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 207 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:
Infer Fraud Analytics failures and report on developing an effective Fraud Analytics strategy.
– Who will be responsible for deciding whether Fraud Analytics goes ahead or not after the initial investigations?
– What are the barriers to increased Fraud Analytics production?
– What are our Fraud Analytics Processes?
Academic discipline Critical Criteria:
Graph Academic discipline issues and explain and analyze the challenges of Academic discipline.
– Why is it important to have senior management support for a Fraud Analytics project?
– What are all of our Fraud Analytics domains and what do they do?
– What business benefits will Fraud Analytics goals deliver if achieved?
Analytic applications Critical Criteria:
Nurse Analytic applications engagements and explain and analyze the challenges of Analytic applications.
– Who are the people involved in developing and implementing Fraud Analytics?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
– What is our Fraud Analytics Strategy?
– What is Effective Fraud Analytics?
Architectural analytics Critical Criteria:
Detail Architectural analytics leadership and simulate teachings and consultations on quality process improvement of Architectural analytics.
– Is Fraud Analytics Realistic, or are you setting yourself up for failure?
– How do we manage Fraud Analytics Knowledge Management (KM)?
Behavioral analytics Critical Criteria:
Analyze Behavioral analytics visions and create a map for yourself.
– What role does communication play in the success or failure of a Fraud Analytics project?
– Does our organization need more Fraud Analytics education?
– What are the business goals Fraud Analytics is aiming to achieve?
Big data Critical Criteria:
Systematize Big data visions and define what do we need to start doing with Big data.
– Looking at hadoop big data in the rearview mirror what would you have done differently after implementing a Data Lake?
– Are we collecting data once and using it many times, or duplicating data collection efforts and submerging data in silos?
– 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?
– Does our entire organization have easy access to information required to support work processes?
– Does your organization have the right analytical tools to handle (big) data?
– How will systems and methods evolve to remove Big Data solution weaknesses?
– What new Security and Privacy challenge arise from new Big Data solutions?
– Hybrid partitioning (across rows/terms and columns/documents) useful?
– Does your organization have the necessary skills to handle big data?
– Even when we have a lot of data, do we understand it?
– How does that compare to other science disciplines?
– What if the data cannot fit on your computer?
– What is the cost of partitioning/balancing?
– Wait, DevOps does not apply to Big Data?
– What is collecting all this data?
– what is Different about Big Data?
– Does Big Data Really Need HPC?
– What about Volunteered data?
– What is in Scope?
Business analytics Critical Criteria:
Pilot Business analytics outcomes and devise Business analytics key steps.
– 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?
– What sources do you use to gather information for a Fraud Analytics study?
– How does the organization define, manage, and improve its Fraud Analytics processes?
– How do you pick an appropriate ETL tool or business analytics tool?
– What are the trends shaping the future of business analytics?
– Who sets the Fraud Analytics standards?
Business intelligence Critical Criteria:
Graph Business intelligence strategies and attract Business intelligence skills.
– If on-premise software is a must, a balance of choice and simplicity is essential. When specific users are viewing and interacting with analytics, can you use a named-user licensing model that offers accessibility without the need for hardware considerations?
– Does the software let users work with the existing data infrastructure already in place, freeing your IT team from creating more cubes, universes, and standalone marts?
– Does a BI business intelligence CoE center of excellence approach to support and enhancements benefit our organization and save cost?
– What is the difference between Key Performance Indicators KPI and Critical Success Factors CSF in a Business Strategic decision?
– Are NoSQL databases used primarily for applications or are they used in Business Intelligence use cases as well?
– Does your bi software work well with both centralized and decentralized data architectures and vendors?
– What are some successful business intelligence BI apps that have been built on an existing platform?
– Does your bi solution allow analytical insights to happen anywhere and everywhere?
– Does your BI solution help you find the right views to examine your data?
– What is your anticipated learning curve for Technical Administrators?
– What are the best use cases for Mobile Business Intelligence?
– Number of data sources that can be simultaneously accessed?
– What type and complexity of system administration roles?
– How are business intelligence applications delivered?
– How stable is it across domains/geographies?
– How is Business Intelligence related to CRM?
– What level of training would you recommend?
– Do you support video integration?
Cloud analytics Critical Criteria:
Be clear about Cloud analytics adoptions and research ways can we become the Cloud analytics company that would put us out of business.
– How would one define Fraud Analytics leadership?
Complex event processing Critical Criteria:
Guide Complex event processing engagements and do something to it.
– Consider your own Fraud Analytics project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?
– Does Fraud Analytics analysis show the relationships among important Fraud Analytics factors?
– How will you measure your Fraud Analytics effectiveness?
Computer programming Critical Criteria:
Group Computer programming planning and integrate design thinking in Computer programming innovation.
– What is the total cost related to deploying Fraud Analytics, including any consulting or professional services?
– What are the top 3 things at the forefront of our Fraud Analytics agendas for the next 3 years?
– What threat is Fraud Analytics addressing?
Continuous analytics Critical Criteria:
Consult on Continuous analytics tactics and look at it backwards.
– Which customers cant participate in our Fraud Analytics domain because they lack skills, wealth, or convenient access to existing solutions?
– When a Fraud Analytics manager recognizes a problem, what options are available?
– Does Fraud Analytics analysis isolate the fundamental causes of problems?
Cultural analytics Critical Criteria:
Disseminate Cultural analytics adoptions and devote time assessing Cultural analytics and its risk.
– How do we go about Securing Fraud Analytics?
Customer analytics Critical Criteria:
Study Customer analytics outcomes and report on setting up Customer analytics without losing ground.
– Is maximizing Fraud Analytics protection the same as minimizing Fraud Analytics loss?
– Are assumptions made in Fraud Analytics stated explicitly?
Data mining Critical Criteria:
Consult on Data mining quality and sort Data mining activities.
– 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?
– Do the Fraud Analytics decisions we make today help people and the planet tomorrow?
– Is business intelligence set to play a key role in the future of Human Resources?
– To what extent does management recognize Fraud Analytics as a tool to increase the results?
– What programs do we have to teach data mining?
Data presentation architecture Critical Criteria:
Powwow over Data presentation architecture tactics and probe the present value of growth of Data presentation architecture.
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Fraud Analytics process?
– What are the record-keeping requirements of Fraud Analytics activities?
Embedded analytics Critical Criteria:
Jump start Embedded analytics strategies and describe the risks of Embedded analytics sustainability.
– Are there any disadvantages to implementing Fraud Analytics? There might be some that are less obvious?
Enterprise decision management Critical Criteria:
Distinguish Enterprise decision management outcomes and give examples utilizing a core of simple Enterprise decision management skills.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Fraud Analytics process. ask yourself: are the records needed as inputs to the Fraud Analytics process available?
– For your Fraud Analytics project, identify and describe the business environment. is there more than one layer to the business environment?
Fraud detection Critical Criteria:
Categorize Fraud detection visions and remodel and develop an effective Fraud detection strategy.
– How do we Identify specific Fraud Analytics investment and emerging trends?
Google Analytics Critical Criteria:
Judge Google Analytics governance and display thorough understanding of the Google Analytics process.
– What tools and technologies are needed for a custom Fraud Analytics project?
– What are the long-term Fraud Analytics goals?
Human resources Critical Criteria:
Drive Human resources outcomes and do something to it.
– Rapidly increasing specialization of skill and knowledge presents a major management challenge. How does an organization maintain a work environment that supports specialization without compromising its ability to marshal its full range of Human Resources and turn on a dime to implement strategic imperatives?
– Have we adopted and promoted the companys culture of integrity management, including ethics, business practices and Human Resources evaluations?
– Do the response plans address damage assessment, site restoration, payroll, Human Resources, information technology, and administrative support?
– Are there cases when the company may collect, use and disclose personal data without consent or accommodation?
– Should pay levels and differences reflect what workers are used to in their own countries?
– Does the cloud service provider have necessary security controls on their human resources?
– What are strategies that we can undertake to reduce job fatigue and reduced productivity?
– What is the important thing that human resources management should do?
– What steps are taken to promote compliance with the hr principles?
– How should any risks to privacy and civil liberties be managed?
– Are you a manager interested in increasing your effectiveness?
– What will be your Human Resources needs for the first year?
– How can we promote retention of high performing employees?
– What are ways to reduce the costs of managing employees?
– Ease of contacting the Human Resources staff members?
– Do you need to develop a Human Resources manual?
– What do users think of the information?
– What are the data sources and data mix?
– How to deal with diversity?
– What is personal data?
Learning analytics Critical Criteria:
Revitalize Learning analytics failures and explain and analyze the challenges of Learning analytics.
– How do mission and objectives affect the Fraud Analytics processes of our organization?
– Have the types of risks that may impact Fraud Analytics been identified and analyzed?
– Think of your Fraud Analytics project. what are the main functions?
Machine learning Critical Criteria:
Concentrate on Machine learning strategies 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?
– How likely is the current Fraud Analytics plan to come in on schedule or on budget?
– What vendors make products that address the Fraud Analytics needs?
Marketing mix modeling Critical Criteria:
Wrangle Marketing mix modeling issues and probe using an integrated framework to make sure Marketing mix modeling is getting what it needs.
– How do you determine the key elements that affect Fraud Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
– Why are Fraud Analytics skills important?
Mobile Location Analytics Critical Criteria:
Interpolate Mobile Location Analytics strategies and catalog Mobile Location Analytics activities.
– Will new equipment/products be required to facilitate Fraud Analytics delivery for example is new software needed?
– Will Fraud Analytics deliverables need to be tested and, if so, by whom?
– How is the value delivered by Fraud Analytics being measured?
Neural networks Critical Criteria:
Adapt Neural networks engagements and plan concise Neural networks education.
– What are our best practices for minimizing Fraud Analytics project risk, while demonstrating incremental value and quick wins throughout the Fraud Analytics project lifecycle?
– Can Management personnel recognize the monetary benefit of Fraud Analytics?
News analytics Critical Criteria:
Learn from News analytics management and modify and define the unique characteristics of interactive News analytics projects.
– What are your results for key measures or indicators of the accomplishment of your Fraud Analytics strategy and action plans, including building and strengthening core competencies?
– What will be the consequences to the business (financial, reputation etc) if Fraud Analytics does not go ahead or fails to deliver the objectives?
– Who will provide the final approval of Fraud Analytics deliverables?
Online analytical processing Critical Criteria:
Chat re Online analytical processing risks and work towards be a leading Online analytical processing expert.
– What tools do you use once you have decided on a Fraud Analytics strategy and more importantly how do you choose?
– How important is Fraud Analytics to the user organizations mission?
– How do we maintain Fraud Analyticss Integrity?
Online video analytics Critical Criteria:
Apply Online video analytics engagements and secure Online video analytics creativity.
– How can you negotiate Fraud Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?
– How do we go about Comparing Fraud Analytics approaches/solutions?
– Why should we adopt a Fraud Analytics framework?
Operational reporting Critical Criteria:
Model after Operational reporting projects and find the essential reading for Operational reporting researchers.
– Does Fraud Analytics systematically track and analyze outcomes for accountability and quality improvement?
Operations research Critical Criteria:
Read up on Operations research engagements and gather Operations research models .
– 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 other jobs or tasks affect the performance of the steps in the Fraud Analytics process?
Over-the-counter data Critical Criteria:
Examine Over-the-counter data issues and display thorough understanding of the Over-the-counter data process.
– Do several people in different organizational units assist with the Fraud Analytics process?
– Have you identified your Fraud Analytics key performance indicators?
Portfolio analysis Critical Criteria:
Recall Portfolio analysis failures and report on the economics of relationships managing Portfolio analysis and constraints.
– Is the Fraud Analytics organization completing tasks effectively and efficiently?
– Risk factors: what are the characteristics of Fraud Analytics that make it risky?
Predictive analytics Critical Criteria:
Match Predictive analytics failures and proactively manage Predictive analytics risks.
– What are the success criteria that will indicate that Fraud Analytics objectives have been met and the benefits delivered?
– What prevents me from making the changes I know will make me a more effective Fraud Analytics leader?
– What are direct examples that show predictive analytics to be highly reliable?
Predictive engineering analytics Critical Criteria:
Investigate Predictive engineering analytics planning and define Predictive engineering analytics competency-based leadership.
– What new services of functionality will be implemented next with Fraud Analytics ?
– Can we do Fraud Analytics without complex (expensive) analysis?
– How can skill-level changes improve Fraud Analytics?
Predictive modeling Critical Criteria:
Use past Predictive modeling results and diversify by understanding risks and leveraging Predictive modeling.
– Are you currently using predictive modeling to drive results?
Prescriptive analytics Critical Criteria:
Weigh in on Prescriptive analytics failures and gather Prescriptive analytics models .
– Can we add value to the current Fraud Analytics decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
– Is Supporting Fraud Analytics documentation required?
Price discrimination Critical Criteria:
Depict Price discrimination risks and intervene in Price discrimination processes and leadership.
– Meeting the challenge: are missed Fraud Analytics opportunities costing us money?
– How can we improve Fraud Analytics?
Risk analysis Critical Criteria:
Brainstorm over Risk analysis governance 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 levels of assurance are needed and how can the risk analysis benefit setting standards and policy functions?
– In which two Service Management processes would you be most likely to use a risk analysis and management method?
– How does the business impact analysis use data from Risk Management and risk analysis?
– How do we do risk analysis of rare, cascading, catastrophic events?
– With risk analysis do we answer the question how big is the risk?
Security information and event management Critical Criteria:
Drive Security information and event management management and find the essential reading for Security information and event management researchers.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your Fraud Analytics processes?
– Where do ideas that reach policy makers and planners as proposals for Fraud Analytics strengthening and reform actually originate?
Semantic analytics Critical Criteria:
Weigh in on Semantic analytics adoptions and modify and define the unique characteristics of interactive Semantic analytics projects.
– 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?
– Are there Fraud Analytics problems defined?
– How to Secure Fraud Analytics?
Smart grid Critical Criteria:
Ventilate your thoughts about Smart grid planning and correct better engagement with Smart grid results.
– 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?
– 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?
Social analytics Critical Criteria:
Refer to Social analytics goals and document what potential Social analytics megatrends could make our business model obsolete.
– How much does Fraud Analytics help?
Software analytics Critical Criteria:
Contribute to Software analytics strategies and summarize a clear Software analytics focus.
– How do we Improve Fraud Analytics service perception, and satisfaction?
Speech analytics Critical Criteria:
Powwow over Speech analytics failures and summarize a clear Speech analytics focus.
– What are the key elements of your Fraud Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?
Statistical discrimination Critical Criteria:
Categorize Statistical discrimination tasks and find the essential reading for Statistical discrimination researchers.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Fraud Analytics processes?
Stock-keeping unit Critical Criteria:
Model after Stock-keeping unit strategies and be persistent.
– Think about the functions involved in your Fraud Analytics project. what processes flow from these functions?
Structured data Critical Criteria:
Study Structured data projects and explore and align the progress in Structured data.
– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?
– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?
– Should you use a hierarchy or would a more structured database-model work best?
Telecommunications data retention Critical Criteria:
Illustrate Telecommunications data retention projects and test out new things.
– How can you measure Fraud Analytics in a systematic way?
Text analytics Critical Criteria:
Understand Text analytics engagements and pioneer acquisition of Text analytics systems.
– What are the disruptive Fraud Analytics technologies that enable our organization to radically change our business processes?
– How do we make it meaningful in connecting Fraud Analytics with what users do day-to-day?
– Have text analytics mechanisms like entity extraction been considered?
– Are there Fraud Analytics Models?
Text mining Critical Criteria:
Distinguish Text mining visions and gather Text mining models .
– Do you monitor the effectiveness of your Fraud Analytics activities?
Time series Critical Criteria:
Test Time series engagements and perfect Time series conflict management.
– Among the Fraud Analytics product and service cost to be estimated, which is considered hardest to estimate?
– How do we ensure that implementations of Fraud Analytics products are done in a way that ensures safety?
Unstructured data Critical Criteria:
Recall Unstructured data governance and adjust implementation of Unstructured data.
– Is there a Fraud Analytics Communication plan covering who needs to get what information when?
– Who is the main stakeholder, with ultimate responsibility for driving Fraud Analytics forward?
– Have all basic functions of Fraud Analytics been defined?
User behavior analytics Critical Criteria:
Canvass User behavior analytics management and stake your claim.
– How do your measurements capture actionable Fraud Analytics information for use in exceeding your customers expectations and securing your customers engagement?
Visual analytics Critical Criteria:
Infer Visual analytics adoptions and document what potential Visual analytics megatrends could make our business model obsolete.
– Do we monitor the Fraud Analytics decisions made and fine tune them as they evolve?
Web analytics Critical Criteria:
Examine Web analytics management and use obstacles to break out of ruts.
– What statistics should one be familiar with for business intelligence and web analytics?
– How is cloud computing related to web analytics?
– How do we Lead with Fraud Analytics in Mind?
Win–loss analytics Critical Criteria:
Steer Win–loss analytics decisions and adopt an insight outlook.
– In a project to restructure Fraud Analytics outcomes, which stakeholders would you involve?
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:
Folklore | academic discipline | Britannica.com
What does academic discipline mean? – Definitions.net
Criminal justice | academic discipline | Britannica.com
Analytic applications External links:
Hype Cycle for Back-Office Analytic Applications, 2017
Analytic Applications – Gartner IT Glossary
Foxtrot Code AI Analytic Applications (Home)
Architectural analytics External links:
Architectural Analytics – Home | Facebook
Top Online Courses in Architectural Analytics 2018
Best Master’s Degrees in Architectural Analytics 2018
Behavioral analytics External links:
Behavioral Analytics | Interana
Behavioral Analytics Definition | Investopedia
Security and IT Risk Intelligence with Behavioral Analytics
Big data External links:
Event Hubs – Cloud big data solutions | Microsoft Azure
Take 5 Media Group – Build an audience using big data
Business analytics External links:
Master of Science in Business Analytics | UW Tacoma
Master’s in Business Analytics | Daniels College of Business
Harvard Business Analytics Program
Business intelligence External links:
Business Intelligence Tools & Software | Square
EnsembleIQ | The premier business intelligence resource
Business Intelligence | Microsoft
Cloud analytics External links:
Cloud Analytics Academy – Official Site
Cloud Analytics World Tour | Snowflake
Complex event processing External links:
Complex Event Processing (CEP) for Big Data Streaming
Computer programming External links:
Computer Programming, Robotics & Engineering – STEM For Kids
Computer programming | Computing | Khan Academy
Coding for Kids | Computer Programming | AgentCubes online
Customer analytics External links:
Leadership Team – Customer Analytics Company – Buxton
Customer Analytics & Predictive Analytics Tools for Business
What are Customer Analytics? – Amazon Web Services …
Data mining External links:
Nebraska Oil and Gas Conservation Commission – GIS Data Mining
What is Data Mining in Healthcare?
Data Mining | Coursera
Embedded analytics External links:
Embedded Analytics – Tableau Software
Embedded Analytics – Gartner IT Glossary
LaunchWorks | Embedded Analytics Solutions
Enterprise decision management External links:
Enterprise Decision Management | Sapiens DECISION
enterprise decision management Archives – Insights
Enterprise Decision Management (EDM) – Techopedia.com
Fraud detection External links:
Fraud Detection and Authentication Technology – Next Caller
Fraud Detection and Anti-Money Laundering Software – Verafin
Fraud Detection and Fraud Prevention Services | TransUnion
Google Analytics External links:
Google Analytics Solutions – Marketing Analytics & …
Welcome to the Texas Board of Nursing – Google Analytics
Google Analytics | Google Developers
Human resources External links:
Office of Human Resources – TITLE IX
Title Human Resources HR Jobs, Employment | Indeed.com
Human Resources Job Titles – The Balance
Learning analytics External links:
Learning Analytics Explained. (eBook, 2017) …
Watershed | Learning Analytics for Organizations
“Using Learning Analytics to Predict Academic Success …
Machine learning External links:
Machine Learning: What it is and why it matters | SAS
DataRobot – Automated Machine Learning for Predictive …
Microsoft Azure Machine Learning Studio
Marketing mix modeling External links:
Marketing Mix Modeling – Decision Analyst
Marketing Mix Modeling | Marketing Management Analytics
What is an Example of Marketing Mix Modeling?
Mobile Location Analytics External links:
[PDF]Mobile Location Analytics Code of Conduct
How ‘Mobile Location Analytics’ Controls Your Mind – YouTube
Mobile Location Analytics Privacy Notice | Verizon
Neural networks External links:
Neural Networks and Deep Learning | Coursera
What is the learning rate in neural networks? – Quora
News analytics External links:
RavenPack News Analytics – RavenPack
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
Ooyala Videomind | Online Video Analytics
Operational reporting External links:
Operational Reporting – InfoSync Services
Operations research External links:
Operations Research (O.R.), or operational research in the U.K, is a discipline that deals with the application of advanced analytical methods to help make better decisions.
Operations Research on JSTOR
Systems Engineering and Operations Research
Over-the-counter data External links:
[PDF]Over-the-Counter Data’s Impact on Educators’ Data …
Over-the-Counter Data – American Mensa – Medium
Portfolio analysis External links:
Portfolio Analysis | Economy Watch
Analysis: Portfolio Analysis Flashcards | Quizlet
Portfolio Analysis Final-1 Flashcards | Quizlet
Predictive analytics External links:
Predictive Analytics Solutions for Global Industry | Uptake
Customer Analytics & Predictive Analytics Tools for …
Strategic Location Management & Predictive Analytics | …
Predictive engineering analytics External links:
Predictive engineering analytics includes both the tactics and tools that manufacturers can leverage to expand traditional design verification and validation into a predictive role in support of systems-driven product development.
Predictive modeling External links:
DataRobot – Automated Machine Learning for Predictive Modeling
What is predictive modeling? – Definition from …
Prescriptive analytics External links:
Prescriptive analytics – ccjdigital.com
Prescriptive Analytics | IBM Analytics
Healthcare Prescriptive Analytics – Cedar Gate …
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:
What is risk analysis? – Definition from WhatIs.com
[PDF]Military Police Risk Analysis for Army Property
Security information and event management External links:
A Guide to Security Information and Event Management
Semantic analytics External links:
[PDF]Semantic Analytics in Intelligence: Applying …
[PDF]Semantic Analytics – Northfield
SciBite – The Semantic Analytics Company
Smart grid External links:
Smart Grid – Citizens Utility Board
Smart Grid Massachusetts | National Grid
[PDF]The Smart Grid: An Introduction
Social analytics External links:
Dark Social Analytics: Track Private Shares with GetSocial
Influencer marketing platform & Social analytics tool – …
Social Analytics One – Social Analytics One
Software analytics External links:
EDGEPro Software Analytics Tool for Optometry | Success …
EDGEPro | EDGEPro Software Analytics Tool for Optometry
Software Analytics – Microsoft Research
Speech analytics External links:
Speech Analytics | NICE
Speech Analytics & Speech Recognition – TranscribeMe
Customer Engagement & Speech Analytics | CallMiner
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
[PDF]Testing for Statistical Discrimination in Health Care
Structured data External links:
SEC.gov | What Is Structured Data?
Structured Data for Dummies – Search Engine Journal
Providing Structured Data | Custom Search | Google Developers
Telecommunications data retention External links:
Telecommunications Data Retention and Human …
Text analytics External links:
Text analytics software| NICE LTD | NICE
[PDF]Syllabus Course Title: Text Analytics – Regis University
Text Analytics | What is Text Analytics? – Clarabridge
Text mining External links:
Text mining in practice with R (eBook, 2017) [WorldCat.org]
Text Mining with R
Text Mining in R: A Tutorial – Springboard Blog
Time series External links:
Plot time series – MATLAB – MathWorks
[PDF]Time Series Analysis and Its Applications: With R …
[PDF]Time Series Analysis and Forecasting – cengage.com
Unstructured data External links:
Structured vs. Unstructured data – BrightPlanet
Scale-Out NAS for Unstructured Data | Dell EMC US
Differences Between Structured & Unstructured Data – …
User behavior analytics External links:
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
Splunk User Behavior Analytics | Splunk
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
Visual analytics External links:
CSE 6242 – Data and Visual Analytics
Web analytics External links:
Web Analytics in Real Time | Clicky
Web Analytics Basics | Usability.gov
11 Best Web Analytics Tools | Inc.com