What is involved in Enterprise Analytics

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

How far is your company on its Enterprise Analytics journey?

Take this short survey to gauge your organization’s progress toward Enterprise 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 Enterprise Analytics related domains to cover and 197 essential critical questions to check off in that domain.

The following domains are covered:

Enterprise 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:

Enterprise Analytics Critical Criteria:

Steer Enterprise Analytics planning and overcome Enterprise Analytics skills and management ineffectiveness.

– Does Enterprise 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?

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

– What are the Essentials of Internal Enterprise Analytics Management?

Academic discipline Critical Criteria:

Read up on Academic discipline goals and adjust implementation of Academic discipline.

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

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

– Are there recognized Enterprise Analytics problems?

Analytic applications Critical Criteria:

Discuss Analytic applications leadership and look at it backwards.

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

– What potential environmental factors impact the Enterprise Analytics effort?

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

– How do you handle Big Data in Analytic Applications?

– Analytic Applications: Build or Buy?

Architectural analytics Critical Criteria:

Troubleshoot Architectural analytics governance and oversee Architectural analytics requirements.

– What role does communication play in the success or failure of a Enterprise Analytics project?

– How do we go about Securing Enterprise Analytics?

– Why are Enterprise Analytics skills important?

Behavioral analytics Critical Criteria:

Scrutinze Behavioral analytics tasks and suggest using storytelling to create more compelling Behavioral analytics projects.

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

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

Big data Critical Criteria:

Rank Big data issues and create Big data explanations for all managers.

– 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?

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

– Do you see the need to address the issues of data ownership or access to non-personal data (e.g. machine-generated data)?

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

– How should we organize to capture the benefit of Big Data and move swiftly to higher maturity stages?

– Do we understand public perception of transportation service delivery at any given time?

– What if the needle in the haystack happens to be a complex data structure?

– How can the benefits of Big Data collection and applications be measured?

– What analytical tools do you consider particularly important?

– How fast can we determine changes in the incoming data?

– Where do you see the need for standardisation actions?

– What happens if/when no longer need cognitive input?

– Can analyses improve with more data to process?

– How to attract and keep the community involved?

– Where Is This Big Data Coming From ?

– What is collecting all this data?

– What about Volunteered data?

– Who is collecting what?

– What is Big Data to us?

Business analytics Critical Criteria:

Chat re Business analytics management and integrate design thinking in Business analytics innovation.

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

– 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?

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

– Is there a mechanism to leverage information for business analytics and optimization?

– What sources do you use to gather information for a Enterprise Analytics study?

– 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?

Business intelligence Critical Criteria:

Familiarize yourself with Business intelligence planning and oversee Business intelligence requirements.

– Does the software let users work with the existing data infrastructure already in place, freeing your IT team from creating more cubes, universes, and standalone marts?

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

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

– How does Tableau stack up against the traditional BI software like Microstrategy or Business Objects?

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

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

– What are the best UI frameworks for Business Intelligence Applications?

– Is Data Warehouseing necessary for a business intelligence service?

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

– What percentage of enterprise apps will be web based in 3 years?

– Number of data sources that can be simultaneously accessed?

– What are the best client side analytics tools today?

– What are alternatives to building a data warehouse?

– What business intelligence systems are available?

– What would true business intelligence look like?

– What level of training would you recommend?

– Do you offer formal user training?

– What is your annual maintenance?

– What is your products direction?

Cloud analytics Critical Criteria:

Review Cloud analytics governance and assess what counts with Cloud analytics that we are not counting.

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

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

– How to deal with Enterprise Analytics Changes?

Complex event processing Critical Criteria:

Debate over Complex event processing leadership and secure Complex event processing creativity.

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

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

Computer programming Critical Criteria:

Meet over Computer programming tasks and devote time assessing Computer programming and its risk.

– Risk factors: what are the characteristics of Enterprise Analytics that make it risky?

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

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

Continuous analytics Critical Criteria:

Reorganize Continuous analytics projects and learn.

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

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

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

Cultural analytics Critical Criteria:

Design Cultural analytics tasks and correct better engagement with Cultural analytics results.

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

– Who needs to know about Enterprise Analytics ?

– Who sets the Enterprise Analytics standards?

Customer analytics Critical Criteria:

Reorganize Customer analytics tactics and spearhead techniques for implementing Customer analytics.

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

– Do you monitor the effectiveness of your Enterprise Analytics activities?

– What is Effective Enterprise Analytics?

Data mining Critical Criteria:

Refer to Data mining results and triple focus on important concepts of Data mining relationship 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?

– 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?

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

– What programs do we have to teach data mining?

– How can we improve Enterprise Analytics?

Data presentation architecture Critical Criteria:

Learn from Data presentation architecture tactics and revise understanding of Data presentation architecture architectures.

– Think about the people you identified for your Enterprise 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?

– How will you measure your Enterprise Analytics effectiveness?

Embedded analytics Critical Criteria:

Do a round table on Embedded analytics tasks and catalog what business benefits will Embedded analytics goals deliver if achieved.

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

– Are there Enterprise Analytics Models?

Enterprise decision management Critical Criteria:

Chat re Enterprise decision management management and interpret which customers can’t participate in Enterprise decision management because they lack skills.

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

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

Fraud detection Critical Criteria:

Collaborate on Fraud detection adoptions and secure Fraud detection creativity.

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

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

Google Analytics Critical Criteria:

Check Google Analytics engagements and reinforce and communicate particularly sensitive Google Analytics decisions.

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

– Do several people in different organizational units assist with the Enterprise Analytics process?

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

Human resources Critical Criteria:

Demonstrate Human resources leadership and cater for concise Human resources education.

– A dramatic step toward becoming a learning organization is to appoint a chief training officer (CTO) or a chief learning officer (CLO). Many organizations claim to value Human Resources, but how many have a Human Resources representative involved in discussions about research and development commercialization, new product development, the strategic vision of the company, or increasing shareholder value?

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

– Are there cases when the company may collect, use and disclose personal data without consent or accommodation?

– Do we perform an environmental scan of hr strategies within the hr community (what/how are others planning)?

– Is the crisis management team comprised of members from Human Resources?

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

– What are the Human Resources we can bring to establishing new business?

– Do you have Human Resources available to support your policies?

– How do you view the department and staff members as a whole?

– How can we promote retention of high performing employees?

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

– What are ways that employee productivity can be measured?

– Does the hr plan make sense to our stakeholders?

– How is Promptness of returning calls or e-mail?

– What do users think of the information?

– What are the data sources and data mix?

– Who should appraise performance?

– What is personal data?

– What is harassment?

Learning analytics Critical Criteria:

Confer re Learning analytics results and reinforce and communicate particularly sensitive Learning analytics decisions.

– What are your current levels and trends in key measures or indicators of Enterprise Analytics product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?

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

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

Machine learning Critical Criteria:

Audit Machine learning visions and work towards be a leading Machine learning expert.

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

– What is our Enterprise Analytics Strategy?

Marketing mix modeling Critical Criteria:

Gauge Marketing mix modeling adoptions and oversee Marketing mix modeling requirements.

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

Mobile Location Analytics Critical Criteria:

See the value of Mobile Location Analytics goals and achieve a single Mobile Location Analytics view and bringing data together.

– What other organizational variables, such as reward systems or communication systems, affect the performance of this Enterprise Analytics process?

Neural networks Critical Criteria:

Consolidate Neural networks tasks and give examples utilizing a core of simple Neural networks skills.

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

News analytics Critical Criteria:

Investigate News analytics visions and grade techniques for implementing News analytics controls.

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

– What are our Enterprise Analytics Processes?

Online analytical processing Critical Criteria:

Have a session on Online analytical processing visions and work towards be a leading Online analytical processing expert.

– How do we make it meaningful in connecting Enterprise Analytics with what users do day-to-day?

– How is the value delivered by Enterprise Analytics being measured?

Online video analytics Critical Criteria:

Categorize Online video analytics engagements and simulate teachings and consultations on quality process improvement of Online video analytics.

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

– What threat is Enterprise Analytics addressing?

Operational reporting Critical Criteria:

Refer to Operational reporting decisions and adjust implementation of Operational reporting.

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

– How do we Lead with Enterprise Analytics in Mind?

Operations research Critical Criteria:

Boost Operations research visions and change contexts.

– What are the usability implications of Enterprise Analytics actions?

Over-the-counter data Critical Criteria:

Have a session on Over-the-counter data decisions and probe Over-the-counter data strategic alliances.

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

Portfolio analysis Critical Criteria:

Scrutinze Portfolio analysis results and test out new things.

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

– How would one define Enterprise Analytics leadership?

– How do we maintain Enterprise Analyticss Integrity?

Predictive analytics Critical Criteria:

Wrangle Predictive analytics adoptions and catalog what business benefits will Predictive analytics goals deliver if achieved.

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

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

– How to Secure Enterprise Analytics?

Predictive engineering analytics Critical Criteria:

Investigate Predictive engineering analytics management and handle a jump-start course to Predictive engineering analytics.

– How can the value of Enterprise Analytics be defined?

Predictive modeling Critical Criteria:

Collaborate on Predictive modeling quality and raise human resource and employment practices for Predictive modeling.

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

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

– Are you currently using predictive modeling to drive results?

Prescriptive analytics Critical Criteria:

Powwow over Prescriptive analytics management and find out what it really means.

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

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

Price discrimination Critical Criteria:

Infer Price discrimination management and look at the big picture.

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

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

– How do we keep improving Enterprise Analytics?

Risk analysis Critical Criteria:

Wrangle Risk analysis quality and drive action.

– 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?

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

– 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?

– Are there Enterprise Analytics problems defined?

Security information and event management Critical Criteria:

Jump start Security information and event management goals and innovate what needs to be done with Security information and event management.

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

Semantic analytics Critical Criteria:

Scan Semantic analytics goals and report on the economics of relationships managing Semantic analytics and constraints.

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

– What are current Enterprise Analytics Paradigms?

Smart grid Critical Criteria:

Consolidate Smart grid planning and be persistent.

– 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?

– Who will be responsible for deciding whether Enterprise Analytics goes ahead or not after the initial investigations?

Social analytics Critical Criteria:

Exchange ideas about Social analytics strategies and mentor Social analytics customer orientation.

– Does the Enterprise Analytics task fit the clients priorities?

Software analytics Critical Criteria:

Chart Software analytics adoptions and oversee Software analytics requirements.

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

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

Speech analytics Critical Criteria:

Infer Speech analytics outcomes and reinforce and communicate particularly sensitive Speech analytics decisions.

Statistical discrimination Critical Criteria:

Consult on Statistical discrimination visions and create Statistical discrimination explanations for all managers.

– How will you know that the Enterprise Analytics project has been successful?

– Are assumptions made in Enterprise Analytics stated explicitly?

Stock-keeping unit Critical Criteria:

Set goals for Stock-keeping unit strategies and get the big picture.

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

Structured data Critical Criteria:

Bootstrap Structured data results and devise Structured data key steps.

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

– 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?

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

Telecommunications data retention Critical Criteria:

Probe Telecommunications data retention adoptions and achieve a single Telecommunications data retention view and bringing data together.

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

– What will drive Enterprise Analytics change?

Text analytics Critical Criteria:

Analyze Text analytics results and report on the economics of relationships managing Text analytics and constraints.

– Have text analytics mechanisms like entity extraction been considered?

Text mining Critical Criteria:

Depict Text mining strategies and diversify disclosure of information – dealing with confidential Text mining information.

– What are the long-term Enterprise Analytics goals?

Time series Critical Criteria:

Drive Time series failures and mentor Time series customer orientation.

– What are the barriers to increased Enterprise Analytics production?

Unstructured data Critical Criteria:

Inquire about Unstructured data outcomes and find answers.

User behavior analytics Critical Criteria:

Interpolate User behavior analytics risks and oversee implementation of User behavior analytics.

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

Visual analytics Critical Criteria:

Read up on Visual analytics management and summarize a clear Visual analytics focus.

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

Web analytics Critical Criteria:

Graph Web analytics management and work towards be a leading Web analytics expert.

– 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:

Transcribe Win–loss analytics planning and visualize why should people listen to you regarding Win–loss analytics.


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


Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]


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:

Enterprise Analytics External links:

Enterprise Analytics Solutions – Dun & Bradstreet

Vice President, Enterprise Analytics – Isaac S. Cronkhite

Our Enterprise Analytics Solutions – D&B Canada

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:

Magnifier Behavioral Analytics – Palo Alto Networks

Behavioral Analytics Definition | Investopedia

Behavioral Analytics | Interana

Big data External links:

Take 5 Media Group – Build an audience using big data

ZestFinance.com: Machine Learning & Big Data Underwriting

Event Hubs – Cloud big data solutions | Microsoft Azure

Business analytics External links:

What is Business Analytics? Webopedia Definition

Big Data & Business Analytics – Wayne State University

Business intelligence External links:

Mortgage Business Intelligence Software :: Motivity Solutions

BIIS – Business Intelligence for Independent Schools

Cloud analytics External links:

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 …

Cultural analytics External links:

Software Studies Initiative: Cultural analytics

Software Studies Initiative: Cultural analytics

Customer analytics External links:

BlueVenn – Customer Analytics and Customer Journey …

Customer Analytics & Predictive Analytics Tools for Business

Zylotech- AI For Customer Analytics

Data mining External links:

Data Mining on the Florida Department of Corrections Website

Data Mining Extensions (DMX) Reference | Microsoft Docs

UT Data Mining

Embedded analytics External links:

Tailored Embedded Analytics from Logi Analytics

Embedded Analytics – icCube

What is embedded analytics ? – Definition from WhatIs.com

Enterprise decision management External links:

Enterprise Decision Management | SAS Italy

Enterprise Decision Management (EDM) – Techopedia.com

Enterprise Decision Management | Sapiens DECISION

Fraud detection External links:

Debit Card Security | Fraud Detection & Protection | RushCard

Fraud Detection and Authentication Technology – Next Caller

Credit Card Fraud Detection | Kaggle

Google Analytics External links:

Enterprise Marketing Analytics – Google Analytics 360 Suite

Google Analytics Opt-out Browser Add-on Download Page

Google Analytics

Human resources External links:

Human Resources Job Titles | Enlighten Jobs

Human Resources – jobs.goodyear.com

Human Resources Job Titles – The Balance

Learning analytics External links:

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

Machine learning External links:

What is machine learning? – Definition from WhatIs.com

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:

Mobile location analytics | Federal Trade Commission

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:

Operations Research on JSTOR

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

Operations Research: INFORMS

Over-the-counter data External links:

What is Over-the-Counter Data | IGI Global

Standards — Over-the-Counter Data

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

Portfolio analysis External links:

[PDF]Portfolio Analysis Tool: Methodologies and Assumptions

Portfolio Analysis | Economy Watch

Loan Portfolio Analysis | Visible Equity

Predictive analytics External links:

Predictive Analytics Software, Social Listening | NewBrand

Strategic Location Management & Predictive Analytics | Tango

Inventory Optimization for Retail | Predictive Analytics

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.
Reference: plm.automation.siemens.com/en/plm/predictive-engineering-a…

Predictive modeling External links:

What is predictive modeling? – Definition from …

Prescriptive analytics External links:

Healthcare Prescriptive Analytics – Cedar Gate Technologies

Price discrimination External links:

Price Discrimination Flashcards | Quizlet

MBAecon – 1st, 2nd and 3rd Price discrimination

A macroeconomic model of international price discrimination

Risk analysis External links:

Risk Analysis
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

Semantic analytics External links:

What is Semantic Analytics | IGI Global

SciBite – The Semantic Analytics Company

[PDF]Geospatial and Temporal Semantic Analytics

Smart grid External links:

Smart Grid – AbeBooks

Smart Grid Massachusetts | National Grid


Social analytics External links:

Google Search with Social Analytics – ctrlq.org

Dark Social Analytics: Track Private Shares with GetSocial

Social Analytics – Votigo

Software analytics External links:

EDGEPro | EDGEPro Software Analytics Tool for Optometry

Speech analytics External links:

Speech Analytics | NICE

What is speech analytics? – Definition from WhatIs.com

Eureka: Speech Analytics Software | CallMiner

Statistical discrimination External links:

“Employer Learning and Statistical Discrimination”

Structured data External links:

SEC.gov | What Is Structured Data?

Structured Data for Dummies – Search Engine Journal

What is structured data? – Definition from WhatIs.com

Telecommunications data retention External links:

Telecommunications Data Retention and Human Rights: …

Text analytics External links:

The Truth about Text Analytics and Sentiment Analysis

[PDF]What Is Text Analytics? – Information Today, Inc. Books

Text analytics software| NICE LTD | NICE

Text mining External links:

Text Mining with R

Applied Text Mining in Python | Coursera

Text Mining in R: A Tutorial – Springboard Blog

Time series External links:

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

Initial State – Analytics for 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:

IBM QRadar User Behavior Analytics – Overview – United States

User Behavior Analytics (UBA) Tools and Solutions | Rapid7

Visual analytics External links:

Visual Analytics Working Group | AMIA

Web analytics External links:

Web analytics | HitsLink

Login – Web analytics | HitsLink

AFS Analytics – Web analytics

Categories: Documents