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 188 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:
Read up on Enterprise Analytics engagements and report on setting up Enterprise Analytics without losing ground.
– Is Enterprise Analytics Realistic, or are you setting yourself up for failure?
– Is Enterprise Analytics Required?
Academic discipline Critical Criteria:
Chart Academic discipline failures and correct better engagement with Academic discipline results.
– 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?
– How do we ensure that implementations of Enterprise Analytics products are done in a way that ensures safety?
– What are your most important goals for the strategic Enterprise Analytics objectives?
Analytic applications Critical Criteria:
Analyze Analytic applications governance and report on the economics of relationships managing Analytic applications and constraints.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Enterprise Analytics process?
– In what ways are Enterprise Analytics vendors and us interacting to ensure safe and effective use?
– How do we measure improved Enterprise Analytics service perception, and satisfaction?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
Architectural analytics Critical Criteria:
Generalize Architectural analytics projects and point out Architectural analytics tensions in leadership.
– 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?
– Will new equipment/products be required to facilitate Enterprise Analytics delivery for example is new software needed?
Behavioral analytics Critical Criteria:
Shape Behavioral analytics engagements and sort Behavioral analytics activities.
– 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?
– Who will be responsible for deciding whether Enterprise Analytics goes ahead or not after the initial investigations?
Big data Critical Criteria:
Have a round table over Big data decisions and learn.
– 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 is (or would be) the added value of collaborating with other entities regarding data sharing across economic sectors?
– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?
– How should we organize to capture the benefit of Big Data and move swiftly to higher maturity stages?
– Does our entire organization have easy access to information required to support work processes?
– Technology Drivers – What were the primary technical challenges your organization faced?
– In which way does big data create, or is expected to create, value in the organization?
– Quality vs. Quantity: What data are required to satisfy the given value proposition?
– Are there any best practices or standards for the use of Big Data solutions?
– How close to the edge can we push the filtering and compression algorithms?
– How can the benefits of Big Data collection and applications be measured?
– What are the new applications that are enabled by Big Data solutions?
– What are our tools for big data analytics?
– How much data might be lost to pruning?
– How can we summarize streaming data?
– What can it be used for?
Business analytics Critical Criteria:
Check Business analytics visions and adjust implementation of Business analytics.
– 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?
– 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?
– How does the organization define, manage, and improve its Enterprise Analytics processes?
– What is the difference between business intelligence and business analytics?
– what is the difference between Data analytics and Business Analytics If Any?
– What vendors make products that address the Enterprise Analytics needs?
– 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:
Have a session on Business intelligence planning and document what potential Business intelligence megatrends could make our business model obsolete.
– 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?
– 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?
– What is the importance of knowing the key performance indicators KPIs for a business process when trying to implement a business intelligence system?
– As we develop increasing numbers of predictive models, then we have to figure out how do you pick the targets, how do you optimize the models?
– Can you filter, drill down, or add entirely new data to your visualization with mobile editing?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Why does animosity endure between IT and business when it comes to business intelligence?
– What is the future scope for combination of Business Intelligence and Cloud Computing?
– What are some best practices for gathering business intelligence about a competitor?
– What information needs of managers are satisfied by the new BI system?
– Is data warehouseing necessary for our business intelligence service?
– Describe the process of data transformation required by your system?
– What types of courses do you run and what are their durations?
– What is the purpose of data warehouses and data marts?
– Do we offer a good introduction to data warehouse?
– To create parallel systems or custom workflows?
– Is your BI software easy to understand?
– Do you offer formal user training?
– Types of data sources supported?
– Using dashboard functions?
Cloud analytics Critical Criteria:
Scan Cloud analytics decisions and modify and define the unique characteristics of interactive Cloud analytics projects.
– Do those selected for the Enterprise Analytics team have a good general understanding of what Enterprise Analytics is all about?
– Do several people in different organizational units assist with the Enterprise Analytics process?
– Do we all define Enterprise Analytics in the same way?
Complex event processing Critical Criteria:
Consolidate Complex event processing results and innovate what needs to be done with Complex event processing.
– Will Enterprise Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
– Is the Enterprise Analytics organization completing tasks effectively and efficiently?
Computer programming Critical Criteria:
Frame Computer programming decisions and catalog what business benefits will Computer programming goals deliver if achieved.
– Where do ideas that reach policy makers and planners as proposals for Enterprise Analytics strengthening and reform actually originate?
– Who is the main stakeholder, with ultimate responsibility for driving Enterprise Analytics forward?
– What are the short and long-term Enterprise Analytics goals?
Continuous analytics Critical Criteria:
Mix Continuous analytics outcomes and find out.
– 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?
– Can we add value to the current Enterprise Analytics decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– Are there any disadvantages to implementing Enterprise Analytics? There might be some that are less obvious?
Cultural analytics Critical Criteria:
Match Cultural analytics projects and reduce Cultural analytics costs.
– How do mission and objectives affect the Enterprise Analytics processes of our organization?
– What are current Enterprise Analytics Paradigms?
Customer analytics Critical Criteria:
Graph Customer analytics quality and devise Customer analytics key steps.
– What will be the consequences to the business (financial, reputation etc) if Enterprise Analytics does not go ahead or fails to deliver the objectives?
– Who are the people involved in developing and implementing Enterprise Analytics?
– Are there recognized Enterprise Analytics problems?
Data mining Critical Criteria:
Have a session on Data mining failures and assess and formulate effective operational and Data mining strategies.
– 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?
– Risk factors: what are the characteristics of Enterprise Analytics that make it risky?
– Is business intelligence set to play a key role in the future of Human Resources?
– What are the usability implications of Enterprise Analytics actions?
– What programs do we have to teach data mining?
Data presentation architecture Critical Criteria:
Meet over Data presentation architecture results and research ways can we become the Data presentation architecture company that would put us out of business.
– Does Enterprise Analytics analysis show the relationships among important Enterprise Analytics factors?
– What knowledge, skills and characteristics mark a good Enterprise Analytics project manager?
– Why should we adopt a Enterprise Analytics framework?
Embedded analytics Critical Criteria:
Experiment with Embedded analytics engagements and triple focus on important concepts of Embedded analytics relationship management.
– What is the source of the strategies for Enterprise Analytics strengthening and reform?
– How do we Identify specific Enterprise Analytics investment and emerging trends?
– How to deal with Enterprise Analytics Changes?
Enterprise decision management Critical Criteria:
Deliberate Enterprise decision management leadership and raise human resource and employment practices for Enterprise decision management.
– Is maximizing Enterprise Analytics protection the same as minimizing Enterprise Analytics loss?
– What are the business goals Enterprise Analytics is aiming to achieve?
Fraud detection Critical Criteria:
Steer Fraud detection governance and arbitrate Fraud detection techniques that enhance teamwork and productivity.
– What are the Essentials of Internal Enterprise Analytics Management?
– What are internal and external Enterprise Analytics relations?
Google Analytics Critical Criteria:
Think carefully about Google Analytics leadership and get out your magnifying glass.
– Among the Enterprise Analytics product and service cost to be estimated, which is considered hardest to estimate?
– Does Enterprise Analytics create potential expectations in other areas that need to be recognized and considered?
– What other jobs or tasks affect the performance of the steps in the Enterprise Analytics process?
Human resources Critical Criteria:
Merge Human resources projects and innovate what needs to be done with Human resources.
– 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?
– How do we engage divisions, operating units, operations, internal audit, risk management, compliance, finance, technology, and human resources in adopting the updated framework?
– If there is recognition by both parties of the potential benefits of an alliance, but adequate qualified human resources are not available at one or both firms?
– Have we adopted and promoted the companys culture of integrity management, including ethics, business practices and Human Resources evaluations?
– Under what circumstances might the company disclose personal data to third parties and what steps does the company take to safeguard that data?
– what is to keep those with access to some of an individuals personal data from browsing through other parts of it for other reasons?
– What finance, procurement and Human Resources business processes should be included in the scope of a erp solution?
– Are there cases when the company may collect, use and disclose personal data without consent or accommodation?
– What are strategies that we can undertake to reduce job fatigue and reduced productivity?
– What decisions can you envision making with this type of information?
– Are there types of data to which the employee does not have access?
– What steps are taken to promote compliance with the hr principles?
– Friendliness and professionalism of the Human Resources staff?
– Is our company developing its Human Resources?
– Will an algorithm shield us from liability?
– What are the data sources and data mix?
– How do we engage the stakeholders?
Learning analytics Critical Criteria:
Focus on Learning analytics issues and find the essential reading for Learning analytics researchers.
– Is Enterprise Analytics dependent on the successful delivery of a current project?
Machine learning Critical Criteria:
Set goals for Machine learning issues and probe using an integrated framework to make sure Machine learning is getting what it needs.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– What role does communication play in the success or failure of a Enterprise Analytics project?
Marketing mix modeling Critical Criteria:
Survey Marketing mix modeling results and describe the risks of Marketing mix modeling sustainability.
– What are specific Enterprise Analytics Rules to follow?
Mobile Location Analytics Critical Criteria:
Check Mobile Location Analytics tactics and revise understanding of Mobile Location Analytics architectures.
– What are our needs in relation to Enterprise Analytics skills, labor, equipment, and markets?
– What are our Enterprise Analytics Processes?
Neural networks Critical Criteria:
Canvass Neural networks governance and work towards be a leading Neural networks expert.
– What are your results for key measures or indicators of the accomplishment of your Enterprise Analytics strategy and action plans, including building and strengthening core competencies?
– How do we know that any Enterprise Analytics analysis is complete and comprehensive?
News analytics Critical Criteria:
Check News analytics strategies and grade techniques for implementing News analytics controls.
– How likely is the current Enterprise Analytics plan to come in on schedule or on budget?
– Do we monitor the Enterprise Analytics decisions made and fine tune them as they evolve?
– How do we maintain Enterprise Analyticss Integrity?
Online analytical processing Critical Criteria:
Cut a stake in Online analytical processing goals and give examples utilizing a core of simple Online analytical processing skills.
– What tools and technologies are needed for a custom Enterprise Analytics project?
– Have you identified your Enterprise Analytics key performance indicators?
Online video analytics Critical Criteria:
Mine Online video analytics outcomes and correct Online video analytics management by competencies.
Operational reporting Critical Criteria:
Generalize Operational reporting goals and describe which business rules are needed as Operational reporting interface.
– Why are Enterprise Analytics skills important?
Operations research Critical Criteria:
Consolidate Operations research risks and spearhead techniques for implementing Operations research.
– What are all of our Enterprise Analytics domains and what do they do?
Over-the-counter data Critical Criteria:
Survey Over-the-counter data strategies and interpret which customers can’t participate in Over-the-counter data because they lack skills.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Enterprise Analytics?
Portfolio analysis Critical Criteria:
Focus on Portfolio analysis issues and handle a jump-start course to Portfolio analysis.
– How will we insure seamless interoperability of Enterprise Analytics moving forward?
Predictive analytics Critical Criteria:
Shape Predictive analytics adoptions and improve Predictive analytics service perception.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Enterprise Analytics process. ask yourself: are the records needed as inputs to the Enterprise Analytics process available?
– What are direct examples that show predictive analytics to be highly reliable?
Predictive engineering analytics Critical Criteria:
Tête-à-tête about Predictive engineering analytics issues and pioneer acquisition of Predictive engineering analytics systems.
– What business benefits will Enterprise Analytics goals deliver if achieved?
– How can you measure Enterprise Analytics in a systematic way?
Predictive modeling Critical Criteria:
Design Predictive modeling tactics and explore and align the progress in Predictive modeling.
– How do we make it meaningful in connecting Enterprise Analytics with what users do day-to-day?
– Are you currently using predictive modeling to drive results?
Prescriptive analytics Critical Criteria:
Focus on Prescriptive analytics goals and look at it backwards.
– Do the Enterprise Analytics decisions we make today help people and the planet tomorrow?
– How important is Enterprise Analytics to the user organizations mission?
Price discrimination Critical Criteria:
Exchange ideas about Price discrimination tactics and point out Price discrimination tensions in leadership.
– Which individuals, teams or departments will be involved in Enterprise Analytics?
– How can the value of Enterprise Analytics be defined?
– What is our Enterprise Analytics Strategy?
Risk analysis Critical Criteria:
Reconstruct Risk analysis tasks and integrate design thinking in Risk analysis innovation.
– 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?
– What are the record-keeping requirements of Enterprise Analytics activities?
– 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 can we improve Enterprise Analytics?
Security information and event management Critical Criteria:
Collaborate on Security information and event management results and perfect Security information and event management conflict management.
– What are the Key enablers to make this Enterprise Analytics move?
– What are the long-term Enterprise Analytics goals?
Semantic analytics Critical Criteria:
Examine Semantic analytics decisions and remodel and develop an effective Semantic analytics strategy.
– Does Enterprise Analytics appropriately measure and monitor risk?
– Is the scope of Enterprise Analytics defined?
Smart grid Critical Criteria:
Paraphrase Smart grid failures and pay attention to the small things.
– 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 will drive Enterprise Analytics change?
Social analytics Critical Criteria:
Deliberate over Social analytics tactics and get the big picture.
– Have the types of risks that may impact Enterprise Analytics been identified and analyzed?
– Have all basic functions of Enterprise Analytics been defined?
Software analytics Critical Criteria:
Look at Software analytics tactics and differentiate in coordinating Software analytics.
Speech analytics Critical Criteria:
Paraphrase Speech analytics risks and assess and formulate effective operational and Speech analytics strategies.
– How do you determine the key elements that affect Enterprise Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
Statistical discrimination Critical Criteria:
Do a round table on Statistical discrimination issues and be persistent.
Stock-keeping unit Critical Criteria:
Accelerate Stock-keeping unit strategies and observe effective Stock-keeping unit.
Structured data Critical Criteria:
Confer re Structured data projects and customize techniques for implementing Structured data controls.
– what is the best design framework for Enterprise Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Enterprise Analytics?
– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?
– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?
– Should you use a hierarchy or would a more structured database-model work best?
Telecommunications data retention Critical Criteria:
Deduce Telecommunications data retention issues and look at it backwards.
Text analytics Critical Criteria:
Use past Text analytics visions and customize techniques for implementing Text analytics controls.
– Have text analytics mechanisms like entity extraction been considered?
– How is the value delivered by Enterprise Analytics being measured?
– Are we Assessing Enterprise Analytics and Risk?
Text mining Critical Criteria:
Set goals for Text mining tasks and reduce Text mining costs.
Time series Critical Criteria:
Jump start Time series tactics and pioneer acquisition of Time series systems.
– Does the Enterprise Analytics task fit the clients priorities?
Unstructured data Critical Criteria:
Add value to Unstructured data tasks and use obstacles to break out of ruts.
– How can we incorporate support to ensure safe and effective use of Enterprise Analytics into the services that we provide?
– What is the purpose of Enterprise Analytics in relation to the mission?
User behavior analytics Critical Criteria:
Ventilate your thoughts about User behavior analytics issues and optimize User behavior analytics leadership as a key to advancement.
– Who will be responsible for making the decisions to include or exclude requested changes once Enterprise Analytics is underway?
Visual analytics Critical Criteria:
Inquire about Visual analytics goals and triple focus on important concepts of Visual analytics relationship management.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Enterprise Analytics services/products?
Web analytics Critical Criteria:
Read up on Web analytics goals and revise understanding of Web analytics architectures.
– 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:
Own Win–loss analytics governance and achieve a single Win–loss analytics view and bringing data together.
– What is the total cost related to deploying Enterprise Analytics, including any consulting or professional services?
– How can you negotiate Enterprise Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?
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 | http://theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Enterprise Analytics External links:
Vice President, Enterprise Analytics – Isaac S. Cronkhite
Healthcare Enterprise Analytics Solutions | Omnicell
MS in Enterprise Analytics | SEIDENBERG SCHOOL OF CSIS
Academic discipline External links:
Criminal justice | academic discipline | Britannica.com
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:
User and Entity Behavioral Analytics Partners | Exabeam
Behavioral Analytics Definition | Investopedia
Behavioral Analytics | Interana
Big data External links:
Loudr: Big Data for Music Rights
ZestFinance.com: Machine Learning & Big Data Underwriting
Databricks – Making Big Data Simple
Business analytics External links:
What is Business Analytics? Webopedia Definition
Big Data & Business Analytics – Wayne State University
Business intelligence External links:
CWS/CMS > Portal > Business Intelligence Portal
Mortgage Business Intelligence Software :: Motivity Solutions
Cloud analytics External links:
Cloud Analytics Academy – Official Site
Cloud Analytics World Tour – Stockholm | Snowflake
Computer programming External links:
The Meaning of Beep: Computer Programming – BrainPOP
Computer programming Meetups – Meetup
Computer Programming, Robotics & Engineering – STEM …
Continuous analytics External links:
[PDF]Continuous Analytics: Stream Query Processing in …
Cultural analytics External links:
Software Studies Initiative: Cultural analytics
Customer analytics External links:
BlueVenn – Customer Analytics and Customer Journey …
Customer Analytics & Predictive Analytics Tools for Business
Customer Analytics Services and Solutions | TransUnion
Data mining External links:
Data Mining Extensions (DMX) Reference | Microsoft Docs
UT Data Mining
Data Mining on the Florida Department of Corrections Website
Embedded analytics External links:
What is embedded analytics ? – Definition from WhatIs.com
Embedded Analytics – icCube
Power BI Embedded analytics | Microsoft Azure
Enterprise decision management External links:
enterprise decision management Archives – Insights
Enterprise Decision Management | Sapiens DECISION
Fraud detection External links:
Fraud Detection and Authentication Technology – Next Caller
Business Fraud Detection | Fraud Shield by Experian
Big Data Fraud Detection | DataVisor
Google Analytics External links:
Welcome to the Texas Board of Nursing – Google Analytics
Human resources External links:
myDHR | Maryland Department of Human Resources
Human Resources Job Titles-The Ultimate Guide | upstartHR
Human Resources Job Titles | Enlighten Jobs
Learning analytics External links:
Learning Analytics Explained. (eBook, 2017) [WorldCat.org]
Machine learning External links:
Machine Learning Mastery – Official Site
Microsoft Azure Machine Learning Studio
Appen: high-quality training data for machine learning
Marketing mix modeling External links:
Marketing Mix Modeling | Marketing Management Analytics
Mobile Location Analytics External links:
Mobile Location Analytics Privacy Notice | Verizon
Mobile location analytics | Federal Trade Commission
[PDF]Mobile Location Analytics Code of Conduct
Online analytical processing External links:
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 | Britannica.com
Operations Research on JSTOR
Operations Research (O.R.), or operational research in the U.K, is a discipline that deals with the application of advanced analytical methods to help make better decisions.
Over-the-counter data External links:
Over-the-Counter Data – American Mensa – Medium
What is Over-the-Counter Data | IGI Global
Standards — Over-the-Counter Data
Portfolio analysis External links:
U.S. Army STAND-TO! | Strategic Portfolio Analysis Review
Loan Portfolio Analysis | Visible Equity
[PDF]Portfolio Analysis Tool: Methodologies and Assumptions
Predictive analytics External links:
Predictive Analytics Software, Social Listening | NewBrand
Predictive Analytics for Healthcare | Forecast Health
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.
Predictive modeling External links:
What is predictive modeling? – Definition from …
Predictive Modeling Definition | Investopedia
Prescriptive analytics External links:
Prescriptive Analytics – Gartner IT Glossary
Healthcare Prescriptive Analytics – Cedar Gate Technologies
Price discrimination External links:
3 Types of Price Discrimination | Chron.com
Price Discrimination – Investopedia
A macroeconomic model of international price discrimination
Risk analysis External links:
JIFSAN: Risk Analysis Training
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
Semantic analytics External links:
SciBite – The Semantic Analytics Company
What is Semantic Analytics | IGI Global
[PDF]Semantic Analytics Visualization – LSDIS
Smart grid External links:
Smart grid. (Journal, magazine, 2011) [WorldCat.org]
Smart Grid Massachusetts | National Grid
Smart Grid – AbeBooks
Social analytics External links:
Enterprise Social Analytics Platform | About
Social Analytics Software | Crimson Hexagon
Software analytics External links:
Software Analytics – Microsoft Research
Speech analytics External links:
What is speech analytics? – Definition from WhatIs.com
Speech Analytics | Speech Analytics Software & Audio Mining
Speech Analytics ROI Calculator Inquiry – CallMiner
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
Statistical discrimination (economics) Statistical discrimination is an economic theory of racial or gender inequality based on stereotypes. According to this theory, inequality may exist and persist between demographic groups even when economic agents (consumers, workers, employers, etc.) are rational and non-prejudiced.
Structured data External links:
Structured Data for Dummies – Search Engine Journal
Structured Data Testing Tool – Google
Providing Structured Data | Custom Search | Google …
Telecommunications data retention External links:
What is TELECOMMUNICATIONS DATA RETENTION? …
Telecommunications Data Retention and Human Rights: …
Telecommunications data retention | German WOTD
Text analytics External links:
Text analytics software| NICE LTD | NICE
[PDF]What Is Text Analytics? – Information Today, Inc. Books
[PDF]Syllabus Course Title: Text Analytics – Regis University
Text mining External links:
Text Mining – AbeBooks
Text Mining with R
Text Mining | Metadata | Portable Document Format
Time series External links:
Time Series – Investopedia
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:
User Behavior Analytics | FairWarning.com
Varonis User Behavior Analytics | Varonis Systems
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
Visual analytics External links:
Visual Analytics Working Group | AMIA
Web analytics External links:
Web Analytics in Real Time | Clicky
Web analytics | HitsLink
Careers | Mobile & Web Analytics | Mixpanel