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 205 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:
Disseminate Pricing Analytics strategies and gather practices for scaling Pricing Analytics.
– Are we making progress? and are we making progress as Pricing Analytics leaders?
– What are the barriers to increased Pricing Analytics production?
– What are the Key enablers to make this Pricing Analytics move?
Academic discipline Critical Criteria:
Meet over Academic discipline issues and change contexts.
– Which customers cant participate in our Pricing Analytics domain because they lack skills, wealth, or convenient access to existing solutions?
– Are we Assessing Pricing Analytics and Risk?
– How do we keep improving Pricing Analytics?
Analytic applications Critical Criteria:
Generalize Analytic applications planning and get the big picture.
– How do we ensure that implementations of Pricing Analytics products are done in a way that ensures safety?
– What are the long-term Pricing Analytics goals?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
– Are there Pricing Analytics problems defined?
Architectural analytics Critical Criteria:
Canvass Architectural analytics projects and tour deciding if Architectural analytics progress is made.
– How can you negotiate Pricing Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?
– Who will be responsible for deciding whether Pricing Analytics goes ahead or not after the initial investigations?
– How will you measure your Pricing Analytics effectiveness?
Behavioral analytics Critical Criteria:
Bootstrap Behavioral analytics quality and question.
– Will new equipment/products be required to facilitate Pricing Analytics delivery for example is new software needed?
– How will you know that the Pricing Analytics project has been successful?
– How can skill-level changes improve Pricing Analytics?
Big data Critical Criteria:
Interpolate Big data outcomes and probe using an integrated framework to make sure Big data is getting what it needs.
– From all data collected by your organization, what is approximately the share of external data (collected from external sources), compared to internal data (produced by your operations)?
– For your Pricing Analytics project, identify and describe the business environment. is there more than one layer to the business environment?
– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?
– The real challenge: are you willing to get better value and more innovation for some loss of privacy?
– Do you see areas in your domain or across domains where vendor lock-in is a potential risk?
– Wheres the evidence that using big data intelligently will improve business performance?
– From what sources does your organization collect, or expects to collect, data?
– What would be needed to support collaboration on data sharing in your sector?
– How will systems and methods evolve to remove Big Data solution weaknesses?
– Can good algorithms, models, heuristics overcome Data Quality problems?
– What (additional) data do these algorithms need to be effective?
– How do we track the provenance of the derived data/information?
– Future Plans What is the future plan to expand this solution?
– Isnt big data just another way of saying analytics?
– How to attract and keep the community involved?
– What is tacit permission and approval, anyway?
– Wait, DevOps does not apply to Big Data?
– WHAT ARE THE NOMINATION CRITERIA?
– Hash tables for term management?
– What can it be used for?
Business analytics Critical Criteria:
Experiment with Business analytics management and remodel and develop an effective Business analytics strategy.
– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?
– What is the difference between business intelligence business analytics and data mining?
– Is there a mechanism to leverage information for business analytics and optimization?
– What is the difference between business intelligence and business analytics?
– what is the difference between Data analytics and Business Analytics If Any?
– How do we Identify specific Pricing Analytics investment and emerging trends?
– How do you pick an appropriate ETL tool or business analytics tool?
– What are the trends shaping the future of business analytics?
– How would one define Pricing Analytics leadership?
Business intelligence Critical Criteria:
Pay attention to Business intelligence risks and track iterative Business intelligence results.
– Does your bi software work well with both centralized and decentralized data architectures and vendors?
– How is Business Intelligence affecting marketing decisions during the Digital Revolution?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Does your bi solution allow analytical insights to happen anywhere and everywhere?
– Does creating or modifying reports or dashboards require a reporting team?
– What should recruiters look for in a business intelligence professional?
– What are some software and skills that every Data Scientist should know?
– Who prioritizes, conducts and monitors business intelligence projects?
– Number of data sources that can be simultaneously accessed?
– What are the pillar concepts of business intelligence?
– How do we use AI algorithms in practical applications?
– How can data extraction from dashboards be automated?
– What level of training would you recommend?
– What is required to present video images?
– How can we maximize our BI investments?
– Do you support video integration?
– Types of data sources supported?
– What is your products direction?
Cloud analytics Critical Criteria:
Tête-à-tête about Cloud analytics visions and look for lots of ideas.
– Think about the people you identified for your Pricing Analytics project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?
– What are our best practices for minimizing Pricing Analytics project risk, while demonstrating incremental value and quick wins throughout the Pricing Analytics project lifecycle?
– Is a Pricing Analytics Team Work effort in place?
Complex event processing Critical Criteria:
Have a round table over Complex event processing tactics and acquire concise Complex event processing education.
– Think about the functions involved in your Pricing Analytics project. what processes flow from these functions?
– Is maximizing Pricing Analytics protection the same as minimizing Pricing Analytics loss?
– Which individuals, teams or departments will be involved in Pricing Analytics?
Computer programming Critical Criteria:
Have a round table over Computer programming decisions and document what potential Computer programming megatrends could make our business model obsolete.
– How does the organization define, manage, and improve its Pricing Analytics processes?
– What are the short and long-term Pricing Analytics goals?
– How to deal with Pricing Analytics Changes?
Continuous analytics Critical Criteria:
Consolidate Continuous analytics planning and assess and formulate effective operational and Continuous analytics strategies.
– Why should we adopt a Pricing Analytics framework?
– Why are Pricing Analytics skills important?
Cultural analytics Critical Criteria:
Meet over Cultural analytics leadership and define what do we need to start doing with Cultural analytics.
– What are the top 3 things at the forefront of our Pricing Analytics agendas for the next 3 years?
– To what extent does management recognize Pricing Analytics as a tool to increase the results?
– What are current Pricing Analytics Paradigms?
Customer analytics Critical Criteria:
Track Customer analytics strategies and budget the knowledge transfer for any interested in Customer analytics.
– 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?
– What threat is Pricing Analytics addressing?
Data mining Critical Criteria:
X-ray Data mining quality and learn.
– In the case of a Pricing Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Pricing Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Pricing Analytics project is implemented as planned, and is it working?
– 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?
– Is business intelligence set to play a key role in the future of Human Resources?
– What programs do we have to teach data mining?
– What are our Pricing Analytics Processes?
Data presentation architecture Critical Criteria:
Value Data presentation architecture management and define Data presentation architecture competency-based leadership.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Pricing Analytics?
– In what ways are Pricing Analytics vendors and us interacting to ensure safe and effective use?
Embedded analytics Critical Criteria:
Extrapolate Embedded analytics decisions and look at the big picture.
– How do your measurements capture actionable Pricing Analytics information for use in exceeding your customers expectations and securing your customers engagement?
– Does Pricing Analytics analysis show the relationships among important Pricing Analytics factors?
Enterprise decision management Critical Criteria:
Test Enterprise decision management leadership and define what do we need to start doing with Enterprise decision management.
– 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?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Pricing Analytics?
– Is the Pricing Analytics organization completing tasks effectively and efficiently?
Fraud detection Critical Criteria:
Huddle over Fraud detection management and optimize Fraud detection leadership as a key to advancement.
– What are your current levels and trends in key measures or indicators of Pricing Analytics product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– Will Pricing Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– What business benefits will Pricing Analytics goals deliver if achieved?
Google Analytics Critical Criteria:
Analyze Google Analytics tactics and look for lots of ideas.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Pricing Analytics process?
– Is Pricing Analytics dependent on the successful delivery of a current project?
– Have the types of risks that may impact Pricing Analytics been identified and analyzed?
Human resources Critical Criteria:
Dissect Human resources visions and acquire concise Human resources education.
– Describe your views on the value of human assets in helping an organization achieve its goals. how important is it for organizations to train and develop their Human Resources?
– Should pay levels and differences reflect the earnings of colleagues in the country of the facility, or earnings at the company headquarters?
– What are the procedures for filing an internal complaint about the handling of personal data?
– Should pay levels and differences reflect what workers are used to in their own countries?
– What are your most important goals for the strategic Pricing Analytics objectives?
– How important is it for organizations to train and develop their Human Resources?
– How do financial reports support the various aspects of accountability?
– Friendliness and professionalism of the Human Resources staff?
– How can we promote retention of high performing employees?
– What internal dispute resolution mechanisms are available?
– To achieve our vision, what customer needs must we serve?
– How is Staffs knowledge of procedures and regulations?
– Do you understand the parameters set by the algorithm?
– How is the Content updated of the hr website?
– Does the hr plan work for our stakeholders?
– How to deal with diversity?
– Is the hr plan effective ?
Learning analytics Critical Criteria:
Extrapolate Learning analytics goals and observe effective Learning analytics.
– What potential environmental factors impact the Pricing Analytics effort?
Machine learning Critical Criteria:
Powwow over Machine learning failures and intervene in Machine learning processes and leadership.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– Does Pricing Analytics systematically track and analyze outcomes for accountability and quality improvement?
– Risk factors: what are the characteristics of Pricing Analytics that make it risky?
Marketing mix modeling Critical Criteria:
Mix Marketing mix modeling visions and get out your magnifying glass.
– Have you identified your Pricing Analytics key performance indicators?
– Will Pricing Analytics deliverables need to be tested and, if so, by whom?
– How can we improve Pricing Analytics?
Mobile Location Analytics Critical Criteria:
Talk about Mobile Location Analytics planning and adjust implementation of Mobile Location Analytics.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Pricing Analytics processes?
– What is the total cost related to deploying Pricing Analytics, including any consulting or professional services?
Neural networks Critical Criteria:
Wrangle Neural networks outcomes and check on ways to get started with Neural networks.
News analytics Critical Criteria:
Guard News analytics decisions and don’t overlook the obvious.
– Who will be responsible for documenting the Pricing Analytics requirements in detail?
– Is Supporting Pricing Analytics documentation required?
Online analytical processing Critical Criteria:
Debate over Online analytical processing planning and find answers.
– How do senior leaders actions reflect a commitment to the organizations Pricing Analytics values?
– What is our formula for success in Pricing Analytics ?
– How to Secure Pricing Analytics?
Online video analytics Critical Criteria:
Be clear about Online video analytics visions and grade techniques for implementing Online video analytics controls.
– What are the disruptive Pricing Analytics technologies that enable our organization to radically change our business processes?
Operational reporting Critical Criteria:
Paraphrase Operational reporting risks and revise understanding of Operational reporting architectures.
– What tools and technologies are needed for a custom Pricing Analytics project?
Operations research Critical Criteria:
Canvass Operations research projects and adopt an insight outlook.
– Think about the kind of project structure that would be appropriate for your Pricing Analytics project. should it be formal and complex, or can it be less formal and relatively simple?
– How do we go about Securing Pricing Analytics?
Over-the-counter data Critical Criteria:
Guard Over-the-counter data adoptions and correct better engagement with Over-the-counter data results.
– What are the Essentials of Internal Pricing Analytics Management?
Portfolio analysis Critical Criteria:
Talk about Portfolio analysis goals and interpret which customers can’t participate in Portfolio analysis because they lack skills.
– How will we insure seamless interoperability of Pricing Analytics moving forward?
– Is there any existing Pricing Analytics governance structure?
Predictive analytics Critical Criteria:
Set goals for Predictive analytics adoptions and plan concise Predictive analytics education.
– What are direct examples that show predictive analytics to be highly reliable?
Predictive engineering analytics Critical Criteria:
Talk about Predictive engineering analytics outcomes and look in other fields.
– Do those selected for the Pricing Analytics team have a good general understanding of what Pricing Analytics is all about?
– Who are the people involved in developing and implementing Pricing Analytics?
Predictive modeling Critical Criteria:
Deliberate Predictive modeling outcomes 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?
– Are you currently using predictive modeling to drive results?
Prescriptive analytics Critical Criteria:
Examine Prescriptive analytics adoptions and oversee implementation of Prescriptive analytics.
Price discrimination Critical Criteria:
Have a session on Price discrimination issues and achieve a single Price discrimination view and bringing data together.
– At what point will vulnerability assessments be performed once Pricing Analytics is put into production (e.g., ongoing Risk Management after implementation)?
– How can you measure Pricing Analytics in a systematic way?
Risk analysis Critical Criteria:
Review Risk analysis leadership and do something to it.
– 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?
– Among the Pricing Analytics product and service cost to be estimated, which is considered hardest to estimate?
– 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:
Win new insights about Security information and event management adoptions and ask questions.
– How likely is the current Pricing Analytics plan to come in on schedule or on budget?
– Does our organization need more Pricing Analytics education?
Semantic analytics Critical Criteria:
Give examples of Semantic analytics tasks and summarize a clear Semantic analytics focus.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Pricing Analytics services/products?
– How is the value delivered by Pricing Analytics being measured?
Smart grid Critical Criteria:
Win new insights about Smart grid quality and acquire concise Smart grid education.
– 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 prevents me from making the changes I know will make me a more effective Pricing Analytics leader?
Social analytics Critical Criteria:
Demonstrate Social analytics visions and interpret which customers can’t participate in Social analytics because they lack 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?
– How do you determine the key elements that affect Pricing Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
Software analytics Critical Criteria:
Do a round table on Software analytics issues and look for lots of ideas.
– When a Pricing Analytics manager recognizes a problem, what options are available?
– Can Management personnel recognize the monetary benefit of Pricing Analytics?
Speech analytics Critical Criteria:
Investigate Speech analytics strategies and overcome Speech analytics skills and management ineffectiveness.
– Are there any easy-to-implement alternatives to Pricing Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– In a project to restructure Pricing Analytics outcomes, which stakeholders would you involve?
Statistical discrimination Critical Criteria:
Mine Statistical discrimination risks and transcribe Statistical discrimination as tomorrows backbone for success.
– Does Pricing Analytics create potential expectations in other areas that need to be recognized and considered?
Stock-keeping unit Critical Criteria:
Concentrate on Stock-keeping unit issues and forecast involvement of future Stock-keeping unit projects in development.
Structured data Critical Criteria:
Devise Structured data decisions and know what your objective is.
– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?
– Do we monitor the Pricing Analytics decisions made and fine tune them as they evolve?
– Should you use a hierarchy or would a more structured database-model work best?
– What are all of our Pricing Analytics domains and what do they do?
Telecommunications data retention Critical Criteria:
Refer to Telecommunications data retention planning and inform on and uncover unspoken needs and breakthrough Telecommunications data retention results.
– Is there a Pricing Analytics Communication plan covering who needs to get what information when?
Text analytics Critical Criteria:
Sort Text analytics engagements and visualize why should people listen to you regarding Text analytics.
– Have text analytics mechanisms like entity extraction been considered?
Text mining Critical Criteria:
Brainstorm over Text mining results and define Text mining competency-based leadership.
– Do the Pricing Analytics decisions we make today help people and the planet tomorrow?
– How do we maintain Pricing Analyticss Integrity?
Time series Critical Criteria:
Look at Time series planning and gather practices for scaling Time series.
– Where do ideas that reach policy makers and planners as proposals for Pricing Analytics strengthening and reform actually originate?
– What is the source of the strategies for Pricing Analytics strengthening and reform?
– Does the Pricing Analytics task fit the clients priorities?
Unstructured data Critical Criteria:
Refer to Unstructured data strategies and track iterative Unstructured data results.
User behavior analytics Critical Criteria:
Jump start User behavior analytics strategies and ask what if.
– Meeting the challenge: are missed Pricing Analytics opportunities costing us money?
Visual analytics Critical Criteria:
Have a session on Visual analytics risks and stake your claim.
– Are assumptions made in Pricing Analytics stated explicitly?
Web analytics Critical Criteria:
Investigate Web analytics engagements and innovate what needs to be done with Web analytics.
– What statistics should one be familiar with for business intelligence and web analytics?
– Think of your Pricing Analytics project. what are the main functions?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Inquire about Win–loss analytics quality and plan concise Win–loss analytics education.
– 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 are the business goals Pricing Analytics is aiming to achieve?
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:
Banking Pricing Analytics | Analytics | Market Segmentation
Academic discipline External links:
criminal justice | academic discipline | Britannica.com
ERIC – Comparative Literature as Academic Discipline., …
Architectural analytics External links:
Architectural Analytics – Home | Facebook
Behavioral analytics External links:
User and Entity Behavioral Analytics Partners | Exabeam
Security and IT Risk Intelligence with Behavioral Analytics
FraudMAP Behavioral Analytics Solutions Brochure | Fiserv
Big data External links:
Pepperdata: DevOps for Big Data
Business intelligence External links:
[PDF]Position Title: Business Intelligence Analyst – ttra
List of Business Intelligence Skills – The Balance
Cloud analytics External links:
Cloud Analytics Academy | Hosted by Snowflake
Complex event processing External links:
SAP HANA Tech: Complex Event Processing – SAP …
Complex Event Processing (CEP) for Big Data Streaming
Computer programming External links:
Computer Programming, Robotics & Engineering – STEM For Kids
Computer programming | Computing | Khan Academy
Gwinnett Technical College- Computer Programming
Continuous analytics External links:
Continuous Analytics: Why You Must Consider It – Zymr
[PDF]Continuous Analytics: Stream Query Processing in …
Cultural analytics External links:
Cultural analytics is the exploration and research of massive cultural data sets of visual material – both digitized visual artifacts and contemporary visual and interactive media.
Customer analytics External links:
Customer Analytics | Precima
Our Team | Customer Analytics Experts | ClickFox
Data mining External links:
Title Data Mining Jobs, Employment | Indeed.com
[PDF]Data Mining Mining Text Data – tutorialspoint.com
Job Titles in Data Mining – KDnuggets
Embedded analytics External links:
Power BI Embedded analytics | Microsoft Azure
Fiori Embedded Analytics | Integrated Business Intelligence
LaunchWorks | Embedded Analytics Solutions
Enterprise decision management External links:
enterprise decision management Archives – Insights
Fraud detection External links:
Title IV fraud detection | University Business Magazine
Google Analytics External links:
Welcome to the Texas Board of Nursing – Google Analytics
Google Analytics Solutions – Marketing Analytics & …
Human resources External links:
Department of Human Resources Home – TN.Gov
Office of Human Resources – Employment & Recruitment …
Phila.gov | Human Resources | Jobs
Learning analytics External links:
Learning analytics – MoodleDocs
Journal of Learning Analytics
Machine learning External links:
Machine Learning Mastery – Official Site
DataRobot – Automated Machine Learning for Predictive …
Microsoft Azure Machine Learning Studio
Marketing mix modeling External links:
Marketing Mix Modeling – Decision Analyst
Marketing Mix Modeling | Marketing Management Analytics
Mobile Location Analytics External links:
Mobile Location Analytics Privacy Notice | Verizon
[PDF]Mobile Location Analytics Code of Conduct
Mobile location analytics | Federal Trade Commission
Neural networks External links:
How Deep Neural Networks Work – YouTube
Online analytical processing External links:
Working with Online Analytical Processing (OLAP)
Oracle Online Analytical Processing (OLAP)
SAS Online Analytical Processing Server
Operations research External links:
Operations research (Book, 2014) [WorldCat.org]
Operations Research Analysis Manager Salaries – Salary.com
Operations Research Dual-Title Degree Graduate …
Over-the-counter data External links:
Portfolio analysis External links:
Portfolio Analysis – AbeBooks
Portfolio Analysis | Economy Watch
Portfolio analysis (Book, 1979) [WorldCat.org]
Predictive analytics External links:
Stategic Location Management & Predictive Analytics | …
Inventory Optimization for Retail | Predictive Analytics
Predictive Analytics Software, Social Listening | NewBrand
Predictive engineering analytics External links:
Predictive Engineering Analytics: Siemens PLM Software
Predictive modeling External links:
Othot Predictive Modeling | Predictive Analytics Company
DataRobot – Automated Machine Learning for Predictive Modeling
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate …
Price discrimination External links:
Introduction to Price Discrimination – YouTube
ERIC – Marketing Theory Applied to Price Discrimination …
Price Discrimination – Investopedia
Risk analysis External links:
What is Risk Analysis? – Definition from Techopedia
HIPAA [HIPPA]Compliance / Risk Analysis Home Page.
[DOC]Risk Analysis Template – hud.gov
Security information and event management External links:
[PDF]Security Information and Event Management (SIEM) …
Magic Quadrant for Security Information and Event Management
Semantic analytics External links:
SciBite – The Semantic Analytics Company
Smart grid External links:
Smart Grid – AbeBooks
[PDF]Smart Grid Asset Descriptions
Honeywell Smart Grid
Social analytics External links:
Dark Social Analytics: Track Private Shares with GetSocial
Social Analytics – Marchex
Enterprise Social Analytics Platform | About
Software analytics External links:
Software Analytics – Microsoft Research
Speech analytics External links:
Webinars for Phone Systems & Speech Analytics | Vaspian
DEVELOPERS – Speech recognition & speech analytics APIs
Eureka: Speech Analytics Software | CallMiner
Statistical discrimination External links:
Statistical discrimination is an economic theory of racial or gender inequality based on stereotypes. According to this theory, inequality may exist and persist between demographic groups even when economic agents (consumers, workers, employers, etc.) are rational and non-prejudiced.
“Employer Learning and Statistical Discrimination”
Structured data External links:
n4e Ltd Structured Data cabling | Electrical Installations
CLnet Solution Sdn Bhd | Structured Data Cabling Malaysia
SEC.gov | What Is Structured Data?
Telecommunications data retention External links:
Telecommunications Data Retention and Human …
Text analytics External links:
Machine Learning, Cognitive Search & Text Analytics | Attivio
Text Analytics — Blogs, Pictures, and more on WordPress
[PDF]Syllabus Course Title: Text Analytics – …
Text mining External links:
Text Mining in R: A Tutorial – Springboard Blog
Text Mining – AbeBooks
Text Mining / Text Analytics Specialist – bigtapp
Time series External links:
SPK WCDS – Hourly Time Series Reports
Unstructured data External links:
Isilon Scale-Out NAS Storage-Unstructured Data | Dell …
User behavior analytics External links:
IBM QRadar User Behavior Analytics – Overview – United …
What is User Behavior Analytics? – YouTube
Visual analytics External links:
Visual Analytics — Blogs, Pictures, and more on WordPress
Web analytics External links:
11 Best Web Analytics Tools | Inc.com
Web Analytics in Real Time | Clicky
20 Best Title:(web Analytics Manager) jobs | Simply Hired