What is involved in Performance Analytics
Find out what the related areas are that Performance 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 Performance Analytics thinking-frame.
How far is your company on its Performance Analytics journey?
Take this short survey to gauge your organization’s progress toward Performance 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 Performance Analytics related domains to cover and 195 essential critical questions to check off in that domain.
The following domains are covered:
Performance 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:
Performance Analytics Critical Criteria:
Accumulate Performance Analytics decisions and explain and analyze the challenges of Performance Analytics.
– Do the Performance Analytics decisions we make today help people and the planet tomorrow?
– Do we have past Performance Analytics Successes?
Academic discipline Critical Criteria:
Pilot Academic discipline strategies and prioritize challenges of Academic discipline.
– Will new equipment/products be required to facilitate Performance Analytics delivery for example is new software needed?
– What are the short and long-term Performance Analytics goals?
– What are current Performance Analytics Paradigms?
Analytic applications Critical Criteria:
Probe Analytic applications management and shift your focus.
– In what ways are Performance Analytics vendors and us interacting to ensure safe and effective use?
– How would one define Performance Analytics leadership?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
Architectural analytics Critical Criteria:
Distinguish Architectural analytics visions and customize techniques for implementing Architectural analytics controls.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Performance Analytics process. ask yourself: are the records needed as inputs to the Performance Analytics process available?
– How is the value delivered by Performance Analytics being measured?
– How do we Lead with Performance Analytics in Mind?
Behavioral analytics Critical Criteria:
Detail Behavioral analytics leadership and be persistent.
– Does Performance Analytics systematically track and analyze outcomes for accountability and quality improvement?
– Have the types of risks that may impact Performance Analytics been identified and analyzed?
– How to Secure Performance Analytics?
Big data Critical Criteria:
Reason over Big data failures and define what do we need to start doing with Big data.
– How we make effective use of the flood of data that will be produced will be a real big data challenge: should we keep it all or could we throw some away?
– If this nomination is completed on behalf of the customer, has that customer been made aware of this nomination in advance of this submission?
– 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?
– How should we organize to capture the benefit of Big Data and move swiftly to higher maturity stages?
– Do we understand public perception of transportation service delivery at any given time?
– From what sources does your organization collect, or expects to collect, data?
– How can the best Big Data solution be chosen based on use case requirements?
– What are the Key enablers to make this Performance Analytics move?
– Are there any best practices or standards for the use of Big Data solutions?
– How will systems and methods evolve to remove Big Data solution weaknesses?
– How can the benefits of Big Data collection and applications be measured?
– Is the process repeatable as we change algorithms and data structures?
– Does your organization have a strategy on big data or data analytics?
– When we plan and design, how well do we capture previous experience?
– How do we track the provenance of the derived data/information?
– What is the limit for value as we add more data?
– Solution for updating (i.e., adding documents)?
– Why are we collecting all this data?
– what is Different about Big Data?
Business analytics Critical Criteria:
Meet over Business analytics leadership and report on the economics of relationships managing Business analytics and constraints.
– 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 Performance Analytics service perception, and satisfaction?
– What are the trends shaping the future of business analytics?
– Are we Assessing Performance Analytics and Risk?
Business intelligence Critical Criteria:
Scrutinze Business intelligence outcomes and devise Business intelligence key steps.
– Does the software let users work with the existing data infrastructure already in place, freeing your IT team from creating more cubes, universes, and standalone marts?
– Does your mobile solution allow you to interact with desktop-authored dashboards using touchscreen gestures like taps, flicks, and pinches?
– Does a BI business intelligence CoE center of excellence approach to support and enhancements benefit our organization and save cost?
– Can your software connect to all forms of data, from text and Excel files to cloud and enterprise-grade databases, with a few clicks?
– Does creating or modifying reports or dashboards require a reporting team?
– What are the key skills a Business Intelligence Analyst should have?
– How would you broadly categorize the different BI tools?
– How are business intelligence applications delivered?
– Do we offer a good introduction to data warehouse?
– What are some real time data analysis frameworks?
– Will your product work from a mobile device?
– What are typical data-mining applications?
– What are our tools for big data analytics?
– What is your licensing model and prices?
– Is your BI software easy to understand?
– What is your expect product life cycle?
– What is your products direction?
– What is Effective Performance Analytics?
Cloud analytics Critical Criteria:
Air ideas re Cloud analytics failures and create a map for yourself.
– What role does communication play in the success or failure of a Performance Analytics project?
– Who is the main stakeholder, with ultimate responsibility for driving Performance Analytics forward?
Complex event processing Critical Criteria:
See the value of Complex event processing goals and drive action.
– Who will be responsible for making the decisions to include or exclude requested changes once Performance Analytics is underway?
– What prevents me from making the changes I know will make me a more effective Performance Analytics leader?
Computer programming Critical Criteria:
Shape Computer programming governance and be persistent.
– What sources do you use to gather information for a Performance Analytics study?
– Does Performance Analytics analysis isolate the fundamental causes of problems?
Continuous analytics Critical Criteria:
Troubleshoot Continuous analytics quality and finalize specific methods for Continuous analytics acceptance.
– When a Performance Analytics manager recognizes a problem, what options are available?
– Do you monitor the effectiveness of your Performance Analytics activities?
Cultural analytics Critical Criteria:
Conceptualize Cultural analytics decisions and catalog what business benefits will Cultural analytics goals deliver if achieved.
– How do we measure improved Performance Analytics service perception, and satisfaction?
– What are your most important goals for the strategic Performance Analytics objectives?
– What are internal and external Performance Analytics relations?
Customer analytics Critical Criteria:
Judge Customer analytics tasks and use obstacles to break out of ruts.
– What tools do you use once you have decided on a Performance Analytics strategy and more importantly how do you choose?
– Who are the people involved in developing and implementing Performance Analytics?
Data mining Critical Criteria:
Powwow over Data mining projects and create Data mining explanations for all managers.
– 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?
– How does the organization define, manage, and improve its Performance Analytics processes?
– Which Performance Analytics goals are the most important?
– What programs do we have to teach data mining?
– Are there recognized Performance Analytics problems?
Data presentation architecture Critical Criteria:
Interpolate Data presentation architecture decisions and intervene in Data presentation architecture processes and leadership.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Performance Analytics in a volatile global economy?
– Are assumptions made in Performance Analytics stated explicitly?
– Is Performance Analytics Required?
Embedded analytics Critical Criteria:
Deliberate over Embedded analytics quality and revise understanding of Embedded analytics architectures.
– How do we make it meaningful in connecting Performance Analytics with what users do day-to-day?
– Is a Performance Analytics Team Work effort in place?
Enterprise decision management Critical Criteria:
Be clear about Enterprise decision management visions and get the big picture.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Performance Analytics process?
– How do we Identify specific Performance Analytics investment and emerging trends?
– What about Performance Analytics Analysis of results?
Fraud detection Critical Criteria:
Deliberate Fraud detection management and describe the risks of Fraud detection sustainability.
– Have all basic functions of Performance Analytics been defined?
– Is the scope of Performance Analytics defined?
Google Analytics Critical Criteria:
Inquire about Google Analytics tasks and report on the economics of relationships managing Google Analytics and constraints.
– Do those selected for the Performance Analytics team have a good general understanding of what Performance Analytics is all about?
– What potential environmental factors impact the Performance Analytics effort?
– Are accountability and ownership for Performance Analytics clearly defined?
Human resources Critical Criteria:
Demonstrate Human resources strategies and point out Human resources tensions in leadership.
– 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?
– Are Human Resources subject to screening, and do they have terms and conditions of employment defining their information security responsibilities?
– Have we adopted and promoted the companys culture of integrity management, including ethics, business practices and Human Resources evaluations?
– Should pay levels and differences reflect the earnings of colleagues in the country of the facility, or earnings at the company headquarters?
– Are there cases when the company may collect, use and disclose personal data without consent or accommodation?
– Does the cloud service provider have necessary security controls on their human resources?
– To satisfy customers and stakeholders, which internal business process must we excel in?
– What problems have you encountered with the department or staff member?
– What are the Human Resources we can bring to establishing new business?
– Can you think of other ways to reduce the costs of managing employees?
– How should any risks to privacy and civil liberties be managed?
– When can an employee access and correct personal data?
– Does the hr plan make sense to our stakeholders?
– How is Promptness of returning calls or e-mail?
– What additional approaches already exist?
– What do users think of the information?
– Who should appraise performance?
– Why is transparency important?
– What is personal data?
Learning analytics Critical Criteria:
Confer re Learning analytics leadership and assess and formulate effective operational and Learning analytics strategies.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Performance Analytics. How do we gain traction?
– Does Performance Analytics analysis show the relationships among important Performance Analytics factors?
Machine learning Critical Criteria:
Rank Machine learning strategies and frame using storytelling to create more compelling Machine learning projects.
– What are your current levels and trends in key measures or indicators of Performance 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 are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– Can we do Performance Analytics without complex (expensive) analysis?
Marketing mix modeling Critical Criteria:
Exchange ideas about Marketing mix modeling management and look at it backwards.
– What is the source of the strategies for Performance Analytics strengthening and reform?
– How do we manage Performance Analytics Knowledge Management (KM)?
– What are the long-term Performance Analytics goals?
Mobile Location Analytics Critical Criteria:
Deliberate over Mobile Location Analytics engagements and oversee Mobile Location Analytics requirements.
– Will Performance Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– What is our formula for success in Performance Analytics ?
Neural networks Critical Criteria:
Huddle over Neural networks risks and find answers.
– What will be the consequences to the business (financial, reputation etc) if Performance Analytics does not go ahead or fails to deliver the objectives?
– Is the Performance Analytics organization completing tasks effectively and efficiently?
– What business benefits will Performance Analytics goals deliver if achieved?
News analytics Critical Criteria:
Face News analytics adoptions and inform on and uncover unspoken needs and breakthrough News analytics results.
– Does Performance 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 are the Essentials of Internal Performance Analytics Management?
– Why should we adopt a Performance Analytics framework?
Online analytical processing Critical Criteria:
Familiarize yourself with Online analytical processing projects and test out new things.
– Among the Performance Analytics product and service cost to be estimated, which is considered hardest to estimate?
– Why is Performance Analytics important for you now?
Online video analytics Critical Criteria:
Closely inspect Online video analytics leadership and forecast involvement of future Online video analytics projects in development.
– What are the success criteria that will indicate that Performance Analytics objectives have been met and the benefits delivered?
Operational reporting Critical Criteria:
Pilot Operational reporting tactics and correct better engagement with Operational reporting results.
– Which individuals, teams or departments will be involved in Performance Analytics?
Operations research Critical Criteria:
Have a round table over Operations research governance and clarify ways to gain access to competitive Operations research services.
– How can you negotiate Performance Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?
– What are the business goals Performance Analytics is aiming to achieve?
Over-the-counter data Critical Criteria:
Consult on Over-the-counter data quality and reinforce and communicate particularly sensitive Over-the-counter data decisions.
– What are our needs in relation to Performance Analytics skills, labor, equipment, and markets?
– Who sets the Performance Analytics standards?
– What are our Performance Analytics Processes?
Portfolio analysis Critical Criteria:
Reconstruct Portfolio analysis failures and look at the big picture.
– How do we know that any Performance Analytics analysis is complete and comprehensive?
Predictive analytics Critical Criteria:
Derive from Predictive analytics issues and gather Predictive analytics models .
– In the case of a Performance Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Performance Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Performance Analytics project is implemented as planned, and is it working?
– What are direct examples that show predictive analytics to be highly reliable?
Predictive engineering analytics Critical Criteria:
Coach on Predictive engineering analytics projects and catalog what business benefits will Predictive engineering analytics goals deliver if achieved.
– How do we keep improving Performance Analytics?
Predictive modeling Critical Criteria:
Depict Predictive modeling issues and overcome Predictive modeling skills and management ineffectiveness.
– Are you currently using predictive modeling to drive results?
– Does Performance Analytics appropriately measure and monitor risk?
Prescriptive analytics Critical Criteria:
Investigate Prescriptive analytics goals and describe the risks of Prescriptive analytics sustainability.
– Is Supporting Performance Analytics documentation required?
Price discrimination Critical Criteria:
Conceptualize Price discrimination engagements and modify and define the unique characteristics of interactive Price discrimination projects.
– How will we insure seamless interoperability of Performance Analytics moving forward?
Risk analysis Critical Criteria:
Recall Risk analysis tactics and sort Risk analysis activities.
– 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?
Security information and event management Critical Criteria:
Consider Security information and event management risks and oversee Security information and event management requirements.
Semantic analytics Critical Criteria:
Talk about Semantic analytics adoptions and question.
– At what point will vulnerability assessments be performed once Performance Analytics is put into production (e.g., ongoing Risk Management after implementation)?
– What vendors make products that address the Performance Analytics needs?
– How can skill-level changes improve Performance Analytics?
Smart grid Critical Criteria:
Devise Smart grid issues and report on setting up Smart grid without losing ground.
– 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?
– Where do ideas that reach policy makers and planners as proposals for Performance Analytics strengthening and reform actually originate?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Performance Analytics?
Social analytics Critical Criteria:
Confer over Social analytics engagements and oversee Social analytics requirements.
– How do you determine the key elements that affect Performance Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
– How do we go about Securing Performance Analytics?
Software analytics Critical Criteria:
Revitalize Software analytics risks and grade techniques for implementing Software analytics controls.
– What are the disruptive Performance Analytics technologies that enable our organization to radically change our business processes?
– What other jobs or tasks affect the performance of the steps in the Performance Analytics process?
– Think of your Performance Analytics project. what are the main functions?
Speech analytics Critical Criteria:
Trace Speech analytics risks and devise Speech analytics key steps.
– What is the purpose of Performance Analytics in relation to the mission?
Statistical discrimination Critical Criteria:
Disseminate Statistical discrimination planning and look in other fields.
Stock-keeping unit Critical Criteria:
Frame Stock-keeping unit tactics and be persistent.
– Consider your own Performance Analytics project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
Structured data Critical Criteria:
Analyze Structured data planning and find the essential reading for Structured data researchers.
– Who will be responsible for deciding whether Performance Analytics goes ahead or not after the initial investigations?
– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?
– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?
– Should you use a hierarchy or would a more structured database-model work best?
Telecommunications data retention Critical Criteria:
Graph Telecommunications data retention planning and frame using storytelling to create more compelling Telecommunications data retention projects.
– Are there Performance Analytics Models?
Text analytics Critical Criteria:
Guide Text analytics adoptions and gather Text analytics models .
– Do several people in different organizational units assist with the Performance Analytics process?
– Have text analytics mechanisms like entity extraction been considered?
Text mining Critical Criteria:
Guard Text mining decisions and prioritize challenges of Text mining.
– Can Management personnel recognize the monetary benefit of Performance Analytics?
Time series Critical Criteria:
Debate over Time series results and remodel and develop an effective Time series strategy.
– How do senior leaders actions reflect a commitment to the organizations Performance Analytics values?
Unstructured data Critical Criteria:
Analyze Unstructured data goals and find out what it really means.
– Why is it important to have senior management support for a Performance Analytics project?
User behavior analytics Critical Criteria:
Have a round table over User behavior analytics tasks and describe the risks of User behavior analytics sustainability.
Visual analytics Critical Criteria:
Pay attention to Visual analytics risks and observe effective Visual analytics.
– What management system can we use to leverage the Performance Analytics experience, ideas, and concerns of the people closest to the work to be done?
– Do we all define Performance Analytics in the same way?
Web analytics Critical Criteria:
Concentrate on Web analytics quality and suggest using storytelling to create more compelling Web analytics projects.
– what is the best design framework for Performance Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– What statistics should one be familiar with for business intelligence and web analytics?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Read up on Win–loss analytics issues and reinforce and communicate particularly sensitive Win–loss analytics decisions.
– What is the total cost related to deploying Performance Analytics, including any consulting or professional services?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Performance Analytics Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | 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:
Academic discipline External links:
Academic Discipline – Earl Warren College
Academic Discipline – Earl Warren College
Analytic applications External links:
Aptos Analytic Applications – Aptos
Foxtrot Code AI Analytic Applications (Home)
Architectural analytics External links:
Architectural Analytics – Home | Facebook
Behavioral analytics External links:
Behavioral Analytics | Interana
User and Entity Behavioral Analytics Partners | Exabeam
Big data External links:
Databricks – Making Big Data Simple
Take 5 Media Group – Build an audience using big data
Business analytics External links:
What is Business Analytics? Webopedia Definition
Business intelligence External links:
Business Intelligence and Big Data Analytics Software
Oracle Business Intelligence – RCI
Mortgage Business Intelligence Software :: Motivity Solutions
Cloud analytics External links:
Cloud Analytics | Big Data Analytics | Vertica
Cloud Analytics – Solutions for Cloud Data Analytics | NetApp
Cloud Analytics Academy – Official Site
Computer programming External links:
Computer Programming – ed2go
Computer Programming, Robotics & Engineering – STEM …
Cultural analytics External links:
Software Studies Initiative: Cultural analytics
Customer analytics External links:
BlueVenn – Customer Analytics and Customer Journey …
Customer Analytics & Predictive Analytics Tools for Business
Zylotech- AI For Customer Analytics
Data mining External links:
Data Mining Courses | Stanford University
What is Data Mining in Healthcare?
UT Data Mining
Embedded analytics External links:
Embedded Analytics | Qlik
Embedded Analytics | ThoughtSpot
Embedded Analytics | Tableau
Enterprise decision management External links:
enterprise decision management Archives – Insights
Enterprise Decision Management | Sapiens DECISION
Enterprise Decision Management | SAS Italy
Fraud detection External links:
Fraud Detection and Anti-Money Laundering Software – Verafin
Debit Card Security | Fraud Detection & Protection | RushCard
Big Data Fraud Detection | DataVisor
Google Analytics External links:
Enterprise Marketing Analytics – Google Analytics 360 Suite
Google Analytics – Sign in
Human resources External links:
Human Resources Job Titles-The Ultimate Guide | upstartHR
Human Resources Job Titles – The Balance
Office of Human Resources – TITLE IX
Learning analytics External links:
Society for Learning Analytics Research (SoLAR)
Learning Analytics | Riptide Elements
Deep Learning Analytics
Machine learning External links:
Titanic: Machine Learning from Disaster | Kaggle
DataRobot – Automated Machine Learning for Predictive …
Machine Learning | Coursera
Marketing mix modeling External links:
Marketing Mix Modeling – Gartner IT Glossary
Marketing Mix Modeling | Marketing Management Analytics
Mobile Location Analytics External links:
[PDF]Mobile Location Analytics Code of Conduct
Mobile Location Analytics Privacy Notice | Verizon
How ‘Mobile Location Analytics’ Controls Your Mind – YouTube
News analytics External links:
Yakshof – Big Data News Analytics
News Analytics | Amareos
Online analytical processing External links:
Working with Online Analytical Processing (OLAP)
[PDF]OLAP (Online Analytical Processing) – SRM University
Online video analytics External links:
Managing Your Online Video Analytics – DaCast
Online Video Analytics & Marketing Software | Vidooly
Operations research External links:
Operations research (Book, 1974) [WorldCat.org]
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.
Over-the-counter data External links:
[PDF]Over-the-Counter Data’s Impact on Educators’ Data …
Standards — Over-the-Counter Data
Over-the-Counter Data – American Mensa – Medium
Portfolio analysis External links:
Portfolio Analysis | Economy Watch
Portfolio Analysis Final-1 Flashcards | Quizlet
[PDF]Portfolio Analysis Tool: Methodologies and Assumptions
Predictive analytics External links:
Inventory Optimization for Retail | Predictive Analytics
Customer Analytics & Predictive Analytics Tools for Business
Predictive Analytics Software, Social Listening | NewBrand
Predictive engineering analytics External links:
Predictive engineering analytics is the application of multidisciplinary engineering simulation and test with intelligent reporting and data analytics, to develop digital twins that can predict the real world behavior of products throughout the product lifecycle.
Predictive modeling External links:
What is predictive modeling? – Definition from …
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate Technologies
Price discrimination External links:
MBAecon – 1st, 2nd and 3rd Price discrimination
Price Discrimination Flashcards | Quizlet
Price Discrimination – Investopedia
Risk analysis External links:
Project Management and Risk Analysis Software | Safran
Risk analysis is the study of the underlying uncertainty of a given course of action. Risk analysis refers to the uncertainty of forecasted future cash flows streams, variance of portfolio/stock returns, statistical analysis to determine the probability of a project’s success or failure, and possible future economic states.
Risk Analysis | Investopedia
Security information and event management External links:
A Guide to Security Information and Event Management
Semantic analytics External links:
SciBite – The Semantic Analytics Company
Semantic Analytics – Get Business Intelligence With Schema …
[PDF]Geospatial and Temporal Semantic Analytics
Smart grid External links:
Smart Grid Security (eBook, 2015) [WorldCat.org]
Smart Grid – AbeBooks
Social analytics External links:
Social Analytics – Votigo
Dark Social Analytics: Track Private Shares with GetSocial
Enterprise Social Analytics Platform | About
Software analytics External links:
Software Analytics – Microsoft Research
Speech analytics External links:
Eureka: Speech Analytics Software | CallMiner
Speech Analytics | NICE
Customer Engagement & Speech Analytics | CallMiner
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
[PDF]statistical discrimination – Andrea Moro Webpage
Structured data External links:
Structured Data for Dummies – Search Engine Journal
Providing Structured Data | Custom Search | Google …
Telecommunications data retention External links:
Telecommunications Data Retention and Human Rights: …
Text analytics External links:
[PDF]Syllabus Course Title: Text Analytics – Regis University
Text Mining / Text Analytics Specialist – bigtapp
Text Analytics – Site Title
Text mining External links:
Text mining — University of Illinois at Urbana-Champaign
Text Mining with R
Applied Text Mining in Python | Coursera
Time series External links:
Stationarity and differencing of time series data
[PDF]Chapter 1 MINING TIME SERIES DATA
[PDF]Time Series Analysis and Forecasting – cengage.com
Unstructured data External links:
Scale-Out NAS for Unstructured Data | Dell EMC US
Structured vs. Unstructured data – BrightPlanet
The Data Difference | Unstructured Data DSP
User behavior analytics External links:
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
IBM QRadar User Behavior Analytics – Overview – United States
Web analytics External links:
Web Analytics in Real Time | Clicky
AFS Analytics – Web analytics
Careers | Mobile & Web Analytics | Mixpanel