Top 195 Sales Analytics Free Questions to Collect the Right answers

What is involved in Sales Analytics

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

How far is your company on its Sales Analytics journey?

Take this short survey to gauge your organization’s progress toward Sales 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 Sales Analytics related domains to cover and 195 essential critical questions to check off in that domain.

The following domains are covered:

Sales 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:

Sales Analytics Critical Criteria:

Explore Sales Analytics tactics and spearhead techniques for implementing Sales Analytics.

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

– Why should we adopt a Sales Analytics framework?

– How to deal with Sales Analytics Changes?

Academic discipline Critical Criteria:

Survey Academic discipline risks and explain and analyze the challenges of Academic discipline.

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

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

– What are specific Sales Analytics Rules to follow?

Analytic applications Critical Criteria:

Derive from Analytic applications engagements and arbitrate Analytic applications techniques that enhance teamwork and productivity.

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

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

– How do you handle Big Data in Analytic Applications?

– How do we maintain Sales Analyticss Integrity?

– Analytic Applications: Build or Buy?

Architectural analytics Critical Criteria:

Paraphrase Architectural analytics decisions and inform on and uncover unspoken needs and breakthrough Architectural analytics results.

– Are accountability and ownership for Sales Analytics clearly defined?

– Why are Sales Analytics skills important?

– Are there Sales Analytics Models?

Behavioral analytics Critical Criteria:

Drive Behavioral analytics outcomes and probe Behavioral analytics strategic alliances.

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

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

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

Big data Critical Criteria:

Give examples of Big data engagements and point out Big data tensions in leadership.

– How we make effective use of the flood of data that will be produced will be a real big data challenge: should we keep it all or could we throw some away?

– Do you see the need to address the issues of data ownership or access to non-personal data (e.g. machine-generated data)?

– Is the software compatible with new database formats for raw, unstructured, and semi-structured big data?

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

– Future: Given the focus on Big Data where should the Chief Executive for these initiatives report?

– Does the in situ hardware have the computational capacity to support such algorithms?

– How will systems and methods evolve to remove Big Data solution weaknesses?

– How close to the edge can we push the filtering and compression algorithms?

– How can the benefits of Big Data collection and applications be measured?

– What is the right technique for distributing domains across processors?

– Hybrid partitioning (across rows/terms and columns/documents) useful?

– Which Oracle Data Integration products are used in your solution?

– Do you see a need to share data processing facilities?

– What is the limit for value as we add more data?

– How much data correction can we do at the edges?

– What load balancing technique should we use?


– What about Volunteered data?

– How robust are the results?

Business analytics Critical Criteria:

Transcribe Business analytics tasks and secure Business analytics creativity.

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

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

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

– What is the difference between business intelligence business analytics and data mining?

– Is there a mechanism to leverage information for business analytics and optimization?

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

– 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?

Business intelligence Critical Criteria:

Drive Business intelligence leadership and separate what are the business goals Business intelligence is aiming to achieve.

– 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?

– What is the importance of knowing the key performance indicators KPIs for a business process when trying to implement a business intelligence system?

– Can you filter, drill down, or add entirely new data to your visualization with mobile editing?

– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?

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

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

– Who prioritizes, conducts and monitors business intelligence projects?

– What social media dashboards are available and how do they compare?

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

– What percentage of enterprise apps will be web based in 3 years?

– What is your anticipated learning curve for Report Users?

– What would true business intelligence look like?

– To create parallel systems or custom workflows?

– How stable is it across domains/geographies?

– What level of training would you recommend?

– What is required to present video images?

– Make or buy BI Business Intelligence?

– What is your annual maintenance?

– Using dashboard functions?

– Why BI?

Cloud analytics Critical Criteria:

Inquire about Cloud analytics planning and budget for Cloud analytics challenges.

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

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Sales Analytics processes?

– Is there any existing Sales Analytics governance structure?

Complex event processing Critical Criteria:

Powwow over Complex event processing risks and correct Complex event processing management by competencies.

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

– What is our formula for success in Sales Analytics ?

Computer programming Critical Criteria:

Interpolate Computer programming issues and probe Computer programming strategic alliances.

– What are your current levels and trends in key measures or indicators of Sales Analytics product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?

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

Continuous analytics Critical Criteria:

Align Continuous analytics risks and customize techniques for implementing Continuous analytics controls.

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

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

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

Cultural analytics Critical Criteria:

Scan Cultural analytics outcomes and reinforce and communicate particularly sensitive Cultural analytics decisions.

– What about Sales Analytics Analysis of results?

Customer analytics Critical Criteria:

Wrangle Customer analytics goals and research ways can we become the Customer analytics company that would put us out of business.

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

Data mining Critical Criteria:

Judge Data mining leadership and diversify disclosure of information – dealing with confidential Data mining information.

– 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?

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

– What business benefits will Sales Analytics goals deliver if achieved?

– What programs do we have to teach data mining?

– Do we all define Sales Analytics in the same way?

Data presentation architecture Critical Criteria:

Illustrate Data presentation architecture management and ask what if.

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

– What are the barriers to increased Sales Analytics production?

Embedded analytics Critical Criteria:

Powwow over Embedded analytics failures and attract Embedded analytics skills.

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

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

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

Enterprise decision management Critical Criteria:

Unify Enterprise decision management engagements and find answers.

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

– Have all basic functions of Sales Analytics been defined?

Fraud detection Critical Criteria:

Discourse Fraud detection quality and reinforce and communicate particularly sensitive Fraud detection decisions.

– What knowledge, skills and characteristics mark a good Sales Analytics project manager?

Google Analytics Critical Criteria:

Learn from Google Analytics adoptions and prioritize challenges of Google Analytics.

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

– Which Sales Analytics goals are the most important?

Human resources Critical Criteria:

Guide Human resources visions and suggest using storytelling to create more compelling Human resources projects.

– Imagine you work in the Human Resources department of a company considering a policy to protect its data on employees mobile devices. in advising on this policy, what rights should be considered?

– Should pay levels and differences reflect the earnings of colleagues in the country of the facility, or earnings at the company headquarters?

– May an employee be retaliated against for making a complaint or reporting potential violations of these principles?

– 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?

– To satisfy customers and stakeholders, which internal business process must we excel in?

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

– Is the crisis management team comprised of members from Human Resources?

– How do financial reports support the various aspects of accountability?

– What problems have you encountered with the department or staff member?

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

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

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

– Do you understand the parameters set by the algorithm?

– What other outreach efforts would be helpful?

– What additional approaches already exist?

– How do we engage the stakeholders?

– How to deal with diversity?

– What is harassment?

Learning analytics Critical Criteria:

Have a round table over Learning analytics issues and revise understanding of Learning analytics architectures.

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

Machine learning Critical Criteria:

Scan Machine learning tasks and get out your magnifying glass.

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

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

– Do you monitor the effectiveness of your Sales Analytics activities?

Marketing mix modeling Critical Criteria:

Scan Marketing mix modeling goals and point out Marketing mix modeling tensions in leadership.

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

– Have you identified your Sales Analytics key performance indicators?

Mobile Location Analytics Critical Criteria:

Cut a stake in Mobile Location Analytics adoptions and integrate design thinking in Mobile Location Analytics innovation.

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

– Is Supporting Sales Analytics documentation required?

– Is a Sales Analytics Team Work effort in place?

Neural networks Critical Criteria:

Examine Neural networks tactics and intervene in Neural networks processes and leadership.

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

News analytics Critical Criteria:

Gauge News analytics goals and mentor News analytics customer orientation.

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

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

Online analytical processing Critical Criteria:

Concentrate on Online analytical processing decisions and explore and align the progress in Online analytical processing.

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

– What are the Essentials of Internal Sales Analytics Management?

Online video analytics Critical Criteria:

Examine Online video analytics management and oversee implementation of Online video analytics.

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

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

Operational reporting Critical Criteria:

Define Operational reporting results and document what potential Operational reporting megatrends could make our business model obsolete.

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

– In what ways are Sales Analytics vendors and us interacting to ensure safe and effective use?

Operations research Critical Criteria:

Communicate about Operations research planning and maintain Operations research for success.

– What are our best practices for minimizing Sales Analytics project risk, while demonstrating incremental value and quick wins throughout the Sales Analytics project lifecycle?

Over-the-counter data Critical Criteria:

Huddle over Over-the-counter data adoptions and check on ways to get started with Over-the-counter data.

– What potential environmental factors impact the Sales Analytics effort?

Portfolio analysis Critical Criteria:

Deliberate over Portfolio analysis engagements and define what do we need to start doing with Portfolio analysis.

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

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

Predictive analytics Critical Criteria:

Refer to Predictive analytics tasks and shift your focus.

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

– What tools and technologies are needed for a custom Sales Analytics project?

Predictive engineering analytics Critical Criteria:

Graph Predictive engineering analytics goals and test out new things.

Predictive modeling Critical Criteria:

Confer re Predictive modeling management and sort Predictive modeling activities.

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

– Are you currently using predictive modeling to drive results?

– Are there Sales Analytics problems defined?

Prescriptive analytics Critical Criteria:

Accommodate Prescriptive analytics visions and be persistent.

– How do we Lead with Sales Analytics in Mind?

Price discrimination Critical Criteria:

Shape Price discrimination outcomes and acquire concise Price discrimination education.

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

– Who is the main stakeholder, with ultimate responsibility for driving Sales Analytics forward?

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

Risk analysis Critical Criteria:

Reason over Risk analysis leadership and oversee Risk analysis management by competencies.

– 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?

– What are internal and external Sales Analytics relations?

Security information and event management Critical Criteria:

Cut a stake in Security information and event management strategies and triple focus on important concepts of Security information and event management relationship management.

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

Semantic analytics Critical Criteria:

Survey Semantic analytics adoptions and remodel and develop an effective Semantic analytics strategy.

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

– Does the Sales Analytics task fit the clients priorities?

– Are there recognized Sales Analytics problems?

Smart grid Critical Criteria:

Depict Smart grid quality and define Smart grid competency-based leadership.

– 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?

– Will Sales Analytics deliverables need to be tested and, if so, by whom?

– What are the long-term Sales Analytics goals?

Social analytics Critical Criteria:

Add value to Social analytics outcomes and define what our big hairy audacious Social analytics goal is.

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

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

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

Software analytics Critical Criteria:

Accommodate Software analytics tasks and stake your claim.

Speech analytics Critical Criteria:

Trace Speech analytics leadership and optimize Speech analytics leadership as a key to advancement.

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

Statistical discrimination Critical Criteria:

Dissect Statistical discrimination visions and modify and define the unique characteristics of interactive Statistical discrimination projects.

Stock-keeping unit Critical Criteria:

Analyze Stock-keeping unit strategies and maintain Stock-keeping unit for success.

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

– Are we Assessing Sales Analytics and Risk?

Structured data Critical Criteria:

Face Structured data governance and maintain Structured data for success.

– 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?

– What is our Sales Analytics Strategy?

– How much does Sales Analytics help?

Telecommunications data retention Critical Criteria:

Review Telecommunications data retention strategies and adopt an insight outlook.

– How do we keep improving Sales Analytics?

– How can we improve Sales Analytics?

Text analytics Critical Criteria:

Merge Text analytics tactics and balance specific methods for improving Text analytics results.

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

– Have text analytics mechanisms like entity extraction been considered?

Text mining Critical Criteria:

Participate in Text mining planning and point out improvements in Text mining.

– Who sets the Sales Analytics standards?

Time series Critical Criteria:

Study Time series visions and correct better engagement with Time series results.

Unstructured data Critical Criteria:

Scan Unstructured data management and create Unstructured data explanations for all managers.

– How can skill-level changes improve Sales Analytics?

– Is Sales Analytics Required?

User behavior analytics Critical Criteria:

Refer to User behavior analytics planning and pay attention to the small things.

– How will you measure your Sales Analytics effectiveness?

Visual analytics Critical Criteria:

Refer to Visual analytics results and get the big picture.

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

Web analytics Critical Criteria:

Audit Web analytics adoptions and check on ways to get started with Web analytics.

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

– How is cloud computing related to web analytics?

Win–loss analytics Critical Criteria:

Recall Win–loss analytics risks and mentor Win–loss analytics customer orientation.

– What are all of our Sales Analytics domains and what do they do?


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

Author: Gerard Blokdijk

CEO at The Art of Service |

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:

Sales Analytics External links:

Sales Analytics Manager Jobs, Employment |


What is Sales Analytics? – Definition from Techopedia

Academic discipline External links:

ERIC – Comparative Literature as Academic Discipline., …

criminal justice | academic discipline |

Analytic applications External links:

Hype Cycle for Back-Office Analytic Applications, 2017

Foxtrot Code AI Analytic Applications (Home)

Architectural analytics External links:

Architectural Analytics – Home | Facebook

Behavioral analytics External links:

Behavioral Analytics | Interana

Fortscale | Behavioral Analytics for Everyone

User and Entity Behavioral Analytics Partners | Exabeam

Big data External links:

Pepperdata: DevOps for Big Data

Business analytics External links:

Power BI Business Analytics Solutions

Harvard Business Analytics Program

Business intelligence External links:

business intelligence jobs |

List of Business Intelligence Skills – The Balance

Cloud analytics External links:

Cloud Analytics Academy | Hosted by Snowflake

Complex event processing External links:

SAP HANA Tech: Complex Event Processing – SAP …

Computer programming External links:

M State – Computer Programming

Computer Programming, Robotics & Engineering – STEM For Kids

Computer programming | Computing | Khan Academy

Continuous analytics External links:

[PDF]Continuous Analytics: Stream Query Processing in …

Continuous Analytics: Why You Must Consider It – Zymr

Customer analytics External links:

Customer Analytics Services and Solutions | TransUnion

Customer Analytics | Precima

Customer Analytics

Data mining External links:

Title Data Mining Jobs, Employment |

[PDF]Project Title: Data Mining to Improve Water Management

Job Titles in Data Mining – KDnuggets

Embedded analytics External links:

What is embedded analytics ? – Definition from

Power BI Embedded analytics | Microsoft Azure

Enterprise decision management External links:

Come to the Enterprise Decision Management Summit in …

enterprise decision management Archives – Insights

Fraud detection External links:

Title IV fraud detection | University Business Magazine

Google Analytics External links:

Welcome to the Texas Board of Nursing – Google Analytics

Google Analytics Solutions – Marketing Analytics & …

Human resources External links:

Home | Human Resources

Home – OU Human Resources | Human Resources | Jobs

Learning analytics External links:

Watershed | Learning Analytics for Organizations

Learning analytics – MoodleDocs

Machine learning External links:

Microsoft Azure Machine Learning Studio

DataRobot – Automated Machine Learning for Predictive …

Machine Learning Mastery – Official Site

Marketing mix modeling External links:

Marketing Mix Modeling | Marketing Management Analytics

Mobile Location Analytics External links:

[PDF]Mobile Location Analytics Code of Conduct

Mobile location analytics | Federal Trade Commission

Mobile Location Analytics Privacy Notice | Verizon

Neural networks External links:

Neural Networks –

Artificial Neural Networks – ScienceDirect

Online analytical processing External links:

Oracle Online Analytical Processing (OLAP)

Working with Online Analytical Processing (OLAP)

Operations research External links:

Operations research (Book, 2014) []

[PDF]Course Syllabus Course Title: Operations Research

Match details for Operations Research Analysts operator

Over-the-counter data External links:

Over-the-Counter Data

Portfolio analysis External links:

Essay on Portfolio Analysis – 1491 Words – StudyMode

What is PORTFOLIO ANALYSIS? definition of …

Portfolio Analysis – AbeBooks

Predictive analytics External links:

Predictive Analytics Software, Social Listening | NewBrand

Inventory Optimization for Retail | Predictive Analytics

Customer Analytics & Predictive Analytics Tools for …

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:

Healthcare Prescriptive Analytics – Cedar Gate …

Price discrimination External links:

What Every Business Should Know About Price Discrimination

Price Discrimination Flashcards | Quizlet

Price Discrimination – Investopedia

Risk analysis External links:

Risk analysis (Book, 1998) []

Risk Analysis is a monthly peer-reviewed academic journal covering all aspects of risk analysis published by Wiley-Blackwell on behalf of the Society for Risk Analysis.
http://[DOC]Risk Analysis Template – / U.S. Department …

Risk analysis (eBook, 2015) []

Security information and event management External links:

Magic Quadrant for Security Information and Event Management

Smart grid External links:

Le Smart Grid – AbeBooks

[PDF]Smart Grid Asset Descriptions

Honeywell Smart Grid

Social analytics External links:

Social Analytics – Marchex

Enterprise Social Analytics Platform | About

Dark Social Analytics: Track Private Shares with GetSocial

Software analytics External links:

Software Analytics – Microsoft Research

Speech analytics External links:

Front Analytics – Speech Analytics Implementation and …

Customer Engagement & Speech Analytics | CallMiner

Speech Analytics – Marchex

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.

“Employer Learning and Statistical Discrimination”

Stock-keeping unit External links:

SKU (stock-keeping unit) – Gartner IT Glossary

Structured data External links: | What Is Structured Data?

n4e Ltd Structured Data cabling | Electrical Installations

Introduction to Structured Data | Search | Google Developers

Text analytics External links:

[PDF]Syllabus Course Title: Text Analytics – …

Text analytics software| NICE LTD | NICE

Machine Learning, Cognitive Search & Text Analytics | Attivio

Text mining External links:

Text Mining / Text Analytics Specialist – bigtapp

Text Mining – AbeBooks

Text Analytics – MeaningCloud text mining solutions

Time series External links:

Initial State – Analytics for Time Series Data

Azure Time Series Insights API | Microsoft Docs

SPK WCDS – Hourly Time Series Reports

Unstructured data External links:

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

User behavior analytics External links:

IBM QRadar User Behavior Analytics – Overview – United …

Varonis User Behavior Analytics | Varonis Systems

Web analytics External links:

11 Best Web Analytics Tools |

Web Analytics in Real Time | Clicky

AFS Analytics – Web analytics