality reduction expert.

– Why should we adopt a Analytics and Decision Support framework?

Data vault modeling Critical Criteria:

Design Data vault modeling quality and prioritize challenges of Data vault modeling.

– How do your measurements capture actionable Analytics and Decision Support information for use in exceeding your customers expectations and securing your customers engagement?

– How can you negotiate Analytics and Decision Support successfully with a stubborn boss, an irate client, or a deceitful coworker?

– How do we keep improving Analytics and Decision Support?

Problem-Oriented Medical Information System Critical Criteria:

Cut a stake in Problem-Oriented Medical Information System issues and intervene in Problem-Oriented Medical Information System processes and leadership.

– Among the Analytics and Decision Support product and service cost to be estimated, which is considered hardest to estimate?

– What tools and technologies are needed for a custom Analytics and Decision Support project?

Judge–advisor system Critical Criteria:

Apply Judge–advisor system leadership and create Judge–advisor system explanations for all managers.

– How do mission and objectives affect the Analytics and Decision Support processes of our organization?

– How do we go about Comparing Analytics and Decision Support approaches/solutions?

Business intelligence Critical Criteria:

Coach on Business intelligence quality and transcribe Business intelligence as tomorrows backbone for success.

– Does your BI solution honor distinctions with dashboards that automatically authenticate and provide the appropriate level of detail based on a users privileges to the data source?

– 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?

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

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

– Which OpenSource ETL tool is easier to use more agile Pentaho Kettle Jitterbit Talend Clover Jasper Rhino?

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

– 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 documentation is provided with the software / system and in what format?

– Can your bi solution quickly locate dashboard on your mobile device?

– Does your client support bi-directional functionality with mapping?

– What is your anticipated learning curve for Report Users?

– Can Business Intelligence BI meet business expectations?

– What are the pillar concepts of business intelligence?

– What are alternatives to building a data warehouse?

– How is Business Intelligence related to CRM?

– Can your product map ad-hoc query results?

– What is your licensing model and prices?

– What is your expect product life cycle?

Product finder Critical Criteria:

Prioritize Product finder decisions and diversify by understanding risks and leveraging Product finder.

– What is the purpose of Analytics and Decision Support in relation to the mission?

– Are assumptions made in Analytics and Decision Support stated explicitly?

– What are our Analytics and Decision Support Processes?

Online deliberation Critical Criteria:

Contribute to Online deliberation engagements and research ways can we become the Online deliberation company that would put us out of business.

– Do the Analytics and Decision Support decisions we make today help people and the planet tomorrow?

– Is there any existing Analytics and Decision Support governance structure?

MultiDimensional eXpressions Critical Criteria:

Exchange ideas about MultiDimensional eXpressions risks and perfect MultiDimensional eXpressions conflict management.

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

– Have all basic functions of Analytics and Decision Support been defined?

– Are there Analytics and Decision Support Models?

Expert system Critical Criteria:

Talk about Expert system governance and devise Expert system key steps.

– Do several people in different organizational units assist with the Analytics and Decision Support process?

– What knowledge, skills and characteristics mark a good Analytics and Decision Support project manager?

Data warehouse automation Critical Criteria:

Adapt Data warehouse automation visions and budget the knowledge transfer for any interested in Data warehouse automation.

– How do we know that any Analytics and Decision Support analysis is complete and comprehensive?

– Is the scope of Analytics and Decision Support defined?

Precision agriculture Critical Criteria:

Analyze Precision agriculture management and differentiate in coordinating Precision agriculture.

– How does the organization define, manage, and improve its Analytics and Decision Support processes?

– What are the short and long-term Analytics and Decision Support goals?

– Do we all define Analytics and Decision Support in the same way?

Sustainable development Critical Criteria:

Give examples of Sustainable development goals and finalize specific methods for Sustainable development acceptance.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Analytics and Decision Support processes?

Decision making process Critical Criteria:

Contribute to Decision making process governance and devote time assessing Decision making process and its risk.

– In what ways are Analytics and Decision Support vendors and us interacting to ensure safe and effective use?

– Is the Analytics and Decision Support organization completing tasks effectively and efficiently?

– What role do analysts play in the decision making process?

– How would one define Analytics and Decision Support leadership?

– Who will be involved in the decision making process?

Preference elicitation Critical Criteria:

Track Preference elicitation failures and ask what if.

– What new services of functionality will be implemented next with Analytics and Decision Support ?

– Who are the people involved in developing and implementing Analytics and Decision Support?

– What is our formula for success in Analytics and Decision Support ?

Argument map Critical Criteria:

Investigate Argument map decisions and create a map for yourself.

– Does Analytics and Decision Support systematically track and analyze outcomes for accountability and quality improvement?

– What potential environmental factors impact the Analytics and Decision Support effort?

– Think of your Analytics and Decision Support project. what are the main functions?

Information overload Critical Criteria:

Map Information overload decisions and catalog Information overload activities.

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

– How to deal with Analytics and Decision Support Changes?

Knowledge environment Critical Criteria:

Guide Knowledge environment projects and pioneer acquisition of Knowledge environment systems.

– How will you measure your Analytics and Decision Support effectiveness?

– How do we Lead with Analytics and Decision Support in Mind?

Dimension table Critical Criteria:

Contribute to Dimension table outcomes and pay attention to the small things.

– Does Analytics and Decision Support 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?

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

Decision making Critical Criteria:

Detail Decision making governance and adjust implementation of Decision making.

– Is there a timely attempt to prepare people for technological and organizational changes, e.g., through personnel management, training, or participatory decision making?

– What kind of processes and tools could serve both the vertical and horizontal analysis and decision making?

– What s the protocol for interaction, decision making, project management?

– Are the data needed for corporate decision making?

Open source Critical Criteria:

Gauge Open source strategies and inform on and uncover unspoken needs and breakthrough Open source results.

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

– Is there any open source personal cloud software which provides privacy and ease of use 1 click app installs cross platform html5?

– How much do political issues impact on the decision in open source projects and how does this ultimately impact on innovation?

– What are the different RDBMS (commercial and open source) options available in the cloud today?

– Is open source software development faster, better, and cheaper than software engineering?

– Vetter, Infectious Open Source Software: Spreading Incentives or Promoting Resistance?

– What are some good open source projects for the internet of things?

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

– Is open source software development essentially an agile method?

– Is there an open source alternative to adobe captivate?

– What can a cms do for an open source project?

– What are the open source alternatives to Moodle?

Data dictionary Critical Criteria:

Administer Data dictionary tasks and mentor Data dictionary customer orientation.

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

– What are the barriers to increased Analytics and Decision Support production?

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

– Is there a data dictionary?

Music Genome Project Critical Criteria:

Guard Music Genome Project outcomes and interpret which customers can’t participate in Music Genome Project because they lack skills.

– How do we Identify specific Analytics and Decision Support investment and emerging trends?

Time series Critical Criteria:

Tête-à-tête about Time series risks and change contexts.

– Think about the people you identified for your Analytics and Decision Support 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?

– How will you know that the Analytics and Decision Support project has been successful?

– What are the usability implications of Analytics and Decision Support actions?

Decision support system Critical Criteria:

Refer to Decision support system visions and get out your magnifying glass.

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

– Have you identified your Analytics and Decision Support key performance indicators?

Fact table Critical Criteria:

Steer Fact table strategies and clarify ways to gain access to competitive Fact table services.

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Analytics and Decision Support. How do we gain traction?

– What is the source of the strategies for Analytics and Decision Support strengthening and reform?

Sixth normal form Critical Criteria:

Value Sixth normal form outcomes and learn.

– What are the success criteria that will indicate that Analytics and Decision Support objectives have been met and the benefits delivered?

– Does Analytics and Decision Support analysis isolate the fundamental causes of problems?

Social Loafing Critical Criteria:

Own Social Loafing risks and cater for concise Social Loafing education.

– Who will be responsible for making the decisions to include or exclude requested changes once Analytics and Decision Support is underway?

Decision-making software Critical Criteria:

Examine Decision-making software visions and budget for Decision-making software challenges.

United Airlines Critical Criteria:

Explore United Airlines tactics and forecast involvement of future United Airlines projects in development.

– Have the types of risks that may impact Analytics and Decision Support been identified and analyzed?

– How to Secure Analytics and Decision Support?

Collaborative filtering Critical Criteria:

Interpolate Collaborative filtering adoptions and be persistent.

User interface Critical Criteria:

Huddle over User interface leadership and spearhead techniques for implementing User interface.

– Who will be responsible for documenting the Analytics and Decision Support requirements in detail?

– Are accountability and ownership for Analytics and Decision Support clearly defined?

– What if we substitute prototyping for user interface screens on paper?

– How is the value delivered by Analytics and Decision Support being measured?

– Does a User interface survey show which search ui is better ?

Decision engineering Critical Criteria:

Gauge Decision engineering governance and suggest using storytelling to create more compelling Decision engineering projects.

– What are the Essentials of Internal Analytics and Decision Support Management?

Column-oriented DBMS Critical Criteria:

Powwow over Column-oriented DBMS governance and assess and formulate effective operational and Column-oriented DBMS strategies.

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Analytics and Decision Support?

– What are the business goals Analytics and Decision Support is aiming to achieve?

O’Hare International Airport Critical Criteria:

Powwow over O’Hare International Airport governance and explain and analyze the challenges of O’Hare International Airport.

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

– Who will provide the final approval of Analytics and Decision Support deliverables?

– Why are Analytics and Decision Support skills important?

Collaborative search engine Critical Criteria:

Sort Collaborative search engine projects and oversee Collaborative search engine management by competencies.

– Are there any disadvantages to implementing Analytics and Decision Support? There might be some that are less obvious?

– Do we monitor the Analytics and Decision Support decisions made and fine tune them as they evolve?

Data transformation Critical Criteria:

Deliberate over Data transformation outcomes and adjust implementation of Data transformation.

– Do you monitor the effectiveness of your Analytics and Decision Support activities?

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

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

GroupLens Research Critical Criteria:

Disseminate GroupLens Research failures and triple focus on important concepts of GroupLens Research relationship management.

– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Analytics and Decision Support?

– What are the disruptive Analytics and Decision Support technologies that enable our organization to radically change our business processes?

Data loading Critical Criteria:

Guard Data loading failures and question.

Predictive analytics Critical Criteria:

Experiment with Predictive analytics management and oversee Predictive analytics management by competencies.

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

– Which Analytics and Decision Support goals are the most important?

Knowledge base Critical Criteria:

Reconstruct Knowledge base goals and work towards be a leading Knowledge base expert.

– Do we support the certified Cybersecurity professional and cyber-informed operations and engineering professionals with advanced problem-solving tools, communities of practice, canonical knowledge bases, and other performance support tools?

– Does our organization need more Analytics and Decision Support education?

– Can specialized social networks replace learning management systems?

– What is Effective Analytics and Decision Support?

Netflix Prize Critical Criteria:

Exchange ideas about Netflix Prize tasks and perfect Netflix Prize conflict management.

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

– How do we measure improved Analytics and Decision Support service perception, and satisfaction?

– Does Analytics and Decision Support appropriately measure and monitor risk?

Similarity search Critical Criteria:

Adapt Similarity search decisions and arbitrate Similarity search techniques that enhance teamwork and productivity.

– How can the value of Analytics and Decision Support be defined?

Data warehouse Critical Criteria:

Graph Data warehouse engagements and budget for Data warehouse challenges.

– 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?

– Risk factors: what are the characteristics of Analytics and Decision Support that make it risky?

– 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?

– Do we offer a good introduction to data warehouse?

– Data Warehouse versus Data Lake (Data Swamp)?

– Do you still need a data warehouse?

Clinical decision support system Critical Criteria:

Interpolate Clinical decision support system failures and triple focus on important concepts of Clinical decision support system relationship management.

– Which individuals, teams or departments will be involved in Analytics and Decision Support?

Spatial decision support system Critical Criteria:

Detail Spatial decision support system outcomes and figure out ways to motivate other Spatial decision support system users.

– Is Analytics and Decision Support dependent on the successful delivery of a current project?

Executive information system Critical Criteria:

Audit Executive information system quality and plan concise Executive information system education.

Content discovery platform Critical Criteria:

Reorganize Content discovery platform visions and find answers.

Online analytical processing Critical Criteria:

Analyze Online analytical processing results and modify and define the unique characteristics of interactive Online analytical processing projects.

– Where do ideas that reach policy makers and planners as proposals for Analytics and Decision Support strengthening and reform actually originate?

– To what extent does management recognize Analytics and Decision Support as a tool to increase the results?

– Are there Analytics and Decision Support problems defined?

Texas Instruments Critical Criteria:

Graph Texas Instruments strategies and budget the knowledge transfer for any interested in Texas Instruments.

– In a project to restructure Analytics and Decision Support outcomes, which stakeholders would you involve?

– When a Analytics and Decision Support manager recognizes a problem, what options are available?

Comparison of OLAP Servers Critical Criteria:

Design Comparison of OLAP Servers issues and remodel and develop an effective Comparison of OLAP Servers strategy.

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

Decision theory Critical Criteria:

Huddle over Decision theory tactics and assess what counts with Decision theory that we are not counting.

– What are current Analytics and Decision Support Paradigms?

Data Mining Extensions Critical Criteria:

Have a meeting on Data Mining Extensions visions and describe which business rules are needed as Data Mining Extensions interface.


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Analytics and Decision Support Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]


Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Operational data store External links:

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

Operational Data Store – YouTube

Operational Data Store – ODS – Gartner Tech Definitions

Extract, transform, load External links:

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

What is ETL (Extract, Transform, Load)? – Talend

Cold start External links:

Crazy cold start ! – YouTube

Light snow today, cold start to the work week | WISH-TV

Instant Pot No Boil Yogurt {Cold Start Method} | This Old Gal

Dimensionality reduction External links:

Why is dimensionality reduction useful? – Quora

Dimensionality Reduction and Feature Extraction – …

Data vault modeling External links:

Data Vault Modeling | Data Warehousing with Oracle

Data Vault Modeling and Snowflake | Snowflake

Intermediate Data Vault Modeling – Data Vault 2.0 – Chapter 5

Problem-Oriented Medical Information System External links:

Problem-Oriented medical information system (PROMIS…

Problem-Oriented Medical Information System | Free …

Promis – Problem-Oriented Medical Information System …

Business intelligence External links:

Business Intelligence Tools & Software | Square

SQL Server Business Intelligence | Microsoft

Business Intelligence Software – ERP & Project …

Product finder External links:

Clairol Professional Product Finder

CHEP Product Finder

Product finder – Metso

MultiDimensional eXpressions External links:

Multidimensional Expressions (MDX) Reference

Multidimensional Expressions (MDX) – Tutorial Gateway

MDX: Multidimensional Expressions | Project Botticelli

Expert system External links:

Expert system | computer science | Britannica.com

Harmony – TOC S&T Expert System Goldratt Research Labs

What is Machine Learning? A definition – Expert System

Data warehouse automation External links:

Data Warehouse Automation | Automic Software

Data Warehouse Automation | Magnitude Software

Data Warehouse Automation Done Right Panoply | Panoply

Precision agriculture External links:

Precision Agriculture, Farming and Agricultural Technology

Precision Agriculture – Building The Farms Of Tomorrow
Ad · spectra.mhi.com/precision/farming

Precision Agriculture and Precision Ag Solutions | Trimble Ag

Sustainable development External links:

Sustainable development goals – United Nations – un.org

Sustainable Development Goals: 17 Goals to Transform …

City of Fort Lauderdale, FL : Sustainable Development

Decision making process External links:


Preference elicitation External links:

[PDF]Chapter 10: Preference Elicitation in Combinatorial …

[PDF]the Preference Elicitation and Welcome – The …

Argument map External links:

Argument Map – YouTube

Argument Map – Anthropology

So, what exactly is an argument map? – Argunet

Information overload External links:

Information overload Flashcards | Quizlet

It’s Enough To Make You Sick! Information Overload – YouTube

Overcoming Information Overload – Psych Central – …

Knowledge environment External links:

Studio E&P Knowledge Environment – Schlumberger …

Sophic Cancer Biomarker Knowledge Environment

Dynamic Knowledge Environment (DKE) – Doug …

Dimension table External links:

What is dimension table? – Definition from WhatIs.com

Pipe Dimension Table – Grating

Dimension Table – msdn.microsoft.com

Decision making External links:

IS-241.B: Decision Making and Problem Solving – FEMA

Essays on decision making – Rutgers University

Effective Decision Making | SkillsYouNeed

Open source External links:

Open Source Center – Official Site

What is open source software? | Opensource.com

Open source
In production and development, open source as a development model promotes a universal access via a free license to a product’s design or blueprint, and universal redistribution of that design or blueprint, including subsequent improvements to it by anyone. Before the phrase open source became widely adopted, developers and producers used a variety of other terms. Open source gained hold with the rise of the Internet, and the attendant need for massive retooling of the computing source code. Opening the source code enabled a self-enhancing diversity of production models, communication paths, and interactive communities. The open-source software movement arose to clarify the environment that the new copyright, licensing, domain, and consumer issues created. Generally, open source refers to a computer program in which the source code is available to the general public for use and/or modification from its original design. Open-source code is typically a collaborative effort where programmers improve upon the source code and share the changes within the community so that other members can help improve it further.

Data dictionary External links:

What is a Data Dictionary? – Bridging the Gap

Tablespace Data Dictionary Views – Oracle

Creating Metadata and a Data Dictionary | data.ca.gov

Music Genome Project External links:

Music Genome Project
The Music Genome Project was first conceived by Will Glaser and Tim Westergren in late 1999. In January 2000, they joined forces with Jon Kraft to found Savage Beast Technologies to bring their idea to market. The Music Genome Project is an effort to “capture the essence of music at the most fundamental level” using over 450 attributes to describe songs and a complex mathematical algorithm to organize them. The Music Genome Project is currently made up of 5 sub-genomes: Pop/Rock, Hip-Hop/Electronica, Jazz, World Music, and Classical. Under the direction of Nolan Gasser and a team of musicological experts, the initial attributes were later refined and extended.

What is the Music Genome Project? (with pictures)

Pandora – Music Genome Project

Time series External links:

1.1 Overview of Time Series Characteristics | STAT 510

[PDF]Time Series Analysis and Forecasting – Cengage

Lesson 1: Time Series Basics | STAT 510

Decision support system External links:

Decision Support System – DSS – Investopedia

[PDF]Global Decision Support System (GDSS) 1 of 5

Maintenance Decision Support System – Iteris

Fact table External links:

Factless Fact Table | Learn about Factless Fact Table

Fact table – Oracle FAQ

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

Sixth normal form External links:

Anchor Modelling: Sixth Normal Form Databases! – vimeo.com

OBM – IT abbreviation – 6NF-Sixth Normal Form – YouTube

6NF abbreviation stands for Sixth normal form – All Acronyms

Social Loafing External links:

What Is Social Loafing in Psychology? – Verywell Mind

In social psychology, social loafing is the phenomenon of a person exerting less effort to achieve a goal when they work in a group than when they work alone.
Reference: en.wikipedia.org/wiki/Social_loafing_theory

[PDF]Social Loafing: A Review of the Literature

Decision-making software External links:

Decision-making software | 1000minds

[PDF]Decision-Making Software for Implementingfor …

Paramount Decisions | Lean Decision-making Software …

United Airlines External links:

United Airlines | Credit Cards | Chase.com

United Airlines

Receipts for Inflight Purchases | United Airlines

Collaborative filtering External links:

Collaborative Filtering | Recommender Systems

User interface External links:

Datatel User Interface 5.4

Portal Web Mail User Interface – MyCopper.net

What is user interface (UI)? – Definition from WhatIs.com

Decision engineering External links:

Information Engineering, Decision Engineering | …

Decision Engineering 9781852339739 – Pubgraphics Direct, LLC

Column-oriented DBMS External links:

CiteSeerX — C-Store: A Column-oriented DBMS

Column-oriented DBMS |THWACK

ClickHouse — open source distributed column-oriented DBMS

O’Hare International Airport External links:

General Parking Information – O’Hare International Airport

Airport Rides at O’Hare International Airport (ORD) – Lyft

(ORD) O’Hare International Airport Current Conditions

Data transformation External links:

Data transformation in R – Stack Overflow

Data Transformation Services (DTS) – msdn.microsoft.com

GroupLens Research External links:

GroupLens Research – YouTube

GroupLens Research · GitHub

Data loading External links:

The Data Loading Performance Guide – technet.microsoft.com

Data Loading and Processing Tutorial — PyTorch …

What is Data Loading? – Definition from Techopedia

Predictive analytics External links:

What is predictive analytics? – Definition from WhatIs.com

Customer Analytics & Predictive Analytics Tools for Business

Predictive Analytics Software, Social Listening | NewBrand

Knowledge base External links:

Knowledge Base – Home · CDSS Customer Portal

Knowledge Base: Download RingCentral Meetings – …

Knowledge Base – technet.microsoft.com

Netflix Prize External links:

Netflix Prize: FAQ

Netflix Prize – Official Site

Netflix Prize data | Kaggle

Similarity search External links:

FALCONN: Similarity Search Over High-Dimensional Data

Similarity Search – LibreOffice Help

Similarity search in visual data – University of Minnesota

Data warehouse External links:

Condition Categories – Chronic Conditions Data Warehouse

Data Warehouse Home Page

HRSA Data Warehouse Home Page

Spatial decision support system External links:

[PDF]The Midwest Spatial Decision Support System …

What is Spatial Decision Support System | IGI Global

Executive information system External links:

Best Executive Information System Software – G2 Crowd

[PDF]Transportation Executive Information System …

Executive information system – YouTube

Content discovery platform External links:

What is Outbrain? Content Discovery Platform | Outbrain…

Personalized Content Discovery Platform | TiVo

Ad.Style – Native Advertising & Content Discovery Platform

Online analytical processing External links:

Working with Online Analytical Processing (OLAP)

Texas Instruments External links:

Free 2-day shipping on qualified orders over $35. Buy Texas Instruments TI-30XS MultiView Calculator at Walmart.com

Free 2-day shipping on qualified orders over $35. Buy Texas Instruments TI-30X IIS Scientific Calculator, 10-Digit LCD at Walmart.com

Comparison of OLAP Servers External links:


Comparison Of OLAP Servers – theinfolist.com

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

Decision theory External links:

Decision Theory Flashcards | Quizlet

Normative decision theory Flashcards | Quizlet

Decision theory | statistics | Britannica.com

Data Mining Extensions External links:

Data Mining Extensions (DMX) Reference | Microsoft Docs

Data Mining Extensions (DMX) Reference

Data Mining Extensions (DMX) Operator Reference