What is involved in Analytics
Find out what the related areas are that 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 Analytics thinking-frame.
How far is your company on its Pricing Analytics journey?
Take this short survey to gauge your organization’s progress toward Pricing 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 Analytics related domains to cover and 215 essential critical questions to check off in that domain.
The following domains are covered:
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:
Analytics Critical Criteria:
Value Analytics engagements and work towards be a leading Analytics expert.
– Now that organizations have fine-tuned human performance, supply chain, operations, processes, cycle time, and other targeted areas, how can they continue to improve performance?
– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?
– Are business intelligence solutions starting to include social media data and analytics features?
– Do you see connections where one variable might affect another at the same or different level?
– Do the drivers of employee engagement differ significantly in different regions of the world?
– What is the biggest value proposition for new BI or analytics functionality at your company?
– What are the key process differences between our most productive plants and others?
– Should we hire only those job candidates who have certain types of college degrees?
– What are the characteristics of managers with the highest employee loyalty?
– what is the difference between Data analytics and Business Analytics If Any?
– what is the sweet spot for job tenure for our sales representatives?
– Which of our talent gaps are most critical to address?
– What are the best client side analytics tools today?
– Can analyses improve with more detailed analytics that we use?
– What are the objectives for voice analytics?
– What are the data sources and data mix?
– Is there a plan for search analytics?
– Have you identified A players?
– What are the implications?
– Too many indicators?
Academic discipline Critical Criteria:
Tête-à-tête about Academic discipline quality and correct better engagement with Academic discipline results.
– What role does communication play in the success or failure of a Analytics project?
– What are the record-keeping requirements of Analytics activities?
– Why should we adopt a Analytics framework?
Analytic applications Critical Criteria:
Face Analytic applications tactics and define Analytic applications competency-based leadership.
– What are your current levels and trends in key measures or indicators of 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?
– Who is the main stakeholder, with ultimate responsibility for driving Analytics forward?
– What are the business goals Analytics is aiming to achieve?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
Architectural analytics Critical Criteria:
Analyze Architectural analytics tactics and describe which business rules are needed as Architectural analytics interface.
– What are all of our Analytics domains and what do they do?
– Are there recognized Analytics problems?
– How much does Analytics help?
Behavioral analytics Critical Criteria:
Check Behavioral analytics issues and grade techniques for implementing Behavioral analytics controls.
– what is the best design framework for Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– Is Analytics dependent on the successful delivery of a current project?
– How will you measure your Analytics effectiveness?
Big data Critical Criteria:
Confer over Big data quality and drive action.
– Have we let algorithms and large centralized data centres not only control the remembering but also the meaning and interpretation of the data?
– Erp versus big data are the two philosophies of information architecture consistent complementary or in conflict with each other?
– Does your organization share data with other entities (with customers, suppliers, companies, government, etc)?
– Is your organizations business affected by regulatory restrictions on data/servers localisation requirements?
– What rules and regulations should exist about combining data about individuals into a central repository?
– To what extent does your organization have experience with big data and data-driven innovation (DDI)?
– What are the disruptive innovations in the middle-term that provide near-term domain leadership?
– How will systems and methods evolve to remove Big Data solution weaknesses?
– Hybrid partitioning (across rows/terms and columns/documents) useful?
– When we plan and design, how well do we capture previous experience?
– Which Oracle Data Integration products are used in your solution?
– Big Data: what is different from large databases?
– Does your organization buy datasets from other entities?
– Are all our algorithms covered by templates?
– How much data might be lost to pruning?
– Wait, DevOps does not apply to Big Data?
– How do I get to there from here?
– what is Different about Big Data?
– What are we missing?
Business analytics Critical Criteria:
Consider Business analytics management and point out Business analytics tensions in leadership.
– What is the difference between business intelligence business analytics and data mining?
– What other jobs or tasks affect the performance of the steps in the Analytics process?
– Is there a mechanism to leverage information for business analytics and optimization?
– What is the difference between business intelligence and business analytics?
– How do you pick an appropriate ETL tool or business analytics tool?
– What are the trends shaping the future of business analytics?
– What are internal and external Analytics relations?
– Why is Analytics important for you now?
Business intelligence Critical Criteria:
Distinguish Business intelligence visions and spearhead techniques for implementing Business intelligence.
– How do you determine the key elements that affect Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
– Does your BI solution create a strong partnership with IT to ensure that data, whether from extracts or live connections, is 100-percent accurate?
– When users are more fluid and guest access is a must, can you choose hardware-based licensing that is tailored to your exact configuration needs?
– Does the software provide fast query performance, either via its own fast in-memory software or by directly connecting to fast data stores?
– Can your software connect to all forms of data, from text and Excel files to cloud and enterprise-grade databases, with a few clicks?
– Are NoSQL databases used primarily for applications or are they used in Business Intelligence use cases as well?
– Does your bi solution require weeks of training before new users can analyze data and publish dashboards?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– What are some common criticisms of Sharepoint as a knowledge sharing tool?
– Describe the process of data transformation required by your system?
– What are some best practices for managing business intelligence?
– Is your software easy for IT to manage and upgrade?
– To create parallel systems or custom workflows?
– Is the product accessible from the internet?
– How stable is it across domains/geographies?
– Will your product work from a mobile device?
– Do you still need a data warehouse?
– Types of data sources supported?
Cloud analytics Critical Criteria:
Unify Cloud analytics issues and find out.
– Think about the functions involved in your Analytics project. what processes flow from these functions?
– What are the usability implications of Analytics actions?
Complex event processing Critical Criteria:
Depict Complex event processing planning and inform on and uncover unspoken needs and breakthrough Complex event processing results.
– Think about the kind of project structure that would be appropriate for your Analytics project. should it be formal and complex, or can it be less formal and relatively simple?
– Do you monitor the effectiveness of your Analytics activities?
– What are the Essentials of Internal Analytics Management?
Computer programming Critical Criteria:
Troubleshoot Computer programming results and look at it backwards.
– Among the Analytics product and service cost to be estimated, which is considered hardest to estimate?
– What are our needs in relation to Analytics skills, labor, equipment, and markets?
Continuous analytics Critical Criteria:
Experiment with Continuous analytics results and devise Continuous analytics key steps.
– Does Analytics analysis show the relationships among important Analytics factors?
– What are specific Analytics Rules to follow?
Cultural analytics Critical Criteria:
Discuss Cultural analytics issues and define what do we need to start doing with Cultural analytics.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Analytics processes?
– What are the top 3 things at the forefront of our Analytics agendas for the next 3 years?
– Is the scope of Analytics defined?
Customer analytics Critical Criteria:
Examine Customer analytics visions and get going.
– Who will be responsible for making the decisions to include or exclude requested changes once Analytics is underway?
– What is the source of the strategies for Analytics strengthening and reform?
– What are the barriers to increased Analytics production?
Data mining Critical Criteria:
Be clear about Data mining planning and finalize the present value of growth of Data mining.
– 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?
– What programs do we have to teach data mining?
– Do we all define Analytics in the same way?
– Is a Analytics Team Work effort in place?
– Is Analytics Required?
Data presentation architecture Critical Criteria:
Check Data presentation architecture visions and sort Data presentation architecture activities.
– Is Analytics Realistic, or are you setting yourself up for failure?
– Are there Analytics problems defined?
Embedded analytics Critical Criteria:
Match Embedded analytics management and differentiate in coordinating Embedded analytics.
– How do we ensure that implementations of Analytics products are done in a way that ensures safety?
– Why are Analytics skills important?
Enterprise decision management Critical Criteria:
Understand Enterprise decision management outcomes and grade techniques for implementing Enterprise decision management controls.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Analytics process. ask yourself: are the records needed as inputs to the Analytics process available?
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Analytics. How do we gain traction?
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Analytics processes?
Fraud detection Critical Criteria:
Apply Fraud detection governance and gather practices for scaling Fraud detection.
– Is maximizing Analytics protection the same as minimizing Analytics loss?
– How is the value delivered by Analytics being measured?
Google Analytics Critical Criteria:
Powwow over Google Analytics engagements and find the ideas you already have.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Analytics process?
– Does Analytics analysis isolate the fundamental causes of problems?
– What will drive Analytics change?
Human resources Critical Criteria:
Adapt Human resources outcomes and document what potential Human resources megatrends could make our business model obsolete.
– Under what circumstances might the company disclose personal data to third parties and what steps does the company take to safeguard that data?
– How often do we hold meaningful conversations at the operating level among sales, finance, operations, IT, and human resources?
– What happens if an individual objects to the collection, use, and disclosure of his or her personal data?
– What are the procedures for filing an internal complaint about the handling of personal data?
– Should pay levels and differences reflect what workers are used to in their own countries?
– What are strategies that we can undertake to reduce job fatigue and reduced productivity?
– How important is it for organizations to train and develop their Human Resources?
– Is the crisis management team comprised of members from Human Resources?
– What problems have you encountered with the department or staff member?
– What decisions can you envision making with this type of information?
– How should any risks to privacy and civil liberties be managed?
– How does the global environment influence management?
– Does all hr data receive the same level of security?
– What other outreach efforts would be helpful?
– How is the Ease of navigating the hr website?
– Why is transparency important?
– How do we Lead with Analytics in Mind?
– How to deal with diversity?
Learning analytics Critical Criteria:
Familiarize yourself with Learning analytics issues and diversify disclosure of information – dealing with confidential Learning analytics information.
– What will be the consequences to the business (financial, reputation etc) if Analytics does not go ahead or fails to deliver the objectives?
– Do we monitor the Analytics decisions made and fine tune them as they evolve?
Machine learning Critical Criteria:
Trace Machine learning quality and report on developing an effective Machine learning strategy.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
Marketing mix modeling Critical Criteria:
Steer Marketing mix modeling failures and gather practices for scaling Marketing mix modeling.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Analytics?
– Does Analytics create potential expectations in other areas that need to be recognized and considered?
– Risk factors: what are the characteristics of Analytics that make it risky?
Mobile Location Analytics Critical Criteria:
Prioritize Mobile Location Analytics strategies and shift your focus.
– What management system can we use to leverage the Analytics experience, ideas, and concerns of the people closest to the work to be done?
Neural networks Critical Criteria:
Jump start Neural networks engagements and handle a jump-start course to Neural networks.
– What are the disruptive Analytics technologies that enable our organization to radically change our business processes?
– How do we Improve Analytics service perception, and satisfaction?
News analytics Critical Criteria:
Explore News analytics outcomes and triple focus on important concepts of News analytics relationship management.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Analytics models, tools and techniques are necessary?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Analytics?
– What is Effective Analytics?
Online analytical processing Critical Criteria:
Study Online analytical processing engagements and figure out ways to motivate other Online analytical processing users.
– For your Analytics project, identify and describe the business environment. is there more than one layer to the business environment?
Online video analytics Critical Criteria:
Merge Online video analytics issues and oversee Online video analytics management by competencies.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Analytics services/products?
– Will Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
Operational reporting Critical Criteria:
Investigate Operational reporting tactics and look at the big picture.
– Does 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?
Operations research Critical Criteria:
Unify Operations research governance and find out.
– Can Management personnel recognize the monetary benefit of Analytics?
– Does the Analytics task fit the clients priorities?
Over-the-counter data Critical Criteria:
Design Over-the-counter data projects and work towards be a leading Over-the-counter data expert.
– Consider your own Analytics project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
Portfolio analysis Critical Criteria:
Look at Portfolio analysis quality and get going.
– When a Analytics manager recognizes a problem, what options are available?
Predictive analytics Critical Criteria:
Examine Predictive analytics projects and overcome Predictive analytics skills and management ineffectiveness.
– What are direct examples that show predictive analytics to be highly reliable?
– How do we maintain Analyticss Integrity?
Predictive engineering analytics Critical Criteria:
Facilitate Predictive engineering analytics decisions and gather practices for scaling Predictive engineering analytics.
– To what extent does management recognize Analytics as a tool to increase the results?
– Are assumptions made in Analytics stated explicitly?
Predictive modeling Critical Criteria:
Nurse Predictive modeling tactics and inform on and uncover unspoken needs and breakthrough Predictive modeling results.
– How do senior leaders actions reflect a commitment to the organizations Analytics values?
– Are you currently using predictive modeling to drive results?
– How can the value of Analytics be defined?
Prescriptive analytics Critical Criteria:
Discourse Prescriptive analytics tactics and assess and formulate effective operational and Prescriptive analytics strategies.
– What is our formula for success in Analytics ?
– Who needs to know about Analytics ?
Price discrimination Critical Criteria:
Incorporate Price discrimination visions and correct better engagement with Price discrimination results.
– How would one define Analytics leadership?
Risk analysis Critical Criteria:
Closely inspect Risk analysis visions and tour deciding if Risk analysis progress is made.
– 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?
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Analytics in a volatile global economy?
– 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 make it meaningful in connecting Analytics with what users do day-to-day?
– How do we do risk analysis of rare, cascading, catastrophic events?
– With risk analysis do we answer the question how big is the risk?
– What about Analytics Analysis of results?
Security information and event management Critical Criteria:
Weigh in on Security information and event management projects and test out new things.
– How do your measurements capture actionable Analytics information for use in exceeding your customers expectations and securing your customers engagement?
– How to deal with Analytics Changes?
Semantic analytics Critical Criteria:
Revitalize Semantic analytics management and mentor Semantic analytics customer orientation.
Smart grid Critical Criteria:
Think carefully about Smart grid results and devote time assessing Smart grid and its risk.
– 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?
– Can we add value to the current Analytics decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– How do we manage Analytics Knowledge Management (KM)?
Social analytics Critical Criteria:
Huddle over Social analytics strategies and find the ideas you already have.
Software analytics Critical Criteria:
Map Software analytics leadership and attract Software analytics skills.
– Are we making progress? and are we making progress as Analytics leaders?
– Do we have past Analytics Successes?
Speech analytics Critical Criteria:
Grasp Speech analytics strategies and explain and analyze the challenges of Speech analytics.
– How can you measure Analytics in a systematic way?
Statistical discrimination Critical Criteria:
Survey Statistical discrimination visions and find the essential reading for Statistical discrimination researchers.
Stock-keeping unit Critical Criteria:
Devise Stock-keeping unit decisions and oversee Stock-keeping unit management by competencies.
– Will new equipment/products be required to facilitate Analytics delivery for example is new software needed?
– How important is Analytics to the user organizations mission?
– Are accountability and ownership for Analytics clearly defined?
Structured data Critical Criteria:
Investigate Structured data failures and figure out ways to motivate other Structured data users.
– 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)?
– Who will be responsible for deciding whether Analytics goes ahead or not after the initial investigations?
– Should you use a hierarchy or would a more structured database-model work best?
– Think of your Analytics project. what are the main functions?
Telecommunications data retention Critical Criteria:
Track Telecommunications data retention tasks and tour deciding if Telecommunications data retention progress is made.
– What potential environmental factors impact the Analytics effort?
Text analytics Critical Criteria:
Reconstruct Text analytics decisions and attract Text analytics skills.
– Have text analytics mechanisms like entity extraction been considered?
– What tools and technologies are needed for a custom Analytics project?
Text mining Critical Criteria:
Meet over Text mining decisions and integrate design thinking in Text mining innovation.
– How does the organization define, manage, and improve its Analytics processes?
Time series Critical Criteria:
Drive Time series outcomes and figure out ways to motivate other Time series users.
Unstructured data Critical Criteria:
Start Unstructured data engagements and devote time assessing Unstructured data and its risk.
– Who are the people involved in developing and implementing Analytics?
User behavior analytics Critical Criteria:
Dissect User behavior analytics issues and get out your magnifying glass.
– How can we incorporate support to ensure safe and effective use of Analytics into the services that we provide?
– How do mission and objectives affect the Analytics processes of our organization?
– What new services of functionality will be implemented next with Analytics ?
Visual analytics Critical Criteria:
Dissect Visual analytics adoptions and maintain Visual analytics for success.
– In what ways are Analytics vendors and us interacting to ensure safe and effective use?
– What business benefits will Analytics goals deliver if achieved?
Web analytics Critical Criteria:
Cut a stake in Web analytics planning and clarify ways to gain access to competitive Web analytics services.
– What statistics should one be familiar with for business intelligence and web analytics?
– Why is it important to have senior management support for a Analytics project?
– How is cloud computing related to web analytics?
– How can we improve Analytics?
Win–loss analytics Critical Criteria:
Guard Win–loss analytics failures and describe which business rules are needed as Win–loss analytics interface.
– What threat is Analytics addressing?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Pricing 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:
Analytics External links:
Google Analytics Solutions – Marketing Analytics & …
SHP: Strategic Healthcare Programs | Real-Time Analytics
Academic discipline External links:
Criminal justice | academic discipline | Britannica.com
Analytic applications External links:
Foxtrot Code AI Analytic Applications (Home)
Architectural analytics External links:
Architectural Analytics – Home | Facebook
Behavioral analytics External links:
Behavioral Analytics | Interana
User and Entity Behavioral Analytics Partners | Exabeam
Behavioral Analytics – Mattersight
Big data External links:
Business Intelligence and Big Data Analytics Software
ZestFinance.com: Machine Learning & Big Data Underwriting
Databricks – Making Big Data Simple
Business analytics External links:
Harvard Business Analytics Program
What is Business Analytics? Webopedia Definition
Business intelligence External links:
EnsembleIQ | The premier business intelligence resource
SQL Server Business Intelligence | Microsoft
Cloud analytics External links:
Cloud Analytics Academy – Official Site
Cloud Analytics – Solutions for Cloud Data Analytics | NetApp
Cloud Analytics | Big Data Analytics | Vertica
Computer programming External links:
Computer Programming, Robotics & Engineering – STEM …
Coding for Kids | Computer Programming | AgentCubes online
Cultural analytics External links:
Software Studies Initiative: Cultural analytics
Customer analytics External links:
Customer Analytics & Predictive Analytics Tools for Business
BlueVenn – Customer Analytics and Customer Journey …
Zylotech- AI For Customer Analytics
Data mining External links:
Data mining | computer science | Britannica.com
UT Data Mining
Data Mining Extensions (DMX) Reference | Microsoft Docs
Embedded analytics External links:
What is embedded analytics ? – Definition from WhatIs.com
Embedded Analytics | ThoughtSpot
Embedded Analytics | Tableau
Enterprise decision management External links:
Enterprise Decision Management (EDM) – Techopedia.com
enterprise decision management Archives – Insights
Enterprise Decision Management | SAS Italy
Fraud detection External links:
Big Data Fraud Detection | DataVisor
Debit Card Security | Fraud Detection & Protection | RushCard
Fraud Detection and Anti-Money Laundering Software – Verafin
Google Analytics External links:
Google Analytics Opt-out Browser Add-on Download Page
Google Analytics | Google Developers
Google Analytics Solutions – Marketing Analytics & …
Human resources External links:
Office of Human Resources – TITLE IX
Human Resources Job Titles | Enlighten Jobs
Human Resources Job Titles-The Ultimate Guide | upstartHR
Learning analytics External links:
Watershed | Learning Analytics for Organizations
Deep Learning Analytics
Society for Learning Analytics Research (SoLAR)
Machine learning External links:
DataRobot – Automated Machine Learning for Predictive …
Machine Learning | Coursera
What is machine learning? – Definition from WhatIs.com
Marketing mix modeling External links:
Marketing Mix Modeling – Gartner IT Glossary
Marketing Mix Modeling | Marketing Management Analytics
Mobile Location Analytics External links:
How ‘Mobile Location Analytics’ Controls Your Mind – YouTube
Mobile location analytics | Federal Trade Commission
Mobile Location Analytics Privacy Notice | Verizon
News analytics External links:
News Analytics | Amareos
Online analytical processing External links:
Working with Online Analytical Processing (OLAP)
Online video analytics External links:
Online Video Analytics & Marketing Software | Vidooly
Global Online Video Analytics Market Market Research
Managing Your Online Video Analytics – DaCast
Operations research External links:
Operations Research on JSTOR
Operations research (Book, 1974) [WorldCat.org]
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:
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:
Loan Portfolio Analysis | Credit Union Analytics | CU Direct
Portfolio Analysis Final-1 Flashcards | Quizlet
iCite | NIH Office of Portfolio Analysis
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:
3 Types of Price Discrimination | Chron.com
Price Discrimination – Investopedia
MBAecon – 1st, 2nd and 3rd Price discrimination
Risk analysis External links:
Project Management and Risk Analysis Software | Safran
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
Security information and event management External links:
A Guide to Security Information and Event Management
Semantic analytics External links:
SciBite – The Semantic Analytics Company
[PDF]Geospatial and Temporal Semantic Analytics
Semantic Analytics – Get Business Intelligence With Schema …
Smart grid External links:
Smart Grid Security (eBook, 2015) [WorldCat.org]
Recovery Act Smart Grid Programs
Smart Grid – AbeBooks
Social analytics External links:
Union Metrics makes social analytics easy – TweetReach
Google Search with Social Analytics – ctrlq.org
Influencer marketing platform & Social analytics tool – HYPR
Software analytics External links:
Software Analytics – Microsoft Research
Speech analytics External links:
What is speech analytics? – Definition from WhatIs.com
Speech Analytics | NICE
Eureka: Speech Analytics Software | CallMiner
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
Structured data External links:
What is structured data? – Definition from WhatIs.com
Providing Structured Data | Custom Search | Google …
Structured Data for Dummies – Search Engine Journal
Telecommunications data retention External links:
Telecommunications Data Retention and Human Rights: …
Text analytics External links:
Text analytics software| NICE LTD | NICE
[PDF]Syllabus Course Title: Text Analytics – Regis University
Text Mining / Text Analytics Specialist – bigtapp
Text mining External links:
Text mining — University of Illinois at Urbana-Champaign
Text Mining – AbeBooks
Applied Text Mining in Python | Coursera
Time series External links:
Ethereum Pending Transactions Queue – Time Series Chart
Time Series – University of Nebraska–Lincoln
1.1 Overview of Time Series Characteristics | STAT 510
Unstructured data External links:
Scale-Out NAS for Unstructured Data | Dell EMC US
User behavior analytics External links:
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
IBM QRadar User Behavior Analytics – Overview – United States
Visual analytics External links:
Dynamic text in SAS VA (Visual Analytics) – Stack Overflow
Web analytics External links:
Web Analytics in Real Time | Clicky
Login – Heap | Mobile and Web Analytics
Careers | Mobile & Web Analytics | Mixpanel