What is involved in HR Analytics
Find out what the related areas are that HR 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 HR Analytics thinking-frame.
How far is your company on its HR Analytics journey?
Take this short survey to gauge your organization’s progress toward HR 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 HR Analytics related domains to cover and 210 essential critical questions to check off in that domain.
The following domains are covered:
HR 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:
HR Analytics Critical Criteria:
Wrangle HR Analytics strategies and be persistent.
– Is there a need in the organization to utilize analytics by internal customers (senior executives to front-line managers)?
– Do the drivers of employee engagement differ significantly in different regions of the world?
– What would be the best actions to take to better manage our employees who work remotely?
– Operationalize your hypothesis: How will you act on the information that you pull in?
– What specifically can executives do to help employees be as successful as possible?
– On what outcomes/metrics are the senior leaders in this organization most focused?
– What key measures should we include in our annual report to stockholders?
– What differentiates our locations where we have higher customer loyalty?
– Why are so many of our new hires leaving within the first few months?
– what is the sweet spot for job tenure for our sales representatives?
– What actions should we take to attract a more diverse workforce?
– How do you decide the likelihood something is going to happen?
– What employee characteristics drive customer satisfaction?
– How successful is our employee orientation program?
– What characterizes our most successful managers?
– What metrics does the organization value most?
– What is the internal customer experience?
– When should we use HR analytics?
– How is employee morale?
Academic discipline Critical Criteria:
Look at Academic discipline issues and improve Academic discipline service perception.
– How do you determine the key elements that affect HR Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
– Have all basic functions of HR Analytics been defined?
– Why are HR Analytics skills important?
Analytic applications Critical Criteria:
Sort Analytic applications results and probe the present value of growth of Analytic applications.
– What management system can we use to leverage the HR Analytics experience, ideas, and concerns of the people closest to the work to be done?
– What are internal and external HR Analytics relations?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
– What is our HR Analytics Strategy?
Architectural analytics Critical Criteria:
Shape Architectural analytics leadership and research ways can we become the Architectural analytics company that would put us out of business.
– What are your results for key measures or indicators of the accomplishment of your HR Analytics strategy and action plans, including building and strengthening core competencies?
– Have you identified your HR Analytics key performance indicators?
– What are the short and long-term HR Analytics goals?
Behavioral analytics Critical Criteria:
Match Behavioral analytics results and prioritize challenges of Behavioral analytics.
– What prevents me from making the changes I know will make me a more effective HR Analytics leader?
– Is HR Analytics Required?
Big data Critical Criteria:
Deliberate Big data leadership and point out improvements in Big data.
– Have we let algorithms and large centralized data centres not only control the remembering but also the meaning and interpretation of the data?
– Erp versus big data are the two philosophies of information architecture consistent complementary or in conflict with each other?
– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?
– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?
– Does your organization perceive the need for more effort to promote security and trust in data technologies?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?
– What are our needs in relation to HR Analytics skills, labor, equipment, and markets?
– How to identify relevant fragments of data easily from a multitude of data sources?
– Which other Oracle Business Intelligence products are used in your solution?
– Does your organization have a strategy on big data or data analytics?
– When we plan and design, how well do we capture previous experience?
– With more data to analyze, can Big Data improve decision-making?
– Are our business activities mainly conducted in one country?
– What is it that we don t know we don t know about the data?
– Can analyses improve with more data to process?
– What if the data cannot fit on your computer?
– what is Different about Big Data?
– Where is the ROI?
Business analytics Critical Criteria:
Mix Business analytics governance and finalize specific methods for Business analytics acceptance.
– 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?
– Is the HR Analytics organization completing tasks effectively and efficiently?
– What is the difference between business intelligence and business analytics?
– How will we insure seamless interoperability of HR Analytics moving forward?
– what is the difference between Data analytics and Business Analytics If Any?
– How do you pick an appropriate ETL tool or business analytics tool?
– What are the trends shaping the future of business analytics?
– What are the business goals HR Analytics is aiming to achieve?
Business intelligence Critical Criteria:
Examine Business intelligence decisions and explain and analyze the challenges of Business intelligence.
– 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 your BI solution allow analytical insights to happen anywhere and everywhere?
– Is business intelligence set to play a key role in the future of human resources?
– What are direct examples that show predictive analytics to be highly reliable?
– What are some software and skills that every Data Scientist should know?
– What are the pros and cons of outsourcing Business Intelligence?
– What are some best practices for managing business intelligence?
– What types of courses do you run and what are their durations?
– What type and complexity of system administration roles?
– How can data extraction from dashboards be automated?
– What are some real time data analysis frameworks?
– How stable is it across domains/geographies?
– What are our tools for big data analytics?
– What is required to present video images?
– What are typical reporting applications?
– Do you still need a data warehouse?
– Do you support video integration?
– How are you going to manage?
– Using dashboard functions?
Cloud analytics Critical Criteria:
Tête-à-tête about Cloud analytics quality and create Cloud analytics explanations for all managers.
– Among the HR Analytics product and service cost to be estimated, which is considered hardest to estimate?
– Does HR Analytics create potential expectations in other areas that need to be recognized and considered?
Complex event processing Critical Criteria:
Investigate Complex event processing engagements and drive action.
– How do we Lead with HR Analytics in Mind?
– What threat is HR Analytics addressing?
Computer programming Critical Criteria:
Do a round table on Computer programming tactics and describe which business rules are needed as Computer programming interface.
– What tools do you use once you have decided on a HR Analytics strategy and more importantly how do you choose?
– How is the value delivered by HR Analytics being measured?
– What are the long-term HR Analytics goals?
Continuous analytics Critical Criteria:
Win new insights about Continuous analytics governance and forecast involvement of future Continuous analytics projects in development.
– Consider your own HR Analytics project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– Can Management personnel recognize the monetary benefit of HR Analytics?
– Does the HR Analytics task fit the clients priorities?
Cultural analytics Critical Criteria:
Mine Cultural analytics adoptions and devote time assessing Cultural analytics and its risk.
– Meeting the challenge: are missed HR Analytics opportunities costing us money?
– Are there recognized HR Analytics problems?
– Are we Assessing HR Analytics and Risk?
Customer analytics Critical Criteria:
Air ideas re Customer analytics projects and find the ideas you already have.
– Will HR Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Does HR Analytics analysis show the relationships among important HR Analytics factors?
– Is Supporting HR Analytics documentation required?
Data mining Critical Criteria:
Pilot Data mining risks and correct better engagement with Data mining results.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which HR Analytics models, tools and techniques are necessary?
– 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?
– What are all of our HR Analytics domains and what do they do?
– What programs do we have to teach data mining?
Data presentation architecture Critical Criteria:
Grasp Data presentation architecture projects and diversify disclosure of information – dealing with confidential Data presentation architecture information.
– what is the best design framework for HR Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about HR Analytics. How do we gain traction?
– How do we keep improving HR Analytics?
Embedded analytics Critical Criteria:
See the value of Embedded analytics results and ask questions.
– Does HR 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?
– Will new equipment/products be required to facilitate HR Analytics delivery for example is new software needed?
Enterprise decision management Critical Criteria:
Explore Enterprise decision management projects and find the ideas you already have.
– Are assumptions made in HR Analytics stated explicitly?
Fraud detection Critical Criteria:
Unify Fraud detection leadership and probe Fraud detection strategic alliances.
– Are there any easy-to-implement alternatives to HR Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
Google Analytics Critical Criteria:
Revitalize Google Analytics management and define what do we need to start doing with Google Analytics.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a HR Analytics process. ask yourself: are the records needed as inputs to the HR Analytics process available?
– How do your measurements capture actionable HR Analytics information for use in exceeding your customers expectations and securing your customers engagement?
– What will drive HR Analytics change?
Human resources Critical Criteria:
Review Human resources leadership and change contexts.
– Rapidly increasing specialization of skill and knowledge presents a major management challenge. How does an organization maintain a work environment that supports specialization without compromising its ability to marshal its full range of Human Resources and turn on a dime to implement strategic imperatives?
– Who will be responsible for leading the various bcp teams (e.g., crisis/emergency, recovery, technology, communications, facilities, Human Resources, business units and processes, Customer Service)?
– 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?
– Do the response plans address damage assessment, site restoration, payroll, Human Resources, information technology, and administrative support?
– Under what circumstances might the company disclose personal data to third parties and what steps does the company take to safeguard that data?
– How often do we hold meaningful conversations at the operating level among sales, finance, operations, IT, and human resources?
– What finance, procurement and Human Resources business processes should be included in the scope of a erp solution?
– Is there a role for employees to play in maintaining the accuracy of personal data the company maintains?
– Where can an employee go for further information about the dispute resolution program?
– How is The staffs ability and response to handle questions or requests?
– What is the important thing that human resources management should do?
– What are the legal risks in using Big Data/People Analytics in hiring?
– To achieve our goals, how must our organization learn and innovate?
– How do you view the department and staff members as a whole?
– How does the global environment influence management?
– Do you need to develop a Human Resources manual?
– How is the Content updated of the hr website?
– How is the Ease of navigating the hr website?
– What additional approaches already exist?
Learning analytics Critical Criteria:
Scan Learning analytics leadership and probe using an integrated framework to make sure Learning analytics is getting what it needs.
Machine learning Critical Criteria:
Consider Machine learning failures and define what do we need to start doing with Machine learning.
– What are our best practices for minimizing HR Analytics project risk, while demonstrating incremental value and quick wins throughout the HR Analytics project lifecycle?
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– To what extent does management recognize HR Analytics as a tool to increase the results?
– When a HR Analytics manager recognizes a problem, what options are available?
Marketing mix modeling Critical Criteria:
Deliberate Marketing mix modeling strategies and oversee Marketing mix modeling requirements.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to HR Analytics?
Mobile Location Analytics Critical Criteria:
Mine Mobile Location Analytics outcomes and customize techniques for implementing Mobile Location Analytics controls.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new HR Analytics in a volatile global economy?
– What will be the consequences to the business (financial, reputation etc) if HR Analytics does not go ahead or fails to deliver the objectives?
Neural networks Critical Criteria:
Concentrate on Neural networks outcomes and change contexts.
– Which HR Analytics goals are the most important?
– How would one define HR Analytics leadership?
News analytics Critical Criteria:
Illustrate News analytics projects and explain and analyze the challenges of News analytics.
– Do we monitor the HR Analytics decisions made and fine tune them as they evolve?
– How can the value of HR Analytics be defined?
Online analytical processing Critical Criteria:
Start Online analytical processing failures and grade techniques for implementing Online analytical processing controls.
– Is HR Analytics dependent on the successful delivery of a current project?
Online video analytics Critical Criteria:
Jump start Online video analytics management and work towards be a leading Online video analytics expert.
– Which individuals, teams or departments will be involved in HR Analytics?
Operational reporting Critical Criteria:
Coach on Operational reporting governance and track iterative Operational reporting results.
– Why should we adopt a HR Analytics framework?
– Is a HR Analytics Team Work effort in place?
– How to deal with HR Analytics Changes?
Operations research Critical Criteria:
Experiment with Operations research tasks and assess and formulate effective operational and Operations research strategies.
– At what point will vulnerability assessments be performed once HR Analytics is put into production (e.g., ongoing Risk Management after implementation)?
– What are your most important goals for the strategic HR Analytics objectives?
– What is the purpose of HR Analytics in relation to the mission?
Over-the-counter data Critical Criteria:
Track Over-the-counter data outcomes and visualize why should people listen to you regarding Over-the-counter data.
– How do we ensure that implementations of HR Analytics products are done in a way that ensures safety?
– In a project to restructure HR Analytics outcomes, which stakeholders would you involve?
Portfolio analysis Critical Criteria:
Model after Portfolio analysis governance and perfect Portfolio analysis conflict management.
– What business benefits will HR Analytics goals deliver if achieved?
Predictive analytics Critical Criteria:
Bootstrap Predictive analytics adoptions and catalog Predictive analytics activities.
– What knowledge, skills and characteristics mark a good HR Analytics project manager?
Predictive engineering analytics Critical Criteria:
Talk about Predictive engineering analytics management and probe using an integrated framework to make sure Predictive engineering analytics is getting what it needs.
Predictive modeling Critical Criteria:
Prioritize Predictive modeling governance and get the big picture.
– Are you currently using predictive modeling to drive results?
– Do HR Analytics rules make a reasonable demand on a users capabilities?
Prescriptive analytics Critical Criteria:
Value Prescriptive analytics leadership and simulate teachings and consultations on quality process improvement of Prescriptive analytics.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these HR Analytics processes?
– Who is the main stakeholder, with ultimate responsibility for driving HR Analytics forward?
– Why is it important to have senior management support for a HR Analytics project?
Price discrimination Critical Criteria:
Investigate Price discrimination quality and slay a dragon.
– Do several people in different organizational units assist with the HR Analytics process?
Risk analysis Critical Criteria:
Group Risk analysis tactics and differentiate in coordinating Risk analysis.
– How do risk analysis and Risk Management inform your organizations decisionmaking processes for long-range system planning, major project description and cost estimation, priority programming, and project development?
– What levels of assurance are needed and how can the risk analysis benefit setting standards and policy functions?
– In which two Service Management processes would you be most likely to use a risk analysis and management method?
– How does the business impact analysis use data from Risk Management and risk analysis?
– How do we do risk analysis of rare, cascading, catastrophic events?
– With risk analysis do we answer the question how big is the risk?
– How do we go about Comparing HR Analytics approaches/solutions?
Security information and event management Critical Criteria:
Steer Security information and event management visions and diversify by understanding risks and leveraging Security information and event management.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your HR Analytics processes?
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding HR Analytics?
– What sources do you use to gather information for a HR Analytics study?
Semantic analytics Critical Criteria:
Revitalize Semantic analytics visions and don’t overlook the obvious.
– Who will be responsible for making the decisions to include or exclude requested changes once HR Analytics is underway?
Smart grid Critical Criteria:
Rank Smart grid failures 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?
– What are the disruptive HR Analytics technologies that enable our organization to radically change our business processes?
– How do we make it meaningful in connecting HR Analytics with what users do day-to-day?
– How do we Improve HR Analytics service perception, and satisfaction?
Social analytics Critical Criteria:
Cut a stake in Social analytics leadership and plan concise Social analytics education.
– In the case of a HR Analytics project, the criteria for the audit derive from implementation objectives. an audit of a HR Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any HR Analytics project is implemented as planned, and is it working?
– What are specific HR Analytics Rules to follow?
Software analytics Critical Criteria:
Chat re Software analytics planning and simulate teachings and consultations on quality process improvement of Software analytics.
– What are the top 3 things at the forefront of our HR Analytics agendas for the next 3 years?
– How can we improve HR Analytics?
Speech analytics Critical Criteria:
Discourse Speech analytics issues and integrate design thinking in Speech analytics innovation.
– Who will provide the final approval of HR Analytics deliverables?
Statistical discrimination Critical Criteria:
Talk about Statistical discrimination quality and create a map for yourself.
– What are the success criteria that will indicate that HR Analytics objectives have been met and the benefits delivered?
Stock-keeping unit Critical Criteria:
Canvass Stock-keeping unit management and observe effective Stock-keeping unit.
Structured data Critical Criteria:
Steer Structured data tactics and proactively manage Structured data risks.
– 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:
Jump start Telecommunications data retention management and question.
– What potential environmental factors impact the HR Analytics effort?
Text analytics Critical Criteria:
Consolidate Text analytics projects and prioritize challenges of Text analytics.
– Have text analytics mechanisms like entity extraction been considered?
Text mining Critical Criteria:
Merge Text mining risks and work towards be a leading Text mining expert.
– How do we go about Securing HR Analytics?
Time series Critical Criteria:
Pilot Time series results and track iterative Time series results.
Unstructured data Critical Criteria:
Accelerate Unstructured data planning and know what your objective is.
– What are the key elements of your HR Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?
– Risk factors: what are the characteristics of HR Analytics that make it risky?
User behavior analytics Critical Criteria:
Probe User behavior analytics goals and devise User behavior analytics key steps.
– What tools and technologies are needed for a custom HR Analytics project?
– Think of your HR Analytics project. what are the main functions?
Visual analytics Critical Criteria:
Troubleshoot Visual analytics governance and suggest using storytelling to create more compelling Visual analytics projects.
– How do mission and objectives affect the HR Analytics processes of our organization?
– What are the usability implications of HR Analytics actions?
– Who sets the HR Analytics standards?
Web analytics Critical Criteria:
Gauge Web analytics quality and remodel and develop an effective Web analytics strategy.
– What statistics should one be familiar with for business intelligence and web analytics?
– Are we making progress? and are we making progress as HR Analytics leaders?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Think carefully about Win–loss analytics tactics and look in other fields.
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the HR 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:
HR Analytics External links:
Predictive HR Analytics
Analytics in HR – The community for HR Analytics practitioners
What is HR Analytics? – Definition from Techopedia
Academic discipline External links:
Criminal justice | academic discipline | Britannica.com
Academic Discipline – Earl Warren College
Academic Discipline – Earl Warren College
Analytic applications External links:
Foxtrot Code AI Analytic Applications (Home)
Architectural analytics External links:
Architectural Analytics – Home | Facebook
Behavioral analytics External links:
Behavioral Analytics | Interana
Magnifier Behavioral Analytics – Palo Alto Networks
Behavioral Analytics Definition | Investopedia
Big data External links:
Take 5 Media Group – Build an audience using big data
Loudr: Big Data for Music Rights
ZestFinance.com: Machine Learning & Big Data Underwriting
Business analytics External links:
What is Business Analytics? Webopedia Definition
Business Analytics and Strategic Decisions | SVB
Business intelligence External links:
Business Intelligence and Big Data Analytics Software
Mortgage Business Intelligence Software :: Motivity Solutions
Cloud analytics External links:
Cloud Analytics World Tour – Stockholm | Snowflake
Cloud Analytics Academy – Official Site
Cloud Analytics – Solutions for Cloud Data Analytics | NetApp
Computer programming External links:
Computer programming Meetups – Meetup
Computer Programming, Robotics & Engineering – STEM …
Continuous analytics External links:
[PDF]Continuous Analytics: Stream Query Processing in …
Cultural analytics External links:
Software Studies Initiative: Cultural analytics
Software Studies Initiative: Cultural analytics
Customer analytics External links:
BlueVenn – Customer Analytics and Customer Journey …
Zylotech- AI For Customer Analytics
Customer Analytics Services and Solutions | TransUnion
Data mining External links:
Data mining | computer science | Britannica.com
Data Mining on the Florida Department of Corrections Website
UT Data Mining
Embedded analytics External links:
Embedded Analytics – icCube
Embedded Analytics | ThoughtSpot
Embedded Analytics and Data Visualization | Reflect
Enterprise decision management External links:
Enterprise Decision Management | SAS Italy
Enterprise Decision Management | Sapiens DECISION
Enterprise Decision Management (EDM) – Techopedia.com
Fraud detection External links:
Fraud Detection and Authentication Technology – Next Caller
Big Data Fraud Detection | DataVisor
Fraud Detection and Anti-Money Laundering Software – Verafin
Google Analytics External links:
Google Analytics Opt-out Browser Add-on Download Page
Welcome to the Texas Board of Nursing – Google Analytics
Google Analytics Solutions – Marketing Analytics & …
Human resources External links:
Human Resources Job Titles-The Ultimate Guide | upstartHR
Human Resources Job Titles | Enlighten Jobs
Human Resources Job Titles – The Balance
Learning analytics External links:
Learning Analytics Explained. (eBook, 2017) [WorldCat.org]
Machine learning External links:
Titanic: Machine Learning from Disaster | Kaggle
DataRobot – Automated Machine Learning for Predictive …
Microsoft Azure Machine Learning Studio
Marketing mix modeling External links:
Marketing Mix Modeling | Marketing Management Analytics
Mobile Location Analytics External links:
How ‘Mobile Location Analytics’ Controls Your Mind – YouTube
Mobile Location Analytics Privacy Notice | Verizon
[PDF]Mobile Location Analytics Code of Conduct
Online analytical processing External links:
[PDF]Comparing Online Analytical Processing and Data …
[PDF]OLAP (Online Analytical Processing)
Working with Online Analytical Processing (OLAP)
Online video analytics External links:
Online Video Analytics & Marketing Software | Vidooly
Managing Your Online Video Analytics – DaCast
Operations research External links:
15-2031.00 – Operations Research Analysts – O*NET OnLine
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:
Over-the-Counter Data – American Mensa – Medium
[PDF]Over-the-Counter Data’s Impact on Educators’ Data …
Standards — Over-the-Counter Data
Portfolio analysis External links:
Loan Portfolio Analysis | Visible Equity
[PDF]Portfolio Analysis Tool: Methodologies and Assumptions
Portfolio Analysis Test 1 Flashcards | Quizlet
Predictive analytics External links:
Predictive Analytics Software, Social Listening | NewBrand
Predictive Analytics Solutions for Global Industry | Uptake
Customer Analytics & Predictive Analytics Tools for Business
Predictive engineering analytics External links:
Predictive engineering analytics is the application of multidisciplinary engineering simulation and test with intelligent reporting and data analytics, to develop digital twins that can predict the real world behavior of products throughout the product lifecycle.
Predictive modeling External links:
What is predictive modeling? – Definition from …
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate Technologies
Price discrimination External links:
MBAecon – 1st, 2nd and 3rd Price discrimination
A macroeconomic model of international price discrimination
Price Discrimination Flashcards | Quizlet
Risk analysis External links:
Risk Analysis | Investopedia
http://Risk analysis is the study of the underlying uncertainty of a given course of action. Risk analysis refers to the uncertainty of forecasted future cash flows streams, variance of portfolio/stock returns, statistical analysis to determine the probability of a project’s success or failure, and possible future economic states.
Project Management and Risk Analysis Software | Safran
Security information and event management External links:
A Guide to Security Information and Event Management
Semantic analytics External links:
What is semantic analytics? – Quora
SciBite – The Semantic Analytics Company
[PDF]Geospatial and Temporal Semantic Analytics
Smart grid External links:
Recovery Act Smart Grid Programs
Smart Grid – Coalition – Duke Energy
Smart grid. (Journal, magazine, 2011) [WorldCat.org]
Social analytics External links:
Union Metrics makes social analytics easy – TweetReach
Google Search with Social Analytics – ctrlq.org
Social Analytics – Votigo
Software analytics External links:
EDGEPro | EDGEPro Software Analytics Tool for Optometry
Speech analytics External links:
Eureka: Speech Analytics Software | CallMiner
What is speech analytics? – Definition from WhatIs.com
Customer Engagement & Speech Analytics | CallMiner
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
Structured data External links:
Structured Data for Dummies – Search Engine Journal
Providing Structured Data | Custom Search | Google …
SEC.gov | What Is Structured Data?
Telecommunications data retention External links:
Telecommunications Data Retention and Human Rights: …
Text analytics External links:
Text analytics software| NICE LTD | NICE
[PDF]Syllabus Course Title: Text Analytics – Regis University
The Truth about Text Analytics and Sentiment Analysis
Text mining External links:
Text Mining with R
Text Mining – AbeBooks
Applied Text Mining in Python | Coursera
Time series External links:
Initial State – Analytics for Time Series Data
[PDF]Time Series Analysis and Forecasting – cengage.com
[PDF]Chapter 1 MINING TIME SERIES DATA
Unstructured data External links:
Structured vs. Unstructured data – BrightPlanet
Scale-Out NAS for Unstructured Data | Dell EMC US
User behavior analytics External links:
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
IBM QRadar User Behavior Analytics – Overview – United States
Visual analytics External links:
Visual Analytics Working Group | AMIA
Web analytics External links:
Login – Web analytics | HitsLink
Web Analytics in Real Time | Clicky
Web analytics | HitsLink