What are the most common misconceptions about data monetization?

Many firms possess a wealth of data but fail to fully monetize it. A study in the MIT Sloan Management Review revealed that only one in 12 companies monetize their data to its fullest extent. A common misconception is the need for perfect or vast amounts of data to begin monetization, which isn't true. Existing or readily available data can serve as a solid starting point. The misconception stems from the perceived complexity or readiness needed to monetize existing data, deterring some from exploring monetization strategies. Effectively leveraging existing data can open new revenue streams and enhance market competitiveness.

Data monetization: Not easy, but rewarding. Understand your data, customers, and market. Not all data is equal. Be careful who you sell to and how you use it. Many ways to monetize data. Not just for big businesses.

Data monetization is very hard, no doubt about this. It involves a lot of work and collaboration to ascertain the right monetization to align with the brand and the future. Big companies too struggle to monetize great products with large following. It is not very obvious and has to tie in with strategy closely.

In order to estimate the cost and complexity of implementing a successful data monetisation programme you have to consider people, tools and processes. You need to hire data engineers, infra/cloud engineers, and data scientists; potentially you may need ML engineers, and full stack developers as well, if you want to build and maintain ML models, and implement and integrate data across various apps and services. You need to purchase a data warehousing solution, as well as deploy orchestration of various data exposures and pipelines. For ML workloads you need either a self hosted or a serverless ML pipeline platform. Finally you need processes for defining schemas, data flow, data quality metrics, security and privacy policies, and MLOps.

A good data monetization process requires documentation and ensuring compliance to regulations like GDPR or HIPAA. A good documentation covers the decision rules for imputation, deletion, normalization, etc., and insight about the initial dataset. This is useful for both long-term project maintainability and (if possible) data marketing. Then, a good data monetization strategy includes compliance with local and global regulations by taking measurements such as separating (or removal, if other regulations permit) personal data from the monetized data.

A product changes throughout its lifecycle and so does company strategy. If data monetization is disconnected from the strategy and brand, it will seem that selling the product is easier than monetizing a product, even for a large company!

No, data monetization is not always ethical and legal. It is important to consider the following factors when monetizing data: Consent: You must obtain informed consent from individuals before collecting and using their data. Transparency: You must be transparent about how you collect and use data, including how you plan to monetize it. Data security: You must implement appropriate security measures to protect data from unauthorized access, disclosure, modification, or destruction. Data privacy: You must comply with all applicable data privacy laws and regulations. Data monetization is not always ethical and legal. Consider consent, transparency, data security, privacy, and ethical implications.

Data monetization encompasses a broader spectrum of strategies beyond simply selling data. While data selling involves transferring data ownership for a fee, data monetization emphasizes extracting value from data for your business or customers without necessarily relinquishing control.

Data science spans a wide array of disciplines, from machine learning to natural language processing, with applications across numerous sectors including healthcare, education, and more. It's essential to recognize that data science serves various goals beyond monetization, such as advancing research, fostering innovation and promoting societal benefits. Instead of being a singular end goal, data monetization is just one facet of the multifaceted data science landscape.

Another misconception about data monetisation is that’s solely about the data product and value. Many underestimate the need for the right business and operating model to successfully run a data monetisation initiative. This involves sales, marketing, operations, customer support, contracting, etc. to effectively deliver data monetisation.

Data monetization isn't for everyone. The most common misconceptions I've encountered are: 1) We are sitting on a golden egg - Big pile of data does not necessarily mean a golden mine. The important thing is whether it yields a solution to a particular problem. 2) We have a lot of data - If the available data does not have enough diversity and statistical significance to cover most common variations of the problem, you don't have a lot of data. 3) We will just put data into some algorithm and it will do all the work for us - No algorithm will do the fundamental analysis of your problem for you and it's very difficult to estimate the project ROI at the beginning, especially if you haven't seen the data yet.