216 Extremely Powerful HR Analytics Questions You Do Not Know

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 216 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:

Probe HR Analytics projects and simulate teachings and consultations on quality process improvement of HR Analytics.

– There are five ways to measure anything in business. They are cost, time, quantity, quality, and human reaction. The central question is: Which is most important to track?

– If you have determined that compensation and benefits are not necessarily creating an incentive for your employees to stay with you, where do you look next?

– What if your analytical model tells you that your hiring and firing policy is not at all sound or is even discriminatory?

– Who were the ones we used to trust, and whom do we want to establish alliances with for tomorrow?

– Do the drivers of employee engagement differ significantly in different regions of the world?

– What characterizes our organizations culture, and where should we seek improvement?

– Does our performance rating system accurately reflect actual employee performance?

– Is our employee rewards/recognition program more successful for certain functions?

– Do HR systems educate leaders about the quality of their human capital decisions?

– What interventions would be most effective in reducing high levels of turnover?

– What are the characteristics of managers with the highest employee loyalty?

– Who owns the specific data/metrics that senior leaders are focused?

– How do you know which variables have an effect on the outcomes?

– Which of our talent gaps are most critical to address?

– How can we reduce fraud/theft at a certain location?

– Does your company use HCMs in a scorecard?

– Are we paying our workers competitively?

– What factors drive employee retention?

– Who should or can do HR analytics?

Academic discipline Critical Criteria:

Jump start Academic discipline projects and define what our big hairy audacious Academic discipline goal is.

– How do your measurements capture actionable HR Analytics information for use in exceeding your customers expectations and securing your customers engagement?

– Are assumptions made in HR Analytics stated explicitly?

– What are the long-term HR Analytics goals?

Analytic applications Critical Criteria:

Participate in Analytic applications goals and find answers.

– How can we incorporate support to ensure safe and effective use of HR Analytics into the services that we provide?

– What are our needs in relation to HR Analytics skills, labor, equipment, and markets?

– What knowledge, skills and characteristics mark a good HR Analytics project manager?

– How do you handle Big Data in Analytic Applications?

– Analytic Applications: Build or Buy?

Architectural analytics Critical Criteria:

X-ray Architectural analytics management and find out what it really means.

– What is the source of the strategies for HR Analytics strengthening and reform?

– Can Management personnel recognize the monetary benefit of HR Analytics?

– Is HR Analytics Required?

Behavioral analytics Critical Criteria:

Powwow over Behavioral analytics tasks and grade techniques for implementing Behavioral analytics controls.

– 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?

– Does our organization need more HR Analytics education?

Big data Critical Criteria:

Bootstrap Big data goals and diversify disclosure of information – dealing with confidential Big data information.

– New roles. Executives interested in leading a big data transition can start with two simple techniques. First, they can get in the habit of asking What do the data say?

– Looking at hadoop big data in the rearview mirror, what would you have done differently after implementing a Data Lake?

– What are the main obstacles that prevent you from having access to all the datasets that are relevant for your organization?

– What is the quantifiable ROI for this solution (cost / time savings / data error minimization / etc)?

– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?

– To what extent does data-driven innovation add to the competitive advantage (CA) of your company?

– Which other Oracle Business Intelligence products are used in your solution?

– How can the best Big Data solution be chosen based on use case requirements?

– Does your organization have a strategy on big data or data analytics?

– What are the new applications that are enabled by Big Data solutions?

– Can analyses improve with better system and environment models?

– Does your organization buy datasets from other entities?

– How to model context in a computational environment?

– How does that compare to other science disciplines?

– What is tacit permission and approval, anyway?

– What metrics do we use to assess the results?

– Wait, DevOps does not apply to Big Data?

– How robust are the results?

– How to use in practice?

Business analytics Critical Criteria:

Test Business analytics engagements and point out Business analytics tensions in leadership.

– 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 are your most important goals for the strategic HR Analytics objectives?

– What is the difference between business intelligence and business analytics?

– what is the difference between Data analytics and Business Analytics If Any?

– How do you pick an appropriate ETL tool or business analytics tool?

– What are the trends shaping the future of business analytics?

– What about HR Analytics Analysis of results?

– Why are HR Analytics skills important?

Business intelligence Critical Criteria:

Think carefully about Business intelligence goals and finalize specific methods for Business intelligence acceptance.

– If on-premise software is a must, a balance of choice and simplicity is essential. When specific users are viewing and interacting with analytics, can you use a named-user licensing model that offers accessibility without the need for hardware considerations?

– 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?

– 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?

– Are NoSQL databases used primarily for applications or are they used in Business Intelligence use cases as well?

– Are business intelligence solutions starting to include social media data and analytics features?

– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

– Does your BI solution allow analytical insights to happen anywhere and everywhere?

– Was your software written by your organization or acquired from a third party?

– What are direct examples that show predictive analytics to be highly reliable?

– Does your BI solution help you find the right views to examine your data?

– Is data warehouseing necessary for our business intelligence service?

– Describe the process of data transformation required by your system?

– What is your anticipated learning curve for report users?

– Does your software integrate with active directory?

– To create parallel systems or custom workflows?

– How is Business Intelligence related to CRM?

– What is required to present video images?

– Why do we need business intelligence?

– Business Intelligence Tools?

Cloud analytics Critical Criteria:

Map Cloud analytics tasks and clarify ways to gain access to competitive Cloud analytics services.

– 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?

– What are your key performance measures or indicators and in-process measures for the control and improvement of your HR Analytics processes?

– What are the disruptive HR Analytics technologies that enable our organization to radically change our business processes?

Complex event processing Critical Criteria:

Coach on Complex event processing outcomes and oversee implementation of Complex event processing.

– Is there a HR Analytics Communication plan covering who needs to get what information when?

– Are there any disadvantages to implementing HR Analytics? There might be some that are less obvious?

– How can you measure HR Analytics in a systematic way?

Computer programming Critical Criteria:

Differentiate Computer programming decisions and finalize specific methods for Computer programming acceptance.

– In what ways are HR Analytics vendors and us interacting to ensure safe and effective use?

– How will we insure seamless interoperability of HR Analytics moving forward?

– How important is HR Analytics to the user organizations mission?

Continuous analytics Critical Criteria:

Reorganize Continuous analytics visions and balance specific methods for improving Continuous analytics results.

– Can we do HR Analytics without complex (expensive) analysis?

– What business benefits will HR Analytics goals deliver if achieved?

– Which HR Analytics goals are the most important?

Cultural analytics Critical Criteria:

Set goals for Cultural analytics issues and triple focus on important concepts of Cultural analytics relationship management.

– Does HR Analytics systematically track and analyze outcomes for accountability and quality improvement?

– Are there recognized HR Analytics problems?

– How can we improve HR Analytics?

Customer analytics Critical Criteria:

Start Customer analytics outcomes and get going.

– 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?

– 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?

– How does the organization define, manage, and improve its HR Analytics processes?

Data mining Critical Criteria:

Devise Data mining adoptions and document what potential Data mining megatrends could make our business model obsolete.

– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?

– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?

– Is business intelligence set to play a key role in the future of Human Resources?

– Who is the main stakeholder, with ultimate responsibility for driving HR Analytics forward?

– Have you identified your HR Analytics key performance indicators?

– How do we go about Comparing HR Analytics approaches/solutions?

– What programs do we have to teach data mining?

Data presentation architecture Critical Criteria:

Dissect Data presentation architecture governance and drive action.

– How do senior leaders actions reflect a commitment to the organizations HR Analytics values?

Embedded analytics Critical Criteria:

Troubleshoot Embedded analytics strategies and correct Embedded analytics management by competencies.

– What are our HR Analytics Processes?

Enterprise decision management Critical Criteria:

Track Enterprise decision management planning and explore and align the progress in Enterprise decision management.

– What are the key elements of your HR Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?

– How do we measure improved HR Analytics service perception, and satisfaction?

Fraud detection Critical Criteria:

Be responsible for Fraud detection tasks and adjust implementation of Fraud detection.

– 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?

– 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 much does HR Analytics help?

Google Analytics Critical Criteria:

Start Google Analytics tactics and spearhead techniques for implementing Google Analytics.

– 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?

– Why is it important to have senior management support for a HR Analytics project?

– How will you measure your HR Analytics effectiveness?

Human resources Critical Criteria:

Set goals for Human resources goals and ask questions.

– Do the response plans address damage assessment, site restoration, payroll, Human Resources, information technology, and administrative support?

– 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?

– What happens if an individual objects to the collection, use, and disclosure of his or her personal data?

– What are the procedures for filing an internal complaint about the handling of personal data?

– Should pay levels and differences reflect what workers are used to in their own countries?

– What are the responsibilities of the company official responsible for compliance?

– How important is it for organizations to train and develop their Human Resources?

– Why does the company collect and use personal data in the employment context?

– What problems have you encountered with the department or staff member?

– How is The staffs ability and response to handle questions or requests?

– What will be your Human Resources needs for the first year?

– How can we promote retention of high performing employees?

– To achieve our vision, what customer needs must we serve?

– Will an algorithm shield us from liability?

– Why is transparency important?

– How to deal with diversity?

Learning analytics Critical Criteria:

Win new insights about Learning analytics tactics and point out improvements in Learning analytics.

– What prevents me from making the changes I know will make me a more effective HR Analytics leader?

– What potential environmental factors impact the HR Analytics effort?

– How do we keep improving HR Analytics?

Machine learning Critical Criteria:

Revitalize Machine learning projects and simulate teachings and consultations on quality process improvement of 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?

– What tools do you use once you have decided on a HR Analytics strategy and more importantly how do you choose?

Marketing mix modeling Critical Criteria:

Confer re Marketing mix modeling governance and find the ideas you already have.

– Is HR Analytics dependent on the successful delivery of a current project?

Mobile Location Analytics Critical Criteria:

Powwow over Mobile Location Analytics results and find out what it really means.

– What are your current levels and trends in key measures or indicators of HR 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?

Neural networks Critical Criteria:

Wrangle Neural networks projects and change contexts.

– What tools and technologies are needed for a custom HR Analytics project?

– What are specific HR Analytics Rules to follow?

News analytics Critical Criteria:

Exchange ideas about News analytics planning and intervene in News analytics processes and leadership.

– Think of your HR Analytics project. what are the main functions?

– How do we maintain HR Analyticss Integrity?

Online analytical processing Critical Criteria:

Distinguish Online analytical processing tactics and proactively manage Online analytical processing risks.

– Think about the people you identified for your HR 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?

– Why should we adopt a HR Analytics framework?

– What is Effective HR Analytics?

Online video analytics Critical Criteria:

Audit Online video analytics risks and secure Online video analytics creativity.

– 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?

– How do we know that any HR Analytics analysis is complete and comprehensive?

Operational reporting Critical Criteria:

Do a round table on Operational reporting management and devote time assessing Operational reporting and its risk.

– Will HR Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?

– Are accountability and ownership for HR Analytics clearly defined?

Operations research Critical Criteria:

Cut a stake in Operations research management and correct better engagement with Operations research results.

– Why is HR Analytics important for you now?

Over-the-counter data Critical Criteria:

Systematize Over-the-counter data governance and probe Over-the-counter data strategic alliances.

– What is the purpose of HR Analytics in relation to the mission?

– Do HR Analytics rules make a reasonable demand on a users capabilities?

Portfolio analysis Critical Criteria:

Air ideas re Portfolio analysis management and find the essential reading for Portfolio analysis researchers.

– What are the Key enablers to make this HR Analytics move?

– How to Secure HR Analytics?

Predictive analytics Critical Criteria:

Study Predictive analytics visions and gather practices for scaling Predictive analytics.

– Will new equipment/products be required to facilitate HR Analytics delivery for example is new software needed?

– Who are the people involved in developing and implementing HR Analytics?

Predictive engineering analytics Critical Criteria:

Concentrate on Predictive engineering analytics tactics and intervene in Predictive engineering analytics processes and leadership.

– What is the total cost related to deploying HR Analytics, including any consulting or professional services?

Predictive modeling Critical Criteria:

Match Predictive modeling planning and explain and analyze the challenges of Predictive modeling.

– How do you determine the key elements that affect HR Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?

– Are you currently using predictive modeling to drive results?

– Is Supporting HR Analytics documentation required?

Prescriptive analytics Critical Criteria:

Meet over Prescriptive analytics quality and give examples utilizing a core of simple Prescriptive analytics skills.

– Do we all define HR Analytics in the same way?

– What are current HR Analytics Paradigms?

Price discrimination Critical Criteria:

Shape Price discrimination issues and be persistent.

– Does HR Analytics analysis isolate the fundamental causes of problems?

Risk analysis Critical Criteria:

Rank Risk analysis visions and triple focus on important concepts of Risk analysis relationship management.

– 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?

– In a project to restructure HR Analytics outcomes, which stakeholders would you involve?

– 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 would one define HR Analytics leadership?

Security information and event management Critical Criteria:

Guide Security information and event management visions and revise understanding of Security information and event management architectures.

– 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?

– Is the HR Analytics organization completing tasks effectively and efficiently?

– How can the value of HR Analytics be defined?

Semantic analytics Critical Criteria:

Do a round table on Semantic analytics engagements and find answers.

– How do we ensure that implementations of HR Analytics products are done in a way that ensures safety?

Smart grid Critical Criteria:

Illustrate Smart grid planning and intervene in Smart grid processes and leadership.

– 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?

– Is a HR Analytics Team Work effort in place?

– How do we Lead with HR Analytics in Mind?

Social analytics Critical Criteria:

Air ideas re Social analytics planning and differentiate in coordinating Social analytics.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent HR Analytics services/products?

Software analytics Critical Criteria:

X-ray Software analytics issues and gather Software analytics models .

– How do mission and objectives affect the HR Analytics processes of our organization?

Speech analytics Critical Criteria:

Design Speech analytics planning and create a map for yourself.

– How can you negotiate HR Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?

– Does HR Analytics analysis show the relationships among important HR Analytics factors?

– What new services of functionality will be implemented next with HR Analytics ?

Statistical discrimination Critical Criteria:

Define Statistical discrimination quality and budget the knowledge transfer for any interested in Statistical discrimination.

– What are the barriers to increased HR Analytics production?

Stock-keeping unit Critical Criteria:

Debate over Stock-keeping unit visions and customize techniques for implementing Stock-keeping unit controls.

– For your HR Analytics project, identify and describe the business environment. is there more than one layer to the business environment?

Structured data Critical Criteria:

Discourse Structured data projects and budget for Structured data challenges.

– 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?

– What are the short and long-term HR Analytics goals?

– What are internal and external HR Analytics relations?

Telecommunications data retention Critical Criteria:

Transcribe Telecommunications data retention adoptions and gather Telecommunications data retention models .

– What are the success criteria that will indicate that HR Analytics objectives have been met and the benefits delivered?

– What will drive HR Analytics change?

Text analytics Critical Criteria:

Meet over Text analytics risks and question.

– Have text analytics mechanisms like entity extraction been considered?

Text mining Critical Criteria:

Have a session on Text mining strategies and mentor Text mining customer orientation.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these HR Analytics processes?

– How do we manage HR Analytics Knowledge Management (KM)?

Time series Critical Criteria:

Audit Time series planning and ask questions.

– Is the scope of HR Analytics defined?

Unstructured data Critical Criteria:

Familiarize yourself with Unstructured data leadership and correct better engagement with Unstructured data results.

User behavior analytics Critical Criteria:

Survey User behavior analytics goals and budget the knowledge transfer for any interested in User behavior analytics.

– What are the top 3 things at the forefront of our HR Analytics agendas for the next 3 years?

– Is maximizing HR Analytics protection the same as minimizing HR Analytics loss?

Visual analytics Critical Criteria:

Check Visual analytics quality and define Visual analytics competency-based leadership.

Web analytics Critical Criteria:

Illustrate Web analytics management and pay attention to the small things.

– 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:

Start Win–loss analytics tactics and give examples utilizing a core of simple Win–loss analytics skills.

– What vendors make products that address the HR Analytics needs?


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.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

HR Analytics External links:

Analytics in HR – The community for HR Analytics …

HR Analytics | Rite Aid Pharmacy Jobs

[PDF]HR Analytics

Academic discipline External links:

Criminal justice | academic discipline | Britannica.com

What does academic discipline mean? – Definitions.net
http://www.definitions.net/definition/academic discipline

Analytic applications External links:

Analytic Applications – Gartner IT Glossary

Foxtrot Code AI Analytic Applications (Home)

Hype Cycle for Back-Office Analytic Applications, 2017

Architectural analytics External links:

Architectural Analytics – Home | Facebook

Behavioral analytics External links:

User and Entity Behavioral Analytics Partners | Exabeam

Behavioral Analytics – Mattersight

FraudMAP Behavioral Analytics Solutions Brochure | Fiserv

Big data External links:

Event Hubs – Cloud big data solutions | Microsoft Azure

Loudr: Big Data for Music Rights

Swiftly – Leverage big data to move your city

Business intelligence External links:

List of Business Intelligence Skills – The Balance

Cloud analytics External links:

Cloud Analytics – Solutions for Cloud Data Analytics | NetApp

Cloud Analytics Academy | Hosted by Snowflake

Computer programming External links:

Computer programming | Computing | Khan Academy

Computer Programming, Robotics & Engineering – STEM For Kids

Continuous analytics External links:

continuous analytics Archives – Iguazio

[PDF]Continuous Analytics: Stream Query Processing in …

Customer analytics External links:

Zylotech- AI For Customer Analytics

Customer Analytics and Customer Journey Management

Customer Analytics Services and Solutions | TransUnion

Data mining External links:

Title Data Mining Jobs, Employment | Indeed.com

Job Titles in Data Mining – KDnuggets

UT Data Mining

Embedded analytics External links:

Power BI Embedded analytics | Microsoft Azure

What is embedded analytics ? – Definition from WhatIs.com


Enterprise decision management External links:

enterprise decision management Archives – Insights

Enterprise Decision Management | Sapiens DECISION

Enterprise Decision Management (EDM) – Techopedia.com

Fraud detection External links:

Title IV fraud detection | University Business Magazine

Big Data Fraud Detection | DataVisor

Google Analytics External links:

Enterprise Marketing Analytics – Google Analytics 360 Suite

Analytics Pros | Google Analytics 360 Consultants & …

Google Analytics Solutions – Marketing Analytics & …

Human resources External links:

myDHR | Maryland Department of Human Resources

Phila.gov | Human Resources | Jobs

Human Resources | Maricopa Community Colleges

Learning analytics External links:

Learning Analytics Explained (eBook, 2017) [WorldCat.org]

Journal of Learning Analytics

Learning analytics – MoodleDocs

Machine learning External links:

Comcast Labs – PHLAI: Machine Learning Conference

DataRobot – Automated Machine Learning for Predictive …

ZestFinance.com: Machine Learning & Big Data …

Marketing mix modeling External links:

Marketing Mix Modeling – Gartner IT Glossary

Marketing Mix Modeling | Marketing Management Analytics

Mobile Location Analytics External links:

Mobile Location Analytics Privacy Notice | Verizon

[PDF]Mobile Location Analytics Code of Conduct

How ‘Mobile Location Analytics’ Controls Your Mind – YouTube

Neural networks External links:

Artificial Neural Networks – ScienceDirect

Neural Networks – ScienceDirect.com

News analytics External links:

Yakshof – Big Data News Analytics

Online video analytics External links:

Managing Your Online Video Analytics – DaCast

Operations research External links:

Operations research (Book, 1974) [WorldCat.org]

Operations Research on JSTOR

Systems Engineering and Operations Research

Over-the-counter data External links:

[PDF]Over-the-Counter Data’s Impact on Educators’ Data …

Over-the-Counter Data

Portfolio analysis External links:

[PPT]Introduction to Portfolio Analysis – DPCPSI

Portfolio analysis (Book, 1979) [WorldCat.org]

Portfolio Analysis | Economy Watch

Predictive analytics External links:

Predictive Analytics for Healthcare | Forecast Health

Stategic Location Management & Predictive Analytics | …

Predictive Analytics Software, Social Listening | NewBrand

Predictive engineering analytics External links:

Predictive Engineering Analytics: Siemens PLM Software

Predictive modeling External links:

What is predictive modeling? – Definition from WhatIs.com

DataRobot – Automated Machine Learning for Predictive Modeling

SDN Predictive Modeling – Student Doctor Network

Prescriptive analytics External links:

Healthcare Prescriptive Analytics – Cedar Gate …

Prescriptive Analytics – Gartner IT Glossary

Risk analysis External links:

What is Risk Analysis? – Definition from Techopedia

SEC.gov | About the Division of Economic and Risk Analysis

JIFSAN: Risk Analysis Training

Security information and event management External links:

Magic Quadrant for Security Information and Event Management

Semantic analytics External links:

What is semantic analytics? – Quora

[PDF]Geospatial and Temporal Semantic Analytics

Smart grid External links:

Honeywell Smart Grid

Smart Grid Solutions | Smart Grid System Integration …

Smart Grid – AbeBooks

Social analytics External links:

Influencer marketing platform & Social analytics tool – …

Enterprise Social Analytics Platform | About

Social Analytics – Marchex

Software analytics External links:

Software Analytics – Microsoft Research

Speech analytics External links:

Yactraq – Speech Analytics & Audio Mining

DEVELOPERS – Speech recognition & speech analytics APIs

Speech Analytics ROI Calculator Inquiry – CallMiner

Statistical discrimination External links:

[PDF]Testing for Statistical Discrimination in Health Care

“Employer Learning and Statistical Discrimination”

Structured data External links:

Introduction to Structured Data | Search | Google Developers

Chapter 11 Structured Data Flashcards | Quizlet

4 ways to improve SEO with schema and structured data

Telecommunications data retention External links:

Telecommunications Data Retention and Human …

Text analytics External links:

Text analytics software| NICE LTD | NICE

[PDF]Syllabus Course Title: Text Analytics – Regis University

Text Mining / Text Analytics Specialist – bigtapp

Text mining External links:

Text mining in practice with R (eBook, 2017) [WorldCat.org]

Text Mining Specialist Jobs, Employment | Indeed.com

Text mining — University of Illinois at Urbana-Champaign

Time series External links:

InfluxDays | Time Series Data & Applications Conference

[PDF]Time Series Analysis and Forecasting – cengage.com

Initial State – Analytics for Time Series Data

Unstructured data External links:

Gigaom | Sector Roadmap: Unstructured Data …

User behavior analytics External links:

User Behavior Analytics (UBA) Tools and Solutions | Rapid7

Web analytics External links:

AFS Analytics – Web analytics

View Web Analytics reports (SharePoint Server 2010)

11 Best Web Analytics Tools | Inc.com

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