Top 196 Advancing Business With Advanced Analytics Goals and Objectives Questions

What is involved in Advanced Analytics

Find out what the related areas are that Advanced 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 Advanced Analytics thinking-frame.

How far is your company on its Advancing Business With Advanced Analytics journey?

Take this short survey to gauge your organization’s progress toward Advancing Business With Advanced 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 Advanced Analytics related domains to cover and 196 essential critical questions to check off in that domain.

The following domains are covered:

Advanced 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:

Advanced Analytics Critical Criteria:

Focus on Advanced Analytics tasks and know what your objective is.

– To what extent does management recognize Advanced Analytics as a tool to increase the results?

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

– Who needs to know about Advanced Analytics ?

– What is Advanced Analytics?

Academic discipline Critical Criteria:

Grade Academic discipline management and maintain Academic discipline for success.

– How likely is the current Advanced Analytics plan to come in on schedule or on budget?

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

– Is a Advanced Analytics Team Work effort in place?

Analytic applications Critical Criteria:

Face Analytic applications risks and observe effective Analytic applications.

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

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

– How do you handle Big Data in Analytic Applications?

– Analytic Applications: Build or Buy?

Architectural analytics Critical Criteria:

Interpolate Architectural analytics quality and modify and define the unique characteristics of interactive Architectural analytics projects.

– Think about the kind of project structure that would be appropriate for your Advanced Analytics project. should it be formal and complex, or can it be less formal and relatively simple?

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

– Are accountability and ownership for Advanced Analytics clearly defined?

Behavioral analytics Critical Criteria:

Deliberate over Behavioral analytics strategies and customize techniques for implementing Behavioral analytics controls.

– What other jobs or tasks affect the performance of the steps in the Advanced Analytics process?

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

– What are the Essentials of Internal Advanced Analytics Management?

Big data Critical Criteria:

Group Big data visions and reduce Big data costs.

– 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?

– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?

– Is your organizations business affected by regulatory restrictions on data/servers localisation requirements?

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

– The real challenge: are you willing to get better value and more innovation for some loss of privacy?

– Does big data threaten the traditional data warehouse business intelligence model stack?

– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?

– Do you see areas in your domain or across domains where vendor lock-in is a potential risk?

– What are the legal risks in using Big Data/People Analytics in hiring?

– How are the new Big Data developments captured in new Reference Architectures?

– Is the process repeatable as we change algorithms and data structures?

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

– Is recruitment of staff with strong data skills crucial?

– Which other Oracle products are used in your solution?

– How to model context in a computational environment?

– How to attract and keep the community involved?

– What business challenges did you face?

– What are some impacts of Big Data?

– What can it be used for?

– What is Big Data to us?

Business analytics Critical Criteria:

Investigate Business analytics outcomes and explain and analyze the challenges of Business analytics.

– 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?

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

– Is the scope of Advanced Analytics defined?

– What is Effective Advanced Analytics?

Business intelligence Critical Criteria:

Infer Business intelligence outcomes and don’t overlook the obvious.

– Does your BI solution honor distinctions with dashboards that automatically authenticate and provide the appropriate level of detail based on a users privileges to the data source?

– 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?

– Choosing good key performance indicators (KPI Key Performance Indicators) did we start from the question How do you measure a companys success?

– Does your mobile solution allow you to interact with desktop-authored dashboards using touchscreen gestures like taps, flicks, and pinches?

– What is the difference between Key Performance Indicators KPI and Critical Success Factors CSF in a Business Strategic decision?

– Does your bi software work well with both centralized and decentralized data architectures and vendors?

– What are some successful business intelligence BI apps that have been built on an existing platform?

– What are the approaches to handle RTB related data 100 GB aggregated for business intelligence?

– What is the difference between Enterprise Information Management and Data Warehousing?

– What is the future scope for combination of Business Intelligence and Cloud Computing?

– What documentation is provided with the software / system and in what format?

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

– What are the pros and cons of outsourcing business intelligence?

– Can Business Intelligence BI meet business expectations?

– No single business unit responsible for enterprise data?

– What are the most efficient ways to create the models?

– How are business intelligence applications delivered?

– How stable is it across domains/geographies?

– Make or buy BI Business Intelligence?

Cloud analytics Critical Criteria:

Audit Cloud analytics visions and get answers.

– What will be the consequences to the business (financial, reputation etc) if Advanced Analytics does not go ahead or fails to deliver the objectives?

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

– How do we go about Securing Advanced Analytics?

Complex event processing Critical Criteria:

Conceptualize Complex event processing decisions and document what potential Complex event processing megatrends could make our business model obsolete.

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

– Which individuals, teams or departments will be involved in Advanced Analytics?

Computer programming Critical Criteria:

Adapt Computer programming governance and get the big picture.

– What management system can we use to leverage the Advanced Analytics experience, ideas, and concerns of the people closest to the work to be done?

– What threat is Advanced Analytics addressing?

Continuous analytics Critical Criteria:

Drive Continuous analytics engagements and cater for concise Continuous analytics education.

– What are the usability implications of Advanced Analytics actions?

– Have all basic functions of Advanced Analytics been defined?

– How would one define Advanced Analytics leadership?

Cultural analytics Critical Criteria:

Focus on Cultural analytics projects and suggest using storytelling to create more compelling Cultural analytics projects.

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

– Do we monitor the Advanced Analytics decisions made and fine tune them as they evolve?

Customer analytics Critical Criteria:

Think about Customer analytics management and figure out ways to motivate other Customer analytics users.

– Are there any easy-to-implement alternatives to Advanced Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project?

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

– Meeting the challenge: are missed Advanced Analytics opportunities costing us money?

Data mining Critical Criteria:

Consolidate Data mining planning and perfect Data mining conflict management.

– 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?

– Does Advanced Analytics create potential expectations in other areas that need to be recognized and considered?

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

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

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

– What are the business goals Advanced Analytics is aiming to achieve?

– What programs do we have to teach data mining?

Data presentation architecture Critical Criteria:

Reason over Data presentation architecture issues and summarize a clear Data presentation architecture focus.

– what is the best design framework for Advanced Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

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

Embedded analytics Critical Criteria:

Participate in Embedded analytics management and find answers.

– Among the Advanced Analytics product and service cost to be estimated, which is considered hardest to estimate?

– How do we Lead with Advanced Analytics in Mind?

Enterprise decision management Critical Criteria:

Think carefully about Enterprise decision management engagements and finalize the present value of growth of Enterprise decision management.

– Which customers cant participate in our Advanced Analytics domain because they lack skills, wealth, or convenient access to existing solutions?

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

– What are the record-keeping requirements of Advanced Analytics activities?

Fraud detection Critical Criteria:

Read up on Fraud detection outcomes and mentor Fraud detection customer orientation.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Advanced Analytics in a volatile global economy?

– Think about the functions involved in your Advanced Analytics project. what processes flow from these functions?

Google Analytics Critical Criteria:

Shape Google Analytics risks and research ways can we become the Google Analytics company that would put us out of business.

– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Advanced Analytics models, tools and techniques are necessary?

– What is our formula for success in Advanced Analytics ?

Human resources Critical Criteria:

Reorganize Human resources quality and probe Human resources strategic alliances.

– 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)?

– Imagine you work in the Human Resources department of a company considering a policy to protect its data on employees mobile devices. in advising on this policy, what rights should be considered?

– 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?

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

– What finance, procurement and Human Resources business processes should be included in the scope of a erp solution?

– Do we identify desired outcomes and key indicators (if not already existing) such as what metrics?

– To satisfy our customers and stakeholders, what financial objectives must we accomplish?

– 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?

– To achieve our goals, how must our organization learn and innovate?

– How does the company provide notice of its information practices?

– How can we more efficiently on-board and off-board employees?

– How can we promote retention of high performing employees?

– Do you understand the parameters set by the algorithm?

– Are we complying with existing security policies?

– How is the Ease of navigating the hr website?

– In what areas do you feel we can improve?

Learning analytics Critical Criteria:

Administer Learning analytics adoptions and assess what counts with Learning analytics that we are not counting.

– Do the Advanced Analytics decisions we make today help people and the planet tomorrow?

– How can skill-level changes improve Advanced Analytics?

Machine learning Critical Criteria:

Tête-à-tête about Machine learning tasks and pioneer acquisition of Machine learning systems.

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

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

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

Marketing mix modeling Critical Criteria:

Systematize Marketing mix modeling management and define what our big hairy audacious Marketing mix modeling goal is.

– Is Advanced Analytics Realistic, or are you setting yourself up for failure?

– Who will be responsible for documenting the Advanced Analytics requirements in detail?

Mobile Location Analytics Critical Criteria:

Boost Mobile Location Analytics strategies and perfect Mobile Location Analytics conflict management.

– Where do ideas that reach policy makers and planners as proposals for Advanced Analytics strengthening and reform actually originate?

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

– Are there Advanced Analytics Models?

Neural networks Critical Criteria:

Be clear about Neural networks outcomes and optimize Neural networks leadership as a key to advancement.

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

– Are there recognized Advanced Analytics problems?

News analytics Critical Criteria:

Adapt News analytics projects and assess and formulate effective operational and News analytics strategies.

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Advanced Analytics. How do we gain traction?

– How will you measure your Advanced Analytics effectiveness?

Online analytical processing Critical Criteria:

Co-operate on Online analytical processing decisions and display thorough understanding of the Online analytical processing process.

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

Online video analytics Critical Criteria:

Judge Online video analytics goals and reduce Online video analytics costs.

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

Operational reporting Critical Criteria:

Pilot Operational reporting decisions and maintain Operational reporting for success.

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

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

– What are the barriers to increased Advanced Analytics production?

Operations research Critical Criteria:

Accelerate Operations research failures and remodel and develop an effective Operations research strategy.

– Is Advanced Analytics Required?

Over-the-counter data Critical Criteria:

Experiment with Over-the-counter data results and innovate what needs to be done with Over-the-counter data.

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

– Are we making progress? and are we making progress as Advanced Analytics leaders?

Portfolio analysis Critical Criteria:

Confer re Portfolio analysis projects and research ways can we become the Portfolio analysis company that would put us out of business.

– What will drive Advanced Analytics change?

Predictive analytics Critical Criteria:

Co-operate on Predictive analytics goals and oversee implementation of Predictive analytics.

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

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

Predictive engineering analytics Critical Criteria:

Mine Predictive engineering analytics issues and drive action.

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

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

Predictive modeling Critical Criteria:

Match Predictive modeling projects and separate what are the business goals Predictive modeling is aiming to achieve.

– Are you currently using predictive modeling to drive results?

Prescriptive analytics Critical Criteria:

Explore Prescriptive analytics failures and mentor Prescriptive analytics customer orientation.

– Who will be responsible for making the decisions to include or exclude requested changes once Advanced Analytics is underway?

Price discrimination Critical Criteria:

Wrangle Price discrimination risks and ask what if.

Risk analysis Critical Criteria:

Explore Risk analysis leadership and pioneer acquisition of Risk analysis systems.

– 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?

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

Security information and event management Critical Criteria:

Focus on Security information and event management goals and describe which business rules are needed as Security information and event management interface.

– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Advanced Analytics?

Semantic analytics Critical Criteria:

Administer Semantic analytics outcomes and find the ideas you already have.

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

Smart grid Critical Criteria:

Refer to Smart grid outcomes and look for lots of ideas.

– 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 our best practices for minimizing Advanced Analytics project risk, while demonstrating incremental value and quick wins throughout the Advanced Analytics project lifecycle?

– How to deal with Advanced Analytics Changes?

Social analytics Critical Criteria:

Coach on Social analytics management and attract Social analytics skills.

– What are the long-term Advanced Analytics goals?

Software analytics Critical Criteria:

Facilitate Software analytics risks and look in other fields.

Speech analytics Critical Criteria:

Check Speech analytics quality and sort Speech analytics activities.

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

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

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

Statistical discrimination Critical Criteria:

Trace Statistical discrimination management and create a map for yourself.

Stock-keeping unit Critical Criteria:

Grade Stock-keeping unit tactics and get out your magnifying glass.

– How do we Identify specific Advanced Analytics investment and emerging trends?

– What are our Advanced Analytics Processes?

Structured data Critical Criteria:

Own Structured data issues and differentiate in coordinating Structured data.

– 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?

– Why should we adopt a Advanced Analytics framework?

Telecommunications data retention Critical Criteria:

Incorporate Telecommunications data retention strategies and do something to it.

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

Text analytics Critical Criteria:

Add value to Text analytics adoptions and learn.

– Have text analytics mechanisms like entity extraction been considered?

– Are we Assessing Advanced Analytics and Risk?

Text mining Critical Criteria:

Investigate Text mining results and finalize specific methods for Text mining acceptance.

– When a Advanced Analytics manager recognizes a problem, what options are available?

Time series Critical Criteria:

Probe Time series failures and find answers.

– What are all of our Advanced Analytics domains and what do they do?

Unstructured data Critical Criteria:

Collaborate on Unstructured data tasks and oversee Unstructured data management by competencies.

– Who will provide the final approval of Advanced Analytics deliverables?

– Which Advanced Analytics goals are the most important?

User behavior analytics Critical Criteria:

Own User behavior analytics risks and summarize a clear User behavior analytics focus.

Visual analytics Critical Criteria:

Nurse Visual analytics results and slay a dragon.

– Are there Advanced Analytics problems defined?

Web analytics Critical Criteria:

Focus on Web analytics strategies and test out new things.

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

– 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:

Analyze Win–loss analytics issues and find out.

– Consider your own Advanced Analytics project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Advancing Business With Advanced Analytics Self Assessment:

Author: Gerard Blokdijk

CEO at The Art of Service |

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:

Advanced Analytics External links:

Advanced Analytics – Big Data Analytics Defined by Gartner

OM1 Outcomes and Advanced Analytics

Academic discipline External links:

Folklore | academic discipline |

Academic Discipline – Earl Warren College

Criminal justice | academic discipline |

Analytic applications External links:

Foxtrot Code AI Analytic Applications (Home)

Aptos Analytic Applications – Aptos

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:

Take 5 Media Group – Build an audience using big data

Event Hubs – Cloud big data solutions | Microsoft Azure Machine Learning & Big Data Underwriting

Business analytics External links:

What is Business Analytics? Webopedia Definition

Business intelligence External links:

Mortgage Business Intelligence Software :: Motivity Solutions

Oracle Business Intelligence – RCI

Cloud analytics External links:

Cloud Analytics – Solutions for Cloud Data Analytics | NetApp

Cloud Analytics | Big Data Analytics | Vertica

Cloud Analytics Academy – Official Site

Computer programming External links:

Computer Programming, Robotics & Engineering – STEM …

Cultural analytics External links:

Cultural Analytics | Nuts and Bolts

Software Studies Initiative: Cultural analytics

Customer analytics External links:

Customer Analytics & Predictive Analytics Tools for Business

Zylotech- AI For Customer Analytics

Customer Analytics Services and Solutions | TransUnion

Data mining External links:

Data mining | computer science |

UT Data Mining

Data Mining Extensions (DMX) Reference | Microsoft Docs

Embedded analytics External links:

Embedded Analytics | ThoughtSpot

Embedded Analytics and Data Visualization | Reflect

Embedded Analytics | Tableau

Enterprise decision management External links:

enterprise decision management Archives – Insights

Enterprise Decision Management and the Payments …

Enterprise Decision Management (EDM) –

Fraud detection External links:

Credit Card Fraud Detection | Kaggle

Big Data Fraud Detection | DataVisor

Fraud Detection and Anti-Money Laundering Software – Verafin

Google Analytics External links:

Google Analytics Solutions – Marketing Analytics & …

Google Analytics – Sign in

Google Analytics Opt-out Browser Add-on Download Page

Human resources External links:

Office of Human Resources – TITLE IX

Human Resources Job Titles | Enlighten Jobs

Title Human Resources HR Jobs, Employment |

Learning analytics External links:

Deep Learning Analytics

Learning Analytics | Riptide Elements

Watershed | Learning Analytics for Organizations

Machine learning External links:

What is machine learning? – Definition from

Titanic: Machine Learning from Disaster | Kaggle

DataRobot – Automated Machine Learning for Predictive …

Marketing mix modeling External links:

Marketing Mix Modeling | Marketing Management Analytics

Marketing Mix Modeling – Gartner IT Glossary

Mobile Location Analytics External links:

Mobile Location Analytics Privacy Notice | Verizon

How ‘Mobile Location Analytics’ Controls Your Mind – YouTube

[PDF]Mobile Location Analytics Code of Conduct

News analytics External links:

Yakshof – Big Data News Analytics

News Analytics | Amareos

Online analytical processing External links:

[PDF]OLAP (Online Analytical Processing) – SRM University

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:

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.

Operations Research on JSTOR

Operations research (Book, 1974) []

Over-the-counter data External links:

Standards — Over-the-Counter Data

Over-the-Counter Data – American Mensa – Medium

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

Portfolio analysis External links:

Portfolio Analysis Final-1 Flashcards | Quizlet

U.S. Army STAND-TO! | Strategic Portfolio Analysis Review

Loan Portfolio Analysis | Visible Equity

Predictive analytics External links:

Customer Analytics & Predictive Analytics Tools for Business

Predictive Analytics Software, Social Listening | NewBrand

Strategic Location Management & Predictive Analytics | Tango

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,++2nd+and+3rd+Price+discrimination

Price Discrimination Flashcards | Quizlet

3 Types of Price Discrimination |

Risk analysis External links:

Risk Analysis
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.

Risk Analysis | Investopedia

Project Management and Risk Analysis Software | Safran

Security information and event management External links:

A Guide to Security Information and Event Management,2-864.html

Semantic analytics External links:

Semantic Analytics – Get Business Intelligence With Schema …

SciBite – The Semantic Analytics Company

[PDF]Geospatial and Temporal Semantic Analytics

Smart grid External links:

Smart grid. (Journal, magazine, 2011) []

Smart Grid Solutions | Smart Grid System Integration Services

Recovery Act Smart Grid Programs

Social analytics External links:

Enterprise Social Analytics Platform | About

Influencer marketing platform & Social analytics tool – HYPR

Dark Social Analytics: Track Private Shares with GetSocial

Software analytics External links:

Software Analytics – Microsoft Research

Speech analytics External links:

Speech Analytics | Speech Analytics Software & Audio Mining

Customer Engagement & Speech Analytics | CallMiner

Eureka: Speech Analytics Software | CallMiner

Statistical discrimination External links:

[PDF]statistical discrimination – Andrea Moro Webpage

“Employer Learning and Statistical Discrimination”

Structured data External links:

Structured Data Testing Tool – Google | What Is Structured Data?

Structured Data for Dummies – Search Engine Journal

Telecommunications data retention External links:

Telecommunications Data Retention and Human Rights: …

Text analytics External links:

Text Mining / Text Analytics Specialist – bigtapp

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

Text analytics software| NICE LTD | NICE

Text mining External links:

Text Mining / Text Analytics Specialist – bigtapp

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 –


Unstructured data External links:

Scale-Out NAS for Unstructured Data | Dell EMC US

The Data Difference | Unstructured Data DSP

Structured vs. Unstructured data – BrightPlanet

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 – Discover Insights in Your Data
http://ad ·

Web Analytics in Real Time | Clicky

Web Analytics – Discover Insights in Your Data
http://ad ·