Unsurprisingly, data is a valuable resource when it comes to software and business. Digital products that involve data are present in our daily lives, whether at work or leisure, as in social networks, for example. Some tools use data to improve our user experience, others use it to prioritize the development of new features, or even recommend which shoes fit perfectly with what we were looking to buy. However, some products facilitate an end goal through data and it is this type of product that we will address in this text, the Data Products.

What are Data Products?

In the article “The Age of the Data Product”, data scientist Benjamin Bengfort defines data products as self-adapting and widely applicable economic mechanisms that derive their value from data, as well as generating more data capable of influencing human behavior or making inferences and predictions. For DJ Patil, writer of the book “Data Jujitsu: The Art of Turning Data into Product”, data products facilitate an end goal through the use of data. In the article “What is Data Science?”, Mike Loukides argues that a data application acquires its value from the data itself and, as a result, creates more data. It’s not just an app with data; it is a data product.

In this way, data products can be used in different areas, contexts, and industries, but as we have seen in the definitions, they need to aim at a specific result, for example: increasing data accessibility, enabling business insights, democratizing access to data, save resources, etc. To achieve this result, the product can provide raw data, transformed data, algorithms, predictions, decision support, or any other output that involves extracting value from the available data.

Data products can be a competitive differentiator for companies, as they assist in decision-making and generate business guidelines for each context if development is oriented to answering strategic questions. Therefore, this type of product requires a team that has knowledge not only of business and software development but also of analysis, science, and data engineering. Furthermore, the planning of the construction of the product must take into account the steps that involve cleaning, analysis, transformation, data mining, and construction of statistical models, which makes the whole process more complex and must be done very carefully.

Differences between “Data as a Product” and “Data Product”

A common misconception is that “data as a product” and “data product” are the same, but they are not. As previously mentioned, data products are products that use data to solve a specific problem, while “data as a product” (DaaP) are a subset of this type of product. Going into more detail, DaaP refers to data products that have data, raw or derived, as the final deliverable of the solution.

In his book “Data Mesh”, Zhamak Dehghani says that teams that provide data must apply the product paradigm to the data sets they provide. Thus, it is ensured that this data transfer has a better Discovery, more security, reliability, etc. Some examples of DaaP would be the construction of a data warehouse, the development of an API that has the purpose of taking data from one environment to another, or even the export and import of transformed data to some file system.

Important features of Data Products

In addition to deriving their value from data, data products need to give back to the context in which they are a part, whether solving a problem, optimizing processes, or generating important insights. For this, this type of product is capable of making inferences and predictions about the business to help users make decisions and is also capable of self-adaptation. That is, the product evolves with the result itself and as the number of users and data available increases.

Apart from these characteristics, there are qualities that a good data product should have, such as:

• Reliability: This type of product needs even more robust tools to identify performance issues as it sometimes handles large amounts of data and can be slow or unstable if not carefully developed.
• Security: This is an essential attribute when dealing with data, as it is up to those responsible to protect the product from possible vulnerabilities and attacks. In addition, you must comply with the laws and regulations established by the government, such as the LGPD, and by the companies involved.
• Scalability: The product must be planned to facilitate its growth, because, in addition to the number of users, it is possible that, as the product evolves, the amount of data and the storage and processing tools need to be prepared for that.
• Good Discovery and Planning: Like any product, the discovery and planning steps are critical to the success of data products. However, for this type of product, attention needs to be even greater, since the research involves not only the business but also the data that the business has to offer to build the desired solution.

Examples of Data Products 

In addition to the raw or derived data that have already been mentioned in the text, there are other examples of data products that may even be present in our daily lives, such as transportation applications, which show the best possible path to take us to our destination, or even those lists of recommended songs that fit our tastes very well and we didn’t know them yet, and that appear in audio streams. The truth is that, if we stop to observe, data products are dominating the markets and making our daily lives much easier.For businesses, dashboards are a good example. Typically, they are used by companies to measure their metrics and assist in decision-making. Social networks themselves provide dashboards for companies, brands, or influencers to generate insights into views, followers, and clicks. Some products provide message and email automation to facilitate user management and that can increase sales if used well. Here at Aquarela we also develop products that use data and artificial intelligence to bring value to companies.

Conclusion

Data products are products that derive their value from data through insights or predictions. These products are present in our daily lives in several applications that we use and are indispensable especially when we talk about business, as they can be the differential to take companies to another level.

In other words, if the company seeks continuous improvement of its processes, it is essential that it uses data in its favor. For this, it is important to look for the characteristics and qualities described in the text in the products, since this product model normally deals with sensitive information, must respect a series of standards, and needs to perform well to provide a solution that really generates a positive impact in business.

What is Aquarela Advanced Analytics?

Aquarela Analytics is the winner of the CNI Innovation Award in Brazil and a national reference in the application of corporate Artificial Intelligence in the industry and large companies. Through the Vorteris platform and the DCM methodology, it serves important clients such as Embraer (aerospace), Scania, Mercedes-Benz, Randon Group (automotive), SolarBR Coca-Cola (food retail), Hospital das Clínicas (healthcare), NTS-Brasil (oil and gas), Auren,SPIC Brasil (energy), Telefônica Vivo (telecommunications), among others.

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