91 Cognitive Computing Criteria for Multi-purpose Projects

What is involved in Cognitive Computing

Find out what the related areas are that Cognitive Computing 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 Cognitive Computing thinking-frame.

How far is your company on its Cognitive Computing journey?

Take this short survey to gauge your organization’s progress toward Cognitive Computing 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 Cognitive Computing related domains to cover and 91 essential critical questions to check off in that domain.

The following domains are covered:

Cognitive Computing, Adaptive system, Adaptive user interface, Affective computing, Artificial intelligence, Artificial neural network, Automated reasoning, Cognitive computer, Cognitive reasoning, Computer vision, Computing platform, Context awareness, Data analysis, Dialog system, Enterprise cognitive system, Face detection, Fraud detection, Human brain, Human–computer interaction, Machine learning, Risk assessment, Sentiment analysis, Signal processing, Social neuroscience, Speech recognition, Synthetic intelligence, Unstructured data, Unstructured information:

Cognitive Computing Critical Criteria:

Troubleshoot Cognitive Computing projects and display thorough understanding of the Cognitive Computing process.

– How do you determine the key elements that affect Cognitive Computing workforce satisfaction? how are these elements determined for different workforce groups and segments?

– what is the best design framework for Cognitive Computing organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

Adaptive system Critical Criteria:

Discuss Adaptive system decisions and visualize why should people listen to you regarding Adaptive system.

– Do several people in different organizational units assist with the Cognitive Computing process?

– To what extent does management recognize Cognitive Computing as a tool to increase the results?

– Is There a Role for Complex Adaptive Systems Theory?

– Do we all define Cognitive Computing in the same way?

Adaptive user interface Critical Criteria:

Confer over Adaptive user interface planning and get the big picture.

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

– What is the total cost related to deploying Cognitive Computing, including any consulting or professional services?

– Risk factors: what are the characteristics of Cognitive Computing that make it risky?

Affective computing Critical Criteria:

Closely inspect Affective computing tactics and remodel and develop an effective Affective computing strategy.

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

– How does the organization define, manage, and improve its Cognitive Computing processes?

– How will you know that the Cognitive Computing project has been successful?

Artificial intelligence Critical Criteria:

Interpolate Artificial intelligence outcomes and change contexts.

– Why is it important to have senior management support for a Cognitive Computing project?

– Is there any existing Cognitive Computing governance structure?

– Is the scope of Cognitive Computing defined?

Artificial neural network Critical Criteria:

Guard Artificial neural network results and suggest using storytelling to create more compelling Artificial neural network projects.

– Think about the functions involved in your Cognitive Computing project. what processes flow from these functions?

– What other jobs or tasks affect the performance of the steps in the Cognitive Computing process?

Automated reasoning Critical Criteria:

Bootstrap Automated reasoning management and question.

– Will Cognitive Computing deliverables need to be tested and, if so, by whom?

Cognitive computer Critical Criteria:

Concentrate on Cognitive computer tactics and cater for concise Cognitive computer education.

– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Cognitive Computing models, tools and techniques are necessary?

– How can we improve Cognitive Computing?

– How to Secure Cognitive Computing?

Cognitive reasoning Critical Criteria:

Bootstrap Cognitive reasoning leadership and describe which business rules are needed as Cognitive reasoning interface.

– What tools and technologies are needed for a custom Cognitive Computing project?

– How important is Cognitive Computing to the user organizations mission?

Computer vision Critical Criteria:

Grasp Computer vision quality and customize techniques for implementing Computer vision controls.

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

– For your Cognitive Computing project, identify and describe the business environment. is there more than one layer to the business environment?

– How do we make it meaningful in connecting Cognitive Computing with what users do day-to-day?

Computing platform Critical Criteria:

Interpolate Computing platform visions and improve Computing platform service perception.

– What knowledge, skills and characteristics mark a good Cognitive Computing project manager?

– How is the value delivered by Cognitive Computing being measured?

Context awareness Critical Criteria:

Survey Context awareness engagements and get out your magnifying glass.

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

– Information/context awareness: how can a developer/participant restore awareness in project activity after having been offline for a few hours, days, or weeks?

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

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

Data analysis Critical Criteria:

Have a session on Data analysis engagements and display thorough understanding of the Data analysis process.

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

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

– Who is the main stakeholder, with ultimate responsibility for driving Cognitive Computing forward?

– Is Cognitive Computing Realistic, or are you setting yourself up for failure?

– What are some real time data analysis frameworks?

Dialog system Critical Criteria:

Communicate about Dialog system strategies and perfect Dialog system conflict management.

– How do we Identify specific Cognitive Computing investment and emerging trends?

Enterprise cognitive system Critical Criteria:

Set goals for Enterprise cognitive system planning and create Enterprise cognitive system explanations for all managers.

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

Face detection Critical Criteria:

Powwow over Face detection tasks and look for lots of ideas.

– Are assumptions made in Cognitive Computing stated explicitly?

– Who needs to know about Cognitive Computing ?

Fraud detection Critical Criteria:

Familiarize yourself with Fraud detection adoptions and arbitrate Fraud detection techniques that enhance teamwork and productivity.

– What is the source of the strategies for Cognitive Computing strengthening and reform?

Human brain Critical Criteria:

Have a meeting on Human brain goals and forecast involvement of future Human brain projects in development.

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

– What are specific Cognitive Computing Rules to follow?

Human–computer interaction Critical Criteria:

Map Human–computer interaction projects and correct better engagement with Human–computer interaction results.

– How will we insure seamless interoperability of Cognitive Computing moving forward?

– What are the Essentials of Internal Cognitive Computing Management?

Machine learning Critical Criteria:

Review Machine learning projects and secure Machine learning creativity.

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– Do you monitor the effectiveness of your Cognitive Computing activities?

Risk assessment Critical Criteria:

Jump start Risk assessment tactics and find answers.

– Have the it security cost for the any investment/project been integrated in to the overall cost including (c&a/re-accreditation, system security plan, risk assessment, privacy impact assessment, configuration/patch management, security control testing and evaluation, and contingency planning/testing)?

– Are interdependent service providers (for example, fuel suppliers, telecommunications providers, meter data processors) included in risk assessments?

– Does the risk assessment approach helps to develop the criteria for accepting risks and identify the acceptable level risk?

– Are standards for risk assessment methodology established, so risk information can be compared across entities?

– Are standards for risk assessment methodology established, so risk information can be compared across entities?

– With Risk Assessments do we measure if Is there an impact to technical performance and to what level?

– How frequently, if at all, do we conduct a business impact analysis (bia) and risk assessment (ra)?

– Does the process include a BIA, risk assessments, Risk Management, and risk monitoring and testing?

– Is the priority of the preventive action determined based on the results of the risk assessment?

– How does your company report on its information and technology risk assessment?

– Who performs your companys information and technology risk assessments?

– How are risk assessment and audit results communicated to executives?

– Do you use any homegrown IT system for ERM or risk assessments?

– Are regular risk assessments executed across all entities?

– Who performs your companys IT risk assessments?

– Do you use any homegrown IT system for risk assessments?

Sentiment analysis Critical Criteria:

Examine Sentiment analysis visions and do something to it.

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

– Is Cognitive Computing dependent on the successful delivery of a current project?

– How representative is twitter sentiment analysis relative to our customer base?

Signal processing Critical Criteria:

Cut a stake in Signal processing adoptions and define what our big hairy audacious Signal processing goal is.

– Who will be responsible for documenting the Cognitive Computing requirements in detail?

– How do we Improve Cognitive Computing service perception, and satisfaction?

Social neuroscience Critical Criteria:

Talk about Social neuroscience projects and adopt an insight outlook.

– What about Cognitive Computing Analysis of results?

– How to deal with Cognitive Computing Changes?

Speech recognition Critical Criteria:

Shape Speech recognition adoptions and drive action.

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

– What sources do you use to gather information for a Cognitive Computing study?

Synthetic intelligence Critical Criteria:

Grasp Synthetic intelligence issues and differentiate in coordinating Synthetic intelligence.

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

Unstructured data Critical Criteria:

Learn from Unstructured data strategies and revise understanding of Unstructured data architectures.

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

– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?

– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?

– What will drive Cognitive Computing change?

Unstructured information Critical Criteria:

Read up on Unstructured information management and secure Unstructured information creativity.

– Is the solution going to generate structured, semistructured, unstructured information for its own use or for use by entities either internal or external to the enterprise?

– How do we keep improving Cognitive Computing?


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


Author: Gerard Blokdijk

CEO at The Art of Service | http://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.

External links:

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

Cognitive Computing External links:

Cognitive Computing Consortium

“Cognitive Computing” by Haluk Demirkan, Seth Earley et al.

What is cognitive computing? – Definition from WhatIs.com

Adaptive system External links:


Adaptive user interface External links:


What is Adaptive User Interface | IGI Global


Affective computing External links:

What is affective computing? – Definition from WhatIs.com

Affective Computing Flashcards | Quizlet

Affective Computing – Gartner IT Glossary

Artificial intelligence External links:

Studying Artificial Intelligence At New York University : NPR

Artificial neural network External links:


Artificial neural network – ScienceDaily

Automated reasoning External links:

Handbook of Automated Reasoning – ScienceDirect

» Automated Reasoning Laboratory – University of …

Cognitive computer External links:

Cognitive Computer Solutions – Home | Facebook

IBM’s Watson cognitive computer has whipped up a cookbook

Cognitive reasoning External links:

Cognitive Reasoning – Parrot Software

Cognitive Reasoning – nnbote.de

Computer vision External links:

Focal Systems – Deep Learning and Computer Vision …

Photogrammetric Computer Vision

Sighthound – Industry Leading Computer Vision

Computing platform External links:

Cloud Foundry Security – Cloud Computing Platform | …

Microsoft Azure Cloud Computing Platform & Services

Context awareness External links:

[PDF]Spatio-Temporal Reasoning and Context Awareness

Semusi – Context Awareness Made Easy

Data analysis External links:

Data Analysis in Excel – EASY Excel Tutorial

Bureau of Justice Statistics (BJS) – All Data Analysis Tools

Research and Data Analysis | DSHS

Dialog system External links:

Dialog system – Object Technology Licensing Corporation

Dialog systems | ACL Member Portal

CiteSeerX — Machine Dialog System

Enterprise cognitive system External links:

Enterprise cognitive system – Revolvy
https://www.revolvy.com/topic/Enterprise cognitive system

Enterprise cognitive system – WOW.com

Face detection External links:

Face Detection using OpenCV and Python: A Beginner’s …

Face detection for C# in ASP.NET – Stack Overflow

CV Dazzle: Camouflage from Face Detection

Fraud detection External links:

Big Data Fraud Detection | DataVisor

Title IV fraud detection | University Business Magazine

Human brain External links:

Brain – Human Brain Diagrams and Detailed Information

Brain Anatomy, Anatomy of the Human Brain

10 Fun Facts About the Human Brain – Take the Quiz!

Machine learning External links:

DataRobot – Automated Machine Learning for Predictive …

Microsoft Azure Machine Learning Studio

Comcast Labs – PHLAI: Machine Learning Conference

Risk assessment External links:

Breast Cancer Risk Assessment Tool

Home | Oklahoma Risk Assessment

Risk Assessment Information | Mass.gov

Sentiment analysis External links:

YUKKA Lab – Sentiment Analysis

Signal processing External links:

Embedded Signal Processing Laboratory

Social neuroscience External links:

Social Neuroscience – Michigan State University

UCLA Social Neuroscience Lab

| Computational Social Neuroscience Lab

Speech recognition External links:

SayIt from nVoq – Speech Recognition in the Cloud

Use speech recognition

Speech API – Speech Recognition | Google Cloud Platform

Synthetic intelligence External links:

Synthetic Intelligence and the Transmutation of …

Synthetic Intelligence Network

Synthetic Intelligence Network – Home | Facebook

Unstructured data External links:

Gigaom | Sector Roadmap: Unstructured Data …

Unstructured information External links:

An unstructured information management system (UIMS…

Unstructured Information Management Architecture SDK – IBM

[PDF]Unstructured Information Processing with Apache …

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