Why Treat Data as a Product? Unraveling Its Worth

Let’s have a reality check.

Everyone is saying that data is the new oil. Agreed. But unlike oil, data is abundant and has the potential to be infinitely reusable.

However, simply having a wealth of data isn’t enough – its true value comes from how it’s utilized.

Many businesses collect vast amounts of data but lack a coherent strategy for its use. This data often ends up in silos, inaccessible, and most of the time unused.

That’s the state of data in many organizations today.

But by treating data as a product, you can capitalize on real value for your business.

Data as a Product refers to a way of thinking about how you manage and use data within your product, and within your organization.

Just like any product, data needs to be designed, built, maintained, and constantly improved.

DaaP focuses on ensuring the data is high quality, readily available, and meets the needs of its users.

The core idea is to make data usable for specific purposes. This involves understanding who will be using the data (both internally and potentially externally) and ensuring it’s presented in a way that’s clear and actionable for them.

Imagine taking raw oil. By itself, it’s not particularly useful. But by refining it into gasoline, you create a valuable product that powers many things.

In the same way, DaaP focuses on refining data into something insightful and beneficial.

A data product includes several parts that work together as a whole, usually stored in one Git repository.

The diagram below shows the typical pieces of a data product.

Data as a Product: Why Do Product Companies Need It?

Think about the most successful products. They solve problems, fill needs, and offer something unique to their users.

Data can do the same!

Here’s why this approach is a game-changer:

DaaP at Work in the Real World

The applications of data as a product span across various industries, each with unique challenges and opportunities.

Here is how top companies are gaining the most out of it.

Background

Objective: To gain deeper insights into customer purchasing behaviors and optimize inventory management.

Data Sources: Point of sale (POS) data, online purchase data, customer feedback, and social media interactions.

Implementation

Data Integration: Combining in-store and online data for a holistic view of customer behavior.

Predictive Analytics: Forecasting demand and trends using historical purchase data.

Personalization: Tailoring marketing campaigns and promotions based on customer preferences. An experienced marketing agency can help leverage these strategies to create targeted campaigns that drive engagement and boost sales.

Outcomes

• Reduced stockouts and overstock situations.
• More effective promotions lead to higher sales.
• Enhanced shopping experience through personalized offers and recommendations.

Background

Objective: To enhance user engagement and retention by offering a highly personalized viewing experience.

Data Sources: Viewing history, user ratings, search queries, and demographic data.

Implementation

Recommendation Engine: Utilizing AI and ML to recommend content based on user preferences and behavior.

Content Insights: Analyzing viewing patterns to guide content creation and acquisition.

User Segmentation: Categorizing users to tailor recommendations more effectively.

Outcomes

• Higher viewer retention and time spent on the platform.
• More informed decisions on content investments.
• Enhanced user experience through accurate and relevant recommendations.

Background

Objective: To enhance fraud detection capabilities and minimize financial losses due to fraudulent activities.

Data Sources: Transaction data, customer behavior data, and external threat intelligence.

Implementation

Real-time Data Processing: Using DaaP to monitor transactions in real-time for anomalies.

Advanced Analytics: AI and ML algorithms to detect unusual patterns and flag potential fraud.

Collaboration: Sharing insights with other financial institutions to stay ahead of emerging fraud techniques.

Outcomes

• Significant reduction in fraudulent transactions.
• Automated fraud detection processes reduce manual intervention.
• Increased trust due to enhanced security measures.

A Framework to Transform Data into a Valuable Product

Taking Data as a Product to the Next Level (With Examples)

Once you have a solid foundation, you can explore ways to make your data product even more valuable.

For that,

Consider offering anonymized or aggregated data sets to external businesses for market research or trend analysis. Whether you have gathered this data through social media analytics tools or by doing manual research, it still represents value, so make sure to use it as best you can.

This can be a great way to generate leads.

Example:

A retail company could sell anonymized customer purchase data to market research firms, helping them understand consumer behavior and product trends.

Move beyond basic reporting and use customer engagement platforms to harness your data to predict future trends and customer behavior.

This allows you to be proactive and make strategic decisions based on what’s coming, not just what’s already happened.

Example:

A logistics company could use data on weather patterns, traffic congestion, and customer locations to predict potential delays and optimize delivery routes.

Partner with other organizations to combine datasets and dig even richer insights. After all, two minds (and two datasets) are often better than one!

Example:

Imagine a healthcare provider collaborating with a pharmaceutical company to analyze patient data and identify new drug targets or treatment options.

Data sharing can lead to groundbreaking discoveries and innovations that benefit the entire industry.