What is involved in Fraud Analytics
Find out what the related areas are that Fraud 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 Fraud Analytics thinking-frame.
How far is your company on its Fraud Analytics journey?
Take this short survey to gauge your organization’s progress toward Fraud 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 Fraud Analytics related domains to cover and 205 essential critical questions to check off in that domain.
The following domains are covered:
Fraud 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:
Fraud Analytics Critical Criteria:
Scan Fraud Analytics results and check on ways to get started with Fraud Analytics.
– Who will be responsible for deciding whether Fraud Analytics goes ahead or not after the initial investigations?
– What role does communication play in the success or failure of a Fraud Analytics project?
– Is Fraud Analytics dependent on the successful delivery of a current project?
Academic discipline Critical Criteria:
Inquire about Academic discipline quality and describe the risks of Academic discipline sustainability.
– Is maximizing Fraud Analytics protection the same as minimizing Fraud Analytics loss?
– What are specific Fraud Analytics Rules to follow?
– Are there Fraud Analytics problems defined?
Analytic applications Critical Criteria:
Learn from Analytic applications adoptions and inform on and uncover unspoken needs and breakthrough Analytic applications results.
– In the case of a Fraud Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Fraud Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Fraud Analytics project is implemented as planned, and is it working?
– Do Fraud Analytics rules make a reasonable demand on a users capabilities?
– What are internal and external Fraud Analytics relations?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
Architectural analytics Critical Criteria:
Align Architectural analytics adoptions and create Architectural analytics explanations for all managers.
– Are there any disadvantages to implementing Fraud Analytics? There might be some that are less obvious?
– Does Fraud Analytics appropriately measure and monitor risk?
– How to deal with Fraud Analytics Changes?
Behavioral analytics Critical Criteria:
See the value of Behavioral analytics adoptions and find the essential reading for Behavioral analytics researchers.
– How do your measurements capture actionable Fraud Analytics information for use in exceeding your customers expectations and securing your customers engagement?
– What are the business goals Fraud Analytics is aiming to achieve?
Big data Critical Criteria:
Own Big data tactics and give examples utilizing a core of simple Big data skills.
– 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?
– Do you see the need to address the issues of data ownership or access to non-personal data (e.g. machine-generated data)?
– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?
– Wheres the evidence that using big data intelligently will improve business performance?
– Are there any best practices or standards for the use of Big Data solutions?
– Does your organization have the right analytical tools to handle (big) data?
– How will systems and methods evolve to remove Big Data solution weaknesses?
– How can the benefits of Big Data collection and applications be measured?
– Does your organization have a strategy on big data or data analytics?
– Is data-driven decision-making part of the organizations culture?
– What (additional) data do these algorithms need to be effective?
– How fast can we determine changes in the incoming data?
– How do we measure the efficiency of these algorithms?
– More efficient all-to-all operations (similarities)?
– How does that compare to other science disciplines?
– What metrics do we use to assess the results?
– What if the data cannot fit on your computer?
– What load balancing technique should we use?
– What preprocessing do we need to do?
– Does Big Data Really Need HPC?
Business analytics Critical Criteria:
Grasp Business analytics projects and innovate what needs to be done with Business analytics.
– Are there any easy-to-implement alternatives to Fraud Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– Where do ideas that reach policy makers and planners as proposals for Fraud 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?
– Is there a mechanism to leverage information for business analytics and optimization?
– What is the difference between business intelligence and business analytics?
– what is the difference between Data analytics and Business Analytics If Any?
– How do you pick an appropriate ETL tool or business analytics tool?
– What are the trends shaping the future of business analytics?
– Are we Assessing Fraud Analytics and Risk?
Business intelligence Critical Criteria:
Closely inspect Business intelligence tasks and grade techniques for implementing Business intelligence controls.
– 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?
– Can you easily add users and features to quickly scale and customize to your organizations specific needs?
– Are business intelligence solutions starting to include social media data and analytics features?
– Why does animosity endure between IT and business when it comes to business intelligence?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– Does your software facilitate the setting of thresholds and provide alerts to users?
– Does your BI solution allow analytical insights to happen anywhere and everywhere?
– Was your software written by your organization or acquired from a third party?
– What tools are there for publishing sharing and visualizing data online?
– Who prioritizes, conducts and monitors business intelligence projects?
– Describe the process of data transformation required by your system?
– How is Business Intelligence and Information Management related?
– What else does the data tell us that we never thought to ask?
– What is your anticipated learning curve for Report Users?
– How would you broadly categorize the different BI tools?
– How do we use AI algorithms in practical applications?
– What programs do we have to teach data mining?
– Where is the business intelligence bottleneck?
– What is your licensing model and prices?
– What is your expect product life cycle?
Cloud analytics Critical Criteria:
See the value of Cloud analytics quality and point out Cloud analytics tensions in leadership.
– Have you identified your Fraud Analytics key performance indicators?
– Is Fraud Analytics Required?
Complex event processing Critical Criteria:
Group Complex event processing governance and plan concise Complex event processing education.
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Fraud Analytics?
– What knowledge, skills and characteristics mark a good Fraud Analytics project manager?
– Have all basic functions of Fraud Analytics been defined?
Computer programming Critical Criteria:
Review Computer programming failures and plan concise Computer programming education.
– Can Management personnel recognize the monetary benefit of Fraud Analytics?
– Can we do Fraud Analytics without complex (expensive) analysis?
Continuous analytics Critical Criteria:
Facilitate Continuous analytics tactics and perfect Continuous analytics conflict management.
– What are your current levels and trends in key measures or indicators of Fraud 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 are the record-keeping requirements of Fraud Analytics activities?
– What will drive Fraud Analytics change?
Cultural analytics Critical Criteria:
Interpolate Cultural analytics governance and get out your magnifying glass.
– What are the key elements of your Fraud Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?
– What management system can we use to leverage the Fraud Analytics experience, ideas, and concerns of the people closest to the work to be done?
– What are the Key enablers to make this Fraud Analytics move?
Customer analytics Critical Criteria:
Experiment with Customer analytics adoptions and report on developing an effective Customer analytics strategy.
– What are our needs in relation to Fraud Analytics skills, labor, equipment, and markets?
– How can we improve Fraud Analytics?
Data mining Critical Criteria:
Systematize Data mining tactics and mentor Data mining customer orientation.
– For your Fraud Analytics project, identify and describe the business environment. is there more than one layer to the business environment?
– 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?
– Do several people in different organizational units assist with the Fraud Analytics process?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– Is business intelligence set to play a key role in the future of Human Resources?
– What is our Fraud Analytics Strategy?
Data presentation architecture Critical Criteria:
Cut a stake in Data presentation architecture goals and visualize why should people listen to you regarding Data presentation architecture.
– How do we Identify specific Fraud Analytics investment and emerging trends?
– Does the Fraud Analytics task fit the clients priorities?
– What are current Fraud Analytics Paradigms?
Embedded analytics Critical Criteria:
Communicate about Embedded analytics adoptions and probe using an integrated framework to make sure Embedded analytics is getting what it needs.
– How important is Fraud Analytics to the user organizations mission?
– How to Secure Fraud Analytics?
Enterprise decision management Critical Criteria:
Prioritize Enterprise decision management quality and maintain Enterprise decision management for success.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Fraud Analytics. How do we gain traction?
– Is the Fraud Analytics organization completing tasks effectively and efficiently?
– When a Fraud Analytics manager recognizes a problem, what options are available?
Fraud detection Critical Criteria:
Reorganize Fraud detection leadership and adopt an insight outlook.
– In what ways are Fraud Analytics vendors and us interacting to ensure safe and effective use?
– Does Fraud Analytics analysis show the relationships among important Fraud Analytics factors?
Google Analytics Critical Criteria:
Transcribe Google Analytics issues and optimize Google Analytics leadership as a key to advancement.
– Have the types of risks that may impact Fraud Analytics been identified and analyzed?
– Does Fraud Analytics analysis isolate the fundamental causes of problems?
– Is there any existing Fraud Analytics governance structure?
Human resources Critical Criteria:
Steer Human resources leadership and cater for concise Human resources education.
– Who will be responsible for leading the various bcp teams (e.g., crisis/emergency, recovery, technology, communications, facilities, Human Resources, business units and processes, Customer Service)?
– Describe your views on the value of human assets in helping an organization achieve its goals. how important is it for organizations to train and develop their Human Resources?
– Have we adopted and promoted the companys culture of integrity management, including ethics, business practices and Human Resources evaluations?
– what is to keep those with access to some of an individuals personal data from browsing through other parts of it for other reasons?
– Do we perform an environmental scan of hr strategies within the hr community (what/how are others planning)?
– Do we identify desired outcomes and key indicators (if not already existing) such as what metrics?
– To satisfy our customers and stakeholders, what financial objectives must we accomplish?
– What tools and technologies are needed for a custom Fraud Analytics project?
– How is Staffs willingness to help or refer questions to the proper level?
– What problems have you encountered with the department or staff member?
– What are the Human Resources we can bring to establishing new business?
– Can you think of other ways to reduce the costs of managing employees?
– Do you have Human Resources available to support your policies?
– How do you view the department and staff members as a whole?
– To achieve our vision, what customer needs must we serve?
– What are ways that employee productivity can be measured?
– Do you understand the parameters set by the algorithm?
– What does the pyramid of information look like?
– Is our company developing its Human Resources?
– How to deal with diversity?
Learning analytics Critical Criteria:
Incorporate Learning analytics outcomes and observe effective Learning analytics.
– Is Fraud Analytics Realistic, or are you setting yourself up for failure?
– How do we manage Fraud Analytics Knowledge Management (KM)?
Machine learning Critical Criteria:
Set goals for Machine learning projects and develop and take control of the Machine learning initiative.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– Are accountability and ownership for Fraud Analytics clearly defined?
Marketing mix modeling Critical Criteria:
Grasp Marketing mix modeling outcomes and revise understanding of Marketing mix modeling architectures.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Fraud Analytics services/products?
– Who will provide the final approval of Fraud Analytics deliverables?
Mobile Location Analytics Critical Criteria:
Detail Mobile Location Analytics leadership and suggest using storytelling to create more compelling Mobile Location Analytics projects.
– What potential environmental factors impact the Fraud Analytics effort?
– How do we Improve Fraud Analytics service perception, and satisfaction?
Neural networks Critical Criteria:
Test Neural networks planning and interpret which customers can’t participate in Neural networks because they lack skills.
– What threat is Fraud Analytics addressing?
News analytics Critical Criteria:
Check News analytics visions and don’t overlook the obvious.
– What is the source of the strategies for Fraud Analytics strengthening and reform?
– How do we measure improved Fraud Analytics service perception, and satisfaction?
Online analytical processing Critical Criteria:
Accommodate Online analytical processing issues and prioritize challenges of Online analytical processing.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Fraud Analytics processes?
– Meeting the challenge: are missed Fraud Analytics opportunities costing us money?
Online video analytics Critical Criteria:
Devise Online video analytics risks and clarify ways to gain access to competitive Online video analytics services.
– Think about the people you identified for your Fraud 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?
– What will be the consequences to the business (financial, reputation etc) if Fraud Analytics does not go ahead or fails to deliver the objectives?
– How is the value delivered by Fraud Analytics being measured?
Operational reporting Critical Criteria:
Design Operational reporting engagements and gather Operational reporting models .
– Do those selected for the Fraud Analytics team have a good general understanding of what Fraud Analytics is all about?
– Is there a Fraud Analytics Communication plan covering who needs to get what information when?
Operations research Critical Criteria:
Check Operations research results and find the ideas you already have.
– Which customers cant participate in our Fraud Analytics domain because they lack skills, wealth, or convenient access to existing solutions?
– How do senior leaders actions reflect a commitment to the organizations Fraud Analytics values?
Over-the-counter data Critical Criteria:
Derive from Over-the-counter data goals and reduce Over-the-counter data costs.
– How can you measure Fraud Analytics in a systematic way?
– What about Fraud Analytics Analysis of results?
Portfolio analysis Critical Criteria:
Align Portfolio analysis outcomes and track iterative Portfolio analysis results.
– How does the organization define, manage, and improve its Fraud Analytics processes?
Predictive analytics Critical Criteria:
Derive from Predictive analytics outcomes and report on setting up Predictive analytics without losing ground.
– What are direct examples that show predictive analytics to be highly reliable?
– What sources do you use to gather information for a Fraud Analytics study?
Predictive engineering analytics Critical Criteria:
Illustrate Predictive engineering analytics tasks and oversee Predictive engineering analytics management by competencies.
– Does Fraud 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?
– Do you monitor the effectiveness of your Fraud Analytics activities?
– Are there Fraud Analytics Models?
Predictive modeling Critical Criteria:
Mix Predictive modeling visions and suggest using storytelling to create more compelling Predictive modeling projects.
– Are you currently using predictive modeling to drive results?
– How do we go about Comparing Fraud Analytics approaches/solutions?
Prescriptive analytics Critical Criteria:
Consolidate Prescriptive analytics results and drive action.
– What are all of our Fraud Analytics domains and what do they do?
Price discrimination Critical Criteria:
Apply Price discrimination engagements and figure out ways to motivate other Price discrimination users.
– What are the usability implications of Fraud Analytics actions?
Risk analysis Critical Criteria:
Demonstrate Risk analysis projects and use obstacles to break out of ruts.
– 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 tools do you use once you have decided on a Fraud Analytics strategy and more importantly how do you choose?
– 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?
– What is the purpose of Fraud Analytics in relation to the mission?
– With risk analysis do we answer the question how big is the risk?
Security information and event management Critical Criteria:
Deliberate over Security information and event management projects and simulate teachings and consultations on quality process improvement of Security information and event management.
Semantic analytics Critical Criteria:
Nurse Semantic analytics results and adopt an insight outlook.
– Can we add value to the current Fraud Analytics decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– What are the barriers to increased Fraud Analytics production?
Smart grid Critical Criteria:
Generalize Smart grid quality and adjust implementation of Smart grid.
– 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?
– How do you determine the key elements that affect Fraud Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
– Will Fraud Analytics have an impact on current business continuity, disaster recovery processes and/or infrastructure?
Social analytics Critical Criteria:
Graph Social analytics planning and assess and formulate effective operational and Social analytics strategies.
– What prevents me from making the changes I know will make me a more effective Fraud Analytics leader?
– What business benefits will Fraud Analytics goals deliver if achieved?
Software analytics Critical Criteria:
Group Software analytics projects and budget for Software analytics challenges.
– How will you know that the Fraud Analytics project has been successful?
– What vendors make products that address the Fraud Analytics needs?
Speech analytics Critical Criteria:
Discuss Speech analytics quality and question.
Statistical discrimination Critical Criteria:
Probe Statistical discrimination tactics and define Statistical discrimination competency-based leadership.
– Is the scope of Fraud Analytics defined?
Stock-keeping unit Critical Criteria:
Illustrate Stock-keeping unit goals and handle a jump-start course to Stock-keeping unit.
– What is the total cost related to deploying Fraud Analytics, including any consulting or professional services?
– Why is it important to have senior management support for a Fraud Analytics project?
Structured data Critical Criteria:
Review Structured data issues and prioritize challenges of Structured data.
– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?
– Does Fraud Analytics create potential expectations in other areas that need to be recognized and considered?
– Should you use a hierarchy or would a more structured database-model work best?
Telecommunications data retention Critical Criteria:
Transcribe Telecommunications data retention risks and find answers.
– Think about the kind of project structure that would be appropriate for your Fraud Analytics project. should it be formal and complex, or can it be less formal and relatively simple?
– At what point will vulnerability assessments be performed once Fraud Analytics is put into production (e.g., ongoing Risk Management after implementation)?
Text analytics Critical Criteria:
Meet over Text analytics strategies and integrate design thinking in Text analytics innovation.
– What new services of functionality will be implemented next with Fraud Analytics ?
– Risk factors: what are the characteristics of Fraud Analytics that make it risky?
– Have text analytics mechanisms like entity extraction been considered?
– How can the value of Fraud Analytics be defined?
Text mining Critical Criteria:
Adapt Text mining issues and forecast involvement of future Text mining projects in development.
– What are our best practices for minimizing Fraud Analytics project risk, while demonstrating incremental value and quick wins throughout the Fraud Analytics project lifecycle?
Time series Critical Criteria:
Meet over Time series goals and arbitrate Time series techniques that enhance teamwork and productivity.
– Who are the people involved in developing and implementing Fraud Analytics?
Unstructured data Critical Criteria:
Demonstrate Unstructured data failures and handle a jump-start course to Unstructured data.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Fraud Analytics in a volatile global economy?
– Are assumptions made in Fraud Analytics stated explicitly?
User behavior analytics Critical Criteria:
X-ray User behavior analytics outcomes and test out new things.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Fraud Analytics process?
Visual analytics Critical Criteria:
Examine Visual analytics governance and tour deciding if Visual analytics progress is made.
Web analytics Critical Criteria:
Wrangle Web analytics management and explore and align the progress in Web analytics.
– What statistics should one be familiar with for business intelligence and web analytics?
– Which Fraud Analytics goals are the most important?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Explore Win–loss analytics planning and summarize a clear Win–loss analytics focus.
– What are the disruptive Fraud Analytics technologies that enable our organization to radically change our business processes?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Fraud 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:
Fraud Analytics External links:
Fraud Analytics | TransUnion
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:
Magnifier Behavioral Analytics – Palo Alto Networks
Behavioral Analytics | Interana
User and Entity Behavioral Analytics Partners | Exabeam
Big data External links:
Databricks – Making Big Data Simple
ZestFinance.com: Machine Learning & Big Data Underwriting
Business Intelligence and Big Data Analytics Software
Business analytics External links:
What is Business Analytics? Webopedia Definition
Harvard Business Analytics Program
Business intelligence External links:
EnsembleIQ | The premier business intelligence resource
SQL Server Business Intelligence | Microsoft
Cloud analytics External links:
Cloud Analytics – Solutions for Cloud Data Analytics | NetApp
Cloud Analytics | Big Data Analytics | Vertica
Cloud Analytics Academy – Official Site
Computer programming External links:
Coding for Kids | Computer Programming | AgentCubes online
Computer Programming, Robotics & Engineering – STEM …
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 …
Customer Analytics Services and Solutions | TransUnion
Data mining External links:
UT Data Mining
Data Mining Extensions (DMX) Reference | Microsoft Docs
What is Data Mining in Healthcare?
Embedded analytics External links:
Embedded Analytics | Qlik
Power BI Embedded analytics | Microsoft Azure
Logi Analytics: The #1 Embedded Analytics Platform
Enterprise decision management External links:
Enterprise Decision Management (EDM) – Techopedia.com
Enterprise Decision Management and Alerts – FICO
Enterprise Decision Management | SAS Italy
Fraud detection External links:
Debit Card Security | Fraud Detection & Protection | RushCard
Fraud Detection and Authentication Technology – Next Caller
Fraud Detection and Fraud Prevention Services | TransUnion
Google Analytics External links:
Google Analytics Solutions – Marketing Analytics & …
Google Analytics Opt-out Browser Add-on Download Page
Google Analytics | Google Developers
Human resources External links:
Human Resources Job Titles-The Ultimate Guide | upstartHR
Human Resources – jobs.goodyear.com
Human Resources Job Titles – The Balance
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 …
Microsoft Azure Machine Learning Studio
What is machine learning? – Definition from WhatIs.com
Marketing mix modeling External links:
Marketing Mix Modeling | Marketing Management Analytics
Marketing Mix Modeling – Gartner IT Glossary
Mobile Location Analytics External links:
Mobile Location Analytics Privacy Notice | Verizon
Mobile location analytics | Federal Trade Commission
How ‘Mobile Location Analytics’ Controls Your Mind – YouTube
News analytics External links:
News Analytics | Amareos
Online analytical processing External links:
Working with Online Analytical Processing (OLAP)
Online video analytics External links:
Global Online Video Analytics Market Market Research
Online Video Analytics & Marketing Software | Vidooly
Managing Your Online Video Analytics – DaCast
Operations research External links:
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.
Operations Research on JSTOR
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:
iCite | NIH Office of Portfolio Analysis
TipRanks Premium | Stock Portfolio Analysis Tool | TipRanks
Portfolio Analysis Final-1 Flashcards | Quizlet
Predictive analytics External links:
Strategic Location Management & Predictive Analytics | Tango
Predictive Analytics Software, Social Listening | NewBrand
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 …
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate Technologies
Price discrimination External links:
MBAecon – 1st, 2nd and 3rd Price discrimination
3 Types of Price Discrimination | Chron.com
Price Discrimination – Investopedia
Risk analysis External links:
Risk Analysis | Investopedia
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.
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:
[PDF]Geospatial and Temporal Semantic Analytics
Semantic Analytics – Get Business Intelligence With Schema …
SciBite – The Semantic Analytics Company
Smart grid External links:
Smart grid. (Journal, magazine, 2011) [WorldCat.org]
Recovery Act Smart Grid Programs
Smart Grid – AbeBooks
Social analytics External links:
Influencer marketing platform & Social analytics tool – HYPR
Union Metrics makes social analytics easy – TweetReach
Dark Social Analytics: Track Private Shares with GetSocial
Software analytics External links:
Software Analytics – Microsoft Research
Speech analytics External links:
What is speech analytics? – Definition from WhatIs.com
Customer Engagement & Speech Analytics | CallMiner
Speech Analytics | Speech Analytics Software & Audio Mining
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
Structured data External links:
Structured Data Testing Tool – Google
Providing Structured Data | Custom Search | Google …
What is structured data? – Definition from WhatIs.com
Telecommunications data retention External links:
Telecommunications Data Retention and Human Rights: …
Text analytics External links:
[PDF]Syllabus Course Title: Text Analytics – Regis University
Text analytics software| NICE LTD | NICE
Text Mining / Text Analytics Specialist – bigtapp
Text mining External links:
Text Mining Specialist Jobs, Employment | Indeed.com
Text mining — University of Illinois at Urbana-Champaign
Text Mining / Text Analytics Specialist – bigtapp
Time series External links:
pandas Time Series Basics – chrisalbon.com
Time Series – University of Nebraska–Lincoln
[PDF]Time Series Analysis and Forecasting – cengage.com
Unstructured data External links:
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:
Dynamic text in SAS VA (Visual Analytics) – Stack Overflow
Web analytics External links:
11 Best Web Analytics Tools | Inc.com
Login – Heap | Mobile and Web Analytics
Careers | Mobile & Web Analytics | Mixpanel