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Summary

This article provides an overview of the various methods of data monetization, such as data as a service, insight as a service, analytics-enabled platform as a service, and embedded analytics. It also discusses the benefits of data monetization, such as scalability, flexibility, and agility, and recommends Sisense, a data monetization tool, that provides users with a free trial and test drive with their own data.

Q&As

What are the main methods for data monetization?
The main methods for data monetization are Data as a Service, Insight as a Service, Analytics-enabled Platform as a Service, and Embedded Analytics.

What are the key features to look for when choosing a data monetization tool?
The key features to look for when choosing a data monetization tool are Future-proofing, Scalability, Flexibility and Agility, User-friendliness, and Visionary in the Magic Quadrant.

How can organizations use data monetization to scale and be more flexible?
Organizations can use data monetization to scale and be more flexible by leveraging the capacity and flexibility of Big Data from a variety of sources, and by choosing a monetization approach that fits best with their larger data strategy.

What is Sisense, and how can it help with data monetization?
Sisense is a data monetization tool that provides the power to the builders, insights for everyone, and is rated #1 in customer success. It can help with data monetization by providing a free trial and test drive with your own data.

What reports are available to assess the capabilities of data monetization tools?
Reports available to assess the capabilities of data monetization tools include Leader on the World's Leading Business Software Review Platform and Trust Leader in Vendor Credibility.

AI Comments

👍 This article is extremely informative and provides a great overview of the various data monetization methods available. It also provides an easy way to compare and contrast different solutions and see which one best fits an organization's needs.

👎 This article is very long and not all of the methods discussed are applicable to all organizations. It could have been more concise and focused on the methods that would be more likely to be used.

AI Discussion

Me: It talks about data monetization methods and how to get the most out of big data. It outlines the different methods, such as data as a service, insight as a service, analytics-enabled platform as a service, and embedded analytics. It also discusses how to future-proof, monetize data, and scale. It mentions Sisense, which is a data monetization tool.

Friend: Ah, that's interesting. What implications does this have?

Me: Well, it's important for organizations to consider the different data monetization methods and decide which one best suits their needs. It also shows that data monetization tools can help organizations get the most out of their data. Additionally, the article mentions Sisense, which is a data monetization tool that can help organizations meet their needs and provide user-friendly and insightful reports.

Action items

Research the different data monetization methods and decide which one is best suited to your organization's needs.
Compare the top data monetization tools and evaluate which one is the best fit for your organization.
Try Sisense for free and take a test drive with your own data to experience the power of data monetization.

Technical terms

Data Monetization: The process of generating revenue from data.
Big Data: A term used to describe large and complex datasets that are difficult to process using traditional data processing applications.
Data as a Service (DaaS): A cloud-based service that provides access to data from multiple sources.
Insight as a Service (IaaS): A cloud-based service that provides insights into data from multiple sources.
Analytics-enabled Platform as a Service (PaaS): A cloud-based platform that provides access to analytics tools and services.
Embedded Analytics: The integration of analytics into applications and processes.
Future-proof: A term used to describe technology that is designed to be able to adapt to future changes.
Monetize Data: The process of generating revenue from data.
Scale: The ability to increase or decrease the size of a system or process.
Flexible and Agile: The ability to quickly adapt to changing conditions.
User-friendly: A term used to describe technology that is easy to use.
Report: A document that provides information about a particular topic.
Magic Quadrant: A report published by Gartner that evaluates vendors in a particular market.
Vendor Credibility: A measure of how trustworthy a vendor is.
Sisense: A business intelligence and analytics platform.