5 Key Pillars of Building Great Data Products

Irrespective of industry or company size, enterprises increasingly rely on data products to differentiate their product offerings and create competitive advantages in their markets. Apart from digital-first entities like Google and Netflix, players in the healthcare, energy, insurance, banking, and even manufacturing sectors rely on data products today to improve decision-making capabilities and increase process efficiency.

Most of us have heard of dynamic pricing, which was popularized by airlines as a way to capitalize on demand while filling planes that would otherwise be empty.  The same concept was applied by tech juggernaut Amazon when it enabled price variations based on urgency, volume, availability, and product requirement. However, what most people are unaware of is that Amazon changes the prices of products an average of 2.5 million times per day!

Building an efficient data product depends on many factors—especially data quality.

5 Essential Data Product Pillars

Here are the five key pillars that every efficient data product must have. 

1. Superior Data Quality

Data quality is one of the most important data product pillars. Data, in all shapes and sizes, is of any value only if it’s accurate, relevant, error-free, and complete. Without all of this, data delivers no actionable value to a data product. Hence, to make sure that data quality is monitored and managed, a robust data management system is required. This system should be able to give data teams a bird's eye view into various data assets and pipelines to ensure data integrity is constant. 

2. User-Centric Design

A well-constructed, user-centric interface is a key element in developing a data product that meets users' needs. Data products need to provide users with a simple way to navigate through them without obstacles. By understanding the target audience and their goals, data teams can achieve product-market fit. A user-centric design paves the way for data products that are intuitive to use, thereby increasing data product engagement.  

3. Scalability

As the data product grows in popularity, and the rate of adoption increases, the amount of data it collects will increase drastically. Hence, they must be able to handle this gradual increase in data volumes. Teams that are looking to build bigger and better data products need to have a data management system that can handle large influxes in data volumes, as well as have the agility to be scaled as the product grows. 

4. Security

In the era of data regulatory laws (e.g., GDPR in the EU) and data protection (e.g., CCPA in the United States), data teams are mandated to ensure data products that deal with sensitive user information are reinforced with the right data security measures. Since the systematic handling of data is required from end-to-end, data products need to have a data management solution that monitors and safeguards user data both at rest and in motion. 

5. Actionable Insights   

The fifth pillar of a data product is its ability to translate intelligent insights through a usable interface. The ultimate goal of a data product is to turn data into actionable insights that drive growth and innovation. Also, these insights need to be presented to users in a format that is easy to understand (through graphs, metrics, charts, and other visual sources). 

Use Data Observability to Build Great Data Products

Beyond these five key pillars, there are many other factors that go into building data products. However, the five data product pillars mentioned above are arguably the most critical to determining success. They help data products deliver value, remain relevant, and provide accurate insights. 

Data quality, security, and scalability are interconnected, as they directly or indirectly depend on the quality, integrity, and value of the data at hand. To manage these pillars, enterprises must have an end-to-end observability platform that helps them take full control of their data assets and pipelines.

By utilizing Acceldata's multi-layered data observability platform, enterprises can obtain a thorough understanding of their data stack, leading to enhancements in both data and pipeline reliability. This enables business teams to develop and maintain exceptional products by closely monitoring compute performance, cost-effectiveness, and the efficient delivery of reliable data.

Learn more about how Acceldata's Data Observability platform can help you accelerate your data product strategy.

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Photo by Hu Jiarui on Unsplash