What is involved in Enterprise Analytics

Find out what the related areas are that Enterprise Analytics connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Enterprise Analytics thinking-frame.

How far is your company on its Enterprise Analytics journey?

Take this short survey to gauge your organization’s progress toward Enterprise Analytics leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which Enterprise Analytics related domains to cover and 218 essential critical questions to check off in that domain.

The following domains are covered:

Enterprise Analytics, Academic discipline, Analytic applications, Architectural analytics, Behavioral analytics, Big data, Business analytics, Business intelligence, Cloud analytics, Complex event processing, Computer programming, Continuous analytics, Cultural analytics, Customer analytics, Data mining, Data presentation architecture, Embedded analytics, Enterprise decision management, Fraud detection, Google Analytics, Human resources, Learning analytics, Machine learning, Marketing mix modeling, Mobile Location Analytics, Neural networks, News analytics, Online analytical processing, Online video analytics, Operational reporting, Operations research, Over-the-counter data, Portfolio analysis, Predictive analytics, Predictive engineering analytics, Predictive modeling, Prescriptive analytics, Price discrimination, Risk analysis, Security information and event management, Semantic analytics, Smart grid, Social analytics, Software analytics, Speech analytics, Statistical discrimination, Stock-keeping unit, Structured data, Telecommunications data retention, Text analytics, Text mining, Time series, Unstructured data, User behavior analytics, Visual analytics, Web analytics, Win–loss analytics:

Enterprise Analytics Critical Criteria:

Win new insights about Enterprise Analytics results and know what your objective is.

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

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

– What are specific Enterprise Analytics Rules to follow?

Academic discipline Critical Criteria:

Align Academic discipline projects and get out your magnifying glass.

– How can you negotiate Enterprise Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?

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

Analytic applications Critical Criteria:

Accommodate Analytic applications visions and develop and take control of the Analytic applications initiative.

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

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

– How do you handle Big Data in Analytic Applications?

– Are there recognized Enterprise Analytics problems?

– Analytic Applications: Build or Buy?

Architectural analytics Critical Criteria:

Weigh in on Architectural analytics quality and devise Architectural analytics key steps.

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

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

– What will drive Enterprise Analytics change?

Behavioral analytics Critical Criteria:

Mine Behavioral analytics quality and spearhead techniques for implementing Behavioral analytics.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Enterprise Analytics in a volatile global economy?

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

– What is the source of the strategies for Enterprise Analytics strengthening and reform?

Big data Critical Criteria:

Tête-à-tête about Big data strategies and budget the knowledge transfer for any interested in Big data.

– Have we let algorithms and large centralized data centres not only control the remembering but also the meaning and interpretation of the data?

– Is your organizations business affected by regulatory restrictions on data/servers localisation requirements?

– What new Security and Privacy challenge arise from new Big Data solutions?

– Is the process repeatable as we change algorithms and data structures?

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

– When we plan and design, how well do we capture previous experience?

– Should we be required to inform individuals when we use their data?

– Can analyses improve with more detailed analytics that we use?

– What analytical tools do you consider particularly important?

– Is the need persistent enough to justify development costs?

– More efficient all-to-all operations (similarities)?

– What is tacit permission and approval, anyway?

– From which country is your organization from?

– Wait, DevOps does not apply to Big Data?

– What preprocessing do we need to do?

– Where Is This Big Data Coming From ?

– What are some impacts of Big Data?

– How to deal with too much data?

– What is Big Data to us?

Business analytics Critical Criteria:

Mine Business analytics quality and look at it backwards.

– Do those selected for the Enterprise Analytics team have a good general understanding of what Enterprise Analytics is all about?

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

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

– 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?

– What are current Enterprise Analytics Paradigms?

Business intelligence Critical Criteria:

Debate over Business intelligence tactics and find the essential reading for Business intelligence researchers.

– 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?

– Research reveals that more than half of business intelligence projects hit a low degree of acceptance or fail. What factors influence the implementation negative or positive?

– 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 your bi solution have dashboards that automatically authenticate and provide the appropriate level of detail based on a users privileges to the data source?

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

– Does your bi software work well with both centralized and decentralized data architectures and vendors?

– Do we have trusted vendors to guide us through the process of adopting business intelligence systems?

– What is the future scope for combination of Business Intelligence and Cloud Computing?

– What is the difference between Enterprise Information Management and Data Warehousing?

– What is the difference between a data scientist and a business intelligence analyst?

– Who prioritizes, conducts and monitors business intelligence projects?

– Is Business Intelligence a more natural fit within Finance or IT?

– What else does the data tell us that we never thought to ask?

– How stable is it across domains/geographies?

– What are our tools for big data analytics?

– Make or buy BI Business Intelligence?

– Do you support video integration?

– Types of data sources supported?

– What is your annual maintenance?

– How are you going to manage?

Cloud analytics Critical Criteria:

Conceptualize Cloud analytics governance and tour deciding if Cloud analytics progress is made.

– In the case of a Enterprise Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Enterprise Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Enterprise Analytics project is implemented as planned, and is it working?

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

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

Complex event processing Critical Criteria:

Add value to Complex event processing planning and oversee implementation of Complex event processing.

– Are we making progress? and are we making progress as Enterprise Analytics leaders?

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

Computer programming Critical Criteria:

Define Computer programming results and interpret which customers can’t participate in Computer programming because they lack skills.

– For your Enterprise Analytics project, identify and describe the business environment. is there more than one layer to the business environment?

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

– Does Enterprise Analytics analysis isolate the fundamental causes of problems?

Continuous analytics Critical Criteria:

Devise Continuous analytics visions and achieve a single Continuous analytics view and bringing data together.

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

– How will you measure your Enterprise Analytics effectiveness?

Cultural analytics Critical Criteria:

Detail Cultural analytics failures and work towards be a leading Cultural analytics expert.

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

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

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

Customer analytics Critical Criteria:

Scrutinze Customer analytics failures and modify and define the unique characteristics of interactive Customer analytics projects.

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

– How would one define Enterprise Analytics leadership?

– How much does Enterprise Analytics help?

Data mining Critical Criteria:

Consolidate Data mining projects and report on setting up Data mining without losing ground.

– Where do ideas that reach policy makers and planners as proposals for Enterprise Analytics strengthening and reform actually originate?

– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?

– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?

– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?

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

– What programs do we have to teach data mining?

Data presentation architecture Critical Criteria:

Accumulate Data presentation architecture quality and grade techniques for implementing Data presentation architecture controls.

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

– How do we maintain Enterprise Analyticss Integrity?

– Is the scope of Enterprise Analytics defined?

Embedded analytics Critical Criteria:

Set goals for Embedded analytics failures and innovate what needs to be done with Embedded analytics.

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

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

– Is Supporting Enterprise Analytics documentation required?

Enterprise decision management Critical Criteria:

Test Enterprise decision management results and correct better engagement with Enterprise decision management results.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Enterprise Analytics processes?

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

Fraud detection Critical Criteria:

Demonstrate Fraud detection adoptions and mentor Fraud detection customer orientation.

– How do we go about Comparing Enterprise Analytics approaches/solutions?

Google Analytics Critical Criteria:

Meet over Google Analytics risks and define Google Analytics competency-based leadership.

– How can skill-level changes improve Enterprise Analytics?

Human resources Critical Criteria:

Air ideas re Human resources tasks and oversee Human resources requirements.

– A dramatic step toward becoming a learning organization is to appoint a chief training officer (CTO) or a chief learning officer (CLO). Many organizations claim to value Human Resources, but how many have a Human Resources representative involved in discussions about research and development commercialization, new product development, the strategic vision of the company, or increasing shareholder value?

– 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 finance, procurement and Human Resources business processes should be included in the scope of a erp solution?

– Are there cases when the company may collect, use and disclose personal data without consent or accommodation?

– What happens if an individual objects to the collection, use, and disclosure of his or her personal data?

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

– Where can an employee go for further information about the dispute resolution program?

– Why does the company collect and use personal data in the employment context?

– Can you think of other ways to reduce the costs of managing employees?

– To achieve our goals, how must our organization learn and innovate?

– What will be your Human Resources needs for the first year?

– Ease of contacting the Human Resources staff members?

– Are we complying with existing security policies?

– Does the hr plan make sense to our stakeholders?

– How is Promptness of returning calls or e-mail?

– How is the Ease of navigating the hr website?

– What do users think of the information?

– Who should appraise performance?

Learning analytics Critical Criteria:

Match Learning analytics goals and know what your objective is.

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

Machine learning Critical Criteria:

Debate over Machine learning governance and maintain Machine learning for success.

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– Is there any existing Enterprise Analytics governance structure?

– Does our organization need more Enterprise Analytics education?

– What is Effective Enterprise Analytics?

Marketing mix modeling Critical Criteria:

Deliberate over Marketing mix modeling results and gather Marketing mix modeling models .

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

– What are the record-keeping requirements of Enterprise Analytics activities?

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

Mobile Location Analytics Critical Criteria:

Design Mobile Location Analytics quality and simulate teachings and consultations on quality process improvement of Mobile Location Analytics.

– What threat is Enterprise Analytics addressing?

– How to Secure Enterprise Analytics?

Neural networks Critical Criteria:

Survey Neural networks failures and get going.

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

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

News analytics Critical Criteria:

Think about News analytics adoptions and slay a dragon.

– Think about the people you identified for your Enterprise 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?

– Risk factors: what are the characteristics of Enterprise Analytics that make it risky?

Online analytical processing Critical Criteria:

Graph Online analytical processing management and summarize a clear Online analytical processing focus.

– Meeting the challenge: are missed Enterprise Analytics opportunities costing us money?

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

Online video analytics Critical Criteria:

Think about Online video analytics risks and intervene in Online video analytics processes and leadership.

– What is the total cost related to deploying Enterprise Analytics, including any consulting or professional services?

– What are the barriers to increased Enterprise Analytics production?

Operational reporting Critical Criteria:

Face Operational reporting issues and be persistent.

– What prevents me from making the changes I know will make me a more effective Enterprise Analytics leader?

Operations research Critical Criteria:

See the value of Operations research projects and define what our big hairy audacious Operations research goal is.

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

– How can we improve Enterprise Analytics?

Over-the-counter data Critical Criteria:

Pilot Over-the-counter data goals and interpret which customers can’t participate in Over-the-counter data because they lack skills.

– How can the value of Enterprise Analytics be defined?

– What is our Enterprise Analytics Strategy?

Portfolio analysis Critical Criteria:

Jump start Portfolio analysis goals and do something to it.

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

– Why are Enterprise Analytics skills important?

Predictive analytics Critical Criteria:

Group Predictive analytics visions and pay attention to the small things.

– Can we add value to the current Enterprise Analytics decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

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

Predictive engineering analytics Critical Criteria:

Have a session on Predictive engineering analytics planning and find the essential reading for Predictive engineering analytics researchers.

– What potential environmental factors impact the Enterprise Analytics effort?

– Who needs to know about Enterprise Analytics ?

– Is Enterprise Analytics Required?

Predictive modeling Critical Criteria:

Study Predictive modeling planning and check on ways to get started with Predictive modeling.

– What other jobs or tasks affect the performance of the steps in the Enterprise Analytics process?

– Are you currently using predictive modeling to drive results?

Prescriptive analytics Critical Criteria:

Paraphrase Prescriptive analytics leadership and visualize why should people listen to you regarding Prescriptive analytics.

– How to deal with Enterprise Analytics Changes?

Price discrimination Critical Criteria:

Participate in Price discrimination quality and intervene in Price discrimination processes and leadership.

– How can you measure Enterprise Analytics in a systematic way?

Risk analysis Critical Criteria:

Set goals for Risk analysis goals and shift your focus.

– 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?

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

– How does the business impact analysis use data from Risk Management and risk analysis?

– Have the types of risks that may impact Enterprise Analytics been identified and analyzed?

– 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 manage Enterprise Analytics Knowledge Management (KM)?

Security information and event management Critical Criteria:

Communicate about Security information and event management quality and be persistent.

– Are there any easy-to-implement alternatives to Enterprise Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project?

– Do the Enterprise Analytics decisions we make today help people and the planet tomorrow?

Semantic analytics Critical Criteria:

Adapt Semantic analytics quality and define what do we need to start doing with Semantic analytics.

Smart grid Critical Criteria:

Meet over Smart grid tactics and explain and analyze the challenges 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?

– What are the Essentials of Internal Enterprise Analytics Management?

Social analytics Critical Criteria:

Confer over Social analytics risks and look for lots of ideas.

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

– Is maximizing Enterprise Analytics protection the same as minimizing Enterprise Analytics loss?

Software analytics Critical Criteria:

Unify Software analytics goals and look for lots of ideas.

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

– How do we measure improved Enterprise Analytics service perception, and satisfaction?

Speech analytics Critical Criteria:

Huddle over Speech analytics visions and look in other fields.

– Why is Enterprise Analytics important for you now?

– Are we Assessing Enterprise Analytics and Risk?

Statistical discrimination Critical Criteria:

Own Statistical discrimination management and point out improvements in Statistical discrimination.

Stock-keeping unit Critical Criteria:

Unify Stock-keeping unit leadership and perfect Stock-keeping unit conflict management.

– Are there Enterprise Analytics problems defined?

Structured data Critical Criteria:

Focus on Structured data projects and diversify by understanding risks and leveraging 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)?

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

– Should you use a hierarchy or would a more structured database-model work best?

– Why should we adopt a Enterprise Analytics framework?

– Is a Enterprise Analytics Team Work effort in place?

Telecommunications data retention Critical Criteria:

Accumulate Telecommunications data retention issues and suggest using storytelling to create more compelling Telecommunications data retention projects.

– What is the purpose of Enterprise Analytics in relation to the mission?

– Have all basic functions of Enterprise Analytics been defined?

Text analytics Critical Criteria:

Weigh in on Text analytics failures and gather practices for scaling Text analytics.

– Do several people in different organizational units assist with the Enterprise Analytics process?

– Have text analytics mechanisms like entity extraction been considered?

Text mining Critical Criteria:

Scan Text mining governance and catalog what business benefits will Text mining goals deliver if achieved.

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

Time series Critical Criteria:

Have a session on Time series failures and adjust implementation of Time series.

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

– Which Enterprise Analytics goals are the most important?

Unstructured data Critical Criteria:

Start Unstructured data results and find the ideas you already have.

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

User behavior analytics Critical Criteria:

Examine User behavior analytics results and intervene in User behavior analytics processes and leadership.

– Among the Enterprise Analytics product and service cost to be estimated, which is considered hardest to estimate?

Visual analytics Critical Criteria:

Steer Visual analytics tasks and display thorough understanding of the Visual analytics process.

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

Web analytics Critical Criteria:

Own Web analytics quality and look at it backwards.

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

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

– How is cloud computing related to web analytics?

Win–loss analytics Critical Criteria:

Powwow over Win–loss analytics decisions and look in other fields.

– How is the value delivered by Enterprise Analytics being measured?


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


Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]


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:

Enterprise Analytics External links:

MS in Enterprise Analytics | SEIDENBERG SCHOOL OF …

Enterprise Analytics and Advanced Research

Enterprise Analytics Division | FEMA.gov

Academic discipline External links:

Academic Discipline Events – Northwest Nazarene …

Architectural analytics External links:

Architectural Analytics – Home | Facebook

Behavioral analytics External links:

Security and IT Risk Intelligence with Behavioral Analytics

The Behavioral Analytics Blog | Interana

FraudMAP Behavioral Analytics Solutions Brochure | Fiserv

Big data External links:

Qognify: Big Data Solutions for Physical Security & …

Customer Journey Analytics | Big Data Solutions | ClickFox

Swiftly – Leverage big data to move your city

Business analytics External links:

Power BI Business Analytics Solutions

Business intelligence External links:

[PDF]Position Title: Business Intelligence Analyst – ttra

business intelligence jobs | Dice.com

Cloud analytics External links:

Cloud Analytics – Datamation

Cloud Analytics Academy | Hosted by Snowflake

Complex event processing External links:

Eclipse IoT Day ECE 2017 – Complex Event Processing of …

Complex Event Processing (CEP) for Big Data Streaming

Computer programming External links:

Coding for Kids | Computer Programming | AgentCubes online

Computer Programming, Robotics & Engineering – STEM For Kids

Gwinnett Technical College- Computer Programming

Continuous analytics External links:

Hydrosphere – Continuous Analytics and DataOps for Big …

Cultural analytics External links:

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 Services and Solutions | TransUnion

Zylotech- AI For Customer Analytics

Customer Analytics & Predictive Analytics for City Government

Data mining External links:

data aggregation in data mining ppt

[PDF]Data Mining Report – fas.org

[PDF]Data Mining Mining Text Data – tutorialspoint.com

Embedded analytics External links:

What is embedded analytics ? – Definition from WhatIs.com

Fraud detection External links:

Title IV fraud detection | University Business Magazine

Big Data Fraud Detection | DataVisor

Google Analytics External links:

Welcome to the Texas Board of Nursing – Google Analytics

Google Analytics Solutions – Marketing Analytics & …

Google Analytics | Google Developers

Human resources External links:

Human Resources

Office of Human Resources

myDHR | Maryland Department of Human Resources

Learning analytics External links:

Society for Learning Analytics Research – YouTube

Journal of Learning Analytics

[PDF]Download and Read Learning Analytics Learning …

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

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:

[PDF]Course Syllabus Course Title: Operations Research

Operations Research: INFORMS

Operations Research – Florida Institute of Technology

Over-the-counter data External links:

Over-the-Counter Data

Bio — Over-the-Counter Data

Portfolio analysis External links:

Portfolio analysis. (Book, 1979) [WorldCat.org]

iCite | NIH Office of Portfolio Analysis

4. TITLE AND SUBTITLE Defense Portfolio Analysis – …

Predictive analytics External links:

Best Predictive Analytics Software in 2017 | G2 Crowd

Predictive Analytics Workers Compensation

Predictive Analytics Software, Social Listening | NewBrand

Predictive engineering analytics External links:

Predictive Engineering Analytics: Siemens PLM Software

Predictive modeling External links:

DataRobot – Automated Machine Learning for Predictive Modeling

Othot Predictive Modeling | Predictive Analytics Company

Price discrimination External links:

ERIC – Price Discrimination in Academic Journals., …

33. Second Degree Price Discrimination – YouTube

Price Discrimination – Investopedia

Risk analysis External links:

The Fed – Risk Analysis

What is Risk Analysis? – Definition from Techopedia

Project Management and Risk Analysis Software | Safran

Smart grid External links:

Honeywell Smart Grid

Smart Grid Solutions | Smart Grid System Integration …

Le Smart Grid – AbeBooks

Social analytics External links:

Social Analytics – Votigo

Enterprise Social Analytics Platform | About

Software analytics External links:

Physician Dispensing Software Analytics | MDScripts

Speech analytics External links:

Impact 360 Speech Analytics

Webinars for Phone Systems & Speech Analytics | Vaspian

Speech Analytics – Marchex

Statistical discrimination External links:

“Employer Learning and Statistical Discrimination”

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_%28economics%…

Stock-keeping unit External links:

SKU (stock-keeping unit) – Gartner IT Glossary

Structured data External links:

Introduction to Structured Data | Search | Google Developers

n4e Ltd Structured Data cabling | Electrical Installations

SEC.gov | What Is Structured Data?

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 — University of Illinois at Urbana-Champaign

Text Mining, Semantics & Data Intelligence | SciBite

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:

Unstructured Data Growth – Veritas

The Data Difference | Unstructured Data DSP

User behavior analytics External links:

IBM QRadar User Behavior Analytics – Overview – United …

What is User Behavior Analytics? – YouTube

Web analytics External links:

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

Web Analytics in Real Time | Clicky

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