What is involved in Pricing Analytics
Find out what the related areas are that Pricing Analytics 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 Pricing Analytics thinking-frame.
How far is your company on its Pricing Analytics journey?
Take this short survey to gauge your organization’s progress toward Pricing Analytics 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 Pricing Analytics related domains to cover and 195 essential critical questions to check off in that domain.
The following domains are covered:
Pricing Analytics, Academic discipline, Analytic applications, Architectural analytics, Behavioral analytics, Big data, Business analytics, Business intelligence, Cloud analytics, Complex event processing, Computer programming, Continuous analytics, Cultural analytics, Customer analytics, Data mining, Data presentation architecture, Embedded analytics, Enterprise decision management, Fraud detection, Google Analytics, Human resources, Learning analytics, Machine learning, Marketing mix modeling, Mobile Location Analytics, Neural networks, News analytics, Online analytical processing, Online video analytics, Operational reporting, Operations research, Over-the-counter data, Portfolio analysis, Predictive analytics, Predictive engineering analytics, Predictive modeling, Prescriptive analytics, Price discrimination, Risk analysis, Security information and event management, Semantic analytics, Smart grid, Social analytics, Software analytics, Speech analytics, Statistical discrimination, Stock-keeping unit, Structured data, Telecommunications data retention, Text analytics, Text mining, Time series, Unstructured data, User behavior analytics, Visual analytics, Web analytics, Win–loss analytics:
Pricing Analytics Critical Criteria:
Give examples of Pricing Analytics results and arbitrate Pricing Analytics techniques that enhance teamwork and productivity.
– At what point will vulnerability assessments be performed once Pricing Analytics is put into production (e.g., ongoing Risk Management after implementation)?
– What prevents me from making the changes I know will make me a more effective Pricing Analytics leader?
– Have the types of risks that may impact Pricing Analytics been identified and analyzed?
Academic discipline Critical Criteria:
Revitalize Academic discipline strategies and reinforce and communicate particularly sensitive Academic discipline decisions.
– What are your current levels and trends in key measures or indicators of Pricing Analytics product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– In a project to restructure Pricing Analytics outcomes, which stakeholders would you involve?
Analytic applications Critical Criteria:
Canvass Analytic applications strategies and work towards be a leading Analytic applications expert.
– Think about the kind of project structure that would be appropriate for your Pricing Analytics project. should it be formal and complex, or can it be less formal and relatively simple?
– Is maximizing Pricing Analytics protection the same as minimizing Pricing Analytics loss?
– What is the source of the strategies for Pricing Analytics strengthening and reform?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
Architectural analytics Critical Criteria:
Concentrate on Architectural analytics strategies and innovate what needs to be done with Architectural analytics.
– Do several people in different organizational units assist with the Pricing Analytics process?
– Are there recognized Pricing Analytics problems?
– What are current Pricing Analytics Paradigms?
Behavioral analytics Critical Criteria:
Gauge Behavioral analytics management and oversee implementation of Behavioral analytics.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Pricing Analytics processes?
– How can we incorporate support to ensure safe and effective use of Pricing Analytics into the services that we provide?
– How to Secure Pricing Analytics?
Big data Critical Criteria:
Detail Big data projects and devise Big data key steps.
– Do we address the daunting challenge of Big Data: how to make an easy use of highly diverse data and provide knowledge?
– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– Wheres the evidence that using big data intelligently will improve business performance?
– Does the in situ hardware have the computational capacity to support such algorithms?
– Is senior management in your organization involved in big data-related projects?
– How are the new Big Data developments captured in new Reference Architectures?
– Are there any best practices or standards for the use of Big Data solutions?
– How will systems and methods evolve to remove Big Data solution weaknesses?
– What are the new applications that are enabled by Big Data solutions?
– How fast can we determine changes in the incoming data?
– From which country is your organization from?
– What are some impacts of Big Data?
– WHAT ARE THE NOMINATION CRITERIA?
– What about Volunteered data?
– How to deal with ambiguity?
– How robust are the results?
– Are we Using Data To Win?
– What is Big Data to us?
Business analytics Critical Criteria:
Reconstruct Business analytics engagements and devote time assessing Business analytics and its risk.
– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?
– What is the difference between business intelligence business analytics and data mining?
– Is there a mechanism to leverage information for business analytics and optimization?
– What is the difference between business intelligence and business analytics?
– what is the difference between Data analytics and Business Analytics If Any?
– How do you pick an appropriate ETL tool or business analytics tool?
– What are the trends shaping the future of business analytics?
– Is Supporting Pricing Analytics documentation required?
– What about Pricing Analytics Analysis of results?
Business intelligence Critical Criteria:
Be clear about Business intelligence leadership and find out.
– 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?
– How should a complicated business setup their business intelligence and analysis to make decisions best?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– Does your bi solution allow analytical insights to happen anywhere and everywhere?
– Does your BI solution help you find the right views to examine your data?
– What should recruiters look for in a business intelligence professional?
– Who prioritizes, conducts and monitors business intelligence projects?
– What information needs of managers are satisfied by the new BI system?
– Does your BI solution require weeks or months to deploy or change?
– What percentage of enterprise apps will be web based in 3 years?
– What are some best practices for managing business intelligence?
– Number of data sources that can be simultaneously accessed?
– What are the most efficient ways to create the models?
– What are the pillar concepts of business intelligence?
– Are there any on demand analytics tools in the cloud?
– How can data extraction from dashboards be automated?
– Is your software easy for IT to manage and upgrade?
– Can your product map ad-hoc query results?
– Do you offer formal user training?
Cloud analytics Critical Criteria:
Air ideas re Cloud analytics strategies and create Cloud analytics explanations for all managers.
– Are accountability and ownership for Pricing Analytics clearly defined?
– How much does Pricing Analytics help?
Complex event processing Critical Criteria:
Discourse Complex event processing results and test out new things.
– How do we go about Comparing Pricing Analytics approaches/solutions?
– What are internal and external Pricing Analytics relations?
– Are there Pricing Analytics problems defined?
Computer programming Critical Criteria:
Depict Computer programming goals and ask questions.
– What sources do you use to gather information for a Pricing Analytics study?
– What are specific Pricing Analytics Rules to follow?
Continuous analytics Critical Criteria:
Review Continuous analytics goals and catalog Continuous analytics activities.
– How does the organization define, manage, and improve its Pricing Analytics processes?
– How can you measure Pricing Analytics in a systematic way?
Cultural analytics Critical Criteria:
Survey Cultural analytics decisions and get going.
– Think about the functions involved in your Pricing Analytics project. what processes flow from these functions?
– How can we improve Pricing Analytics?
Customer analytics Critical Criteria:
Explore Customer analytics failures and oversee Customer analytics management by competencies.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Pricing Analytics models, tools and techniques are necessary?
– How do you determine the key elements that affect Pricing Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
– Are we Assessing Pricing Analytics and Risk?
Data mining Critical Criteria:
Understand Data mining engagements and report on developing an effective Data mining strategy.
– 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?
– How do mission and objectives affect the Pricing Analytics processes of our organization?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Is business intelligence set to play a key role in the future of Human Resources?
– What programs do we have to teach data mining?
Data presentation architecture Critical Criteria:
Survey Data presentation architecture projects and secure Data presentation architecture creativity.
– What are the key elements of your Pricing Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?
– How do we maintain Pricing Analyticss Integrity?
Embedded analytics Critical Criteria:
Bootstrap Embedded analytics projects and get out your magnifying glass.
Enterprise decision management Critical Criteria:
Deliberate Enterprise decision management leadership and define what do we need to start doing with Enterprise decision management.
– In what ways are Pricing Analytics vendors and us interacting to ensure safe and effective use?
– Does Pricing Analytics analysis isolate the fundamental causes of problems?
Fraud detection Critical Criteria:
Accumulate Fraud detection tactics and define Fraud detection competency-based leadership.
Google Analytics Critical Criteria:
Depict Google Analytics tactics and perfect Google Analytics conflict management.
– Who will be responsible for making the decisions to include or exclude requested changes once Pricing Analytics is underway?
– Is the Pricing Analytics organization completing tasks effectively and efficiently?
– How will you know that the Pricing Analytics project has been successful?
Human resources Critical Criteria:
Reorganize Human resources decisions and don’t overlook the obvious.
– A dramatic step toward becoming a learning organization is to appoint a chief training officer (CTO) or a chief learning officer (CLO). Many organizations claim to value Human Resources, but how many have a Human Resources representative involved in discussions about research and development commercialization, new product development, the strategic vision of the company, or increasing shareholder value?
– Imagine you work in the Human Resources department of a company considering a policy to protect its data on employees mobile devices. in advising on this policy, what rights should be considered?
– How do we engage divisions, operating units, operations, internal audit, risk management, compliance, finance, technology, and human resources in adopting the updated framework?
– Are Human Resources subject to screening, and do they have terms and conditions of employment defining their information security responsibilities?
– Do the response plans address damage assessment, site restoration, payroll, Human Resources, information technology, and administrative support?
– Have we adopted and promoted the companys culture of integrity management, including ethics, business practices and Human Resources evaluations?
– What are strategies that we can undertake to reduce job fatigue and reduced productivity?
– To satisfy customers and stakeholders, which internal business process must we excel in?
– What decisions can you envision making with this type of information?
– Friendliness and professionalism of the Human Resources staff?
– Are you a manager interested in increasing your effectiveness?
– How do you view the department and staff members as a whole?
– How does the global environment influence management?
– Ease of contacting the Human Resources staff members?
– Does the hr plan make sense to our stakeholders?
– Why study Human Resources management (hrm)?
– How do we engage the stakeholders?
– Who should appraise performance?
– Can you trust the algorithm?
Learning analytics Critical Criteria:
Check Learning analytics engagements and inform on and uncover unspoken needs and breakthrough Learning analytics results.
– What role does communication play in the success or failure of a Pricing Analytics project?
– Do we monitor the Pricing Analytics decisions made and fine tune them as they evolve?
– What is our Pricing Analytics Strategy?
Machine learning Critical Criteria:
Design Machine learning planning and ask what if.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– How do we know that any Pricing Analytics analysis is complete and comprehensive?
– What are the business goals Pricing Analytics is aiming to achieve?
Marketing mix modeling Critical Criteria:
Analyze Marketing mix modeling tasks and clarify ways to gain access to competitive Marketing mix modeling services.
– What are the Key enablers to make this Pricing Analytics move?
– Do we have past Pricing Analytics Successes?
Mobile Location Analytics Critical Criteria:
Use past Mobile Location Analytics governance and find out.
– Does our organization need more Pricing Analytics education?
– Is Pricing Analytics Required?
Neural networks Critical Criteria:
Merge Neural networks visions and know what your objective is.
– Do those selected for the Pricing Analytics team have a good general understanding of what Pricing Analytics is all about?
– Why is it important to have senior management support for a Pricing Analytics project?
News analytics Critical Criteria:
Drive News analytics quality and define what our big hairy audacious News analytics goal is.
– What are your results for key measures or indicators of the accomplishment of your Pricing Analytics strategy and action plans, including building and strengthening core competencies?
– How important is Pricing Analytics to the user organizations mission?
Online analytical processing Critical Criteria:
Check Online analytical processing leadership and handle a jump-start course to Online analytical processing.
– What is the total cost related to deploying Pricing Analytics, including any consulting or professional services?
– How do we Identify specific Pricing Analytics investment and emerging trends?
– Does the Pricing Analytics task fit the clients priorities?
Online video analytics Critical Criteria:
Reorganize Online video analytics visions and get going.
– Meeting the challenge: are missed Pricing Analytics opportunities costing us money?
– Is there any existing Pricing Analytics governance structure?
Operational reporting Critical Criteria:
Depict Operational reporting failures and triple focus on important concepts of Operational reporting relationship management.
– How do we measure improved Pricing Analytics service perception, and satisfaction?
– What are the short and long-term Pricing Analytics goals?
Operations research Critical Criteria:
Canvass Operations research strategies and display thorough understanding of the Operations research process.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Pricing Analytics?
– Does Pricing Analytics create potential expectations in other areas that need to be recognized and considered?
Over-the-counter data Critical Criteria:
Set goals for Over-the-counter data leadership and change contexts.
– Consider your own Pricing Analytics project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– Why are Pricing Analytics skills important?
Portfolio analysis Critical Criteria:
Map Portfolio analysis visions and report on the economics of relationships managing Portfolio analysis and constraints.
– Will new equipment/products be required to facilitate Pricing Analytics delivery for example is new software needed?
– What potential environmental factors impact the Pricing Analytics effort?
Predictive analytics Critical Criteria:
Sort Predictive analytics projects and budget the knowledge transfer for any interested in Predictive analytics.
– What are direct examples that show predictive analytics to be highly reliable?
Predictive engineering analytics Critical Criteria:
Examine Predictive engineering analytics tasks and forecast involvement of future Predictive engineering analytics projects in development.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Pricing Analytics process. ask yourself: are the records needed as inputs to the Pricing Analytics process available?
– What are the usability implications of Pricing Analytics actions?
Predictive modeling Critical Criteria:
Gauge Predictive modeling failures and grade techniques for implementing Predictive modeling controls.
– Are you currently using predictive modeling to drive results?
Prescriptive analytics Critical Criteria:
Think about Prescriptive analytics tactics and slay a dragon.
– Does Pricing Analytics appropriately measure and monitor risk?
Price discrimination Critical Criteria:
Depict Price discrimination governance and oversee implementation of Price discrimination.
– Are there any easy-to-implement alternatives to Pricing Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– How do we manage Pricing Analytics Knowledge Management (KM)?
– How is the value delivered by Pricing Analytics being measured?
Risk analysis Critical Criteria:
Map Risk analysis quality and research ways can we become the Risk analysis company that would put us out of business.
– How do risk analysis and Risk Management inform your organizations decisionmaking processes for long-range system planning, major project description and cost estimation, priority programming, and project development?
– What levels of assurance are needed and how can the risk analysis benefit setting standards and policy functions?
– In which two Service Management processes would you be most likely to use a risk analysis and management method?
– Who is the main stakeholder, with ultimate responsibility for driving Pricing Analytics forward?
– How does the business impact analysis use data from Risk Management and risk analysis?
– How do we do risk analysis of rare, cascading, catastrophic events?
– With risk analysis do we answer the question how big is the risk?
– How will you measure your Pricing Analytics effectiveness?
Security information and event management Critical Criteria:
Categorize Security information and event management tasks and oversee Security information and event management management by competencies.
Semantic analytics Critical Criteria:
Incorporate Semantic analytics quality and correct better engagement with Semantic analytics results.
Smart grid Critical Criteria:
Discuss Smart grid engagements and develop and take control of the Smart grid initiative.
– Does your organization perform vulnerability assessment activities as part of the acquisition cycle for products in each of the following areas: Cybersecurity, SCADA, smart grid, internet connectivity, and website hosting?
– How can you negotiate Pricing Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?
– Will Pricing Analytics deliverables need to be tested and, if so, by whom?
– How do we Improve Pricing Analytics service perception, and satisfaction?
Social analytics Critical Criteria:
Troubleshoot Social analytics outcomes and find out.
– What tools do you use once you have decided on a Pricing Analytics strategy and more importantly how do you choose?
– How do we ensure that implementations of Pricing Analytics products are done in a way that ensures safety?
– Who needs to know about Pricing Analytics ?
Software analytics Critical Criteria:
Infer Software analytics visions and remodel and develop an effective Software analytics strategy.
– What is the purpose of Pricing Analytics in relation to the mission?
– Do we all define Pricing Analytics in the same way?
Speech analytics Critical Criteria:
Be responsible for Speech analytics planning and catalog what business benefits will Speech analytics goals deliver if achieved.
– Does Pricing Analytics analysis show the relationships among important Pricing Analytics factors?
– What will drive Pricing Analytics change?
Statistical discrimination Critical Criteria:
Set goals for Statistical discrimination decisions and pioneer acquisition of Statistical discrimination systems.
– Is Pricing Analytics dependent on the successful delivery of a current project?
Stock-keeping unit Critical Criteria:
Contribute to Stock-keeping unit goals and reduce Stock-keeping unit costs.
Structured data Critical Criteria:
Pay attention to Structured data goals and summarize a clear Structured data focus.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Pricing Analytics in a volatile global economy?
– 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)?
– Should you use a hierarchy or would a more structured database-model work best?
Telecommunications data retention Critical Criteria:
Generalize Telecommunications data retention adoptions and slay a dragon.
– How do we make it meaningful in connecting Pricing Analytics with what users do day-to-day?
Text analytics Critical Criteria:
Test Text analytics planning and diversify disclosure of information – dealing with confidential Text analytics information.
– Who will be responsible for documenting the Pricing Analytics requirements in detail?
– Have text analytics mechanisms like entity extraction been considered?
– What are all of our Pricing Analytics domains and what do they do?
– How do we keep improving Pricing Analytics?
Text mining Critical Criteria:
Mix Text mining goals and sort Text mining activities.
– What are the barriers to increased Pricing Analytics production?
Time series Critical Criteria:
Pay attention to Time series leadership and balance specific methods for improving Time series results.
Unstructured data Critical Criteria:
Face Unstructured data engagements and cater for concise Unstructured data education.
– In the case of a Pricing Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Pricing Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Pricing Analytics project is implemented as planned, and is it working?
– What is our formula for success in Pricing Analytics ?
User behavior analytics Critical Criteria:
Confer over User behavior analytics quality and remodel and develop an effective User behavior analytics strategy.
Visual analytics Critical Criteria:
Do a round table on Visual analytics tasks and report on setting up Visual analytics without losing ground.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Pricing Analytics. How do we gain traction?
Web analytics Critical Criteria:
Administer Web analytics failures and finalize specific methods for Web analytics acceptance.
– What statistics should one be familiar with for business intelligence and web analytics?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Check Win–loss analytics outcomes and oversee Win–loss analytics management by competencies.
– What will be the consequences to the business (financial, reputation etc) if Pricing Analytics does not go ahead or fails to deliver the objectives?
– Who sets the Pricing Analytics standards?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Pricing Analytics 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.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Academic discipline External links:
Academic Discipline – Earl Warren College
Academic Discipline – Earl Warren College
Criminal justice | academic discipline | Britannica.com
Analytic applications External links:
Foxtrot Code AI Analytic Applications (Home)
Architectural analytics External links:
Architectural Analytics – Home | Facebook
Architectural Analytics – Home | Facebook
Behavioral analytics External links:
Magnifier Behavioral Analytics – Palo Alto Networks
Behavioral Analytics Definition | Investopedia
Behavioral Analytics | Interana
Big data External links:
ZestFinance.com: Machine Learning & Big Data Underwriting
Business Intelligence and Big Data Analytics Software
Loudr: Big Data for Music Rights
Business analytics External links:
Big Data & Business Analytics – Wayne State University
What is Business Analytics? Webopedia Definition
Business intelligence External links:
BIIS – Business Intelligence for Independent Schools
Mortgage Business Intelligence Software :: Motivity Solutions
Cloud analytics External links:
Cloud Analytics Academy – Official Site
Cloud Analytics World Tour – Stockholm | Snowflake
Cloud Analytics – Solutions for Cloud Data Analytics | NetApp
Computer programming External links:
Computer programming Meetups – Meetup
Computer Programming, Robotics & Engineering – STEM …
Cultural analytics External links:
Software Studies Initiative: Cultural analytics
Software Studies Initiative: Cultural analytics
Customer analytics External links:
Customer Analytics & Predictive Analytics Tools for Business
BlueVenn – Customer Analytics and Customer Journey …
Zylotech- AI For Customer Analytics
Data mining External links:
Data Mining Extensions (DMX) Reference | Microsoft Docs
Data Mining on the Florida Department of Corrections Website
UT Data Mining
Embedded analytics External links:
What is embedded analytics ? – Definition from WhatIs.com
Power BI Embedded analytics | Microsoft Azure
Embedded Analytics and Data Visualization | Reflect
Enterprise decision management External links:
enterprise decision management Archives – Insights
Enterprise Decision Management (EDM) – Techopedia.com
Enterprise Decision Management | SAS Italy
Fraud detection External links:
Credit Card Fraud Detection | Kaggle
Debit Card Security | Fraud Detection & Protection | RushCard
Big Data Fraud Detection | DataVisor
Google Analytics External links:
Google Analytics Solutions – Marketing Analytics & …
Google Analytics Opt-out Browser Add-on Download Page
Human resources External links:
Office of Human Resources – TITLE IX
Human Resources Job Titles-The Ultimate Guide | upstartHR
Human Resources Job Titles – The Balance
Learning analytics External links:
Learning Analytics Explained. (eBook, 2017) [WorldCat.org]
Machine learning External links:
What is machine learning? – Definition from WhatIs.com
Titanic: Machine Learning from Disaster | Kaggle
DataRobot – Automated Machine Learning for Predictive …
Marketing mix modeling External links:
Marketing Mix Modeling | Marketing Management Analytics
Mobile Location Analytics External links:
[PDF]Mobile Location Analytics Code of Conduct
How ‘Mobile Location Analytics’ Controls Your Mind – YouTube
Mobile Location Analytics Privacy Notice | Verizon
Online analytical processing External links:
[PDF]Comparing Online Analytical Processing and Data …
Working with Online Analytical Processing (OLAP)
[PDF]OLAP (Online Analytical Processing)
Online video analytics External links:
Managing Your Online Video Analytics – DaCast
Online Video Analytics & Marketing Software | Vidooly
Operations research External links:
Operations Research on JSTOR
Operations Research (O.R.), or operational research in the U.K, is a discipline that deals with the application of advanced analytical methods to help make better decisions.
Operations research | Britannica.com
Over-the-counter data External links:
What is Over-the-Counter Data | IGI Global
Over-the-Counter Data – American Mensa – Medium
[PDF]Over-the-Counter Data’s Impact on Educators’ Data …
Portfolio analysis External links:
[PDF]Portfolio Analysis Tool: Methodologies and Assumptions
Portfolio Analysis | Economy Watch
U.S. Army STAND-TO! | Strategic Portfolio Analysis Review
Predictive analytics External links:
Predictive Analytics Software, Social Listening | NewBrand
Strategic Location Management & Predictive Analytics | Tango
Customer Analytics & Predictive Analytics Tools for Business
Predictive modeling External links:
What is predictive modeling? – Definition from …
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate Technologies
Price discrimination External links:
MBAecon – 1st, 2nd and 3rd Price discrimination
Price Discrimination Flashcards | Quizlet
A macroeconomic model of international price discrimination
Risk analysis External links:
Project Management and Risk Analysis Software | Safran
Risk Analysis | Investopedia
http://Risk analysis is the study of the underlying uncertainty of a given course of action. Risk analysis refers to the uncertainty of forecasted future cash flows streams, variance of portfolio/stock returns, statistical analysis to determine the probability of a project’s success or failure, and possible future economic states.
Security information and event management External links:
A Guide to Security Information and Event Management
Semantic analytics External links:
What is Semantic Analytics | IGI Global
[PDF]Geospatial and Temporal Semantic Analytics
Smart grid External links:
Smart Grid – AbeBooks
Smart Grid Solutions | Smart Grid System Integration Services
Smart grid. (Journal, magazine, 2011) [WorldCat.org]
Social analytics External links:
Dark Social Analytics: Track Private Shares with GetSocial
Social Analytics – Votigo
Union Metrics makes social analytics easy – TweetReach
Software analytics External links:
Software Analytics – Microsoft Research
Speech analytics External links:
Customer Engagement & Speech Analytics | CallMiner
Eureka: Speech Analytics Software | CallMiner
What is speech analytics? – Definition from WhatIs.com
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
Structured data External links:
Structured Data Testing Tool – Google
Structured Data for Dummies – Search Engine Journal
SEC.gov | What Is Structured Data?
Telecommunications data retention External links:
Telecommunications Data Retention and Human Rights: …
Text analytics External links:
[PDF]Syllabus Course Title: Text Analytics – Regis University
[PDF]What Is Text Analytics? – Information Today, Inc. Books
The Truth about Text Analytics and Sentiment Analysis
Text mining External links:
Text mining — University of Illinois at Urbana-Champaign
Text Mining – AbeBooks
Text Mining in R: A Tutorial – Springboard Blog
Time series External links:
Initial State – Analytics for Time Series Data
[PDF]Time Series Analysis and Forecasting – cengage.com
USGS Current Conditions for Texas: Build Time Series
Unstructured data External links:
Scale-Out NAS for Unstructured Data | Dell EMC US
User behavior analytics External links:
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
IBM QRadar User Behavior Analytics – Overview – United States
Visual analytics External links:
Visual Analytics Working Group | AMIA
Web analytics External links:
Web Analytics in Real Time | Clicky
Web analytics | HitsLink
AFS Analytics – Web analytics