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 214 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:
Group Pricing Analytics decisions and slay a dragon.
– How can we incorporate support to ensure safe and effective use of Pricing Analytics into the services that we provide?
– Is maximizing Pricing Analytics protection the same as minimizing Pricing Analytics loss?
– How do we keep improving Pricing Analytics?
Academic discipline Critical Criteria:
Steer Academic discipline visions and finalize specific methods for Academic discipline acceptance.
– Does Pricing Analytics analysis isolate the fundamental causes of problems?
– Are accountability and ownership for Pricing Analytics clearly defined?
– How do we maintain Pricing Analyticss Integrity?
Analytic applications Critical Criteria:
Revitalize Analytic applications goals and correct better engagement with Analytic applications results.
– 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?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Pricing Analytics?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
– What is Effective Pricing Analytics?
Architectural analytics Critical Criteria:
Differentiate Architectural analytics visions and transcribe Architectural analytics as tomorrows backbone for success.
– Think about the functions involved in your Pricing Analytics project. what processes flow from these functions?
– Do we monitor the Pricing Analytics decisions made and fine tune them as they evolve?
Behavioral analytics Critical Criteria:
Be clear about Behavioral analytics outcomes and drive action.
– Think of your Pricing Analytics project. what are the main functions?
– Which individuals, teams or departments will be involved in Pricing Analytics?
Big data Critical Criteria:
Review Big data tasks and report on developing an effective Big data strategy.
– From all the data collected by your organization, what is approximately the percentage that is further processed for value generation?
– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?
– What is (or would be) the added value of collaborating with other entities regarding data sharing in your sector?
– Does your organization share data with other entities (with customers, suppliers, companies, government, etc)?
– What rules and regulations should exist about combining data about individuals into a central repository?
– What are some strategies for capacity planning for big data processing and cloud computing?
– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?
– Do you see areas in your domain or across domains where vendor lock-in is a potential risk?
– Technology Drivers – What were the primary technical challenges your organization faced?
– Is senior management in your organization involved in big data-related projects?
– What would be needed to support collaboration on data sharing in your sector?
– With more data to analyze, can Big Data improve decision-making?
– At which levels do you see the need for standardisation actions?
– How to model context in a computational environment?
– More efficient all-to-all operations (similarities)?
– Which Oracle applications are used in your project?
– What is the cost of partitioning/balancing?
– What are we missing?
Business analytics Critical Criteria:
Troubleshoot Business analytics projects and summarize a clear Business analytics focus.
– 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?
– What prevents me from making the changes I know will make me a more effective Pricing Analytics leader?
– 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?
– To what extent does management recognize Pricing Analytics as a tool to increase the results?
– 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?
Business intelligence Critical Criteria:
Grasp Business intelligence visions and forecast involvement of future Business intelligence projects in development.
– 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?
– As we develop increasing numbers of predictive models, then we have to figure out how do you pick the targets, how do you optimize the models?
– Can your software connect to all forms of data, from text and excel files to cloud and enterprise-grade databases, with a few clicks?
– Can you easily add users and features to quickly scale and customize to your organizations specific needs?
– What strategies will we pursue to ensure the success of the business intelligence competency center?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– What is the future scope for combination of Business Intelligence and Cloud Computing?
– What is your anticipated learning curve for technical administrators?
– What are some best practices for managing business intelligence?
– What BI functionality do we need, and what are we using today?
– What is your anticipated learning curve for Report Users?
– What type and complexity of system administration roles?
– No single business unit responsible for enterprise data?
– How do we use AI algorithms in practical applications?
– Can users easily create these thresholds and alerts?
– To create parallel systems or custom workflows?
– What programs do we have to teach data mining?
– Where is the business intelligence bottleneck?
– Describe any training materials offered?
Cloud analytics Critical Criteria:
Wrangle Cloud analytics governance and drive action.
– What is the total cost related to deploying Pricing Analytics, including any consulting or professional services?
– Who will be responsible for documenting the Pricing Analytics requirements in detail?
– What is our Pricing Analytics Strategy?
Complex event processing Critical Criteria:
Talk about Complex event processing leadership and gather practices for scaling Complex event processing.
– 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?
– What new services of functionality will be implemented next with Pricing Analytics ?
– What tools and technologies are needed for a custom Pricing Analytics project?
Computer programming Critical Criteria:
Unify Computer programming adoptions and budget the knowledge transfer for any interested in Computer programming.
– For your Pricing Analytics project, identify and describe the business environment. is there more than one layer to the business environment?
– What are the short and long-term Pricing Analytics goals?
– How do we go about Securing Pricing Analytics?
Continuous analytics Critical Criteria:
Design Continuous analytics tactics and sort Continuous analytics activities.
– Is the Pricing Analytics organization completing tasks effectively and efficiently?
– How does the organization define, manage, and improve its Pricing Analytics processes?
Cultural analytics Critical Criteria:
Demonstrate Cultural analytics tactics and do something to it.
– What are the key elements of your Pricing Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Pricing Analytics process?
– Are assumptions made in Pricing Analytics stated explicitly?
Customer analytics Critical Criteria:
Devise Customer analytics failures and oversee implementation of Customer analytics.
– What vendors make products that address the Pricing Analytics needs?
– What are the usability implications of Pricing Analytics actions?
Data mining Critical Criteria:
Have a round table over Data mining decisions and raise human resource and employment practices for Data mining.
– 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?
– 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?
– Who will be responsible for deciding whether Pricing Analytics goes ahead or not after the initial investigations?
– 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?
Data presentation architecture Critical Criteria:
Explore Data presentation architecture visions and probe using an integrated framework to make sure Data presentation architecture is getting what it needs.
– How do we Identify specific Pricing Analytics investment and emerging trends?
– Why are Pricing Analytics skills important?
Embedded analytics Critical Criteria:
Troubleshoot Embedded analytics projects and do something to it.
– Will Pricing Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– What tools do you use once you have decided on a Pricing Analytics strategy and more importantly how do you choose?
– Who is the main stakeholder, with ultimate responsibility for driving Pricing Analytics forward?
Enterprise decision management Critical Criteria:
Talk about Enterprise decision management strategies and look in other fields.
– How do you determine the key elements that affect Pricing Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
Fraud detection Critical Criteria:
Scan Fraud detection risks and finalize specific methods for Fraud detection acceptance.
– Where do ideas that reach policy makers and planners as proposals for Pricing Analytics strengthening and reform actually originate?
– Will Pricing Analytics deliverables need to be tested and, if so, by whom?
Google Analytics Critical Criteria:
Apply Google Analytics goals and look at the big picture.
– Why is it important to have senior management support for a Pricing Analytics project?
Human resources Critical Criteria:
Check Human resources visions and handle a jump-start course to 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?
– Does the information security function actively engage with other critical functions, such as it, Human Resources, legal, and the privacy officer, to develop and enforce compliance with information security and privacy policies and practices?
– 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 often do we hold meaningful conversations at the operating level among sales, finance, operations, IT, and human resources?
– Is there a role for employees to play in maintaining the accuracy of personal data the company maintains?
– What are the procedures for filing an internal complaint about the handling of personal data?
– Where can an employee go for further information about the dispute resolution program?
– What are the legal risks in using Big Data/People Analytics in hiring?
– Are there types of data to which the employee does not have access?
– How should any risks to privacy and civil liberties be managed?
– Does all hr data receive the same level of security?
– What does the pyramid of information look like?
– What additional approaches already exist?
– What are the data sources and data mix?
– How to deal with diversity?
– Is the hr plan effective ?
– What is personal data?
– What is harassment?
Learning analytics Critical Criteria:
Be responsible for Learning analytics goals and cater for concise Learning analytics education.
– How do we ensure that implementations of Pricing Analytics products are done in a way that ensures safety?
– How do we know that any Pricing Analytics analysis is complete and comprehensive?
– What potential environmental factors impact the Pricing Analytics effort?
Machine learning Critical Criteria:
Face Machine learning management and grade techniques for implementing Machine learning controls.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– Is Pricing Analytics dependent on the successful delivery of a current project?
– Are there recognized Pricing Analytics problems?
Marketing mix modeling Critical Criteria:
Talk about Marketing mix modeling quality and correct Marketing mix modeling management by competencies.
– How do we manage Pricing Analytics Knowledge Management (KM)?
– What threat is Pricing Analytics addressing?
– How to Secure Pricing Analytics?
Mobile Location Analytics Critical Criteria:
Air ideas re Mobile Location Analytics planning and plan concise Mobile Location Analytics education.
– Can Management personnel recognize the monetary benefit of Pricing Analytics?
– What are specific Pricing Analytics Rules to follow?
Neural networks Critical Criteria:
Familiarize yourself with Neural networks visions and reduce Neural networks costs.
– Will new equipment/products be required to facilitate Pricing Analytics delivery for example is new software needed?
– How will we insure seamless interoperability of Pricing Analytics moving forward?
– Why should we adopt a Pricing Analytics framework?
News analytics Critical Criteria:
Paraphrase News analytics quality and observe effective News analytics.
– At what point will vulnerability assessments be performed once Pricing Analytics is put into production (e.g., ongoing Risk Management after implementation)?
– Do the Pricing Analytics decisions we make today help people and the planet tomorrow?
– What are the record-keeping requirements of Pricing Analytics activities?
Online analytical processing Critical Criteria:
Extrapolate Online analytical processing tactics and arbitrate Online analytical processing techniques that enhance teamwork and productivity.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Pricing Analytics?
– Is there any existing Pricing Analytics governance structure?
Online video analytics Critical Criteria:
Scrutinze Online video analytics risks and probe using an integrated framework to make sure Online video analytics is getting what it needs.
– How do your measurements capture actionable Pricing Analytics information for use in exceeding your customers expectations and securing your customers engagement?
– Can we add value to the current Pricing Analytics decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– How do we Lead with Pricing Analytics in Mind?
Operational reporting Critical Criteria:
Wrangle Operational reporting leadership and get going.
– What are the top 3 things at the forefront of our Pricing Analytics agendas for the next 3 years?
– Do Pricing Analytics rules make a reasonable demand on a users capabilities?
– Are we Assessing Pricing Analytics and Risk?
Operations research Critical Criteria:
Categorize Operations research tactics and probe using an integrated framework to make sure Operations research is getting what it needs.
– What are the success criteria that will indicate that Pricing Analytics objectives have been met and the benefits delivered?
– Among the Pricing Analytics product and service cost to be estimated, which is considered hardest to estimate?
Over-the-counter data Critical Criteria:
Closely inspect Over-the-counter data failures and use obstacles to break out of ruts.
– what is the best design framework for Pricing Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– What are the barriers to increased Pricing Analytics production?
Portfolio analysis Critical Criteria:
Experiment with Portfolio analysis outcomes and give examples utilizing a core of simple Portfolio analysis skills.
– Does Pricing Analytics include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?
– What is the source of the strategies for Pricing Analytics strengthening and reform?
Predictive analytics Critical Criteria:
Detail Predictive analytics tasks and gather Predictive analytics models .
– Do several people in different organizational units assist with the Pricing Analytics process?
– What are direct examples that show predictive analytics to be highly reliable?
– Do we all define Pricing Analytics in the same way?
Predictive engineering analytics Critical Criteria:
Start Predictive engineering analytics decisions and document what potential Predictive engineering analytics megatrends could make our business model obsolete.
– What other jobs or tasks affect the performance of the steps in the Pricing Analytics process?
– Have you identified your Pricing Analytics key performance indicators?
– What are the business goals Pricing Analytics is aiming to achieve?
Predictive modeling Critical Criteria:
Have a session on Predictive modeling issues and adjust implementation of Predictive modeling.
– Who are the people involved in developing and implementing Pricing Analytics?
– Are you currently using predictive modeling to drive results?
Prescriptive analytics Critical Criteria:
Devise Prescriptive analytics engagements and pioneer acquisition of Prescriptive analytics systems.
– Is Supporting Pricing Analytics documentation required?
Price discrimination Critical Criteria:
Familiarize yourself with Price discrimination projects and attract Price discrimination skills.
– What are current Pricing Analytics Paradigms?
Risk analysis Critical Criteria:
Scan Risk analysis management and revise understanding of Risk analysis architectures.
– 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?
– How can skill-level changes improve Pricing Analytics?
Security information and event management Critical Criteria:
Have a session on Security information and event management results and look for lots of ideas.
– What are our needs in relation to Pricing Analytics skills, labor, equipment, and markets?
Semantic analytics Critical Criteria:
Meet over Semantic analytics strategies and secure Semantic analytics creativity.
– Does Pricing Analytics systematically track and analyze outcomes for accountability and quality improvement?
– How do we measure improved Pricing Analytics service perception, and satisfaction?
Smart grid Critical Criteria:
Graph Smart grid tactics and get answers.
– 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 are internal and external Pricing Analytics relations?
– What about Pricing Analytics Analysis of results?
Social analytics Critical Criteria:
Shape Social analytics leadership and define what our big hairy audacious Social analytics goal is.
Software analytics Critical Criteria:
Recall Software analytics strategies and test out new things.
– In a project to restructure Pricing Analytics outcomes, which stakeholders would you involve?
Speech analytics Critical Criteria:
Accelerate Speech analytics goals and intervene in Speech analytics processes and leadership.
– What management system can we use to leverage the Pricing Analytics experience, ideas, and concerns of the people closest to the work to be done?
– Can we do Pricing Analytics without complex (expensive) analysis?
Statistical discrimination Critical Criteria:
Design Statistical discrimination issues and figure out ways to motivate other Statistical discrimination users.
– Why is Pricing Analytics important for you now?
– How to deal with Pricing Analytics Changes?
– What are our Pricing Analytics Processes?
Stock-keeping unit Critical Criteria:
Judge Stock-keeping unit outcomes and transcribe Stock-keeping unit as tomorrows backbone for success.
Structured data Critical Criteria:
Extrapolate Structured data tasks and oversee Structured data requirements.
– 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 our organization need more Pricing Analytics education?
Telecommunications data retention Critical Criteria:
Focus on Telecommunications data retention management and probe the present value of growth of Telecommunications data retention.
– How can you negotiate Pricing Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?
Text analytics Critical Criteria:
Boost Text analytics leadership and define what do we need to start doing with Text analytics.
– Have text analytics mechanisms like entity extraction been considered?
– How will you measure your Pricing Analytics effectiveness?
Text mining Critical Criteria:
Have a session on Text mining visions and tour deciding if Text mining progress is made.
– What are your most important goals for the strategic Pricing Analytics objectives?
– How can the value of Pricing Analytics be defined?
Time series Critical Criteria:
Infer Time series risks and oversee Time series management by competencies.
– What sources do you use to gather information for a Pricing Analytics study?
– What is the purpose of Pricing Analytics in relation to the mission?
Unstructured data Critical Criteria:
Learn from Unstructured data outcomes and find the ideas you already have.
– What role does communication play in the success or failure of a Pricing Analytics project?
– How can we improve Pricing Analytics?
User behavior analytics Critical Criteria:
Judge User behavior analytics outcomes and achieve a single User behavior analytics view and bringing data together.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Pricing Analytics services/products?
Visual analytics Critical Criteria:
Focus on Visual analytics issues and catalog Visual analytics activities.
– Do those selected for the Pricing Analytics team have a good general understanding of what Pricing Analytics is all about?
– Are we making progress? and are we making progress as Pricing Analytics leaders?
Web analytics Critical Criteria:
Co-operate on Web analytics management and inform on and uncover unspoken needs and breakthrough Web analytics results.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Pricing Analytics processes?
– What statistics should one be familiar with for business intelligence and web analytics?
– Who will provide the final approval of Pricing Analytics deliverables?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Check Win–loss analytics governance and find the essential reading for Win–loss analytics researchers.
– How do senior leaders actions reflect a commitment to the organizations Pricing Analytics values?
– In what ways are Pricing Analytics vendors and us interacting to ensure safe and effective use?
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:
Pricing Analytics External links:
Pricing Analytics | Core Pricing Services
Pricing Analytics: Optimizing Price – YouTube
Academic discipline External links:
Folklore | academic discipline | Britannica.com
What does academic discipline mean? – Definitions.net
Criminal justice | academic discipline | Britannica.com
Analytic applications External links:
Foxtrot Code AI Analytic Applications (Home)
Analytic Applications – Gartner IT Glossary
Hype Cycle for Back-Office Analytic Applications, 2017
Architectural analytics External links:
Top Online Courses in Architectural Analytics 2018
Best Master’s Degrees in Architectural Analytics 2018
Architectural Analytics – Home | Facebook
Behavioral analytics External links:
Behavioral Analytics | Interana
User and Entity Behavioral Analytics Partners | Exabeam
Behavioral Analytics – Mattersight
Big data External links:
Event Hubs – Cloud big data solutions | Microsoft Azure
Take 5 Media Group – Build an audience using big data
Business analytics External links:
Harvard Business Analytics Program
Master of Science in Business Analytics | UW Tacoma
What is business analytics (BA)? – Definition from …
Business intelligence External links:
Business Intelligence Software – ERP & Project …
Business Intelligence Tools & Software | Square
EnsembleIQ | The premier business intelligence resource
Cloud analytics External links:
Cloud Analytics World Tour | Snowflake
Cloud Analytics Academy – Official Site
Complex event processing External links:
Complex Event Processing (CEP) for Big Data Streaming
Computer programming External links:
Gwinnett Technical College- Computer Programming
Computer Programming – ed2go
Computer Programming, Robotics & Engineering – STEM For Kids
Customer analytics External links:
Customer Analytics Services and Solutions | TransUnion
Customer Analytics & Predictive Analytics Tools for Business
What are Customer Analytics? – Amazon Web Services …
Data mining External links:
What is Data Mining in Healthcare?
Data Mining | Coursera
Nebraska Oil and Gas Conservation Commission – GIS Data Mining
Embedded analytics External links:
Embedded Analytics – Gartner IT Glossary
Embedded Analytics – Gartner IT Glossary
LaunchWorks | Embedded Analytics Solutions
Enterprise decision management External links:
Enterprise Decision Management (EDM) – Techopedia.com
Enterprise Decision Management | Cutter Consortium
Enterprise Decision Management | Sapiens DECISION
Fraud detection External links:
Fraud Detection and Authentication Technology – Next Caller
Fraud Detection and Fraud Prevention Services | TransUnion
Debit Card Security | Fraud Detection & Protection | RushCard
Google Analytics External links:
Welcome to the Texas Board of Nursing – Google Analytics
Google Analytics Solutions – Marketing Analytics & …
Google Analytics | Google Developers
Human resources External links:
Human Resources Job Titles – The Balance
Title Human Resources HR Jobs, Employment | Indeed.com
Office of Human Resources – TITLE IX
Learning analytics External links:
Learning Analytics Explained. (eBook, 2017) …
“Using Learning Analytics to Predict Academic Success …
Watershed | Learning Analytics for Organizations
Machine learning External links:
Microsoft Azure Machine Learning Studio
DataRobot – Automated Machine Learning for Predictive …
Endpoint Protection – Machine Learning Security | …
Marketing mix modeling External links:
Marketing Mix Modeling – Decision Analyst
What is an Example of Marketing Mix Modeling?
Marketing Mix Modeling | Marketing Management Analytics
Mobile Location Analytics External links:
Mobile location analytics | Federal Trade Commission
How ‘Mobile Location Analytics’ Controls Your Mind – YouTube
[PDF]Mobile Location Analytics Code of Conduct
Neural networks External links:
Neural Networks and Deep Learning | Coursera
News analytics External links:
RavenPack News Analytics – RavenPack
Online analytical processing External links:
Working with Online Analytical Processing (OLAP)
Online video analytics External links:
Online Video Analytics & Marketing Software | Vidooly
Global Online Video Analytics Market Market Research
Ooyala Videomind | Online Video Analytics
Operational reporting External links:
Operational Reporting – InfoSync Services
Operations research External links:
[PDF]Operations Research and Financial Engineering …
http://orfe.princeton.edu/sites/default/files/Senior Thesis Titles 00-10.pdf
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 …
Portfolio analysis External links:
Analysis: Portfolio Analysis Flashcards | Quizlet
Portfolio Analysis Final-1 Flashcards | Quizlet
Portfolio Analysis | Economy Watch
Predictive analytics External links:
Strategic Location Management & Predictive Analytics | …
Predictive Analytics Solutions for Global Industry | Uptake
What is predictive analytics? – Definition from WhatIs.com
Predictive engineering analytics External links:
Predictive engineering analytics includes both the tactics and tools that manufacturers can leverage to expand traditional design verification and validation into a predictive role in support of systems-driven product development.
Predictive modeling External links:
DataRobot – Automated Machine Learning for Predictive Modeling
What is predictive modeling? – Definition from …
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate …
Prescriptive Analytics | IBM Analytics
Prescriptive analytics – ccjdigital.com
Price discrimination External links:
3 Types of Price Discrimination | Chron.com
MBAecon – 1st, 2nd and 3rd Price discrimination
Price Discrimination – Investopedia
Risk analysis External links:
[PDF]Military Police Risk Analysis for Army Property
What is risk analysis? – Definition from WhatIs.com
Security information and event management External links:
A Guide to Security Information and Event Management
Magic Quadrant for Security Information and Event Management
Semantic analytics External links:
Semantic Analytics – Get Business Intelligence With …
SciBite – The Semantic Analytics Company
[PDF]Semantic Analytics – Northfield
Smart grid External links:
[PDF]The Smart Grid: An Introduction
Smart Grid Massachusetts | National Grid
Smart Grid Solutions | Smart Grid System Integration …
Social analytics External links:
Dark Social Analytics: Track Private Shares with GetSocial
Social Analytics One – Social Analytics One
Google Search with Social Analytics – ctrlq.org
Software analytics External links:
EDGEPro Software Analytics Tool for Optometry | Success …
EDGEPro | EDGEPro Software Analytics Tool for Optometry
Speech analytics External links:
Speech Analytics | NICE
Customer Engagement & Speech Analytics | CallMiner
Best Speech Analytics Solutions in 2018 | IT Central Station
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
[PDF]Testing for Statistical Discrimination in Health Care
Structured data External links:
Structured Data for Dummies – Search Engine Journal
Structured Data Testing Tool – Google
SEC.gov | What Is Structured Data?
Telecommunications data retention External links:
What is TELECOMMUNICATIONS DATA RETENTION? …
Telecommunications Data Retention and Human …
Text analytics External links:
Text Mining / Text Analytics Specialist – bigtapp
The Truth about Text Analytics and Sentiment Analysis
Text Analytics | What is Text Analytics? – Clarabridge
Text mining External links:
Text Mining with R
Text Mining – AbeBooks
Text Mining with R
Time series External links:
[PDF]Time Series Analysis and Its Applications: With R …
[PDF]Time Series Analysis and Forecasting – cengage.com
Unstructured data External links:
Structured vs. Unstructured data – BrightPlanet
Differences Between Structured & Unstructured Data – …
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 …
Splunk User Behavior Analytics | Splunk
Visual analytics External links:
CSE 6242 – Data and Visual Analytics
Web analytics External links:
Web Analytics in Real Time | Clicky
11 Best Web Analytics Tools | Inc.com
Web Analytics Basics | Usability.gov