What is involved in Product Analytics
Find out what the related areas are that Product 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 Product Analytics thinking-frame.
How far is your company on its Product Analytics journey?
Take this short survey to gauge your organization’s progress toward Product 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 Product Analytics related domains to cover and 194 essential critical questions to check off in that domain.
The following domains are covered:
Product 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:
Product Analytics Critical Criteria:
Own Product Analytics risks and use obstacles to break out of ruts.
– Do those selected for the Product Analytics team have a good general understanding of what Product Analytics is all about?
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to Product Analytics?
– What will drive Product Analytics change?
Academic discipline Critical Criteria:
Communicate about Academic discipline visions and probe Academic discipline strategic alliances.
– Which Product Analytics goals are the most important?
– Are there Product Analytics Models?
Analytic applications Critical Criteria:
Accelerate Analytic applications failures and proactively manage Analytic applications risks.
– What are our needs in relation to Product Analytics skills, labor, equipment, and markets?
– What vendors make products that address the Product Analytics needs?
– How do you handle Big Data in Analytic Applications?
– Analytic Applications: Build or Buy?
– How can we improve Product Analytics?
Architectural analytics Critical Criteria:
Study Architectural analytics tasks and pay attention to the small things.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Product Analytics. How do we gain traction?
– What other organizational variables, such as reward systems or communication systems, affect the performance of this Product Analytics process?
– What tools and technologies are needed for a custom Product Analytics project?
Behavioral analytics Critical Criteria:
Accumulate Behavioral analytics tactics and know what your objective is.
– Have the types of risks that may impact Product Analytics been identified and analyzed?
– How does the organization define, manage, and improve its Product Analytics processes?
– Is Supporting Product Analytics documentation required?
Big data Critical Criteria:
Closely inspect Big data tasks and find answers.
– 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 support the development and implementation of technical solutions that are enhancing data protection by design and by default?
– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?
– What rules and regulations should exist about combining data about individuals into a central repository?
– What are some strategies for capacity planning for big data processing and cloud computing?
– What are the disruptive innovations in the middle-term that provide near-term domain leadership?
– Do we monitor the Product Analytics decisions made and fine tune them as they evolve?
– What are the ways in which cloud computing and big data can work together?
– Is senior management in your organization involved in big data-related projects?
– What if the needle in the haystack happens to be a complex data structure?
– Can good algorithms, models, heuristics overcome Data Quality problems?
– Can analyses improve with better system and environment models?
– What analytical tools do you consider particularly important?
– What happens if/when no longer need cognitive input?
– Are our Big Data investment programs results driven?
– Wait, DevOps does not apply to Big Data?
– How can we summarize streaming data?
– Who is collecting all this data?
Business analytics Critical Criteria:
Gauge Business analytics projects and interpret which customers can’t participate in Business analytics because they lack skills.
– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?
– What is the difference between business intelligence business analytics and data mining?
– Is there a mechanism to leverage information for business analytics and optimization?
– What is the difference between business intelligence and business analytics?
– what is the difference between Data analytics and Business Analytics If Any?
– How do you pick an appropriate ETL tool or business analytics tool?
– Can Management personnel recognize the monetary benefit of Product Analytics?
– What are the trends shaping the future of business analytics?
– Is there any existing Product Analytics governance structure?
– Is the scope of Product Analytics defined?
Business intelligence Critical Criteria:
Be clear about Business intelligence adoptions and cater for concise Business intelligence education.
– Forget right-click and control+z. mobile interactions are fundamentally different from those on a desktop. does your mobile solution allow you to interact with desktop-authored dashboards using touchscreen gestures like taps, flicks, and pinches?
– Does your software provide roleand group-based security options that allow business users to securely create and publish their work?
– Can you easily add users and features to quickly scale and customize to your organizations specific needs?
– Will new equipment/products be required to facilitate Product Analytics delivery for example is new software needed?
– Which OpenSource ETL tool is easier to use more agile Pentaho Kettle Jitterbit Talend Clover Jasper Rhino?
– How should a complicated business setup their business intelligence and analysis to make decisions best?
– Do we have trusted vendors to guide us through the process of adopting business intelligence systems?
– What strategies will we pursue to ensure the success of the business intelligence competency center?
– what is the BI software application landscape going to look like in the next 5 years?
– Social Data Analytics Are you integrating social into your business intelligence?
– What documentation is provided with the software / system and in what format?
– What specialized bi knowledge does your business have that can be leveraged?
– Which other Oracle Business Intelligence products are used in your solution?
– Is Business Intelligence a more natural fit within Finance or IT?
– What are the best use cases for Mobile Business Intelligence?
– Is the product accessible from the internet?
– What is required to present video images?
Cloud analytics Critical Criteria:
Shape Cloud analytics strategies and report on developing an effective Cloud analytics strategy.
– Which individuals, teams or departments will be involved in Product Analytics?
– How will you measure your Product Analytics effectiveness?
Complex event processing Critical Criteria:
Accelerate Complex event processing tasks and spearhead techniques for implementing Complex event processing.
– What management system can we use to leverage the Product Analytics experience, ideas, and concerns of the people closest to the work to be done?
– Are there any disadvantages to implementing Product Analytics? There might be some that are less obvious?
Computer programming Critical Criteria:
Ventilate your thoughts about Computer programming leadership and develop and take control of the Computer programming initiative.
– What prevents me from making the changes I know will make me a more effective Product Analytics leader?
– Risk factors: what are the characteristics of Product Analytics that make it risky?
– Why are Product Analytics skills important?
Continuous analytics Critical Criteria:
Transcribe Continuous analytics visions and intervene in Continuous analytics processes and leadership.
– How do senior leaders actions reflect a commitment to the organizations Product Analytics values?
– What is our Product Analytics Strategy?
Cultural analytics Critical Criteria:
Win new insights about Cultural analytics tasks and triple focus on important concepts of Cultural analytics relationship management.
– At what point will vulnerability assessments be performed once Product Analytics is put into production (e.g., ongoing Risk Management after implementation)?
– Who needs to know about Product Analytics ?
– What are our Product Analytics Processes?
Customer analytics Critical Criteria:
Graph Customer analytics failures and question.
– How do your measurements capture actionable Product Analytics information for use in exceeding your customers expectations and securing your customers engagement?
– How can skill-level changes improve Product Analytics?
Data mining Critical Criteria:
Wrangle Data mining leadership and spearhead techniques for implementing Data mining.
– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?
– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– What is the source of the strategies for Product Analytics strengthening and reform?
– 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:
Talk about Data presentation architecture visions and adopt an insight outlook.
– Does Product Analytics include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?
– What sources do you use to gather information for a Product Analytics study?
Embedded analytics Critical Criteria:
Be responsible for Embedded analytics strategies and find out.
– Does Product Analytics systematically track and analyze outcomes for accountability and quality improvement?
– How can you measure Product Analytics in a systematic way?
– How do we Lead with Product Analytics in Mind?
Enterprise decision management Critical Criteria:
Generalize Enterprise decision management results and reinforce and communicate particularly sensitive Enterprise decision management decisions.
– Think about the kind of project structure that would be appropriate for your Product Analytics project. should it be formal and complex, or can it be less formal and relatively simple?
– Can we add value to the current Product Analytics decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?
Fraud detection Critical Criteria:
Powwow over Fraud detection goals and look at the big picture.
– Why should we adopt a Product Analytics framework?
– Are we Assessing Product Analytics and Risk?
Google Analytics Critical Criteria:
Discourse Google Analytics results and know what your objective is.
– Does Product Analytics analysis show the relationships among important Product Analytics factors?
– Have you identified your Product Analytics key performance indicators?
Human resources Critical Criteria:
Deliberate over Human resources outcomes and sort Human resources activities.
– 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?
– How do we engage divisions, operating units, operations, internal audit, risk management, compliance, finance, technology, and human resources in adopting the updated framework?
– Should pay levels and differences reflect the earnings of colleagues in the country of the facility, or earnings at the company headquarters?
– How often do we hold meaningful conversations at the operating level among sales, finance, operations, IT, and human resources?
– Do we perform an environmental scan of hr strategies within the hr community (what/how are others planning)?
– 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?
– How is The staffs ability and response to handle questions or requests?
– What problems have you encountered with the department or staff member?
– Can you think of other ways to reduce the costs of managing employees?
– What decisions can you envision making with this type of information?
– Are you a manager interested in increasing your effectiveness?
– How can we promote retention of high performing employees?
– What are ways that employee productivity can be measured?
– How is Staffs knowledge of procedures and regulations?
– How is Promptness of returning calls or e-mail?
– How is the Content updated of the hr website?
– May an employee make an anonymous complaint?
– Is the hr plan effective ?
Learning analytics Critical Criteria:
Drive Learning analytics tactics and check on ways to get started with Learning analytics.
– What are the top 3 things at the forefront of our Product Analytics agendas for the next 3 years?
– What is our formula for success in Product Analytics ?
Machine learning Critical Criteria:
Grade Machine learning quality and inform on and uncover unspoken needs and breakthrough Machine learning results.
– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?
– Does our organization need more Product Analytics education?
– How do we keep improving Product Analytics?
Marketing mix modeling Critical Criteria:
Meet over Marketing mix modeling governance and document what potential Marketing mix modeling megatrends could make our business model obsolete.
– What is the total cost related to deploying Product Analytics, including any consulting or professional services?
– How do we ensure that implementations of Product Analytics products are done in a way that ensures safety?
Mobile Location Analytics Critical Criteria:
Sort Mobile Location Analytics results and oversee implementation of Mobile Location Analytics.
– In the case of a Product Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Product Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Product Analytics project is implemented as planned, and is it working?
– What is the purpose of Product Analytics in relation to the mission?
Neural networks Critical Criteria:
Graph Neural networks management and gather Neural networks models .
– Does Product Analytics analysis isolate the fundamental causes of problems?
– Why is Product Analytics important for you now?
News analytics Critical Criteria:
Participate in News analytics risks and define News analytics competency-based leadership.
– What are our best practices for minimizing Product Analytics project risk, while demonstrating incremental value and quick wins throughout the Product Analytics project lifecycle?
– What is Effective Product Analytics?
Online analytical processing Critical Criteria:
Deliberate over Online analytical processing goals and create a map for yourself.
– What are your most important goals for the strategic Product Analytics objectives?
Online video analytics Critical Criteria:
Drive Online video analytics management and give examples utilizing a core of simple Online video analytics skills.
– How can we incorporate support to ensure safe and effective use of Product Analytics into the services that we provide?
– In a project to restructure Product Analytics outcomes, which stakeholders would you involve?
Operational reporting Critical Criteria:
Revitalize Operational reporting governance and research ways can we become the Operational reporting company that would put us out of business.
– What will be the consequences to the business (financial, reputation etc) if Product Analytics does not go ahead or fails to deliver the objectives?
Operations research Critical Criteria:
Design Operations research decisions and describe the risks of Operations research sustainability.
– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Product Analytics process. ask yourself: are the records needed as inputs to the Product Analytics process available?
Over-the-counter data Critical Criteria:
Define Over-the-counter data planning and prioritize challenges of Over-the-counter data.
– How is the value delivered by Product Analytics being measured?
– Have all basic functions of Product Analytics been defined?
Portfolio analysis Critical Criteria:
Consider Portfolio analysis governance and visualize why should people listen to you regarding Portfolio analysis.
– What are current Product Analytics Paradigms?
Predictive analytics Critical Criteria:
Start Predictive analytics issues and secure Predictive analytics creativity.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Product Analytics processes?
– Is Product Analytics dependent on the successful delivery of a current project?
– What are direct examples that show predictive analytics to be highly reliable?
Predictive engineering analytics Critical Criteria:
Accelerate Predictive engineering analytics tactics and question.
– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Product Analytics in a volatile global economy?
– Who will provide the final approval of Product Analytics deliverables?
Predictive modeling Critical Criteria:
Guard Predictive modeling visions and attract Predictive modeling skills.
– Are you currently using predictive modeling to drive results?
– What about Product Analytics Analysis of results?
Prescriptive analytics Critical Criteria:
Administer Prescriptive analytics outcomes and diversify by understanding risks and leveraging Prescriptive analytics.
– What are the key elements of your Product Analytics performance improvement system, including your evaluation, organizational learning, and innovation processes?
– Do we all define Product Analytics in the same way?
Price discrimination Critical Criteria:
Consult on Price discrimination tasks and catalog Price discrimination activities.
– What are the record-keeping requirements of Product Analytics activities?
– What are specific Product Analytics Rules to follow?
Risk analysis Critical Criteria:
Grasp Risk analysis quality and pioneer acquisition of Risk analysis systems.
– 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?
– What role does communication play in the success or failure of a Product Analytics project?
– How does the business impact analysis use data from Risk Management and risk analysis?
– How do we do risk analysis of rare, cascading, catastrophic events?
– With risk analysis do we answer the question how big is the risk?
– How would one define Product Analytics leadership?
Security information and event management Critical Criteria:
Graph Security information and event management tactics and revise understanding of Security information and event management architectures.
– Among the Product Analytics product and service cost to be estimated, which is considered hardest to estimate?
– How can you negotiate Product Analytics successfully with a stubborn boss, an irate client, or a deceitful coworker?
– Is there a Product Analytics Communication plan covering who needs to get what information when?
Semantic analytics Critical Criteria:
Incorporate Semantic analytics tactics and visualize why should people listen to you regarding Semantic analytics.
– Are accountability and ownership for Product Analytics clearly defined?
– Does Product Analytics appropriately measure and monitor risk?
Smart grid Critical Criteria:
Mine Smart grid visions and display thorough understanding of the Smart grid process.
– 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?
– When a Product Analytics manager recognizes a problem, what options are available?
– What are the business goals Product Analytics is aiming to achieve?
Social analytics Critical Criteria:
Reorganize Social analytics strategies and mentor Social analytics customer orientation.
Software analytics Critical Criteria:
Understand Software analytics engagements and create Software analytics explanations for all managers.
– How do we measure improved Product Analytics service perception, and satisfaction?
Speech analytics Critical Criteria:
Illustrate Speech analytics results and gather practices for scaling Speech analytics.
Statistical discrimination Critical Criteria:
Tête-à-tête about Statistical discrimination tactics and integrate design thinking in Statistical discrimination innovation.
– How do we make it meaningful in connecting Product Analytics with what users do day-to-day?
Stock-keeping unit Critical Criteria:
Review Stock-keeping unit tasks and pay attention to the small things.
– What business benefits will Product Analytics goals deliver if achieved?
Structured data Critical Criteria:
Map Structured data governance and develop and take control of the Structured data initiative.
– 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?
– How do we Identify specific Product Analytics investment and emerging trends?
Telecommunications data retention Critical Criteria:
Boost Telecommunications data retention strategies and look for lots of ideas.
– What knowledge, skills and characteristics mark a good Product Analytics project manager?
– What are the Essentials of Internal Product Analytics Management?
– Do we have past Product Analytics Successes?
Text analytics Critical Criteria:
Grasp Text analytics quality and finalize the present value of growth of Text analytics.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Product Analytics models, tools and techniques are necessary?
– Have text analytics mechanisms like entity extraction been considered?
– What are internal and external Product Analytics relations?
Text mining Critical Criteria:
Judge Text mining outcomes and secure Text mining creativity.
Time series Critical Criteria:
Accelerate Time series visions and report on developing an effective Time series strategy.
– Is the Product Analytics organization completing tasks effectively and efficiently?
– Is a Product Analytics Team Work effort in place?
Unstructured data Critical Criteria:
Revitalize Unstructured data engagements and correct better engagement with Unstructured data results.
– Who will be responsible for deciding whether Product Analytics goes ahead or not after the initial investigations?
– What are all of our Product Analytics domains and what do they do?
User behavior analytics Critical Criteria:
Jump start User behavior analytics issues and know what your objective is.
– Are there Product Analytics problems defined?
Visual analytics Critical Criteria:
Participate in Visual analytics decisions and improve Visual analytics service perception.
– Do the Product Analytics decisions we make today help people and the planet tomorrow?
Web analytics Critical Criteria:
Deduce Web analytics issues and explain and analyze the challenges of Web analytics.
– What statistics should one be familiar with for business intelligence and web analytics?
– How likely is the current Product Analytics plan to come in on schedule or on budget?
– How is cloud computing related to web analytics?
Win–loss analytics Critical Criteria:
Tête-à-tête about Win–loss analytics adoptions and look at the big picture.
– Is maximizing Product Analytics protection the same as minimizing Product Analytics loss?
– Are we making progress? and are we making progress as Product Analytics leaders?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Product Analytics Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | http://theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
Product Analytics External links:
Product Analytics 101 – Treehouse Blog
Mixpanel Trends – Mixpanel | Product Analytics
Product Analytics | Customer Involvement Program | Autodesk
Academic discipline External links:
What does academic discipline mean? – Definitions.net
Criminal justice | academic discipline | Britannica.com
Analytic applications External links:
Foxtrot Code AI Analytic Applications (Home)
Analytic Applications – Gartner IT Glossary
Hype Cycle for Back-Office Analytic Applications, 2017
Architectural analytics External links:
Architectural Analytics – Home | Facebook
Behavioral analytics External links:
Behavioral Analytics | Interana
FraudMAP Behavioral Analytics Solutions Brochure | Fiserv
Behavioral Analytics – Mattersight
Big data External links:
Databricks – Making Big Data Simple
Swiftly – Leverage big data to move your city
ZestFinance.com: Machine Learning & Big Data …
Business intelligence External links:
List of Business Intelligence Skills – The Balance
Cloud analytics External links:
Cloud Analytics Academy | Hosted by Snowflake
Cloud Analytics – Solutions for Cloud Data Analytics | NetApp
Computer programming External links:
Online Computer Programming & Coding Courses | One …
Computer Programming – Augusta Technical College
Computer Programming, Robotics & Engineering – STEM For Kids
Continuous analytics External links:
continuous analytics Archives – Iguazio
[PDF]Continuous Analytics: Stream Query Processing in …
Customer analytics External links:
Customer Analytics & Predictive Analytics Tools for Business
Customer Analytics and Customer Journey Management
Zylotech- AI For Customer Analytics
Data mining External links:
Data Mining (eBook, 2016) [WorldCat.org]
[PDF]Data Mining Report – Federation of American Scientists
Data mining | computer science | Britannica.com
Embedded analytics External links:
What is embedded analytics ? – Definition from WhatIs.com
Power BI Embedded analytics | Microsoft Azure
Embedded Analytics | ThoughtSpot
Enterprise decision management External links:
Enterprise Decision Management | Sapiens DECISION
Enterprise Decision Management (EDM) – Techopedia.com
enterprise decision management Archives – Insights
Fraud detection External links:
Big Data Fraud Detection | DataVisor
Title IV fraud detection | University Business Magazine
Google Analytics External links:
Enterprise Marketing Analytics – Google Analytics 360 Suite
Google Analytics Solutions – Marketing Analytics & …
Google Analytics for Firebase | Firebase
Human resources External links:
Home | Human Resources
Human Resources | Maricopa Community Colleges
Phila.gov | Human Resources | Jobs
Learning analytics External links:
Learning analytics – MoodleDocs
Journal of Learning Analytics
Learning Analytics Explained (eBook, 2017) [WorldCat.org]
Machine learning External links:
Microsoft Azure Machine Learning Studio
DataRobot – Automated Machine Learning for Predictive …
Comcast Labs – PHLAI: Machine Learning Conference
Marketing mix modeling External links:
Marketing Mix Modeling – Gartner IT Glossary
Marketing Mix Modeling | Marketing Management Analytics
Mobile Location Analytics External links:
How ‘Mobile Location Analytics’ Controls Your Mind – YouTube
Mobile Location Analytics Privacy Notice | Verizon
Mobile Location Analytics
Neural networks External links:
Neural Networks – ScienceDirect.com
News analytics External links:
Yakshof – Big Data News Analytics
Online video analytics External links:
Managing Your Online Video Analytics – DaCast
Operations research External links:
Operations research (Book, 1974) [WorldCat.org]
Operations Research on JSTOR
Systems Engineering and Operations Research
Over-the-counter data External links:
[PDF]Over-the-Counter Data’s Impact on Educators’ Data …
Portfolio analysis External links:
[PPT]Introduction to Portfolio Analysis – DPCPSI
[PDF]Portfolio Analysis – Morningstar Log In
Portfolio Analysis – AbeBooks
Predictive analytics External links:
Stategic Location Management & Predictive Analytics | …
Predictive Analytics for Healthcare | Forecast Health
Predictive Analytics Software, Social Listening | NewBrand
Predictive engineering analytics External links:
Predictive Engineering Analytics: Siemens PLM Software
Predictive modeling External links:
What is predictive modeling? – Definition from WhatIs.com
SDN Predictive Modeling – Student Doctor Network
DataRobot – Automated Machine Learning for Predictive Modeling
Prescriptive analytics External links:
Healthcare Prescriptive Analytics – Cedar Gate …
Risk analysis External links:
Full Monte Project Risk Analysis from Barbecana
SEC.gov | About the Division of Economic and Risk Analysis
What is Risk Analysis? – Definition from Techopedia
Semantic analytics External links:
What is semantic analytics? – Quora
[PDF]Geospatial and Temporal Semantic Analytics
Smart grid External links:
Smart Grid – AbeBooks
[PDF]The Smart Grid?
Smart Grid Solutions | Smart Grid System Integration …
Social analytics External links:
Enterprise Social Analytics Platform | About
Influencer marketing platform & Social analytics tool – …
Social Analytics – Marchex
Software analytics External links:
EDGEPro Software Analytics Tool for Optometry | Success …
Software Analytics – Microsoft Research
Speech analytics External links:
Reverse a Pattern of Poor Sales With Speech Analytics
Speech Analytics – Marchex
Yactraq – Speech Analytics & Audio Mining
Statistical discrimination External links:
“Employer Learning and Statistical Discrimination”
[PDF]Testing for Statistical Discrimination in Health Care
Structured data External links:
4 ways to improve SEO with schema and structured data
Introduction to Structured Data | Search | Google Developers
C# HttpWebRequest with XML Structured Data – Stack Overflow
Telecommunications data retention External links:
Telecommunications Data Retention and Human …
Text analytics External links:
Text analytics software| NICE LTD | NICE
[PDF]Syllabus Course Title: Text Analytics – Regis University
Text Mining / Text Analytics Specialist – bigtapp
Text mining External links:
Text mining in practice with R (eBook, 2017) [WorldCat.org]
Text Mining / Text Analytics Specialist – bigtapp
Text Mining – AbeBooks
Time series External links:
Initial State – Analytics for Time Series Data
CLIMATE TIME SERIES Browser – University of Chicago
[PDF]Time Series Analysis and Forecasting – cengage.com
Unstructured data External links:
Gigaom | Sector Roadmap: Unstructured Data …
User behavior analytics External links:
User Behavior Analytics (UBA) Tools and Solutions | Rapid7
Web analytics External links:
View Web Analytics reports (SharePoint Server 2010)
AFS Analytics – Web analytics
11 Best Web Analytics Tools | Inc.com