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 207 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:
Analyze Pricing Analytics risks and describe which business rules are needed as Pricing Analytics interface.
– Do the Pricing Analytics decisions we make today help people and the planet tomorrow?
– Are we making progress? and are we making progress as Pricing Analytics leaders?
– What threat is Pricing Analytics addressing?
Academic discipline Critical Criteria:
Dissect Academic discipline risks and summarize a clear Academic discipline focus.
– What are all of our Pricing Analytics domains and what do they do?
– How can you measure Pricing Analytics in a systematic way?
Analytic applications Critical Criteria:
Accommodate Analytic applications engagements and improve Analytic applications service perception.
– 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?
– What knowledge, skills and characteristics mark a good Pricing Analytics project manager?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
– Is the scope of Pricing Analytics defined?
Architectural analytics Critical Criteria:
Unify Architectural analytics strategies and test out new things.
– What are the key elements of your Pricing Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?
– What are the Key enablers to make this Pricing Analytics move?
– How will you measure your Pricing Analytics effectiveness?
Behavioral analytics Critical Criteria:
Distinguish Behavioral analytics leadership and customize techniques for implementing Behavioral analytics controls.
– Does Pricing Analytics create potential expectations in other areas that need to be recognized and considered?
– Do Pricing Analytics rules make a reasonable demand on a users capabilities?
– Who will provide the final approval of Pricing Analytics deliverables?
Big data Critical Criteria:
Reorganize Big data tactics and look at it backwards.
– Looking at hadoop big data in the rearview mirror, what would you have done differently after implementing a Data Lake?
– Do you see the need to address the issues of data ownership or access to non-personal data (e.g. machine-generated data)?
– What rules and regulations should exist about combining data about individuals into a central repository?
– What would be needed to support collaboration on data sharing across economic sectors?
– Quality vs. Quantity: What data are required to satisfy the given value proposition?
– Is senior management in your organization involved in big data-related projects?
– What are the legal risks in using Big Data/People Analytics in hiring?
– How are the new Big Data developments captured in new Reference Architectures?
– Hybrid partitioning (across rows/terms and columns/documents) useful?
– What are the new applications that are enabled by Big Data solutions?
– At which levels do you see the need for standardisation actions?
– How is the value delivered by Pricing Analytics being measured?
– What analytical tools do you consider particularly important?
– What are our tools for big data analytics?
– Isnt big data just another way of saying analytics?
– What is tacit permission and approval, anyway?
– what is Different about Big Data?
– What is Big Data to us?
– What are we collecting?
– Where is the ROI?
Business analytics Critical Criteria:
Match Business analytics results and grade techniques for implementing Business analytics controls.
– Think about the functions involved in your Pricing Analytics project. what processes flow from these functions?
– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?
– What is the difference between business intelligence business analytics and data mining?
– Is there a mechanism to leverage information for business analytics and optimization?
– What is the difference between business intelligence and business analytics?
– what is the difference between Data analytics and Business Analytics If Any?
– How do you pick an appropriate ETL tool or business analytics tool?
– How do we Improve Pricing Analytics service perception, and satisfaction?
– What are the trends shaping the future of business analytics?
– What is our formula for success in Pricing Analytics ?
Business intelligence Critical Criteria:
Examine Business intelligence outcomes and assess what counts with Business intelligence that we are not counting.
– Research reveals that more than half of business intelligence projects hit a low degree of acceptance or fail. What factors influence the implementation negative or positive?
– What is the importance of knowing the key performance indicators KPIs for a business process when trying to implement a business intelligence system?
– Does your software provide roleand group-based security options that allow business users to securely create and publish their work?
– Can you easily add users and features to quickly scale and customize to your organizations specific needs?
– What are the approaches to handle RTB related data 100 GB aggregated for business intelligence?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– What should recruiters look for in a business intelligence professional?
– What are the best UI frameworks for Business Intelligence Applications?
– Who prioritizes, conducts and monitors business intelligence projects?
– What are the key skills a Business Intelligence Analyst should have?
– Can users easily create these thresholds and alerts?
– What business intelligence systems are available?
– To create parallel systems or custom workflows?
– What level of training would you recommend?
– How is business intelligence disseminated?
– Can your product map ad-hoc query results?
– What are typical reporting applications?
– Do you offer formal user training?
– Do you support video integration?
Cloud analytics Critical Criteria:
Investigate Cloud analytics projects and budget the knowledge transfer for any interested in Cloud analytics.
– How important is Pricing Analytics to the user organizations mission?
– Are we Assessing Pricing Analytics and Risk?
Complex event processing Critical Criteria:
Think about Complex event processing strategies and gather Complex event processing models .
– 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 disruptive Pricing Analytics technologies that enable our organization to radically change our business processes?
Computer programming Critical Criteria:
Match Computer programming planning and ask questions.
– How do your measurements capture actionable Pricing Analytics information for use in exceeding your customers expectations and securing your customers engagement?
– What are your most important goals for the strategic Pricing Analytics objectives?
– Why are Pricing Analytics skills important?
Continuous analytics Critical Criteria:
Win new insights about Continuous analytics adoptions and probe using an integrated framework to make sure Continuous analytics is getting what it needs.
– Is Pricing Analytics Required?
Cultural analytics Critical Criteria:
Merge Cultural analytics engagements and get going.
– 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 tools and technologies are needed for a custom Pricing Analytics project?
– What business benefits will Pricing Analytics goals deliver if achieved?
Customer analytics Critical Criteria:
Detail Customer analytics quality and find the essential reading for Customer analytics researchers.
Data mining Critical Criteria:
Pilot Data mining issues and shift your focus.
– What will be the consequences to the business (financial, reputation etc) if Pricing Analytics does not go ahead or fails to deliver the objectives?
– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?
– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Is business intelligence set to play a key role in the future of Human Resources?
– What programs do we have to teach data mining?
Data presentation architecture Critical Criteria:
Mine Data presentation architecture leadership and check on ways to get started with Data presentation architecture.
– In the case of a Pricing Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Pricing Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Pricing Analytics project is implemented as planned, and is it working?
– What is the total cost related to deploying Pricing Analytics, including any consulting or professional services?
– Is Pricing Analytics Realistic, or are you setting yourself up for failure?
Embedded analytics Critical Criteria:
Focus on Embedded analytics governance and simulate teachings and consultations on quality process improvement of Embedded analytics.
– What other jobs or tasks affect the performance of the steps in the Pricing Analytics process?
Enterprise decision management Critical Criteria:
Guard Enterprise decision management risks and differentiate in coordinating Enterprise decision management.
– How can skill-level changes improve Pricing Analytics?
– How to Secure Pricing Analytics?
Fraud detection Critical Criteria:
Confer re Fraud detection strategies and cater for concise Fraud detection education.
– In a project to restructure Pricing Analytics outcomes, which stakeholders would you involve?
– To what extent does management recognize Pricing Analytics as a tool to increase the results?
– What are the usability implications of Pricing Analytics actions?
Google Analytics Critical Criteria:
Pilot Google Analytics goals and create a map for yourself.
– Are accountability and ownership for Pricing Analytics clearly defined?
– What are specific Pricing Analytics Rules to follow?
Human resources Critical Criteria:
See the value of Human resources tasks and attract Human resources skills.
– A dramatic step toward becoming a learning organization is to appoint a chief training officer (CTO) or a chief learning officer (CLO). Many organizations claim to value Human Resources, but how many have a Human Resources representative involved in discussions about research and development commercialization, new product development, the strategic vision of the company, or increasing shareholder value?
– 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?
– How often do we hold meaningful conversations at the operating level among sales, finance, operations, IT, and human resources?
– Do we identify desired outcomes and key indicators (if not already existing) such as what metrics?
– What are the procedures for filing an internal complaint about the handling of personal data?
– Available personnel – what are the available Human Resources within the organization?
– What problems have you encountered with the department or staff member?
– How is The staffs ability and response to handle questions or requests?
– What are the Human Resources we can bring to establishing new business?
– What is the important thing that human resources management should do?
– How do you view the department and staff members as a whole?
– What internal dispute resolution mechanisms are available?
– What are ways to reduce the costs of managing employees?
– When can an employee access and correct personal data?
– What does the pyramid of information look like?
– What other outreach efforts would be helpful?
– What do users think of the information?
– How to deal with Pricing Analytics Changes?
Learning analytics Critical Criteria:
Accommodate Learning analytics governance and find answers.
– Risk factors: what are the characteristics of Pricing Analytics that make it risky?
– Is the Pricing Analytics organization completing tasks effectively and efficiently?
Machine learning Critical Criteria:
Meet over Machine learning failures and acquire concise Machine learning education.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– How do we Identify specific Pricing Analytics investment and emerging trends?
Marketing mix modeling Critical Criteria:
Judge Marketing mix modeling adoptions and spearhead techniques for implementing Marketing mix modeling.
– Can we add value to the current Pricing Analytics decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
Mobile Location Analytics Critical Criteria:
Consult on Mobile Location Analytics leadership and interpret which customers can’t participate in Mobile Location Analytics because they lack skills.
– Have the types of risks that may impact Pricing Analytics been identified and analyzed?
– How do we go about Securing Pricing Analytics?
Neural networks Critical Criteria:
Steer Neural networks goals and define Neural networks competency-based leadership.
– What role does communication play in the success or failure of a Pricing Analytics project?
– How do we measure improved Pricing Analytics service perception, and satisfaction?
News analytics Critical Criteria:
Use past News analytics visions and attract News analytics skills.
– 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?
– How do we make it meaningful in connecting Pricing Analytics with what users do day-to-day?
Online analytical processing Critical Criteria:
Facilitate Online analytical processing outcomes and report on developing an effective Online analytical processing strategy.
– How do we manage Pricing Analytics Knowledge Management (KM)?
Online video analytics Critical Criteria:
Shape Online video analytics decisions and test out new things.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Pricing Analytics?
– Which Pricing Analytics goals are the most important?
Operational reporting Critical Criteria:
Pay attention to Operational reporting projects and clarify ways to gain access to competitive Operational reporting services.
– 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?
Operations research Critical Criteria:
Air ideas re Operations research goals and oversee Operations research requirements.
– What will drive Pricing Analytics change?
– How do we keep improving Pricing Analytics?
Over-the-counter data Critical Criteria:
Graph Over-the-counter data adoptions and find out what it really means.
– How do we ensure that implementations of Pricing Analytics products are done in a way that ensures safety?
– Who needs to know about Pricing Analytics ?
Portfolio analysis Critical Criteria:
Consider Portfolio analysis planning and reduce Portfolio analysis costs.
– What are current Pricing Analytics Paradigms?
Predictive analytics Critical Criteria:
Illustrate Predictive analytics risks and visualize why should people listen to you regarding Predictive analytics.
– What are direct examples that show predictive analytics to be highly reliable?
– How can we improve Pricing Analytics?
Predictive engineering analytics Critical Criteria:
Study Predictive engineering analytics quality and find out.
– 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?
– What are our Pricing Analytics Processes?
Predictive modeling Critical Criteria:
Interpolate Predictive modeling tasks and ask questions.
– Are you currently using predictive modeling to drive results?
– Does our organization need more Pricing Analytics education?
Prescriptive analytics Critical Criteria:
Chat re Prescriptive analytics outcomes and clarify ways to gain access to competitive Prescriptive analytics services.
– Will Pricing Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
Price discrimination Critical Criteria:
Design Price discrimination adoptions and grade techniques for implementing Price discrimination controls.
– Who will be responsible for documenting the Pricing Analytics requirements in detail?
– What is the purpose of Pricing Analytics in relation to the mission?
– How do we Lead with Pricing Analytics in Mind?
Risk analysis Critical Criteria:
Rank Risk analysis leadership and interpret which customers can’t participate in Risk analysis because they lack skills.
– How do risk analysis and Risk Management inform your organizations decisionmaking processes for long-range system planning, major project description and cost estimation, priority programming, and project development?
– What levels of assurance are needed and how can the risk analysis benefit setting standards and policy functions?
– In which two Service Management processes would you be most likely to use a risk analysis and management method?
– What prevents me from making the changes I know will make me a more effective Pricing Analytics leader?
– How does the business impact analysis use data from Risk Management and risk analysis?
– What is the source of the strategies for Pricing Analytics strengthening and reform?
– 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 planning and find out.
– Is there a Pricing Analytics Communication plan covering who needs to get what information when?
– Is there any existing Pricing Analytics governance structure?
Semantic analytics Critical Criteria:
Dissect Semantic analytics risks and get answers.
– Do we all define Pricing Analytics in the same way?
Smart grid Critical Criteria:
Communicate about Smart grid visions and integrate design thinking in Smart grid innovation.
– 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?
– Does Pricing Analytics analysis show the relationships among important Pricing Analytics factors?
– Do you monitor the effectiveness of your Pricing Analytics activities?
Social analytics Critical Criteria:
Meet over Social analytics goals and balance specific methods for improving Social analytics results.
– Are there any disadvantages to implementing Pricing Analytics? There might be some that are less obvious?
– How do mission and objectives affect the Pricing Analytics processes of our organization?
Software analytics Critical Criteria:
Wrangle Software analytics risks and get the big picture.
– What tools do you use once you have decided on a Pricing Analytics strategy and more importantly how do you choose?
– Does Pricing Analytics appropriately measure and monitor risk?
Speech analytics Critical Criteria:
Deliberate Speech analytics tactics and forecast involvement of future Speech analytics projects in development.
Statistical discrimination Critical Criteria:
Exchange ideas about Statistical discrimination decisions and give examples utilizing a core of simple Statistical discrimination skills.
– What is our Pricing Analytics Strategy?
Stock-keeping unit Critical Criteria:
Confer re Stock-keeping unit planning and modify and define the unique characteristics of interactive Stock-keeping unit projects.
– Have all basic functions of Pricing Analytics been defined?
Structured data Critical Criteria:
Revitalize Structured data issues and catalog Structured data activities.
– 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?
– How would one define Pricing Analytics leadership?
Telecommunications data retention Critical Criteria:
Check Telecommunications data retention results and budget for Telecommunications data retention challenges.
Text analytics Critical Criteria:
Scrutinze Text analytics projects and give examples utilizing a core of simple Text analytics skills.
– Have text analytics mechanisms like entity extraction been considered?
– What about Pricing Analytics Analysis of results?
– Are there Pricing Analytics problems defined?
Text mining Critical Criteria:
Grade Text mining tasks and describe the risks of Text mining sustainability.
Time series Critical Criteria:
Reason over Time series planning and balance specific methods for improving Time series results.
– What are the record-keeping requirements of Pricing Analytics activities?
– Which individuals, teams or departments will be involved in Pricing Analytics?
Unstructured data Critical Criteria:
Explore Unstructured data failures and give examples utilizing a core of simple Unstructured data skills.
– Is Pricing Analytics dependent on the successful delivery of a current project?
– What are internal and external Pricing Analytics relations?
User behavior analytics Critical Criteria:
Focus on User behavior analytics governance and simulate teachings and consultations on quality process improvement of User behavior analytics.
– Will new equipment/products be required to facilitate Pricing Analytics delivery for example is new software needed?
– Can we do Pricing Analytics without complex (expensive) analysis?
Visual analytics Critical Criteria:
Study Visual analytics tactics and describe which business rules are needed as Visual analytics interface.
Web analytics Critical Criteria:
Disseminate Web analytics adoptions and test out new things.
– What statistics should one be familiar with for business intelligence and web analytics?
– Are assumptions made in Pricing Analytics stated explicitly?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Jump start Win–loss analytics planning and don’t overlook the obvious.
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: Optimizing Price – YouTube
Pricing Analytics | Core Pricing Services
Pricing Analytics Scientist – IoT BigData Jobs
Academic discipline External links:
Australian PhD Listings by Academic Discipline – PhDSeek.com
Is Accounting an Academic Discipline? | Accounting Horizons
Academic Discipline – Paper Masters
Analytic applications External links:
Hype Cycle for Back-Office Analytic Applications, 2017
Flexible Scheduling of Distributed Analytic Applications
IDC Innovators: Analytic Applications for Manufacturing, 2017
Architectural analytics External links:
Architectural Analytics – Home | Facebook
Best Master’s Degrees in Architectural Analytics 2017/2018
Top Online Bachelors in Architectural Analytics 2017/2018
Behavioral analytics External links:
Behavioral Analytics – Security Intelligence
MOBOTIX – Behavioral Analytics
NYC Behavioral Analytics Meetup (New York, NY) | Meetup
Big data External links:
Fighting fraud in banking with big data and analytics – IBM
Exclusive insights into IP Big Data – Practice Insight
Home – Transport Big Data
Business analytics External links:
Power BI Business Analytics Solutions
MIS171: Business Analytics at Deakin – StudentVIP Subjects
Business analytics applications | Infor
Business intelligence External links:
Business Intelligence & Analytics, BI Software – Birst
Business Intelligence – RMIT University
Ruralco | Business Intelligence
Cloud analytics External links:
Cloud Analytics | Big Data Analytics | HPE Vertica
Big Cloud Analytics – YouTube
Cloud Analytics Overview – Buttonwood Cloud Exchange
Complex event processing External links:
2015-02-17 – Complex Event Processing on Azure – YouTube
Blending Complex Event Processing with the RETE Algorithm
Complex Event Processing (CEP) for Big Data Streaming
Computer programming External links:
Computer Programming, Robotics & Engineering – STEM For Kids
What Is Java Computer Programming Language?
Bernard Weekes – Computer Programming – Home | Facebook
Continuous analytics External links:
Continuous Analytics: Stream Query Processing in Practice
Hydrosphere – Continuous Analytics and DataOps for Big Data
Continuous Analytics Over Discontinuous Streams – CiteSeerX
Cultural analytics External links:
CULTURAL ANALYTICS: – Software Studies Initiative
Customer analytics External links:
Customer Analytics | Pulse | Bullhorn AU
Customer Analytics | SAS
Customer Analytics & Reporting with Zendesk Explore
Data mining External links:
Data Mining – RMIT University
Title Data Mining Jobs, Employment | Indeed.com
Project Title: Data Mining to Improve Water Management
Data presentation architecture External links:
Data Presentation Architecture – muncharoo.com
http://www.muncharoo.com/topic/Data Presentation Architecture&item_type=topic
【Data presentation architecture】-data …Translate this page
【Data presentation architecture】|DataV数据可 …Translate this page
Embedded analytics External links:
What is embedded analytics ? – Definition from WhatIs.com
SAP S/4HANA Embedded Analytics: An Overview
Power BI Embedded analytics | Microsoft Azure
Enterprise decision management External links:
Enterprise Decision Management (EDM) – Techopedia.com
Enterprise Decision Management – ResearchGate
Enterprise Decision Management | Cutter Consortium
Fraud detection External links:
Fraud Detection Software – Fraud Detection Software | about.com
http://Ad · www.about.com/software
Synthetic Financial Datasets For Fraud Detection | Kaggle
PwC settles $5.5bn fraud detection lawsuit – Financial Times
Google Analytics External links:
Embed API — Google Analytics Demos & Tools
How to Add Google Analytics to a Facebook Page Tab — SitePoint
Human resources External links:
The Human Resources Research Organization – HumRRO
Human Resources Analytics | Kaggle
Careers at Ohio State – Human Resources at Ohio State
Learning analytics External links:
TrackOne Studio – Learning Analytics
Learning analytics – MoodleDocs
Designing for student-facing learning analytics | QUT ePrints
Machine learning External links:
Machine Learning | Microsoft Azure
This Week in Machine Learning and AI Podcast
Titanic: Machine Learning from Disaster | Kaggle
Marketing mix modeling External links:
Article marketing mix modeling final by RoddSL – issuu
Marketing Mix Modeling by bottomlineanalytics – issuu
Media Marketing Mix Modeling – YouTube
Mobile Location Analytics External links:
Mobile Location Analytics – Android Apps on Google Play
Towards Privacy-preserving Mobile Location Analytics
Mobile location analytics | Federal Trade Commission
Neural networks External links:
Business VoIP | Neural Networks
Fax Error Codes – Neural Networks Helpdesk
Neural Networks Consulting | About Us
News analytics External links:
RavenPack News Analytics – RavenPack
RavenPack News Analytics – MATLAB & Simulink
Online analytical processing External links:
Working with Online Analytical Processing (OLAP)
Online Analytical Processing (OLAP), assignment help
OLAP | Online Analytical Processing | OLAP cubes
Online video analytics External links:
Global Online Video Analytics Marketplace Market Research
TubeMogul 2.0 Democratizes Online Video Analytics on Vimeo
Getting Started with Online Video Analytics
Operational reporting External links:
Central Solution for Operational Reporting WaterOutlook
operational reporting | Guidewire
Operational Reporting – asu-sant.asn.au
Operations research External links:
Course Syllabus Course Title: Operations Research
Operations Research Letters – Journal – Elsevier
International Abstracts in Operations Research – Palgrave
Over-the-counter data External links:
Over-the-Counter Data (OTCD) – Funderbeam markets
Over-the-Counter Data – American Mensa – Medium
Portfolio analysis External links:
Personal Portfolio Analysis – VectorVest
CME SPAN: Standard Portfolio Analysis of Risk – CME Group
Insurance Portfolio Analysis – Capital Innovation
Predictive analytics External links:
TrendMiner – predictive analytics for the process industry
Predictiva | Predictive Analytics | Real Time Scoring
Practical Predictive Analytics Course – IAPA
Predictive engineering analytics External links:
Predictive Engineering Analytics: Siemens PLM Software
Predictive engineering analytics – Revolvy
https://www.revolvy.com/topic/Predictive engineering analytics
Simcenter Portfolio for Predictive Engineering Analytics
Predictive modeling External links:
Predicting the future, Part 2: Predictive modeling techniques
Predictive Modeling – Predictive Modeling.
http://Ad · www.ask.com/Predictive Modeling
Limited Attendance Seminar – Predictive Modeling
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate Technologies
How to Get Started With Prescriptive Analytics
prescriptive analytics – IoT Hub
Price discrimination External links:
ECO – Price Discrimination Questions | Assignment Essays
ECO 321 – Price Discrimination Assignment | Essay Hand
33. Second Degree Price Discrimination – YouTube
Risk analysis External links:
Risk analysis – Food Standards Australia New Zealand
Global Country Risk Analysis – BMI Research
Country Risk Analysis – The University of Sydney
Security information and event management External links:
Security Information and Event Management (SIEM) – YouTube
Semantic analytics External links:
SciBite – The Semantic Analytics Company
Smart grid External links:
IEC TR 62357-1:2016 | IEC Webstore | smart energy, smart grid
Honeywell Smart Grid
The National Smart Grid Laboratory – NTNU
Social analytics External links:
Microsoft Codename “Social Analytics”
IBM Social Analytics on Cloud
Social Analytics Software, Customer Community Software
Software analytics External links:
Software Analytics Solutions | New Relic
Software Analytics – Microsoft Research
Nalpeiron Software Analytics and Software Licensing Demo
Speech analytics External links:
North american speech analytics market by tonyandrew – issuu
Real-Time Speech Analytics | NICE
Reverse a Pattern of Poor Sales With Speech Analytics
Statistical discrimination External links:
Statistical Discrimination and Efﬁciency
Testing for Statistical Discrimination in Health Care
Stock-keeping unit External links:
Stock-Keeping Unit (SKU) – Techopedia.com
SKU (stock-keeping unit) – Gartner IT Glossary
Structured data External links:
n4e Ltd Structured Data cabling | Electrical Installations
Introduction to Structured Data | Search | Google Developers
Formulas and Structured Data in Excel Tables | Excel Semi-Pro
Telecommunications data retention External links:
Telecommunications data retention | SBS News
Telecommunications data retention – an overview | APO
Text analytics External links:
Text Analytics – Gartner IT Glossary
Order Text Analytics Software from Provalis Research
Rosette – Text Analytics
Text mining External links:
Text Analytics & Text Mining Software Solution | Confirmit
http://Text mining, also referred to as text data mining, roughly equivalent to text analytics, is the process of deriving high-quality information from text. High-quality information is typically derived through the devising of patterns and trends through means such as statistical pattern learning. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. ‘High quality’ in text mining usually refers to some combination of relevance, novelty, and interestingness. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
Text Mining, Semantics & Data Intelligence | SciBite
Time series External links:
Web Traffic Time Series Forecasting | Kaggle
Forecasting time series using R – Rob J Hyndman
Excel – Time Series Forecasting – Part 1 of 3 – YouTube
Unstructured data External links:
Instaknow – ACE – Unstructured data processing
Next Generation Solutions for Unstructured Data
Unstructured Data Management in the Cloud | Panzura
User behavior analytics External links:
User behavior analytics | Dynatrace
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
Varonis User Behavior Analytics | Varonis Systems
Visual analytics External links:
Visual Analytics Part 2: Get a Perspective | Nuix
Classroom Training – Visual Analytics | Tableau Software
SAS Visual Analytics – SAS Support Communities
Web analytics External links:
Lead Generation, CRO, and Web Analytics For In-House Marketers
Web Analytics Experts – Panalysis
Web analytics | HitsLink