Designing Data Products to Streamline Decision-Making

Data products define a product that facilitates an end goal through the use of data

We are living in an era of information that has a transformative effect on society, academia, and business. The present shifts center around the internet that has created surplus value for big data. Big data has been a promising topic since 2011 when its hype started. It eventually led to the creation of an innovative solution called data product. The term defines a product that facilitates an end goal through the use of data.

The amount of big data that we generate every day is tremendous. They affect every aspect of our lives, starting from the food we consume to our social interactions. However, even in general, a lot of products we use in daily life are data-related. But the evolution of big data and the efforts of data scientists have brought us to a place where data becomes a product, specifically, a consumer product. Data products center around data and solve customers' needs, learn from feedback, prioritize relentlessly, etc. Business owners who spot and build the new data products will lead their companies towards development in the industry, such as how Google became the leader of search and Amazon, the leader in retail. Data products will be at the forefront of customers' choices in the upcoming years.

What is a data product?

Traditionally, a data product is anything that combines big data and algorithms to come up with relative solutions. But as technology evolved, the definition has moved to a collaboration of big data and statistical algorithms that are used for inference or prediction. Many data scientists are also statisticians, who play a big role in developing products. However, the product combats between the ability of data scientists and the material that business owner wants to deliver to consumers based on their needs. Generally, data scientists are concerned about building products with a lot of knobs, dials, and fancy details. But that is not what consumers want. They want products with extraordinary features; yet, expect them to work on a single touch. Therefore, business owners are currently caught between the two worlds and are managing the balance.

CDDB, the single database product marked the beginning of the evolution of data products. After that, many similar products emerged as a source of customers' choice.

LinkedIn's Skills: LinkedIn Skills incorporates databases of users, employers, job listings, skill descriptions, employment histories, and more. It also facilitates users to join different databases to answer questions that couldn't be answered by either database alone.

Google Analytics: Google Analytics' prime objective is to bring a quantitative understanding of online behavior to the user. In the system, data is central to interaction with the user and unlike the other products mentioned thus far, is explicit in its use.

Salesforce's Einstein AI: Einstein's AI leverages customers with luxury tools like predictive analytics, finance terminals such as the Bloomberg Terminal, website analytics tools such as Google Analytics. However, business owners develop their own internal products for privacy, data integrity, and adaptability.

Facebook's Facial Recognition: Facebook's facial recognition stands as an extraordinary example for linked databases. It solves the complex task of face identification and learns who took the picture and who that person's friends are. It is a reasonable guess that any face in someone's feed could circle around their Facebook friend list. Henceforth, Facebook matches it with pictures on the friend's list and tags them automatically.

Types of data product

Data products are of different types. Starting from narrowing down the field of possible products to those that satisfy our definition, data products are a part of internal products in an organization that supports decisions and automates the decision-making process.

Raw data: Raw data is the base material that data scientists use to create products. Business owners also work on collecting and making available data into products that define customers' choices. The user can then choose to use the data as appropriate, but most of the work is done on the user's side.

Benchmarking: The concept of benchmarking is making a comeback as it is used to measure data in every process within a modern company. Call centers measure who picks up the phone faster, online retailers measure everything via A/B tests, and recruiting departments monitor the close rate for applicants over time. As the benchmark industry matures, we see development on actual platform retrieving and displaying data becomes easier to be used.

Automated decision-making: Automated decision-making is famous in applications like Netflix, Spotify, etc. They provide recommendations based on our previous preferences. It also plays a critical role in self-driving cars or automated drones through the physical manifestation of closed decision-loop. Business owners make great use of the technology to streamline automated decisions.