Building data products using the five stages of the product life cycle

Building data products involves using data to create products or services that solve a specific problem or meet a particular need. The concept was developed by German economist Theodore Levitt, who published his Product Life Cycle model in the Harvard Business Review in 1965.

In this sense, we can utilize the five product life cycle stages to help build a good data product. These five stages are development, introduction, growth, maturity, and decline.

1. Development stage:

In this stage, we research and develop the data product idea. For example, we can conduct surveys, focus groups, and interviews to gather feedback and insights from potential users. We then define the product requirements, such as features, functionality, infrastructure, and data sources. At this stage, we should create prototypes to test our ideas and refine the product concept.

2. Introduction stage:

In this stage, we can launch our data product for internal or external consumption. For example, marketing campaigns, social media, and advertising to create awareness and attract external users, or we can use the company town hall to communicate with the internal users. It is also essential to gather feedback and measure the product’s performance to identify areas for improvement. We may also adjust the pricing, packaging, and messaging to suit the target audience better.

3. Growth stage:

In this stage, our data product gains momentum and grows in popularity. As a result, we may expand our customer base, add new features, and improve the product’s performance. We may also explore new markets and partnerships to increase our reach. At this stage, we can also consider creating premium versions or offering additional services to generate revenue.

4. Maturity stage: In this stage, our data product has peaked and may start to plateau. Focus on retaining existing customers and improving their experience with the product. We may also optimize the product’s efficiency and reduce costs. We should also consider diversifying our data product offerings or creating new revenue streams.

5. Decline stage: Our data product may lose market share or become obsolete. We may need to retire or revamp the product to address new user needs and preferences with new data sources. We could also explore new technologies or partnerships to reposition the product and extend its life cycle. Alternatively, we may exit the market and invest in a new data product idea.

Following these five stages of the product life cycle can help build data products that are relevant, scalable, and profitable. In addition, each step presents unique challenges and opportunities that can help create data products that meet the changing needs of the users.