Top 216 Pricing Analytics Free Questions to Collect the Right answers

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 216 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:

Investigate Analytics goals and oversee Analytics management by competencies.

– We have some locations where employees have much more input into hiring decisions; are those locations more successful?

– What is it that we do not know that could fundamentally change the environment in which we work?

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

– What specifically can executives do to help employees be as successful as possible?

– What are the key process differences between our most productive plants and others?

– Analysis Stagnation. What are you going to do with the data now that you have it?

– Do we have locations or offices that can serve as models for other locations?

– Why is employee engagement higher for some job functions than for others?

– What is going on outside and inside that might affect future operations?

– What is the important thing that human resources management should do?

– How does managerial span of control affect sales results?

– Can analyses improve with more detailed analytics that we use?

– Do you maintain coaching or mentoring programs?

– What are the best social crm analytics tools?

– What are the objectives for voice analytics?

– Does your company use HCMs in a scorecard?

– What are the data sources and data mix?

– Is there a plan for search analytics?

– How are analytics done today?

– How do we retain talent?

Academic discipline Critical Criteria:

Model after Academic discipline management and finalize the present value of growth of Academic discipline.

– 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?

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

Analytic applications Critical Criteria:

Prioritize Analytic applications visions and don’t overlook the obvious.

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

– How do you handle Big Data in Analytic Applications?

– Analytic Applications: Build or Buy?

– Who needs to know about Analytics ?

– What about Analytics Analysis of results?

Architectural analytics Critical Criteria:

Deliberate Architectural analytics tasks and catalog Architectural analytics activities.

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

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

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

Behavioral analytics Critical Criteria:

Do a round table on Behavioral analytics issues and reduce Behavioral analytics costs.

– What are your current levels and trends in key measures or indicators of 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?

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

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

Big data Critical Criteria:

X-ray Big data adoptions and display thorough understanding of the Big data process.

– New roles. Executives interested in leading a big data transition can start with two simple techniques. First, they can get in the habit of asking What do the data say?

– Looking at hadoop big data in the rearview mirror, what would you have done differently after implementing a Data Lake?

– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?

– Does big data threaten the traditional data warehouse business intelligence model stack?

– What are the disruptive innovations in the middle-term that provide near-term domain leadership?

– what is needed to build a data-driven application that runs on streams of fast and big data?

– In which way does big data create, or is expected to create, value in the organization?

– What would be needed to support collaboration on data sharing across economic sectors?

– How are the new Big Data developments captured in new Reference Architectures?

– What is the Quality of the Result if the Quality of the Data/Metadata is poor?

– Is data-driven decision-making part of the organizations culture?

– How do we track the provenance of the derived data/information?

– How do we measure the efficiency of these algorithms?

– What are our tools for big data analytics?

– Isnt big data just another way of saying analytics?

– What is the limit for value as we add more data?

– Where Is This Big Data Coming From ?

– What preprocessing do we need to do?

– what is Different about Big Data?

Business analytics Critical Criteria:

Consolidate Business analytics engagements and correct Business analytics management by competencies.

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

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

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

– 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 a Analytics Team Work effort in place?

Business intelligence Critical Criteria:

Familiarize yourself with Business intelligence risks and look at the big picture.

– 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?

– How should a complicated business setup their business intelligence and analysis to make decisions best?

– What is the future scope for combination of Business Intelligence and Cloud Computing?

– Does your BI solution allow analytical insights to happen anywhere and everywhere?

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

– Does your BI solution help you find the right views to examine your data?

– What are some best practices for managing business intelligence?

– What are the top trends in the business intelligence space?

– Number of data sources that can be simultaneously accessed?

– What type and complexity of system administration roles?

– What are the pillar concepts of business intelligence?

– Are there any on demand analytics tools in the cloud?

– How does social media redefine business intelligence?

– What level of training would you recommend?

– Describe any training materials offered?

Cloud analytics Critical Criteria:

Align Cloud analytics outcomes and simulate teachings and consultations on quality process improvement of Cloud analytics.

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

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

Complex event processing Critical Criteria:

Ventilate your thoughts about Complex event processing quality and differentiate in coordinating Complex event processing.

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

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

– Which Analytics goals are the most important?

Computer programming Critical Criteria:

Deliberate Computer programming goals and overcome Computer programming skills and management ineffectiveness.

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

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

– Is there any existing Analytics governance structure?

Continuous analytics Critical Criteria:

Exchange ideas about Continuous analytics failures and raise human resource and employment practices for Continuous analytics.

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

– What are the Essentials of Internal Analytics Management?

– What is Effective Analytics?

Cultural analytics Critical Criteria:

Merge Cultural analytics projects and finalize specific methods for Cultural analytics acceptance.

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

– How can skill-level changes improve Analytics?

Customer analytics Critical Criteria:

Own Customer analytics failures and assess what counts with Customer analytics that we are not counting.

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

Data mining Critical Criteria:

Grade Data mining outcomes and report on setting up Data mining without losing ground.

– 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?

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

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

– Does the Analytics task fit the clients priorities?

– What programs do we have to teach data mining?

– What are our Analytics Processes?

Data presentation architecture Critical Criteria:

Investigate Data presentation architecture adoptions and look in other fields.

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

– Who sets the Analytics standards?

Embedded analytics Critical Criteria:

Generalize Embedded analytics quality and probe Embedded analytics strategic alliances.

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

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

– What are the barriers to increased Analytics production?

Enterprise decision management Critical Criteria:

Co-operate on Enterprise decision management leadership and slay a dragon.

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

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

– Is Analytics Required?

Fraud detection Critical Criteria:

Model after Fraud detection management and report on the economics of relationships managing Fraud detection and constraints.

– How will you measure your Analytics effectiveness?

Google Analytics Critical Criteria:

Differentiate Google Analytics results and cater for concise Google Analytics 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?

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

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

Human resources Critical Criteria:

See the value of Human resources issues and spearhead techniques for implementing Human resources.

– Rapidly increasing specialization of skill and knowledge presents a major management challenge. How does an organization maintain a work environment that supports specialization without compromising its ability to marshal its full range of Human Resources and turn on a dime to implement strategic imperatives?

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

– Are there cases when the company may collect, use and disclose personal data without consent or accommodation?

– Is there a role for employees to play in maintaining the accuracy of personal data the company maintains?

– To satisfy our customers and stakeholders, what financial objectives must we accomplish?

– What are the responsibilities of the company official responsible for compliance?

– Why does the company collect and use personal data in the employment context?

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

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

– Do you have Human Resources available to support your policies?

– How can we promote retention of high performing employees?

– What are ways that employee productivity can be measured?

– How is Staffs knowledge of procedures and regulations?

– Are we complying with existing security policies?

– Does the hr plan make sense to our stakeholders?

– Is our company developing its Human Resources?

– What additional approaches already exist?

– How do we engage the stakeholders?

Learning analytics Critical Criteria:

Explore Learning analytics outcomes and document what potential Learning analytics megatrends could make our business model obsolete.

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

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

– Why is Analytics important for you now?

Machine learning Critical Criteria:

Nurse Machine learning outcomes and oversee Machine learning management by competencies.

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

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

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

Marketing mix modeling Critical Criteria:

Scrutinze Marketing mix modeling governance and triple focus on important concepts of Marketing mix modeling relationship management.

– Are there Analytics Models?

Mobile Location Analytics Critical Criteria:

Bootstrap Mobile Location Analytics strategies and learn.

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

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

Neural networks Critical Criteria:

Value Neural networks governance and finalize specific methods for Neural networks acceptance.

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

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

News analytics Critical Criteria:

Revitalize News analytics risks and budget the knowledge transfer for any interested in News analytics.

Online analytical processing Critical Criteria:

Devise Online analytical processing engagements and adjust implementation of Online analytical processing.

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

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

– How can you measure Analytics in a systematic way?

Online video analytics Critical Criteria:

Unify Online video analytics management and tour deciding if Online video analytics progress is made.

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

Operational reporting Critical Criteria:

Air ideas re Operational reporting engagements and stake your claim.

– Does Analytics appropriately measure and monitor risk?

Operations research Critical Criteria:

Extrapolate Operations research visions and describe the risks of Operations research sustainability.

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

– What are the usability implications of Analytics actions?

Over-the-counter data Critical Criteria:

Accelerate Over-the-counter data leadership and oversee Over-the-counter data management by competencies.

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

Portfolio analysis Critical Criteria:

Steer Portfolio analysis planning and gather practices for scaling Portfolio analysis.

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

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

Predictive analytics Critical Criteria:

Troubleshoot Predictive analytics goals and gather practices for scaling Predictive analytics.

– How do we go about Securing Analytics?

Predictive engineering analytics Critical Criteria:

Merge Predictive engineering analytics planning and improve Predictive engineering analytics service perception.

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

Predictive modeling Critical Criteria:

Derive from Predictive modeling planning and create Predictive modeling explanations for all managers.

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

– Are you currently using predictive modeling to drive results?

Prescriptive analytics Critical Criteria:

Probe Prescriptive analytics planning and improve Prescriptive analytics service perception.

Price discrimination Critical Criteria:

Closely inspect Price discrimination planning and oversee Price discrimination management by competencies.

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

– Have you identified your Analytics key performance indicators?

Risk analysis Critical Criteria:

Chat re Risk analysis strategies and use obstacles to break out of ruts.

– 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?

– 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?

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

Security information and event management Critical Criteria:

Detail Security information and event management failures and ask what if.

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

Semantic analytics Critical Criteria:

Brainstorm over Semantic analytics engagements and figure out ways to motivate other Semantic analytics users.

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

Smart grid Critical Criteria:

Guide Smart grid failures and slay a dragon.

– 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 Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?

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

Social analytics Critical Criteria:

Closely inspect Social analytics leadership and learn.

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

Software analytics Critical Criteria:

Start Software analytics goals and ask questions.

– What is our formula for success in Analytics ?

Speech analytics Critical Criteria:

Conceptualize Speech analytics leadership and point out Speech analytics tensions in leadership.

– Are accountability and ownership for Analytics clearly defined?

– Why should we adopt a Analytics framework?

Statistical discrimination Critical Criteria:

Guide Statistical discrimination issues and create a map for yourself.

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

– How likely is the current Analytics plan to come in on schedule or on budget?

Stock-keeping unit Critical Criteria:

Cut a stake in Stock-keeping unit planning and budget the knowledge transfer for any interested in Stock-keeping unit.

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

– What will drive Analytics change?

Structured data Critical Criteria:

Apply Structured data decisions and get going.

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

– 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:

Recall Telecommunications data retention decisions and visualize why should people listen to you regarding Telecommunications data retention.

– 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 is the value delivered by Analytics being measured?

– Have all basic functions of Analytics been defined?

Text analytics Critical Criteria:

Discuss Text analytics risks and use obstacles to break out of ruts.

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

– Have text analytics mechanisms like entity extraction been considered?

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

– How would one define Analytics leadership?

Text mining Critical Criteria:

Track Text mining projects and work towards be a leading Text mining expert.

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

Time series Critical Criteria:

Investigate Time series projects and pay attention to the small things.

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

– How do we maintain Analyticss Integrity?

Unstructured data Critical Criteria:

Win new insights about Unstructured data risks and display thorough understanding of the Unstructured data process.

– 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 know that any Analytics analysis is complete and comprehensive?

– How can the value of Analytics be defined?

User behavior analytics Critical Criteria:

Meet over User behavior analytics risks and gather practices for scaling User behavior analytics.

– Are there recognized Analytics problems?

Visual analytics Critical Criteria:

Study Visual analytics adoptions and maintain Visual analytics for success.

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

– Does our organization need more Analytics education?

Web analytics Critical Criteria:

Investigate Web analytics quality and devise Web analytics key steps.

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

– 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:

Derive from Win–loss analytics issues and know what your objective is.

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


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 |

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 –

SHP: Strategic Healthcare Programs | Real-Time Analytics

Google Analytics Solutions – Marketing Analytics & …

Academic discipline External links:

Criminal justice | academic discipline |

Analytic applications External links:

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

Magnifier Behavioral Analytics – Palo Alto Networks

Behavioral Analytics | Interana

Big data External links: Machine Learning & Big Data Underwriting

Take 5 Media Group – Build an audience using big data

Business Intelligence and Big Data Analytics Software

Business analytics External links:

What is Business Analytics? Webopedia Definition

Harvard Business Analytics Program

Business intelligence External links:

EnsembleIQ | The premier business intelligence resource

SQL Server Business Intelligence | Microsoft

Cloud analytics External links:

Cloud Analytics Academy – Official Site

Cloud Analytics – Solutions for Cloud Data Analytics | NetApp

Cloud Analytics | Big Data Analytics | Vertica

Computer programming External links:

Computer Programming, Robotics & Engineering – STEM …

Cultural analytics External links:

Software Studies Initiative: Cultural analytics

Customer analytics External links:

Zylotech- AI For Customer Analytics

BlueVenn – Customer Analytics and Customer Journey …

Customer Analytics Services and Solutions | TransUnion

Data mining External links:

Data mining | computer science |

What is Data Mining in Healthcare?

UT Data Mining

Embedded analytics External links:

Embedded Analytics | Tableau

Embedded Analytics | ThoughtSpot

Power BI Embedded analytics | Microsoft Azure

Enterprise decision management External links:

Enterprise Decision Management and Alerts – FICO

enterprise decision management Archives – Insights

Enterprise Decision Management (EDM) –

Fraud detection External links:

Fraud Detection and Anti-Money Laundering Software – Verafin

Debit Card Security | Fraud Detection & Protection | RushCard

Fraud Detection and Authentication Technology – Next Caller

Google Analytics External links:

Google Analytics – Sign in

Google Analytics | Google Developers

Google Analytics

Human resources External links:

Human Resources –

Human Resources Job Titles | Enlighten Jobs

Office of Human Resources – TITLE IX

Learning analytics External links:

Society for Learning Analytics Research (SoLAR)

Learning Analytics | Riptide Elements

Deep Learning Analytics

Machine learning External links:

What is machine learning? – Definition from

Microsoft Azure Machine Learning Studio

Endpoint Protection – Machine Learning Security | Symantec

Marketing mix modeling External links:

Marketing Mix Modeling – Gartner IT Glossary

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 | Federal Trade Commission

News analytics External links:

Yakshof – Big Data News Analytics

News Analytics | Amareos

Online analytical processing External links:

Working with Online Analytical Processing (OLAP)

Online video analytics External links:

Managing Your Online Video Analytics – DaCast

Online Video Analytics & Marketing Software | Vidooly

Global Online Video Analytics Market Market Research

Operations research External links:

Operations research (Book, 1974) []

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 on JSTOR

Over-the-counter data External links:

Over-the-Counter Data – American Mensa – Medium

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

Standards — Over-the-Counter Data

Portfolio analysis External links:

iCite | NIH Office of Portfolio Analysis

Portfolio Analysis Final-1 Flashcards | Quizlet

Loan Portfolio Analysis | Credit Union Analytics | CU Direct

Predictive analytics External links:

Inventory Optimization for Retail | Predictive Analytics

Strategic Location Management & Predictive Analytics | Tango

Customer Analytics & Predictive Analytics Tools for Business

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.

Predictive modeling External links:

What is predictive modeling? – Definition from …

Prescriptive analytics External links:

Healthcare Prescriptive Analytics – Cedar Gate Technologies

Price discrimination External links:

3 Types of Price Discrimination |

Price Discrimination – Investopedia

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

Risk analysis External links:

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.

Project Management and Risk Analysis Software | Safran

Risk Analysis | Investopedia

Security information and event management External links:

A Guide to Security Information and Event Management,2-864.html

Semantic analytics External links:

SciBite – The Semantic Analytics Company

Semantic Analytics – Get Business Intelligence With Schema …

[PDF]Geospatial and Temporal Semantic Analytics

Smart grid External links:

Recovery Act Smart Grid Programs

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

Smart Grid – AbeBooks

Social analytics External links:

Google Search with Social Analytics –

Social Analytics – Votigo

Dark Social Analytics: Track Private Shares with GetSocial

Software analytics External links:

Software Analytics – Microsoft Research

Speech analytics External links:

Eureka: Speech Analytics Software | CallMiner

Customer Engagement & Speech Analytics | CallMiner

Speech Analytics | NICE

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

Providing Structured Data | Custom Search | Google …

Telecommunications data retention External links:

Telecommunications Data Retention and Human Rights: …

Text analytics External links:

[PDF]Syllabus Course Title: Text Analytics – Regis University

Text Mining / Text Analytics Specialist – bigtapp

Text analytics software| NICE LTD | NICE

Text mining External links:

Text Mining / Text Analytics Specialist – bigtapp

Text Mining – AbeBooks

Applied Text Mining in Python | Coursera

Time series External links:

pandas Time Series Basics –

[PDF]Time Series Analysis and Forecasting –

1.1 Overview of Time Series Characteristics | STAT 510

Unstructured data External links:

Structured vs. Unstructured data – BrightPlanet

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:

Dynamic text in SAS VA (Visual Analytics) – Stack Overflow

Web analytics External links:

11 Best Web Analytics Tools |

Web Analytics in Real Time | Clicky

Login – Heap | Mobile and Web Analytics