We delve into different types of data products and share how you can implement them for your own organization.
Everyone (yes, even Forbes) talks about data products today. However, there's a gap in understanding their practical applications and successful use cases. It’s time to change that.
In this article, we will:
A data product is a solution that takes advantage of data assets to provide tangible value (removes a pain point or delights with benefits) to end-users.
The emphasis is on the value generation for product users, not the “solution” aspect.
Traditionally, data teams focused on the “solution” part, tweaking data pipelines that extracted volumes of raw data from data sources, optimizing data models for analyses, or building SOTA machine learning algorithms for predictive analytics. Often, this focus overshadowed the primary goal of addressing specific business problems or delivering additional benefits.
Zhamak Dehghani was the first to argue that the data mesh architecture puts data teams into a product development role:
“For a distributed data platform to be successful, domain data teams must apply product thinking with similar rigor to the datasets that they provide; considering their data assets as their products and the rest of the organization's data scientists, ML, and data engineers as their customers.”
Does that mean a dashboard can be a data product? Absolutely. To further understand this, let's delve into the various types of data products.
With data productization emerging as a new field, experts have various ways to classify them:
We believe there is a better way to understand the different types of data products.
The types of data products should be organized by the value they bring to the end user:
This might all sound abstract. So let’s look at concrete use cases of data products in real-world industry case studies.
Rohlik, a major e-commerce food retailer, faced a challenge common to those selling perishable items like fruits and dairy: the products' short shelf life.
Using Keboola, Rohlik integrated various data sources to create a dataset. This dataset took into account current stock, historical sales, expected sale duration, and other factors.
This information was then utilized to introduce dynamic discounts, where products nearing their expiration date were priced lower. These discounted products were showcased separately for the benefit of the buyers.
The outcome? Rohlik minimized waste, consumers enjoyed better prices, and there was a positive environmental impact.
📚Read Rohlik’s full case study here.
Česká spořitelna, the leading retail bank in the Czech Republic, leverages Keboola and AI tools to automate credit risk scoring for its customers.
Credit risk scores guide the type of loans customers are eligible for.
Previously, bank employees had to manually sift through various data sources to obtain these scores. Now, with Keboola, this process is significantly expedited.
This automation equips bank staff with immediate information on suitable products for customers, reducing research time from days to mere minutes and enhancing the overall user experience.
📚Learn how the biggest Czech retail bank uses data insights to streamline and automate their work.
Olfin Car, a prominent car seller in the Czech Republic, offers not only new and used cars but also services like financing, car maintenance, and insurance.
Facing stiff competition in a saturated market, Olfin Car turned to competitive intelligence to gain an advantage. Partnering with Keboola, they automated the collection of competitor pricing and product offerings, ranging from other resellers to manufacturers.
By integrating this competitive intelligence data with historical supply and demand trends, online behavior patterns, and more, they trained advanced recommender algorithms. These algorithms intelligently adjusted product recommendations on Olfin Car's website to maximize conversions and sales.
The outcome? A staggering 760% revenue surge in just one quarter.
📚Check what else Olfin Car did with big data to drive growth or learn how to set up your data-driven pricing.
These are just some of the examples of how data products helped companies grow. Let’s look at the wider advantages of data products.
Data products offer many benefits to their consumers and the company that develops data products.
Building data products is a continuous process. The product development lifecycle goes through these stages:
Any successful data product manager or product owner will tell you that product development is about fast iterations.
Sadly, most companies become bogged down in the initial stages of product development, such as creating data science algorithms, designing a metadata schema to increase data quality, or ensuring their federated access control complies with data governance policies. As a result, they aren't able to iterate and progress quickly.
That’s why Keboola built tools to get you building data products faster:
Both data apps and templates come pre-built with out-of-the-box data governance, data quality controls, data access management, automated deployment, and all the back-office work. So you can spend more time focusing on the value of product development and less time on the overhead.
Or, you can use the pre-built solutions as a blueprint and tap into the wide ecosystem of Keboola’s features (Streamlit integrations, data science workbench, CDC, etc.) to build your data products.
Keboola helps you set up and deploy data products in minutes with its data apps, data templates, and ecosystem of product development features.
So you can spend more time on value delivery and less time on the engineering overheads.
