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There have been great advancements in monetization opportunities in the last decade, but there are still challenges when it comes to generating big data analytics that can be used for machine learning models

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Every organization big or small produces some kind of useful data that contributes to the wider Big Data ecosystem. Take for example your local independent shop, they're likely to understand the buying habits of specific regular customers 100x better than your large chain supermarkets, but do they monetize and sell data?

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There have been great advancements in monetization opportunities in the last decade, but there are still challenges when it comes to generating big data analytics that can be used for machine learning models.

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 ![Graph showing data monetization](https://cdn.hackernoon.com/images/2jqChkrv03exBUgkLrDzIbfM99q2-q092as0.avif)

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## **General Lack of Understanding**

Times have changed you no longer need a full team of data scientists to make money from your data instead, you can use third-party services such as Trovalo (www.trovalo.io) or Tableau (www.tableau.com) to help structure your data for you.

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The biggest thing businesses need to be careful of is making sure that the data they're trying to monetize meets regional laws surrounding data protection and personal data:

* in the EU and the UK: [GDPR](https://gdpr-info.eu/chapter-1/)
* in the US: It is state-dependent but [CCPA](https://oag.ca.gov/privacy/ccpa) governs California

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With this potential minefield businesses often see this as a cost and a labor-intensive process. Perhaps right now this is true, more focus needs to be made by innovative startups around providing small and medium enterprises the opportunity to structure their data and optimize it for data monetization.

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However, the revenue benefits of selling data far outweigh the costs associated at the beginning. When you're ready to sell data there is a whole host of data brokers who would be willing to help you do so.

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Some examples of data you can sell as an SME are:

* **Email Marketing Data**
* **Phone Number Data**
* **Location Data**
* **Mailing Lists**

## **Lack of Commitment**

One of the main challenges businesses face when selling data is commitment. Becoming a data aggregator and selling your data is another revenue stream and with that comes responsibility.

What some businesses fail to understand is that you have to update these data sources on a regular basis, when another organization buys data they expect to gain a unique insight and this is rarely the case with bulk data transactions. Bulk data transactions are most useful for identifying basic trends and training past machine learning models.

## **Data is Siloed**

Data in established corporations are often siloed and locked away, which can make it difficult and costly to access. To avoid this, it is important to implement ways of practicing data monetization from the beginning.

In today’s ecosystem, data is the key to decision-making for all businesses. Data-driven decisions create competitive advantages with can lead to longer-term success and additional revenue streams. If you wish to monetize data, your business needs to conduct research to understand the true market value of all the information you generate.

## **Benefits of Monetizing Data**

* Companies who monetize their data often see many benefits, including new products, services, or business models, and increased customer engagement and loyalty.
* There are plenty of opportunities to monetize, including benchmarking and profiling customers, credit scoring, fraud detection, and analyzing purchasing and ordering processes.

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***Also published [here](https://www.trovalo.io/the-data-blog/what-are-the-data-monetization-challenges).***

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