220 Pricing Analytics Success Criteria

What is involved in Analytics

Find out what the related areas are that 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 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 Analytics related domains to cover and 220 essential critical questions to check off in that domain.

The following domains are covered:

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:

Analytics Critical Criteria:

Familiarize yourself with Analytics failures and observe effective Analytics.

– Merely identifying and managing human capital is important, but it is not sufficient. We know that we manage only what we measure. So how can measurement make a difference?

– What actions are necessary to retain mission-critical talent under certain market conditions?

– How will HR work with managers to gain an understanding of why a metric is moving as it is?

– What statistics should one be familiar with for business intelligence and web analytics?

– What is the difference between business intelligence business analytics and data mining?

– What key measures should we include in our annual report to our Board of Directors?

– Do HR systems educate leaders about the quality of their human capital decisions?

– Start with your objective(s): What do you want to find out in a strategic sense?

– Will the participant be able to use the information you present immediately?

– What leadership characteristics lead to better team sales results?

– Do you understand the parameters set by the algorithm?

– What can we do to foster greater levels of innovation?

– What will HR metrics look like ten years from today?

– How successful is our employee orientation program?

– What is/are the corollaries for non-algorithmic analytics?

– What are our tools for big data analytics?

– Are we maintaining a diverse workforce?

– Do you see any other tendencies?

– How do we retain talent?

Academic discipline Critical Criteria:

Detail Academic discipline leadership and report on developing an effective Academic discipline strategy.

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

– What are the Essentials of Internal Analytics Management?

– How is the value delivered by Analytics being measured?

Analytic applications Critical Criteria:

Accommodate Analytic applications projects and acquire concise Analytic applications education.

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

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

– How do you handle Big Data in Analytic Applications?

– How can skill-level changes improve Analytics?

– Analytic Applications: Build or Buy?

Architectural analytics Critical Criteria:

Apply Architectural analytics visions and define what our big hairy audacious Architectural analytics goal is.

– Does Analytics analysis show the relationships among important Analytics factors?

– How can the value of Analytics be defined?

– Why are Analytics skills important?

Behavioral analytics Critical Criteria:

Accumulate Behavioral analytics decisions and summarize a clear Behavioral analytics focus.

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

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

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

Big data Critical Criteria:

Conceptualize Big data results and finalize the present value of growth of Big data.

– What are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?

– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?

– Is your organizations business affected by regulatory restrictions on data/servers localisation requirements?

– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?

– Which departments in your organization are involved in using data technologies and data analytics?

– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?

– To what extent does data-driven innovation add to the competitive advantage (CA) of your company?

– Technology Drivers – What were the primary technical challenges your organization faced?

– What are the legal risks in using Big Data/People Analytics in hiring?

– How can the best Big Data solution be chosen based on use case requirements?

– Can Management personnel recognize the monetary benefit of Analytics?

– Which Oracle Data Integration products are used in your solution?

– Are our Big Data investment programs results driven?

– How does that compare to other science disciplines?

– Can analyses improve with more data to process?

– What is tacit permission and approval, anyway?

– what is Different about Big Data?

– Find traffic bottlenecks ?

– What are we missing?

Business analytics Critical Criteria:

Review Business analytics quality and correct better engagement with Business analytics results.

– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?

– 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?

– Are accountability and ownership for Analytics clearly defined?

– Is a Analytics Team Work effort in place?

– How can we improve Analytics?

Business intelligence Critical Criteria:

Guide Business intelligence visions and create a map for yourself.

– 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?

– Does your BI solution create a strong partnership with IT to ensure that data, whether from extracts or live connections, is 100-percent accurate?

– Does your mobile solution allow you to interact with desktop-authored dashboards using touchscreen gestures like taps, flicks, and pinches?

– What is the difference between Key Performance Indicators KPI and Critical Success Factors CSF in a Business Strategic decision?

– Does your bi software work well with both centralized and decentralized data architectures and vendors?

– What should recruiters look for in a business intelligence professional?

– What is your anticipated learning curve for Technical Administrators?

– What information needs of managers are satisfied by the bi system?

– Does your bi solution require weeks or months to deploy or change?

– What are the best use cases for Mobile Business Intelligence?

– Where is the business intelligence bottleneck?

– How stable is it across domains/geographies?

– How is business intelligence disseminated?

– What is your expect product life cycle?

– Do you offer formal user training?

Cloud analytics Critical Criteria:

Cut a stake in Cloud analytics results and customize techniques for implementing Cloud analytics controls.

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

– Do we have past Analytics Successes?

Complex event processing Critical Criteria:

Review Complex event processing management and define what do we need to start doing with Complex event processing.

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

– Does Analytics appropriately measure and monitor risk?

Computer programming Critical Criteria:

Face Computer programming failures and cater for concise Computer programming education.

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

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

Continuous analytics Critical Criteria:

Accommodate Continuous analytics risks and report on developing an effective Continuous analytics strategy.

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

– How do we maintain Analyticss Integrity?

– What is Effective Analytics?

Cultural analytics Critical Criteria:

Audit Cultural analytics risks and question.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Analytics processes?

– What vendors make products that address the Analytics needs?

– Are assumptions made in Analytics stated explicitly?

Customer analytics Critical Criteria:

Pay attention to Customer analytics quality and point out Customer analytics tensions in leadership.

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

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

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

Data mining Critical Criteria:

Participate in Data mining quality and reduce Data mining costs.

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

– 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?

– Is business intelligence set to play a key role in the future of Human Resources?

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

– What programs do we have to teach data mining?

– Why should we adopt a Analytics framework?

Data presentation architecture Critical Criteria:

Mine Data presentation architecture visions and report on developing an effective Data presentation architecture strategy.

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

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

Embedded analytics Critical Criteria:

Learn from Embedded analytics leadership and finalize the present value of growth of Embedded analytics.

– Consider your own Analytics 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 current Analytics Paradigms?

Enterprise decision management Critical Criteria:

Graph Enterprise decision management management and suggest using storytelling to create more compelling Enterprise decision management projects.

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

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

– What are all of our Analytics domains and what do they do?

Fraud detection Critical Criteria:

Concentrate on Fraud detection management and maintain Fraud detection for success.

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

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

Google Analytics Critical Criteria:

Paraphrase Google Analytics failures and triple focus on important concepts of Google Analytics relationship management.

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

– What are the long-term Analytics goals?

– Do we all define Analytics in the same way?

Human resources Critical Criteria:

Detail Human resources goals and check on ways to get started with Human resources.

– Do the response plans address damage assessment, site restoration, payroll, Human Resources, information technology, and administrative support?

– Should pay levels and differences reflect the earnings of colleagues in the country of the facility, or earnings at the company headquarters?

– what is to keep those with access to some of an individuals personal data from browsing through other parts of it for other reasons?

– What finance, procurement and Human Resources business processes should be included in the scope of a erp solution?

– What happens if an individual objects to the collection, use, and disclosure of his or her personal data?

– Does the cloud service provider have necessary security controls on their human resources?

– Where can an employee go for further information about the dispute resolution program?

– What problems have you encountered with the department or staff member?

– What decisions can you envision making with this type of information?

– How should any risks to privacy and civil liberties be managed?

– How can we promote retention of high performing employees?

– What are ways that employee productivity can be measured?

– Does all hr data receive the same level of security?

– What other outreach efforts would be helpful?

– May an employee make an anonymous complaint?

– Will an algorithm shield us from liability?

– What additional approaches already exist?

– Who should appraise performance?

– How to deal with diversity?

– What is personal data?

Learning analytics Critical Criteria:

Familiarize yourself with Learning analytics governance and ask questions.

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

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

– What threat is Analytics addressing?

Machine learning Critical Criteria:

Refer to Machine learning planning and plan concise Machine learning education.

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

– Are there Analytics Models?

Marketing mix modeling Critical Criteria:

Reorganize Marketing mix modeling leadership and describe which business rules are needed as Marketing mix modeling interface.

– Does the Analytics task fit the clients priorities?

Mobile Location Analytics Critical Criteria:

Look at Mobile Location Analytics goals and revise understanding of Mobile Location Analytics architectures.

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

– Who will provide the final approval of Analytics deliverables?

– Does our organization need more Analytics education?

Neural networks Critical Criteria:

Derive from Neural networks visions and integrate design thinking in Neural networks innovation.

– Will Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?

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

– How much does Analytics help?

News analytics Critical Criteria:

Extrapolate News analytics strategies and report on the economics of relationships managing News analytics and constraints.

Online analytical processing Critical Criteria:

Review Online analytical processing goals and question.

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

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

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

Online video analytics Critical Criteria:

Analyze Online video analytics results and catalog Online video analytics activities.

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

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

– How do we keep improving Analytics?

Operational reporting Critical Criteria:

Accelerate Operational reporting goals and explore and align the progress in Operational reporting.

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

– Who sets the Analytics standards?

Operations research Critical Criteria:

Devise Operations research visions and secure Operations research creativity.

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

– What are our Analytics Processes?

Over-the-counter data Critical Criteria:

Coach on Over-the-counter data engagements and remodel and develop an effective Over-the-counter data strategy.

– How can you measure Analytics in a systematic way?

Portfolio analysis Critical Criteria:

Merge Portfolio analysis tasks and be persistent.

– Is maximizing Analytics protection the same as minimizing Analytics loss?

– What about Analytics Analysis of results?

Predictive analytics Critical Criteria:

Closely inspect Predictive analytics adoptions and ask what if.

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

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

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

– What is our formula for success in Analytics ?

Predictive engineering analytics Critical Criteria:

Tête-à-tête about Predictive engineering analytics projects and know what your objective is.

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

– How do we manage Analytics Knowledge Management (KM)?

Predictive modeling Critical Criteria:

Have a round table over Predictive modeling leadership and don’t overlook the obvious.

– Are you currently using predictive modeling to drive results?

– What are the barriers to increased Analytics production?

Prescriptive analytics Critical Criteria:

Depict Prescriptive analytics strategies and get answers.

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

– How would one define Analytics leadership?

Price discrimination Critical Criteria:

Match Price discrimination strategies and find the ideas you already have.

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

– Is the scope of Analytics defined?

Risk analysis Critical Criteria:

Revitalize Risk analysis engagements and shift your focus.

– 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?

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

– 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?

Security information and event management Critical Criteria:

Confer over Security information and event management tactics and gather practices for scaling Security information and event management.

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

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

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

Semantic analytics Critical Criteria:

Grasp Semantic analytics quality and do something to it.

– How do we ensure that implementations of Analytics products are done in a way that ensures safety?

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

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

Smart grid Critical Criteria:

Differentiate Smart grid goals and triple focus on important concepts of Smart grid relationship management.

– 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?

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

Social analytics Critical Criteria:

Accumulate Social analytics visions and check on ways to get started with Social analytics.

– Who needs to know about Analytics ?

– What are specific Analytics Rules to follow?

Software analytics Critical Criteria:

Investigate Software analytics planning and budget for Software analytics challenges.

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

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

Speech analytics Critical Criteria:

Co-operate on Speech analytics tactics and intervene in Speech analytics processes and leadership.

– What are internal and external Analytics relations?

Statistical discrimination Critical Criteria:

Boost Statistical discrimination visions and look at the big picture.

– How will you measure your Analytics effectiveness?

Stock-keeping unit Critical Criteria:

Frame Stock-keeping unit results and suggest using storytelling to create more compelling Stock-keeping unit projects.

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

– Which Analytics goals are the most important?

– Have all basic functions of Analytics been defined?

Structured data Critical Criteria:

Reason over Structured data risks and figure out ways to motivate other Structured data users.

– 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?

– Does Analytics analysis isolate the fundamental causes of problems?

Telecommunications data retention Critical Criteria:

Start Telecommunications data retention planning and get the big picture.

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

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

Text analytics Critical Criteria:

Consolidate Text analytics tasks and find out.

– Have text analytics mechanisms like entity extraction been considered?

Text mining Critical Criteria:

Have a session on Text mining visions and cater for concise Text mining education.

– What potential environmental factors impact the Analytics effort?

Time series Critical Criteria:

Align Time series quality and catalog Time series activities.

Unstructured data Critical Criteria:

Deduce Unstructured data leadership and intervene in Unstructured data processes and leadership.

– Is there any existing Analytics governance structure?

User behavior analytics Critical Criteria:

Familiarize yourself with User behavior analytics visions and look for lots of ideas.

Visual analytics Critical Criteria:

Consolidate Visual analytics outcomes and don’t overlook the obvious.

Web analytics Critical Criteria:

Nurse Web analytics governance and correct better engagement with Web analytics results.

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

– Have you identified your Analytics key performance indicators?

– How is cloud computing related to web analytics?

Win–loss analytics Critical Criteria:

Demonstrate Win–loss analytics issues and reinforce and communicate particularly sensitive Win–loss analytics decisions.

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

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


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.

External links:

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

Analytics External links:

Reporting and Analytics – mymicros.net

Twitter Analytics

SHP: Strategic Healthcare Programs | Real-Time Analytics

Academic discipline External links:

Academic Discipline – Earl Warren College

Academic Discipline – Earl Warren College

Criminal justice | academic discipline | Britannica.com

Analytic applications External links:

Aptos Analytic Applications – Aptos

Foxtrot Code AI Analytic Applications (Home)

Architectural analytics External links:

Architectural Analytics – Home | Facebook

Behavioral analytics External links:

User and Entity Behavioral Analytics Partners | Exabeam

Behavioral Analytics | Interana

Big data External links:

Business Intelligence and Big Data Analytics Software

ZestFinance.com: Machine Learning & Big Data Underwriting

Event Hubs – Cloud big data solutions | Microsoft Azure

Business analytics External links:

What is Business Analytics? Webopedia Definition

Business intelligence External links:

Mortgage Business Intelligence Software :: Motivity Solutions

Oracle Business Intelligence – RCI

Cloud analytics External links:

Cloud Analytics Academy – Official Site

Cloud Analytics | Big Data Analytics | Vertica

Cloud Analytics – Solutions for Cloud Data Analytics | NetApp

Computer programming External links:

Computer Programming, Robotics & Engineering – STEM …

Cultural analytics External links:

Cultural Analytics | Nuts and Bolts

Software Studies Initiative: Cultural analytics

Customer analytics External links:

Customer Analytics Services and Solutions | TransUnion

Customer Analytics & Predictive Analytics Tools for Business

BlueVenn – Customer Analytics and Customer Journey …

Data mining External links:

UT Data Mining

Data Mining Extensions (DMX) Reference | Microsoft Docs

Data mining | computer science | Britannica.com

Embedded analytics External links:

Embedded Analytics | Qlik

Embedded Analytics and Data Visualization | Reflect

Logi Analytics: The #1 Embedded Analytics Platform

Enterprise decision management External links:

enterprise decision management Archives – Insights

Enterprise Decision Management | Sapiens DECISION

Enterprise Decision Management | SAS Italy

Fraud detection External links:

Debit Card Security | Fraud Detection & Protection | RushCard

Fraud Detection and Authentication Technology – Next Caller

Big Data Fraud Detection | DataVisor

Google Analytics External links:

Google Analytics | Google Developers

Google Analytics – Sign in

Google Analytics Opt-out Browser Add-on Download Page

Human resources External links:

Human Resources Job Titles-The Ultimate Guide | upstartHR

Human Resources Job Titles – The Balance

Title Human Resources HR Jobs, Employment | Indeed.com

Learning analytics External links:

Watershed | Learning Analytics for Organizations

Learning Analytics | Riptide Elements

Society for Learning Analytics Research (SoLAR)

Machine learning External links:

Microsoft Azure Machine Learning Studio

Machine Learning | Coursera

Titanic: Machine Learning from Disaster | Kaggle

Marketing mix modeling External links:

Marketing Mix Modeling – Gartner IT Glossary

Marketing Mix Modeling | Marketing Management Analytics

Mobile Location Analytics External links:

How ‘Mobile Location Analytics’ Controls Your Mind – YouTube

[PDF]Mobile Location Analytics Code of Conduct

Mobile Location Analytics Privacy Notice | Verizon

News analytics External links:

Yakshof – Big Data News Analytics

News Analytics | Amareos

Online analytical processing External links:

[PDF]OLAP (Online Analytical Processing) – SRM University

Working with Online Analytical Processing (OLAP)

Online video analytics External links:

Online Video Analytics & Marketing Software | Vidooly

Managing Your Online Video Analytics – DaCast

Operations research External links:

Operations research | Britannica.com

Operations Research: INFORMS

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.
http://Reference: informs.org/about-informs/what-is-operations-research

Over-the-counter data External links:

Over-the-Counter Data – American Mensa – Medium

Standards — Over-the-Counter Data

[PDF]Over-the-Counter Data’s Impact on Educators’ Data …

Portfolio analysis External links:

[PDF]Portfolio Analysis Tool: Methodologies and Assumptions

Portfolio Analysis Final-1 Flashcards | Quizlet

Portfolio Analysis | Economy Watch

Predictive analytics External links:

Predictive Analytics Software, Social Listening | NewBrand

Customer Analytics & Predictive Analytics Tools for Business

Strategic Location Management & Predictive Analytics | Tango

Predictive engineering analytics External links:

Predictive engineering analytics is the application of multidisciplinary engineering simulation and test with intelligent reporting and data analytics, to develop digital twins that can predict the real world behavior of products throughout the product lifecycle.
http://Reference: plm.automation.siemens.com/en/plm/predictive-engineering-a…

Predictive modeling External links:

What is predictive modeling? – Definition from …

Prescriptive analytics External links:

Healthcare Prescriptive Analytics – Cedar Gate Technologies

Price discrimination External links:

Price Discrimination – Investopedia

3 Types of Price Discrimination | Chron.com

MBAecon – 1st, 2nd and 3rd Price discrimination

Risk analysis External links:

Risk Analysis | Investopedia

Project Management and Risk Analysis Software | Safran

Risk Analysis
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:

Semantic Analytics – Get Business Intelligence With Schema …

SciBite – The Semantic Analytics Company

[PDF]Geospatial and Temporal Semantic Analytics

Smart grid External links:

Recovery Act Smart Grid Programs

Smart Grid – Coalition – Duke Energy

Smart grid. (Journal, magazine, 2011) [WorldCat.org]

Social analytics External links:

Google Search with Social Analytics – ctrlq.org

Influencer marketing platform & Social analytics tool – HYPR

Enterprise Social Analytics Platform | About

Software analytics External links:

Software Analytics – Microsoft Research

Speech analytics External links:

Speech Analytics | Speech Analytics Software & Audio Mining

What is speech analytics? – Definition from WhatIs.com

Customer Engagement & Speech Analytics | CallMiner

Statistical discrimination External links:

[PDF]statistical discrimination – Andrea Moro Webpage

“Employer Learning and Statistical Discrimination”

Structured data External links:

Providing Structured Data | Custom Search | Google …

Structured Data for Dummies – Search Engine Journal

What is structured data? – Definition from WhatIs.com

Telecommunications data retention External links:

Telecommunications Data Retention and Human Rights: …

Text analytics External links:

Text Mining / Text Analytics Specialist – bigtapp

Text analytics software| NICE LTD | NICE

Text Analytics – Site Title

Text mining External links:

Text mining — University of Illinois at Urbana-Champaign

Text Mining / Text Analytics Specialist – bigtapp

Applied Text Mining in Python | Coursera

Time series External links:

[PDF]Time Series Analysis and Forecasting – cengage.com

Initial State – Analytics for Time Series Data

Stationarity and differencing of time series data

Unstructured data External links:

Structured vs. Unstructured data – BrightPlanet

Scale-Out NAS for Unstructured Data | Dell EMC US

The Data Difference | Unstructured Data DSP

User behavior analytics External links:

User Behavior Analytics (UBA) Tools and Solutions | Rapid7

IBM QRadar User Behavior Analytics – Overview – United States

Web analytics External links:

Careers | Mobile & Web Analytics | Mixpanel

AFS Analytics – Web analytics

Web Analytics in Real Time | Clicky