Top 184 Analytics and Decision Support Things You Should Know

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 184 essential critical questions to check off in that domain.

The following domains are covered:

Analytics and Decision Support, Data Mining Extensions, Comparison of OLAP Servers, Collective intelligence, Cold start, O’Hare International Airport, Business intelligence software, Slowly changing dimension, Data loading, Self service software, Texas Instruments, Decision support system, Integrated Authority File, Judge–advisor system, Recommender system, Sixth normal form, Anchor Modeling, Spatial decision support system, Data transformation, Data mining, Project management software, Quality assurance, Operational data store, Item-item collaborative filtering, Executive information system, Medical diagnosis, Time series, Data warehouse, Precision agriculture, Sustainable development, Henk G. Sol, Canadian National Railway, MultiDimensional eXpressions, Decision making process, Systems architecture, GroupLens Research, Data extraction, Data mart, Information overload, Online deliberation, Microsoft SharePoint Workspace, Cognitive assets, Data access, Open source, Data vault modeling, Decision engineering, Predictive analytics, Similarity search, Knowledge environment, Fact table, Analytics and Decision Support, Knowledge-based systems, Decision making, Netflix Prize:

Analytics and Decision Support Critical Criteria:

Brainstorm over Analytics and Decision Support planning and be persistent.

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

– Why are Analytics and Decision Support skills important?

Data Mining Extensions Critical Criteria:

Study Data Mining Extensions projects and integrate design thinking in Data Mining Extensions innovation.

– 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?

– Will new equipment/products be required to facilitate Analytics and Decision Support delivery for example is new software needed?

– How important is Analytics and Decision Support to the user organizations mission?

Comparison of OLAP Servers Critical Criteria:

Review Comparison of OLAP Servers planning and test out new things.

– What other organizational variables, such as reward systems or communication systems, affect the performance of this Analytics and Decision Support process?

– Is there a Analytics and Decision Support Communication plan covering who needs to get what information when?

– What role does communication play in the success or failure of a Analytics and Decision Support project?

Collective intelligence Critical Criteria:

Revitalize Collective intelligence decisions and stake your claim.

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

– What is Effective Analytics and Decision Support?

Cold start Critical Criteria:

Differentiate Cold start tasks and improve Cold start service perception.

– Do those selected for the Analytics and Decision Support team have a good general understanding of what Analytics and Decision Support is all about?

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

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

O’Hare International Airport Critical Criteria:

Merge O’Hare International Airport tactics and transcribe O’Hare International Airport as tomorrows backbone for success.

– What will drive Analytics and Decision Support change?

– Do we have past Analytics and Decision Support Successes?

Business intelligence software Critical Criteria:

Track Business intelligence software leadership and assess and formulate effective operational and Business intelligence software strategies.

– What tools do you use once you have decided on a Analytics and Decision Support strategy and more importantly how do you choose?

– What business benefits will Analytics and Decision Support goals deliver if achieved?

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

Slowly changing dimension Critical Criteria:

Drive Slowly changing dimension quality and budget the knowledge transfer for any interested in Slowly changing dimension.

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

– Are we making progress? and are we making progress as Analytics and Decision Support leaders?

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

Data loading Critical Criteria:

Examine Data loading results and grade techniques for implementing Data loading controls.

– How can we incorporate support to ensure safe and effective use of Analytics and Decision Support into the services that we provide?

– Who sets the Analytics and Decision Support standards?

Self service software Critical Criteria:

Meet over Self service software adoptions and budget the knowledge transfer for any interested in Self service software.

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

– How much does Analytics and Decision Support help?

Texas Instruments Critical Criteria:

Graph Texas Instruments risks and gather practices for scaling Texas Instruments.

– 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?

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

Decision support system Critical Criteria:

X-ray Decision support system projects and secure Decision support system creativity.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Analytics and Decision Support in a volatile global economy?

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

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

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

Integrated Authority File Critical Criteria:

Adapt Integrated Authority File quality and adjust implementation of Integrated Authority File.

– What are our needs in relation to Analytics and Decision Support skills, labor, equipment, and markets?

– Are there Analytics and Decision Support Models?

Judge–advisor system Critical Criteria:

Ventilate your thoughts about Judge–advisor system goals and adjust implementation of Judge–advisor system.

– Think about the kind of project structure that would be appropriate for your Analytics and Decision Support project. should it be formal and complex, or can it be less formal and relatively simple?

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

– What vendors make products that address the Analytics and Decision Support needs?

Recommender system Critical Criteria:

Illustrate Recommender system tactics and optimize Recommender system leadership as a key to advancement.

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

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

Sixth normal form Critical Criteria:

Brainstorm over Sixth normal form issues and catalog Sixth normal form activities.

– At what point will vulnerability assessments be performed once Analytics and Decision Support is put into production (e.g., ongoing Risk Management after implementation)?

– Who will be responsible for deciding whether Analytics and Decision Support goes ahead or not after the initial investigations?

– Is maximizing Analytics and Decision Support protection the same as minimizing Analytics and Decision Support loss?

Anchor Modeling Critical Criteria:

Experiment with Anchor Modeling failures and use obstacles to break out of ruts.

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

– What threat is Analytics and Decision Support addressing?

Spatial decision support system Critical Criteria:

Derive from Spatial decision support system engagements and give examples utilizing a core of simple Spatial decision support system skills.

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

– Are we Assessing Analytics and Decision Support and Risk?

Data transformation Critical Criteria:

Track Data transformation failures and intervene in Data transformation processes and leadership.

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

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

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

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

Data mining Critical Criteria:

Revitalize Data mining engagements and improve Data mining service perception.

– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?

– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?

– What 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?

– Is the scope of Analytics and Decision Support defined?

Project management software Critical Criteria:

Weigh in on Project management software projects and diversify by understanding risks and leveraging Project management software.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Analytics and Decision Support process. ask yourself: are the records needed as inputs to the Analytics and Decision Support process available?

– 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?

– How will we insure seamless interoperability of Analytics and Decision Support moving forward?

Quality assurance Critical Criteria:

Co-operate on Quality assurance strategies and pioneer acquisition of Quality assurance systems.

– Does the quality plan prescribe the type(s) of Quality Assurance activities (such as reviews, audits, inspections) to be performed to achieve the objectives of the quality plan?

– What is your Quality Assurance process to ensure that the large volumes of data gathered in the monitoring process are handled efficiently?

– What is the availability of and cost of internal Quality Assurance manpower necessary to monitor each performance indicator?

– Have records center personnel received training on the records management aspects of the Quality Assurance program?

– Is at least one person engaged in software Quality Assurance for every ten engaged in its fabrication?

– How does the company create high performance relationships with its independent trade partners?

– Are records maintained in fireproof enclosures that are sealed to prevent moisture intrusion?

– What is/are the major contractors qa plan(s), and is it/are they implemented?

– How does improper or incomplete documentation affect disciplinary actions?

– Does the Quality Assurance record center contain the selected documents?

– Is the system or component adequately labeled for ease of operation?

– Who closes the loop with the person that submitted a complaint?

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

– Do you keep track of unsuccessfully performed skills?

– Are retention requirements specified for records?

– Is the target achievable by the current process?

– Is the system/component user friendly?

– How often are the protocols reviewed?

– How much does Quality Assurance cost?

– Is the qa plan approved?

Operational data store Critical Criteria:

Adapt Operational data store goals and develop and take control of the Operational data store initiative.

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

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

Item-item collaborative filtering Critical Criteria:

Incorporate Item-item collaborative filtering strategies and inform on and uncover unspoken needs and breakthrough Item-item collaborative filtering results.

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

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

– How can skill-level changes improve Analytics and Decision Support?

Executive information system Critical Criteria:

Closely inspect Executive information system results and grade techniques for implementing Executive information system controls.

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

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

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

Medical diagnosis Critical Criteria:

Study Medical diagnosis engagements and plan concise Medical diagnosis education.

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

Time series Critical Criteria:

Confer over Time series outcomes and diversify disclosure of information – dealing with confidential Time series information.

– Can we add value to the current Analytics and Decision Support decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

Data warehouse Critical Criteria:

Trace Data warehouse quality and acquire concise Data warehouse education.

– Which customers cant participate in our Analytics and Decision Support domain because they lack skills, wealth, or convenient access to existing solutions?

– 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?

– Is data warehouseing necessary for our business intelligence service?

– Is Data Warehouseing necessary for a business intelligence service?

– 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?

– Do you still need a data warehouse?

– Centralized data warehouse?

Precision agriculture Critical Criteria:

Familiarize yourself with Precision agriculture engagements and tour deciding if Precision agriculture progress is made.

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

Sustainable development Critical Criteria:

Interpolate Sustainable development engagements and give examples utilizing a core of simple Sustainable development skills.

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

Henk G. Sol Critical Criteria:

Collaborate on Henk G. Sol projects and proactively manage Henk G. Sol risks.

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

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

Canadian National Railway Critical Criteria:

Trace Canadian National Railway outcomes and observe effective Canadian National Railway.

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

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

MultiDimensional eXpressions Critical Criteria:

Have a round table over MultiDimensional eXpressions decisions and finalize specific methods for MultiDimensional eXpressions acceptance.

– What are the top 3 things at the forefront of our Analytics and Decision Support agendas for the next 3 years?

– What are internal and external Analytics and Decision Support relations?

Decision making process Critical Criteria:

Discuss Decision making process strategies and define what do we need to start doing with Decision making process.

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

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

– Who will be involved in the decision making process?

Systems architecture Critical Criteria:

Think carefully about Systems architecture adoptions and pioneer acquisition of Systems architecture systems.

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

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

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

GroupLens Research Critical Criteria:

Depict GroupLens Research adoptions and do something to it.

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

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

Data extraction Critical Criteria:

Investigate Data extraction failures and optimize Data extraction leadership as a key to advancement.

– What management system can we use to leverage the Analytics and Decision Support experience, ideas, and concerns of the people closest to the work to be done?

– Can Management personnel recognize the monetary benefit of Analytics and Decision Support?

– How can data extraction from dashboards be automated?

Data mart Critical Criteria:

Prioritize Data mart strategies and mentor Data mart customer orientation.

– 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?

Information overload Critical Criteria:

Experiment with Information overload adoptions and ask questions.

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

Online deliberation Critical Criteria:

Participate in Online deliberation leadership and look at it backwards.

Microsoft SharePoint Workspace Critical Criteria:

Drive Microsoft SharePoint Workspace issues and explain and analyze the challenges of Microsoft SharePoint Workspace.

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

Cognitive assets Critical Criteria:

Face Cognitive assets tactics and ask what if.

– 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?

Data access Critical Criteria:

Accumulate Data access strategies and oversee Data access management by competencies.

– Have internal procedural controls been established to manage user data access, including security screenings, training, and confidentiality agreements required for staff with pii access privileges?

– What impact would the naming conventions and the use of homegrown software have on later data access?

– What are the data access requirements for standard file, message, and data management?

– What should be our public authorities policy with regards to data access?

– What impact would the naming conventions have on later data access?

– What are the effects software updates have on later data access?

– What are the implications of tracking/monitoring data access?

– How can you measure Analytics and Decision Support in a systematic way?

– How are data accessed?

Open source Critical Criteria:

Grasp Open source results and report on developing an effective Open source strategy.

– 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?

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

– 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?

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

– Is there an open source alternative to adobe captivate?

– What are the open source alternatives to Moodle?

Data vault modeling Critical Criteria:

Tête-à-tête about Data vault modeling governance and do something to it.

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

– Is Supporting Analytics and Decision Support documentation required?

Decision engineering Critical Criteria:

Meet over Decision engineering outcomes and look at it backwards.

– How would one define Analytics and Decision Support leadership?

Predictive analytics Critical Criteria:

Investigate Predictive analytics governance and ask what if.

– Meeting the challenge: are missed Analytics and Decision Support opportunities costing us money?

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

Similarity search Critical Criteria:

Administer Similarity search planning and find answers.

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

– What are our Analytics and Decision Support Processes?

Knowledge environment Critical Criteria:

Chat re Knowledge environment tactics and suggest using storytelling to create more compelling Knowledge environment projects.

– What are your most important goals for the strategic Analytics and Decision Support objectives?

Fact table Critical Criteria:

Disseminate Fact table decisions and don’t overlook the obvious.

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

Analytics and Decision Support Critical Criteria:

Talk about Analytics and Decision Support issues and finalize specific methods for Analytics and Decision Support acceptance.

– How to deal with Analytics and Decision Support Changes?

Knowledge-based systems Critical Criteria:

Derive from Knowledge-based systems management and check on ways to get started with Knowledge-based systems.

– Are there Analytics and Decision Support problems defined?

Decision making Critical Criteria:

Survey Decision making failures and catalog what business benefits will Decision making goals deliver if achieved.

– 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?

Netflix Prize Critical Criteria:

Audit Netflix Prize planning and suggest using storytelling to create more compelling Netflix Prize projects.

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


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 |

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 Mining Extensions External links:

DMX abbreviation stands for Data Mining Extensions

Data Mining Extensions (DMX) Reference | Microsoft Docs

Data Mining Extensions (DMX) Reference

Comparison of OLAP Servers External links:

Comparison of OLAP Servers – of OLAP Servers


Comparison of OLAP Servers – of OLAP Servers

Collective intelligence External links:

The Collective Intelligence – Home | Facebook

Colleaga | The Power of Collective Intelligence

Cold start External links:

Very cold start to 2018 –

Cold Start, Weekend Warm Up –

A bitter cold start to 2018 | WISH-TV

O’Hare International Airport External links:

Request Uber at Chicago O’Hare International Airport …

O’Hare International Airport – Chicago Department of …

Business intelligence software External links:

Business Intelligence Software Explained – Webopedia…

Best Business Intelligence Software in 2018 | G2 Crowd

Jaspersoft Business Intelligence Software

Slowly changing dimension External links:

SSIS Slowly Changing Dimension Type 2 – Tutorial Gateway

Data loading External links:

The Data Loading Performance Guide –

Self service software External links:

Employee Self Service Software – ESS | Dominion Systems

Texas Instruments External links:

Instructions for using Texas Instruments BA II Plus …

Texas Instruments Perks at Work

TI Analog, DSP and Semiconductor Products – Texas Instruments

Decision support system External links:

CureMatch Decision Support System For Oncologist to …

VisualDx – Visual Clinical Decision Support System (CDSS)

North Carolina Accounting System Decision Support System

Integrated Authority File External links:

MEDLARS indexing integrated authority file : chemical section

MEDLARS indexing: integrated authority file

Integrated Authority File (GND) – Deutsche Nationalbibliothek

Sixth normal form External links:

6NF abbreviation stands for Sixth normal form – All Acronyms

Sixth normal form – Google Groups

sql – design – sixth normal form – Stack Overflow

Anchor Modeling External links:

Anchor Modeling (@anchormodeling) | Twitter

Anchor Modeling – Home | Facebook

About – Anchor Modeling

Spatial decision support system External links:

Spatial Decision Support System (SDSS)

Data transformation External links:

[PDF]Data transformation and normality – Evaluation

Data mining External links:

Nebraska Oil and Gas Conservation Commission – GIS Data Mining

What is Data Mining in Healthcare?

Data Mining Extensions (DMX) Reference | Microsoft Docs

Project management software External links:

Project Management Software –
http://ad ·

Project Management Software –
http://ad ·

Quality assurance External links:

Title Quality Assurance Jobs, Employment |

Quality Assurance | AmeriTitle Inc.

[PDF]Title: Quality Assurance On-Line QA On-line.pdf

Operational data store External links:

Operational Data Store – YouTube

ODS-Operational Data Store

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

Executive information system External links:


[PDF]Transportation Executive Information System …

Medical diagnosis External links:

Online Medical Diagnosis & Advice | UPMC AnywhereCare

Medical diagnosis overview –

Time series External links:

Initial State – Analytics for Time Series Data

[PDF]Time Series Analysis and Forecasting –

Data warehouse External links:

Data Warehouse Specialist Salaries –

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

Enterprise Data Warehouse | IT@UMN

Precision agriculture External links:

Precision Agriculture » Raven Slingshot®

LaserAg Soil Testing – An Innovation in Precision Agriculture

Sustainable development External links:

Sustainable Development Goals: 17 Goals to Transform …

Home .:. Sustainable Development Knowledge Platform

U.S. Indicators For The Sustainable Development Goals

Henk G. Sol External links:

Henk G. Sol | University of Groningen (RUG) | ResearchGate

Canadian National Railway External links:

Canadian National Railway Company – Home | Facebook

Canadian National Railway Map – ACW Railway Company

Canadian National Railway Company Common Stock …

Decision making process External links:


Systems architecture External links:

SOSA (Sensor Open Systems Architecture) | The Open …

Systems Architecture Ch 11/13 Flashcards | Quizlet

Systems Architecture and Concepts | Management …

GroupLens Research External links:

GroupLens Research – Minneapolis, Minnesota – …

GroupLens Research · GitHub

Data extraction External links:

Data Extraction Specialist Jobs, Employment |

NeXtraction – Intelligent Data Extraction

[PDF]Data extraction Presentation – PBworks

Data mart External links:

UNC Data Mart – University of North Carolina

MPR Data Mart

Information overload External links:

Overcoming Information Overload –

Information overload (Book, 2001) []

Information overload Flashcards | Quizlet

Online deliberation External links:

Online Deliberation among Regional Civil Society …

Cognitive assets External links:

25 Cognitive Assets – MS. COVENEY’S WEBSITE 2017-2018

Cognitive assets. Cognitive assets are tangible and intangible organizational assets that constitute sources of the cognition that is necessary for action coordination. These assets allow for the integrity and efficiency of the multiple conversions of individual knowledge into organizational knowledge.

Data access External links:

StaffLink Online Data Access

NOAA: Data Access Viewer

Colorado State Courts – Data Access – Home

Open source External links:

Open source
http://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.

Open Source Center – Official Site

Data vault modeling External links:

Data Vault Modeling and Snowflake | Snowflake

Data Vault Modeling Methodology Jobs, Employment |

Decision engineering External links:

Decision Engineering (SM) Partners – Google Sites

Predictive analytics External links:

Predictive Analytics for Healthcare | Forecast Health

Predictive Analytics Software, Social Listening | NewBrand

Similarity search External links:

eTBLAST and Déjà vu: a Text Similarity Search Engine and …

[PDF]Similarity Search on Time Series Data

Similarity search in visual data – University of Minnesota

Fact table External links:

Factless Fact Table – Wisdomschema

Fact table – Oracle FAQ

Factless Fact Table | Learn about Factless Fact Table

Knowledge-based systems External links:

[PDF]Knowledge-Based Systems for Natural Language …

Decision making External links:

Essays on decision making – Rutgers University

Effective Decision Making | SkillsYouNeed

Netflix Prize External links:

Netflix Prize: Home

Netflix Prize data | Kaggle