What is involved in Data Monetization
Find out what the related areas are that Data Monetization 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 Data Monetization thinking-frame.
How far is your company on its Data Monetization journey?
Take this short survey to gauge your organization’s progress toward Data Monetization 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 Data Monetization related domains to cover and 128 essential critical questions to check off in that domain.
The following domains are covered:
Data Monetization, Business intelligence, Credit card, Crowd sourced, Customer experience, Data as a service, Data capitalism, Data supply chain, European Union, Federated identity, Financial services, General Motors, Gramm–Leach–Bliley Act, Information banking, Internet of things, Location data, Market share, Mobile devices, Patient privacy, Personal cloud, Personal data vaults, Privacy rights, Real time, Retail banks, Reward programs, Risk factors, The Guardian, Trade value, United States Congress, Vendor relationship management, Venture capital:
Data Monetization Critical Criteria:
Systematize Data Monetization planning and describe which business rules are needed as Data Monetization interface.
– Do several people in different organizational units assist with the Data Monetization process?
– Meeting the challenge: are missed Data Monetization opportunities costing us money?
– How would one define Data Monetization leadership?
Business intelligence Critical Criteria:
Investigate Business intelligence risks and sort Business intelligence activities.
– Research reveals that more than half of business intelligence projects hit a low degree of acceptance or fail. What factors influence the implementation negative or positive?
– Are business intelligence solutions starting to include social media data and analytics features?
– What does a typical data warehouse and business intelligence organizational structure look like?
– Can you filter, drill down, or add entirely new data to your visualization with mobile editing?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– What statistics should one be familiar with for business intelligence and web analytics?
– Does your software facilitate the setting of thresholds and provide alerts to users?
– What is the difference between business intelligence and business analytics?
– Who prioritizes, conducts and monitors business intelligence projects?
– Do you monitor the effectiveness of your Data Monetization activities?
– What is your anticipated learning curve for technical administrators?
– What are the key skills a Business Intelligence Analyst should have?
– Are there any on demand analytics tools in the cloud?
– Is the product accessible from the internet?
– Is your BI software easy to understand?
– Do you offer formal user training?
– Do you support video integration?
– Does your system provide APIs?
Credit card Critical Criteria:
Refer to Credit card tactics and be persistent.
– What management system can we use to leverage the Data Monetization experience, ideas, and concerns of the people closest to the work to be done?
– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Data Monetization?
– If credit card payments are accepted, do we currently have a payment gateway?
– Will mobile payments ever replace credit cards?
– Are there recognized Data Monetization problems?
Crowd sourced Critical Criteria:
Adapt Crowd sourced outcomes and differentiate in coordinating Crowd sourced.
– What sources do you use to gather information for a Data Monetization study?
– Can Management personnel recognize the monetary benefit of Data Monetization?
– How do we maintain Data Monetizations Integrity?
Customer experience Critical Criteria:
Be responsible for Customer experience results and devote time assessing Customer experience and its risk.
– What are the top 3 things at the forefront of our Data Monetization agendas for the next 3 years?
– When a person has a bad Customer Service experience how many people do they tell?
– How does mystery shopping help us improve our Customer Service and experience?
– What is the difference between customer experience and user experience?
– How important is real time for providing social media Customer Service?
– what is Different Between B2C B2B Customer Experience Management?
– What are the Key enablers to make this Data Monetization move?
– What are the best community tools for Customer Service?
– So how do we add value to the customer experience?
– What is the internal customer experience?
– How can Customer Service be improved?
– What is our Data Monetization Strategy?
Data as a service Critical Criteria:
Co-operate on Data as a service goals and learn.
– Who are the people involved in developing and implementing Data Monetization?
– What potential environmental factors impact the Data Monetization effort?
– What is the purpose of Data Monetization in relation to the mission?
Data capitalism Critical Criteria:
Focus on Data capitalism outcomes and describe the risks of Data capitalism sustainability.
– Are assumptions made in Data Monetization stated explicitly?
– Who sets the Data Monetization standards?
– How to Secure Data Monetization?
Data supply chain Critical Criteria:
Start Data supply chain risks and gather Data supply chain models .
– what is the best design framework for Data Monetization organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– Does our organization need more Data Monetization education?
– Is the scope of Data Monetization defined?
European Union Critical Criteria:
Paraphrase European Union quality and pioneer acquisition of European Union systems.
– Who will be responsible for making the decisions to include or exclude requested changes once Data Monetization is underway?
– Does Data Monetization systematically track and analyze outcomes for accountability and quality improvement?
– How important is Data Monetization to the user organizations mission?
Federated identity Critical Criteria:
Merge Federated identity risks and mentor Federated identity customer orientation.
– What are the disruptive Data Monetization technologies that enable our organization to radically change our business processes?
– Is Data Monetization dependent on the successful delivery of a current project?
Financial services Critical Criteria:
Use past Financial services tactics and correct better engagement with Financial services results.
– Is maximizing Data Monetization protection the same as minimizing Data Monetization loss?
– What tools and technologies are needed for a custom Data Monetization project?
General Motors Critical Criteria:
Paraphrase General Motors quality and grade techniques for implementing General Motors controls.
– How do we measure improved Data Monetization service perception, and satisfaction?
– Will Data Monetization deliverables need to be tested and, if so, by whom?
Gramm–Leach–Bliley Act Critical Criteria:
Have a round table over Gramm–Leach–Bliley Act results and assess what counts with Gramm–Leach–Bliley Act that we are not counting.
– Does Data Monetization 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?
– Which customers cant participate in our Data Monetization domain because they lack skills, wealth, or convenient access to existing solutions?
Information banking Critical Criteria:
Mine Information banking issues and spearhead techniques for implementing Information banking.
– Who is the main stakeholder, with ultimate responsibility for driving Data Monetization forward?
– How do we Improve Data Monetization service perception, and satisfaction?
Internet of things Critical Criteria:
Detail Internet of things planning and find out.
– Fog Computing is internet computing where the devices responsible for the computing surround us. Instead of having a data center where all of the processing and storage occurs, fog computing would allow us to bring the devices closer to us and these devices would be responsible for their own processing and storage. So how does this concept help us deal with the problems created by the IoT, and what benefits would this provide us that upgrading the cloud infrastructure couldnt?
– What are the constraints that massive deployment of objects/sensor at the network periphery do put on network capabilities and architectures?
– Were competing technologies evaluated to assess and compare their ability to effectively achieve system goals?
– Can/how do the SWE standards work in an IoT environment on a large scale -billions/trillions or more sensors/ things ?
– What auditing measures and technical safeguards are in place to prevent misuse of data?
– Weve already invested in PKI how can we reuse it for mobility and internet of things?
– What new services of functionality will be implemented next with Data Monetization ?
– How will it help your business compete in the context of Digital Marketing?
– Who will be responsible for shipping and delivery, including costs?
– How can we integrate emerging M2M solutions in available platforms?
– Do we have industrial internet-of-things (iiot) on our radar?
– Are customers going to gravitate specific technologies?
– What is the effect of agent diversity on the system?
– Which user group(s) will have access to the system?
– How can the RoI of IoT applications be assessed and measured?
– What middlewares are used in Internet of things?
– Do certain types of agents dominate?
– When do we use agent modeling?
– What are the disruptive aspects of IoT?
– How would we network them?
Location data Critical Criteria:
Accumulate Location data engagements and visualize why should people listen to you regarding Location data.
– Who will be responsible for deciding whether Data Monetization goes ahead or not after the initial investigations?
– Why are Data Monetization skills important?
– What are our Data Monetization Processes?
Market share Critical Criteria:
Derive from Market share leadership and suggest using storytelling to create more compelling Market share projects.
– Are the calculated sales volumes realistic, taking into account the competitive position, realistic market share, importance of customer problem/pain and stage/maturity of customer needs?
– For your Data Monetization project, identify and describe the business environment. is there more than one layer to the business environment?
– What business benefits will Data Monetization goals deliver if achieved?
– Why is Data Monetization important for you now?
– What drives market share?
Mobile devices Critical Criteria:
Chart Mobile devices strategies and use obstacles to break out of ruts.
– 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?
– If mobile technologies are supported, how is the software optimized for use on smartphone, tables, and other mobile devices?
– Does the tool we use provide the ability for mobile devices to access critical portions of the management interface?
– Does Data Monetization analysis show the relationships among important Data Monetization factors?
Patient privacy Critical Criteria:
Map Patient privacy outcomes and diversify by understanding risks and leveraging Patient privacy.
– When a Data Monetization manager recognizes a problem, what options are available?
– How can you measure Data Monetization in a systematic way?
Personal cloud Critical Criteria:
Drive Personal cloud issues and forecast involvement of future Personal cloud projects in development.
– Is there any open source personal cloud software which provides privacy and ease of use 1 click app installs cross platform html5?
– What are the record-keeping requirements of Data Monetization activities?
– What are the usability implications of Data Monetization actions?
Personal data vaults Critical Criteria:
Powwow over Personal data vaults risks and stake your claim.
– Will new equipment/products be required to facilitate Data Monetization delivery for example is new software needed?
– Think of your Data Monetization project. what are the main functions?
Privacy rights Critical Criteria:
Add value to Privacy rights adoptions and maintain Privacy rights for success.
– Does Data Monetization create potential expectations in other areas that need to be recognized and considered?
– Are we making progress? and are we making progress as Data Monetization leaders?
Real time Critical Criteria:
Discourse Real time tactics and define what do we need to start doing with Real time.
– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Data Monetization processes?
– Do you monitor your network in real time to detect possible intrusions or abnormalities in the performance of your system?
– How is it possible to deliver real time self service BI with a legacy RDBMS source?
– Is it important to have access to information in real time?
– What are some real time data analysis frameworks?
Retail banks Critical Criteria:
Accumulate Retail banks visions and acquire concise Retail banks education.
– Are there any easy-to-implement alternatives to Data Monetization? Sometimes other solutions are available that do not require the cost implications of a full-blown project?
– How likely is the current Data Monetization plan to come in on schedule or on budget?
Reward programs Critical Criteria:
Generalize Reward programs governance and differentiate in coordinating Reward programs.
– What role does communication play in the success or failure of a Data Monetization project?
Risk factors Critical Criteria:
Troubleshoot Risk factors results and get out your magnifying glass.
– Risk factors: what are the characteristics of Data Monetization that make it risky?
– How do we go about Comparing Data Monetization approaches/solutions?
– How can you mitigate the risk factors?
The Guardian Critical Criteria:
Face The Guardian risks and catalog The Guardian activities.
Trade value Critical Criteria:
Study Trade value management and get answers.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Data Monetization models, tools and techniques are necessary?
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Data Monetization services/products?
– How do we manage Data Monetization Knowledge Management (KM)?
United States Congress Critical Criteria:
Contribute to United States Congress strategies and transcribe United States Congress as tomorrows backbone for success.
– What is the total cost related to deploying Data Monetization, including any consulting or professional services?
– Have the types of risks that may impact Data Monetization been identified and analyzed?
Vendor relationship management Critical Criteria:
Debate over Vendor relationship management governance and reinforce and communicate particularly sensitive Vendor relationship management decisions.
– What other jobs or tasks affect the performance of the steps in the Data Monetization process?
– Why should we adopt a Data Monetization framework?
Venture capital Critical Criteria:
Win new insights about Venture capital projects and plan concise Venture capital education.
– Do the Data Monetization decisions we make today help people and the planet tomorrow?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Data Monetization 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:
Data Monetization External links:
The Key to Data Monetization – KDnuggets
The Data Monetization Dilemma – Acxiom
Data Monetization – Accenture Insurance Blog
Business intelligence External links:
[PDF]Position Title: Business Intelligence Analyst – ttra
Credit card External links:
Consumer Credit Card Programs – Wells Fargo Retail Services
BuyPower Card Credit Card – Apply Online | Capital One
Merchant e-Solutions – Accept credit card payments …
Crowd sourced External links:
PiggybaQ – Crowd Sourced eCommerce
Customer experience External links:
Customer Experience Is… What, Exactly?
Customer Experience Manager Jobs, Employment | Indeed.com
Chick-fil-A Customer Experience Survey – Welcome
Data as a service External links:
M-DaaS: Master Data as a Service Solution – D&B
European Union External links:
U.S. Mission to the European Union
The European Union’s new GDPR, and what it means for you
Jobs in United Nations, NGO, European Union
Federated identity External links:
Federated Identity Service | University of Colorado Boulder
Financial services External links:
L&N Federal Credit Union – Louisville, KY – Financial Services
Harley-Davidson Financial Services
Wealth Management and Financial Services from Merrill Lynch
General Motors External links:
Internet of things External links:
Internet of Things World Forum – IoTWF Home
Leverege: Internet of Things (IoT) Platform and Solutions
AT&T M2X: Build solutions for the Internet of Things
Location data External links:
2017 Marketer’s Guide To Location Data | AdExchanger
Home | Zip Code and Location Data Analytics | CDXTech
Market share External links:
Market Share Reports in Title Insurance – HDEP …
Title Market Share
Title Market Share
Mobile devices External links:
Shop Mobile Devices & Accessories at Lowes.com
Mr Aberthon – Unlocked Hotspots and Mobile Devices
Microsoft Office 365 for Mobile Devices, Tablets, Phones
Patient privacy External links:
Patient Privacy | Stony Brook Medicine
Johns Hopkins Medicine: HIPAA & Patient Privacy
EHR Software|Substance Abuse Records|Patient Privacy
Personal cloud External links:
Personal Cloud Backup Pricing, Plans & Features | Carbonite
Personal Cloud – FREE download Personal Cloud
Privacy rights External links:
Privacy Rights Clearinghouse – Privacy Rights Clearinghouse
Privacy Rights Clearinghouse
Real time External links:
Real Time Pain Relief
Real Time Sports Bar and Grill l Elk Grove Village
Retail banks External links:
Courier | Intelligent Marketing for Retail Banks
For Retail Banks – MeridianLink
Template:Commercial and retail banks in the United …
Reward programs External links:
Smile.io | Reward Programs for eCommerce
UECU’s Reward Programs | Utilities Employees Credit Union
Employee Reward Programs | Award Programs at Awards …
Risk factors External links:
@ Diabetestor Title ?? Risk Factors For Type 2 Diabetes
Sortable Risk Factors and Health Indicators
WHO | Risk factors
The Guardian External links:
The Guardian (TV Series 2001–2004) – IMDb
Latest UK news and comment | The Guardian
World news and comment from the Guardian | The Guardian
Trade value External links:
Subaru Trade Value | Guaranteed Trade-In Program
Online Vehicle Trade Value – Phoenix Chevy Dealer
United States Congress External links:
Darryl Glenn | United States Congress
Venture capital External links:
Elevar Equity – Human Centered Venture Capital
FundRx | Healthcare and Life Science Venture Capital | FundRx