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 192 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:
Prioritize Sales Analytics issues and report on developing an effective Sales Analytics strategy.
– Who are the people involved in developing and implementing Sales Analytics?
– Are accountability and ownership for Sales Analytics clearly defined?
– How is the value delivered by Sales Analytics being measured?
Academic discipline Critical Criteria:
Survey Academic discipline visions and cater for concise Academic discipline education.
– Where do ideas that reach policy makers and planners as proposals for Sales Analytics strengthening and reform actually originate?
– Who will be responsible for making the decisions to include or exclude requested changes once Sales Analytics is underway?
– Think about the functions involved in your Sales Analytics project. what processes flow from these functions?
Analytic applications Critical Criteria:
Value Analytic applications results and describe which business rules are needed as Analytic applications interface.
– To what extent does management recognize Sales Analytics as a tool to increase the results?
– What are the Essentials of Internal Sales Analytics Management?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
– Do we have past Sales Analytics Successes?
Architectural analytics Critical Criteria:
Infer Architectural analytics issues and define what our big hairy audacious Architectural analytics goal is.
– Risk factors: what are the characteristics of Sales Analytics that make it risky?
– Is Supporting Sales Analytics documentation required?
Behavioral analytics Critical Criteria:
Add value to Behavioral analytics engagements and balance specific methods for improving Behavioral analytics results.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Sales Analytics?
– What are the long-term Sales Analytics goals?
– Why are Sales Analytics skills important?
Big data Critical Criteria:
Categorize Big data results and correct Big data management by competencies.
– What are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?
– From all the data collected by your organization, what is approximately the percentage that is further processed for value generation?
– What is (or would be) the added value of collaborating with other entities regarding data sharing in your sector?
– Which core Oracle Business Intelligence or Big Data Analytics products are used in your solution?
– what is needed to build a data-driven application that runs on streams of fast and big data?
– What new definitions are needed to describe elements of new Big Data solutions?
– Is the process repeatable as we change algorithms and data structures?
– Which Oracle Data Integration products are used in your solution?
– With more data to analyze, can Big Data improve decision-making?
– How does that compare to other science disciplines?
– What preprocessing do we need to do?
– So how are managers using big data?
– What are some impacts of Big Data?
– Who is collecting all this data?
– what is Different about Big Data?
– Does Big Data Really Need HPC?
– How to deal with ambiguity?
– What s limiting the task?
– How much data so far?
– What is in Scope?
Business analytics Critical Criteria:
Dissect Business analytics governance and check on ways to get started with Business analytics.
– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?
– What is the difference between business intelligence business analytics and data mining?
– Is there a mechanism to leverage information for business analytics and optimization?
– Do we monitor the Sales Analytics decisions made and fine tune them as they evolve?
– What is the difference between business intelligence and business analytics?
– what is the difference between Data analytics and Business Analytics If Any?
– How do you pick an appropriate ETL tool or business analytics tool?
– What are the trends shaping the future of business analytics?
– Are there Sales Analytics Models?
Business intelligence Critical Criteria:
Cut a stake in Business intelligence planning and define what do we need to start doing with Business intelligence.
– Does your BI solution honor distinctions with dashboards that automatically authenticate and provide the appropriate level of detail based on a users privileges to the data source?
– What is the importance of knowing the key performance indicators KPIs for a business process when trying to implement a business intelligence system?
– As we develop increasing numbers of predictive models, then we have to figure out how do you pick the targets, how do you optimize the models?
– Are NoSQL databases used primarily for applications or are they used in Business Intelligence use cases as well?
– Does your bi software work well with both centralized and decentralized data architectures and vendors?
– What are the approaches to handle RTB related data 100 GB aggregated for business intelligence?
– What are some best practices for gathering business intelligence about a competitor?
– Does your bi solution allow analytical insights to happen anywhere and everywhere?
– What documentation is provided with the software / system and in what format?
– What are some common criticisms of Sharepoint as a knowledge sharing tool?
– What tools are there for publishing sharing and visualizing data online?
– Who prioritizes, conducts and monitors business intelligence projects?
– What is your anticipated learning curve for Report Users?
– Are there any on demand analytics tools in the cloud?
– Do we offer a good introduction to data warehouse?
– What level of training would you recommend?
– What is your licensing model and prices?
– Using dashboard functions?
Cloud analytics Critical Criteria:
Analyze Cloud analytics issues and revise understanding of Cloud analytics architectures.
– What new services of functionality will be implemented next with Sales Analytics ?
– What are the usability implications of Sales Analytics actions?
Complex event processing Critical Criteria:
Boost Complex event processing tasks and define what our big hairy audacious Complex event processing goal is.
– Which customers cant participate in our Sales Analytics domain because they lack skills, wealth, or convenient access to existing solutions?
– Does our organization need more Sales Analytics education?
– How can the value of Sales Analytics be defined?
Computer programming Critical Criteria:
Huddle over Computer programming projects and define Computer programming competency-based leadership.
– What are the success criteria that will indicate that Sales Analytics objectives have been met and the benefits delivered?
– In what ways are Sales Analytics vendors and us interacting to ensure safe and effective use?
– Do several people in different organizational units assist with the Sales Analytics process?
Continuous analytics Critical Criteria:
Differentiate Continuous analytics tasks and do something to it.
– 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 the barriers to increased Sales Analytics production?
Cultural analytics Critical Criteria:
Gauge Cultural analytics planning and devote time assessing Cultural analytics and its risk.
– 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 is our Sales Analytics Strategy?
Customer analytics Critical Criteria:
Steer Customer analytics adoptions and report on the economics of relationships managing Customer analytics and constraints.
– 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?
Data mining Critical Criteria:
Frame Data mining tasks and devote time assessing Data mining and its risk.
– 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 know that any Sales Analytics analysis is complete and comprehensive?
– What programs do we have to teach data mining?
– How to deal with Sales Analytics Changes?
Data presentation architecture Critical Criteria:
Drive Data presentation architecture visions and reduce Data presentation architecture costs.
– What sources do you use to gather information for a Sales Analytics study?
– What potential environmental factors impact the Sales Analytics effort?
– Do Sales Analytics rules make a reasonable demand on a users capabilities?
Embedded analytics Critical Criteria:
Administer Embedded analytics visions and assess what counts with Embedded analytics that we are not counting.
– How would one define Sales Analytics leadership?
Enterprise decision management Critical Criteria:
Grade Enterprise decision management issues and find out what it really means.
– When a Sales Analytics manager recognizes a problem, what options are available?
– Are we Assessing Sales Analytics and Risk?
Fraud detection Critical Criteria:
Derive from Fraud detection risks and interpret which customers can’t participate in Fraud detection because they lack skills.
– How will we insure seamless interoperability of Sales Analytics moving forward?
– How do we Lead with Sales Analytics in Mind?
Google Analytics Critical Criteria:
Confer re Google Analytics goals and suggest using storytelling to create more compelling Google Analytics projects.
– Are there any easy-to-implement alternatives to Sales Analytics? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– How do we make it meaningful in connecting Sales Analytics with what users do day-to-day?
– Have all basic functions of Sales Analytics been defined?
Human resources Critical Criteria:
Add value to Human resources decisions and don’t overlook the obvious.
– How do we engage divisions, operating units, operations, internal audit, risk management, compliance, finance, technology, and human resources in adopting the updated framework?
– Do the response plans address damage assessment, site restoration, payroll, Human Resources, information technology, and administrative support?
– Have we adopted and promoted the companys culture of integrity management, including ethics, business practices and Human Resources evaluations?
– Should pay levels and differences reflect the earnings of colleagues in the country of the facility, or earnings at the company headquarters?
– what is to keep those with access to some of an individuals personal data from browsing through other parts of it for other reasons?
– What are the responsibilities of the company official responsible for compliance?
– How is The staffs ability and response to handle questions or requests?
– How do financial reports support the various aspects of accountability?
– What are the Human Resources we can bring to establishing new business?
– What is the important thing that human resources management should do?
– Do you have Human Resources available to support your policies?
– How is Staffs knowledge of procedures and regulations?
– How does the global environment influence management?
– Does the company retain personal data indefinitely?
– May an employee make an anonymous complaint?
– Why study Human Resources management (hrm)?
– What additional approaches already exist?
– How do we engage the stakeholders?
Learning analytics Critical Criteria:
Look at Learning analytics issues and proactively manage Learning analytics risks.
– How do you determine the key elements that affect Sales Analytics workforce satisfaction? how are these elements determined for different workforce groups and segments?
Machine learning Critical Criteria:
Think about Machine learning decisions and get answers.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– What are your most important goals for the strategic Sales Analytics objectives?
– Does Sales Analytics appropriately measure and monitor risk?
Marketing mix modeling Critical Criteria:
Talk about Marketing mix modeling adoptions and ask questions.
– In a project to restructure Sales Analytics outcomes, which stakeholders would you involve?
– Are there Sales Analytics problems defined?
Mobile Location Analytics Critical Criteria:
Systematize Mobile Location Analytics risks and suggest using storytelling to create more compelling Mobile Location Analytics projects.
– What prevents me from making the changes I know will make me a more effective Sales Analytics leader?
– Are there any disadvantages to implementing Sales Analytics? There might be some that are less obvious?
– Why is Sales Analytics important for you now?
Neural networks Critical Criteria:
Pilot Neural networks results and look for lots of ideas.
– Does Sales 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?
– How can you measure Sales Analytics in a systematic way?
News analytics Critical Criteria:
Communicate about News analytics strategies and display thorough understanding of the News analytics process.
– Who will be responsible for documenting the Sales Analytics requirements in detail?
Online analytical processing Critical Criteria:
Mine Online analytical processing planning and find the essential reading for Online analytical processing researchers.
– 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?
– How can we incorporate support to ensure safe and effective use of Sales Analytics into the services that we provide?
Online video analytics Critical Criteria:
Design Online video analytics issues and ask questions.
– 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?
Operational reporting Critical Criteria:
Discourse Operational reporting risks and point out Operational reporting tensions in leadership.
– Who is the main stakeholder, with ultimate responsibility for driving Sales Analytics forward?
– How can we improve Sales Analytics?
Operations research Critical Criteria:
Coach on Operations research governance and assess what counts with Operations research that we are not counting.
– Are we making progress? and are we making progress as Sales Analytics leaders?
Over-the-counter data Critical Criteria:
Start Over-the-counter data visions and attract Over-the-counter data skills.
– How do your measurements capture actionable Sales Analytics information for use in exceeding your customers expectations and securing your customers engagement?
– Will new equipment/products be required to facilitate Sales Analytics delivery for example is new software needed?
– How do we keep improving Sales Analytics?
Portfolio analysis Critical Criteria:
Huddle over Portfolio analysis visions and gather practices for scaling Portfolio analysis.
– What are the Key enablers to make this Sales Analytics move?
– Is there any existing Sales Analytics governance structure?
– What are the short and long-term Sales Analytics goals?
Predictive analytics Critical Criteria:
Adapt Predictive analytics engagements and get going.
– What are direct examples that show predictive analytics to be highly reliable?
– What about Sales Analytics Analysis of results?
Predictive engineering analytics Critical Criteria:
Grade Predictive engineering analytics issues and ask questions.
– Why is it important to have senior management support for a Sales Analytics project?
– How do we go about Comparing Sales Analytics approaches/solutions?
Predictive modeling Critical Criteria:
Scan Predictive modeling projects and test out new things.
– At what point will vulnerability assessments be performed once Sales Analytics is put into production (e.g., ongoing Risk Management after implementation)?
– Are you currently using predictive modeling to drive results?
Prescriptive analytics Critical Criteria:
Deliberate Prescriptive analytics projects and summarize a clear Prescriptive analytics focus.
– Is the Sales Analytics organization completing tasks effectively and efficiently?
Price discrimination Critical Criteria:
Demonstrate Price discrimination goals and inform on and uncover unspoken needs and breakthrough Price discrimination results.
– Do you monitor the effectiveness of your Sales Analytics activities?
Risk analysis Critical Criteria:
Depict Risk analysis tactics and finalize specific methods for Risk analysis acceptance.
– How do risk analysis and Risk Management inform your organizations decisionmaking processes for long-range system planning, major project description and cost estimation, priority programming, and project development?
– What levels of assurance are needed and how can the risk analysis benefit setting standards and policy functions?
– In which two Service Management processes would you be most likely to use a risk analysis and management method?
– How does the business impact analysis use data from Risk Management and risk analysis?
– How do we do risk analysis of rare, cascading, catastrophic events?
– With risk analysis do we answer the question how big is the risk?
Security information and event management Critical Criteria:
Wrangle Security information and event management risks and proactively manage Security information and event management risks.
– What are the disruptive Sales Analytics technologies that enable our organization to radically change our business processes?
– What other jobs or tasks affect the performance of the steps in the Sales Analytics process?
– How do we measure improved Sales Analytics service perception, and satisfaction?
Semantic analytics Critical Criteria:
Focus on Semantic analytics decisions and customize techniques for implementing Semantic analytics controls.
– What are the business goals Sales Analytics is aiming to achieve?
– Is Sales Analytics Required?
Smart grid Critical Criteria:
Coach on Smart grid planning and cater for concise Smart grid education.
– 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?
– Does Sales Analytics create potential expectations in other areas that need to be recognized and considered?
– Think of your Sales Analytics project. what are the main functions?
Social analytics Critical Criteria:
Graph Social analytics leadership and visualize why should people listen to you regarding Social analytics.
– How will you know that the Sales Analytics project has been successful?
– Is a Sales Analytics Team Work effort in place?
Software analytics Critical Criteria:
Add value to Software analytics projects and don’t overlook the obvious.
Speech analytics Critical Criteria:
Exchange ideas about Speech analytics decisions and probe using an integrated framework to make sure Speech analytics is getting what it needs.
– Do the Sales Analytics decisions we make today help people and the planet tomorrow?
Statistical discrimination Critical Criteria:
Talk about Statistical discrimination management and find the ideas you already have.
– What are specific Sales Analytics Rules to follow?
Stock-keeping unit Critical Criteria:
Model after Stock-keeping unit goals and budget the knowledge transfer for any interested in Stock-keeping unit.
– Meeting the challenge: are missed Sales Analytics opportunities costing us money?
Structured data Critical Criteria:
Trace Structured data projects and question.
– 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:
Track Telecommunications data retention strategies and handle a jump-start course to Telecommunications data retention.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Sales Analytics processes?
Text analytics Critical Criteria:
Coach on Text analytics risks and clarify ways to gain access to competitive Text analytics services.
– What are the record-keeping requirements of Sales Analytics activities?
– Have text analytics mechanisms like entity extraction been considered?
Text mining Critical Criteria:
Categorize Text mining tasks and probe the present value of growth of Text mining.
– Do we cover the five essential competencies-Communication, Collaboration, Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Sales Analytics in a volatile global economy?
– Will Sales Analytics deliverables need to be tested and, if so, by whom?
Time series Critical Criteria:
Look at Time series issues and define what do we need to start doing with Time series.
Unstructured data Critical Criteria:
Graph Unstructured data governance and arbitrate Unstructured data techniques that enhance teamwork and productivity.
– Are assumptions made in Sales Analytics stated explicitly?
– How will you measure your Sales Analytics effectiveness?
User behavior analytics Critical Criteria:
Add value to User behavior analytics risks and adopt an insight outlook.
– What are our Sales Analytics Processes?
Visual analytics Critical Criteria:
Participate in Visual analytics management and correct Visual analytics management by competencies.
– Who will be responsible for deciding whether Sales Analytics goes ahead or not after the initial investigations?
– What threat is Sales Analytics addressing?
Web analytics Critical Criteria:
Use past Web analytics governance and find out.
– What statistics should one be familiar with for business intelligence and web analytics?
– How do mission and objectives affect the Sales Analytics processes of our organization?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Disseminate Win–loss analytics failures and find answers.
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 | theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Sales Analytics External links:
Sales Analytics Feature Details – Salesforce.com
Sales analytics software designed for wholesale distribution
Use Tableau With Sales Analytics
Architectural analytics External links:
Behavioral analytics External links:
Fortscale | Behavioral Analytics for Everyone
Behavioral Analytics | Interana
Kissmetrics | Behavioral analytics and engagement platform
Big data External links:
Enabling Organizations to Operationalize Big Data | NICE
Big data and analytics by Alberto Labarga – issuu
Big data and health analytics by Alberto Labarga – issuu
Business analytics External links:
PGDM Research and Business Analytics | Welingkar
Data Spark | Imperial Business Analytics
IBM developerWorks : Business analytics proven practices
Business intelligence External links:
Analytics, Business Intelligence and Data Management | SAS
Business Intelligence – BI – Gartner IT Glossary
Business Intelligence and Analytics | Tableau Software
Cloud analytics External links:
Microsoft Azure Cloud Analytics Workshops | BlueGranite
What is cloud analytics? – Definition from WhatIs.com
Cloud Analytics – Solutions for Cloud Data Analytics | NetApp
Complex event processing External links:
复合事件处理(Complex Event Processing)介绍 – 张 …Translate this page
Computer programming External links:
101 Great Computer Programming Quotes – DevTopics
Tutorials for Computer Programming Languages
Knuth: Computer Programming as an Art – Paul Graham
Customer analytics External links:
Customer Analytics | Precima
Customer Analytics PowerPoint Template
www.free-power-point-templates.com › Topic › Business / Finance
Leadership Team – Customer Analytics Company – Buxton
Data mining External links:
RuleQuest Research Data Mining Tools
What is data mining? – Definition from WhatIs.com
Introduction to Data Mining – University of Minnesota
Embedded analytics External links:
Reporting Software and Embedded Analytics – JReport
Power BI Embedded analytics | Microsoft Azure
Embedded Analytics in S/4HANA (with demo videos)
Fraud detection External links:
Fraud Detection and Anti-Money Laundering Software – Verafin
Next Wave Fraud Detection System
Fraud Detection & Prevention Solutions | NICE Actimize
Google Analytics External links:
Google Analytics Opt-out Browser Add-on Download Page
Google Analytics – YouTube
Human resources External links:
What is HR? | Human Resources Explained
Careers @ UO | Human Resources – University of Oregon Jobs
Division of State Human Resources – State of South Carolina
Learning analytics External links:
Learning Analytics for Higher Education | X-Ray Analytics
Society for Learning Analytics Research (SoLAR)
TrackOne Studio – Learning Analytics
Machine learning External links:
Transfer Learning – Machine Learning’s Next Frontier
Endpoint Protection – Machine Learning Security | Symantec
UCI Machine Learning Repository
Marketing mix modeling External links:
Marketing Mix Modeling | Marketing Management Analytics
Neural networks External links:
Neural Networks – Home
Deep learning in neural networks: An overview – ScienceDirect
Online analytical processing External links:
MOLAP (multidimensional online analytical processing)
[텀즈] OLAP (online analytical processing)Translate this page
Operations research External links:
Operations Research Analysts – Bureau of Labor Statistics
Principles and Applications of Operations Research
Portfolio analysis External links:
Portfolio Analysis – Invest Excel
What is Business Portfolio Analysis? definition and meaning
Loan Portfolio Analysis Prepared for – TWENTY
Predictive modeling External links:
337-2012: Introduction to Predictive Modeling with Examples
Predictive Modeling using SAS & R Online Training
SAS Global Forum 2008 Data Mining and Predictive Modeling
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate Technologies
Prescriptive Analytics and Supply Chain Management | AIMMS
Beginning Prescriptive Analytics with Optimization Modeling
Price discrimination External links:
가격차별(Price Discrimination)과 사회후생
Degrees Of Price Discrimination | TutorsOnNet
What Every Business Should Know About Price Discrimination
Risk analysis External links:
Zymax Risk Analysis – UK Flash Density Calculator
@RISK — Risk analysis software – Palisade
WORKING Quantitative Risk Analysis for Project Management
Security information and event management External links:
A Guide to Security Information and Event Management
Smart grid External links:
Smart Grid — Huawei solutions
Digital Grid – Smart Grid Solutions – Siemens
Social analytics External links:
The Complete Social Analytics Solution | Simply Measured
Social Analytics – Gartner IT Glossary
Software analytics External links:
Software Analytics Platform – Kiuwan
Speech analytics External links:
Speech Analytics | NICE
Speech Analytics | Speech Analytics Software & Audio Mining
Speech Analytics | Conversation Analytics Solution – Sayint.ai
Structured data External links:
C# HttpWebRequest with XML Structured Data – Stack Overflow
How to Add Structured Data to Your Website – Neil Patel
What is Structured Data ? Webopedia Definition
Text analytics External links:
Text Analytics – Medallia
Repustate – Multilingual Text Analytics for Businesses
Provalis Research | Text Analytics Software Leader
Text mining External links:
テキストマイニングツール Text Mining StudioTranslate this page
Text Mining Solutions – Expert System
Time series External links:
Time Series Analysis and Its Applications: With R Examples
Time series plot – KNMI
[TS] Time Series – Stata
Unstructured data External links:
Unstructured Data Management in the Cloud | Panzura
What is Unstructured Data? Webopedia Definition
Isilon Scale-Out NAS Storage-Unstructured Data | Dell EMC US
Visual analytics External links:
Planning Your SAS Visual Analytics Dashboard – BI Notes
SAS Visual Analytics | SAS
The Power of R and Visual Analytics | Tableau Software
Web analytics External links:
What is Web Analytics? Webopedia Definition
Download « Open Web Analytics
Heap | Mobile and Web Analytics