208 Risk Analytics Criteria for Multi-purpose Projects

What is involved in Risk Analytics

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

How far is your company on its Risk Analytics journey?

Take this short survey to gauge your organization’s progress toward Risk 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 Risk Analytics related domains to cover and 208 essential critical questions to check off in that domain.

The following domains are covered:

Risk 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:

Risk Analytics Critical Criteria:

Scrutinze Risk Analytics risks and modify and define the unique characteristics of interactive Risk Analytics projects.

– How will we insure seamless interoperability of Risk Analytics moving forward?

– What are specific Risk Analytics Rules to follow?

Academic discipline Critical Criteria:

Cut a stake in Academic discipline projects and look for lots of ideas.

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Risk Analytics?

– Are we Assessing Risk Analytics and Risk?

– Is the scope of Risk Analytics defined?

Analytic applications Critical Criteria:

Categorize Analytic applications goals and pay attention to the small things.

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

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

– What are internal and external Risk Analytics relations?

– How do you handle Big Data in Analytic Applications?

– Analytic Applications: Build or Buy?

Architectural analytics Critical Criteria:

Model after Architectural analytics leadership and differentiate in coordinating Architectural analytics.

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

– Is a Risk Analytics Team Work effort in place?

– How do we Lead with Risk Analytics in Mind?

Behavioral analytics Critical Criteria:

Incorporate Behavioral analytics engagements and spearhead techniques for implementing Behavioral analytics.

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

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

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

Big data Critical Criteria:

Grasp Big data outcomes and secure Big data creativity.

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

– To what extent does data-driven innovation add to the competitive advantage (CA) of your company?

– what is needed to build a data-driven application that runs on streams of fast and big data?

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

– Can we measure the basic performance measures consistently and comprehensively?

– How are the new Big Data developments captured in new Reference Architectures?

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

– What is the contribution of subsets of the data to the problem solution?

– What are the new applications that are enabled by Big Data solutions?

– How to visualize non-numeric data, e.g. text, icons, or images?

– Is recruitment of staff with strong data skills crucial?

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

– How to model context in a computational environment?

– Isnt big data just another way of saying analytics?

– Is our data collection and acquisition optimized?

– Can analyses improve with more data to process?

– What load balancing technique should we use?

– what is Different about Big Data?

– Who is collecting what?

Business analytics Critical Criteria:

Be clear about Business analytics leadership and adopt an insight outlook.

– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?

– In a project to restructure Risk Analytics outcomes, which stakeholders would you involve?

– 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?

– How can skill-level changes improve Risk Analytics?

– How can the value of Risk Analytics be defined?

Business intelligence Critical Criteria:

Consider Business intelligence tasks and define what do we need to start doing with Business intelligence.

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

– Can your software connect to all forms of data, from text and Excel files to cloud and enterprise-grade databases, with a few clicks?

– Can you easily add users and features to quickly scale and customize to your organizations specific needs?

– What are some successful business intelligence BI apps that have been built on an existing platform?

– What strategies will we pursue to ensure the success of the business intelligence competency center?

– How is Business Intelligence affecting marketing decisions during the Digital Revolution?

– Was your software written by your organization or acquired from a third party?

– What specialized bi knowledge does your business have that can be leveraged?

– Does creating or modifying reports or dashboards require a reporting team?

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

– What information needs of managers are satisfied by the new BI system?

– Who prioritizes, conducts and monitors business intelligence projects?

– What is your anticipated learning curve for Technical Administrators?

– Does your client support bi-directional functionality with mapping?

– Does your BI solution require weeks or months to deploy or change?

– Do we offer a good introduction to data warehouse?

– Where is the business intelligence bottleneck?

– Will your product work from a mobile device?

– What is required to present video images?

– Does your system provide APIs?

Cloud analytics Critical Criteria:

Think carefully about Cloud analytics quality and correct better engagement with Cloud analytics results.

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

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

Complex event processing Critical Criteria:

Shape Complex event processing projects and learn.

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

– Who are the people involved in developing and implementing Risk Analytics?

– How do we manage Risk Analytics Knowledge Management (KM)?

Computer programming Critical Criteria:

Transcribe Computer programming visions and optimize Computer programming leadership as a key to advancement.

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

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

– How does the organization define, manage, and improve its Risk Analytics processes?

Continuous analytics Critical Criteria:

Dissect Continuous analytics adoptions and ask what if.

– Does Risk 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?

– What are your results for key measures or indicators of the accomplishment of your Risk Analytics strategy and action plans, including building and strengthening core competencies?

– Is Supporting Risk Analytics documentation required?

Cultural analytics Critical Criteria:

Adapt Cultural analytics strategies and assess and formulate effective operational and Cultural analytics strategies.

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

– Which customers cant participate in our Risk Analytics domain because they lack skills, wealth, or convenient access to existing solutions?

– How do we keep improving Risk Analytics?

Customer analytics Critical Criteria:

Investigate Customer analytics tactics and track iterative Customer analytics results.

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

– Which individuals, teams or departments will be involved in Risk Analytics?

Data mining Critical Criteria:

Analyze Data mining risks and probe 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?

– What are our needs in relation to Risk Analytics skills, labor, equipment, and markets?

– Is business intelligence set to play a key role in the future of Human Resources?

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

– What programs do we have to teach data mining?

Data presentation architecture Critical Criteria:

Check Data presentation architecture quality and know what your objective is.

– What are the disruptive Risk Analytics technologies that enable our organization to radically change our business processes?

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

– What are current Risk Analytics Paradigms?

Embedded analytics Critical Criteria:

Illustrate Embedded analytics adoptions and mentor Embedded analytics customer orientation.

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

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

– What about Risk Analytics Analysis of results?

Enterprise decision management Critical Criteria:

Ventilate your thoughts about Enterprise decision management tactics and grade techniques for implementing Enterprise decision management controls.

– what is the best design framework for Risk Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

– Can we do Risk Analytics without complex (expensive) analysis?

Fraud detection Critical Criteria:

Distinguish Fraud detection strategies and display thorough understanding of the Fraud detection process.

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

– What will drive Risk Analytics change?

Google Analytics Critical Criteria:

Jump start Google Analytics engagements and get the big picture.

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

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

– Why is Risk Analytics important for you now?

Human resources Critical Criteria:

Boost Human resources engagements and observe effective Human resources.

– Does the information security function actively engage with other critical functions, such as it, Human Resources, legal, and the privacy officer, to develop and enforce compliance with information security and privacy policies and practices?

– If there is recognition by both parties of the potential benefits of an alliance, but adequate qualified human resources are not available at one or both firms?

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

– 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)?

– Is there a role for employees to play in maintaining the accuracy of personal data the company maintains?

– Do we identify desired outcomes and key indicators (if not already existing) such as what metrics?

– 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?

– How is The staffs ability and response to handle questions or requests?

– How should any risks to privacy and civil liberties be managed?

– Do you need to develop a Human Resources manual?

– How is the Ease of navigating the hr website?

– May an employee make an anonymous complaint?

– What additional approaches already exist?

– Who should appraise performance?

– What is personal data?

– What is harassment?

Learning analytics Critical Criteria:

Map Learning analytics governance and pay attention to the small things.

– Why is it important to have senior management support for a Risk Analytics project?

– How do we Improve Risk Analytics service perception, and satisfaction?

Machine learning Critical Criteria:

Value Machine learning planning and frame using storytelling to create more compelling Machine learning projects.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Risk Analytics process. ask yourself: are the records needed as inputs to the Risk Analytics process available?

– 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:

Own Marketing mix modeling adoptions and frame using storytelling to create more compelling Marketing mix modeling projects.

– Are accountability and ownership for Risk Analytics clearly defined?

Mobile Location Analytics Critical Criteria:

Participate in Mobile Location Analytics outcomes and prioritize challenges of Mobile Location Analytics.

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

– Are there Risk Analytics Models?

Neural networks Critical Criteria:

Give examples of Neural networks outcomes and improve Neural networks service perception.

– How do senior leaders actions reflect a commitment to the organizations Risk Analytics values?

– What are the barriers to increased Risk Analytics production?

News analytics Critical Criteria:

Audit News analytics results and find answers.

Online analytical processing Critical Criteria:

Analyze Online analytical processing goals and observe effective Online analytical processing.

– At what point will vulnerability assessments be performed once Risk Analytics is put into production (e.g., ongoing Risk Management after implementation)?

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

Online video analytics Critical Criteria:

Chart Online video analytics leadership and be persistent.

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

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

Operational reporting Critical Criteria:

Reconstruct Operational reporting outcomes and shift your focus.

– Have you identified your Risk Analytics key performance indicators?

– Does Risk Analytics appropriately measure and monitor risk?

Operations research Critical Criteria:

Tête-à-tête about Operations research management and find out.

– How to Secure Risk Analytics?

Over-the-counter data Critical Criteria:

Interpolate Over-the-counter data quality and plan concise Over-the-counter data education.

Portfolio analysis Critical Criteria:

Set goals for Portfolio analysis outcomes and observe effective Portfolio analysis.

Predictive analytics Critical Criteria:

Value Predictive analytics decisions and simulate teachings and consultations on quality process improvement of Predictive analytics.

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

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

– What is our formula for success in Risk Analytics ?

Predictive engineering analytics Critical Criteria:

Merge Predictive engineering analytics failures and look in other fields.

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

– How do mission and objectives affect the Risk Analytics processes of our organization?

Predictive modeling Critical Criteria:

Accommodate Predictive modeling tasks and figure out ways to motivate other Predictive modeling users.

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

– Are you currently using predictive modeling to drive results?

– How can we improve Risk Analytics?

Prescriptive analytics Critical Criteria:

Drive Prescriptive analytics planning and describe the risks of Prescriptive analytics sustainability.

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

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Risk Analytics services/products?

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

Price discrimination Critical Criteria:

Chat re Price discrimination management and plan concise Price discrimination education.

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

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

Risk analysis Critical Criteria:

X-ray Risk analysis planning and document what potential Risk analysis megatrends could make our business model obsolete.

– 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?

– How do we Identify specific Risk Analytics investment and emerging trends?

– Is Risk Analytics Required?

Security information and event management Critical Criteria:

Scrutinze Security information and event management adoptions and maintain Security information and event management for success.

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

Semantic analytics Critical Criteria:

Use past Semantic analytics projects and find the ideas you already have.

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

– How to deal with Risk Analytics Changes?

Smart grid Critical Criteria:

Reconstruct Smart grid goals and be persistent.

– Does your organization perform vulnerability assessment activities as part of the acquisition cycle for products in each of the following areas: Cybersecurity, SCADA, smart grid, internet connectivity, and website hosting?

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

Social analytics Critical Criteria:

Experiment with Social analytics quality and raise human resource and employment practices for Social analytics.

Software analytics Critical Criteria:

Detail Software analytics projects and devise Software analytics key steps.

– Do we have past Risk Analytics Successes?

Speech analytics Critical Criteria:

Study Speech analytics governance and find answers.

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

– Is there any existing Risk Analytics governance structure?

Statistical discrimination Critical Criteria:

Face Statistical discrimination governance and inform on and uncover unspoken needs and breakthrough Statistical discrimination results.

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

– Are assumptions made in Risk Analytics stated explicitly?

Stock-keeping unit Critical Criteria:

Look at Stock-keeping unit adoptions and frame using storytelling to create more compelling Stock-keeping unit projects.

– How important is Risk Analytics to the user organizations mission?

Structured data Critical Criteria:

Inquire about Structured data outcomes and find the ideas you already have.

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

– 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:

Debate over Telecommunications data retention engagements and gather Telecommunications data retention models .

Text analytics Critical Criteria:

Win new insights about Text analytics management and assess what counts with Text analytics that we are not counting.

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

– Have text analytics mechanisms like entity extraction been considered?

– Who sets the Risk Analytics standards?

Text mining Critical Criteria:

Apply Text mining tactics and perfect Text mining conflict management.

– Why are Risk Analytics skills important?

Time series Critical Criteria:

Have a session on Time series adoptions and visualize why should people listen to you regarding Time series.

– Who will be responsible for documenting the Risk Analytics requirements in detail?

Unstructured data Critical Criteria:

Depict Unstructured data adoptions and research ways can we become the Unstructured data company that would put us out of business.

– Why should we adopt a Risk Analytics framework?

User behavior analytics Critical Criteria:

Guide User behavior analytics strategies and define what do we need to start doing with User behavior analytics.

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

Visual analytics Critical Criteria:

Track Visual analytics outcomes and probe Visual analytics strategic alliances.

– What is Effective Risk Analytics?

Web analytics Critical Criteria:

Review Web analytics projects and report on setting up Web analytics without losing ground.

– What statistics should one be familiar with for business intelligence and web analytics?

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

– How is cloud computing related to web analytics?

– Are there recognized Risk Analytics problems?

Win–loss analytics Critical Criteria:

Demonstrate Win–loss analytics failures and diversify disclosure of information – dealing with confidential Win–loss analytics information.

– Are there any disadvantages to implementing Risk Analytics? There might be some that are less obvious?


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


Author: Gerard Blokdijk

CEO at The Art of Service | 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.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Risk Analytics External links:

Financial Risk Analytics – Markit

Leader in Credit Risk Analytics – CreditEdge.com

CoStar Risk Analytics

Academic discipline External links:

Academic Discipline Events – Northwest Nazarene …

Architectural analytics External links:

Architectural Analytics – Home | Facebook

Behavioral analytics External links:

The Behavioral Analytics Blog | Interana

Security and IT Risk Intelligence with Behavioral Analytics

FraudMAP Behavioral Analytics Solutions Brochure | Fiserv

Big data External links:

Swiftly – Leverage big data to move your city

Qognify: Big Data Solutions for Physical Security & …

Pepperdata: DevOps for Big Data

Business analytics External links:

Business Analytics Using R – A Practical Approach | …

Business Analytics | Coursera

Business intelligence External links:

[PDF]Position Title: Business Intelligence Analyst – ttra

List of Business Intelligence Skills – The Balance

Cloud analytics External links:

Cloud Analytics Academy | Hosted by Snowflake

Cloud Analytics – Datamation

Complex event processing External links:

Eclipse IoT Day ECE 2017 – Complex Event Processing of …

Complex event processing Market – TMR Research

Computer programming External links:

Computer programming | Computing | Khan Academy

Computer Programming Degrees and Certificates – …

Gwinnett Technical College- Computer Programming

Continuous analytics External links:

Hydrosphere – Continuous Analytics and DataOps for Big …

Cultural analytics External links:

Jump to: navigation, search. Cultural Analytics is the exploration and research of massive cultural data sets of visual material-both digitized visual artifacts and contemporary visual and interactive media.
Reference: en.wikipedia.org/wiki/cultural_analytics

Customer analytics External links:

Customer Analytics | Precima

Customer Analytics

Customer Analytics – Gartner IT Glossary

Data mining External links:

Title Data Mining Jobs, Employment | Indeed.com

Data Mining – RMIT University

Data mining techniques (Book, 2002) [WorldCat.org]

Embedded analytics External links:

What is embedded analytics ? – Definition from WhatIs.com

Fraud detection External links:

Big Data Fraud Detection | DataVisor

Title IV fraud detection | University Business Magazine

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 – Employment & Recruitment …

Phila.gov | Human Resources | Jobs

Home | Human Resources | Washington University in St. …

Learning analytics External links:

Journal of Learning Analytics

[PDF]Download and Read Learning Analytics Learning …

Society for Learning Analytics Research – YouTube

Machine learning External links:

DataRobot – Automated Machine Learning for Predictive …

IT Operations Analytics, Machine Learning Tools – Perspica

Machine Learning Mastery – Official Site

Marketing mix modeling External links:

Marketing Mix Modeling | Marketing Management Analytics

Marketing Mix Modeling – Decision Analyst

Mobile Location Analytics External links:

How ‘Mobile Location Analytics’ Controls Your Mind – …

Neural networks External links:

How Deep Neural Networks Work – YouTube

Online video analytics External links:

Operational reporting External links:

Operational Reporting Manager Jobs, Employment | Indeed.com

Operations research External links:

Operations Research on JSTOR

Match details for Operations Research Analysts
www.onetonline.org/find/score/15-2031.00?s=sorter operator

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

Over-the-counter data External links:

Bio — Over-the-Counter Data

Over-the-Counter Data

Portfolio analysis External links:

Essay on Portfolio Analysis – 1491 Words – StudyMode

Portfolio Analysis Essays – ManyEssays.com

iCite | NIH Office of Portfolio Analysis

Predictive analytics External links:

Predictive Analytics Software, Social Listening | NewBrand

Tookitaki – Predictive Analytics Platform

PredictX Homepage – Predictive Analytics and …

Predictive engineering analytics External links:

Predictive Engineering Analytics: Siemens PLM Software

Predictive modeling External links:

Othot Predictive Modeling | Predictive Analytics Company

DataRobot – Automated Machine Learning for Predictive Modeling

Prescriptive analytics External links:

IBM Prescriptive analytics

Price discrimination External links:

What Every Business Should Know About Price Discrimination

ERIC – Marketing Theory Applied to Price Discrimination …

Price Discrimination Flashcards | Quizlet

Risk analysis External links:

Risk analysis (Book, 1998) [WorldCat.org]

What is Risk Analysis? – Definition from Techopedia

Full Monte Project Risk Analysis from Barbecana

Security information and event management External links:

[PDF]Security Information and Event Management (SIEM) …

Smart grid External links:

Smart Grid Solutions | Smart Grid System Integration …

[PDF]Smart Grid Asset Descriptions

Honeywell Smart Grid

Social analytics External links:

Enterprise Social Analytics Platform | About

Social Analytics – Votigo

Software analytics External links:

Physician Dispensing Software Analytics | MDScripts

Speech analytics External links:

Impact 360 Speech Analytics

Speech Analytics – Marchex

Eureka: Speech Analytics Software | CallMiner

Statistical discrimination External links:

Statistical discrimination is an economic theory of racial or gender inequality based on stereotypes. According to this theory, inequality may exist and persist between demographic groups even when economic agents (consumers, workers, employers, etc.) are rational and non-prejudiced.
Reference: en.wikipedia.org/wiki/statistical_discrimination_(economics)

“Employer Learning and Statistical Discrimination”

Stock-keeping unit External links:

SKU (stock-keeping unit) – Gartner IT Glossary

Structured data External links:

SEC.gov | What Is Structured Data?

Structured vs. Unstructured data – BrightPlanet

n4e Ltd Structured Data cabling | Electrical Installations

Telecommunications data retention External links:

[PDF]Telecommunications Data Retention and Human …

Telecommunications Data Retention and Human …

Text analytics External links:

Text analytics software| NICE LTD | NICE

How to Use Text Analytics in Business – Data Informed

Text mining External links:

Text Mining, Semantics & Data Intelligence | SciBite

Text mining — University of Illinois at Urbana-Champaign

Time series External links:

Initial State – Analytics for Time Series Data

Time Series Insights | Microsoft Azure

Azure Time Series Insights API | Microsoft Docs

Unstructured data External links:

Isilon Scale-Out NAS Storage-Unstructured Data | Dell …

User behavior analytics External links:

What is User Behavior Analytics? – YouTube

IBM QRadar User Behavior Analytics – Overview – United …

Web analytics External links:

Web Analytics in Real Time | Clicky

20 Best Title:(web Analytics Manager) jobs | Simply Hired