What Exactly is a Data Product? Beyond the Hype of AI
It’s impossible to have a conversation about artificial intelligence these days without the topic quickly shifting to ChatGPT. The ability to generate human-quality text is undeniably impressive, but let’s not forget – AI is a much broader field with a rich history.
Decades ago, AI researchers were building chess-playing algorithms and rudimentary spam filters, all requiring significant technical expertise to use and maintain. These were powerful tools, but hardly accessible to the average user.
ChatGPT (and competitors like Gemini and Claude), however, changed the game.
They made AI accessible, easy to use, and immediately useful. You don’t need to be a programmer to leverage its capabilities. This, along with its user-friendly interface and clear value proposition, are all hallmarks of a well-designed data product.
Data products are the bridge between raw data and actionable insights.
They take complex data sets, process them using AI and other techniques, and deliver valuable information in a way that’s readily consumable, solving a specific problem or fulfilling a particular need.
Think of them as pre-packaged intelligence, ready to be used by anyone, regardless of their technical background.
Just like ChatGPT, data products are designed with ease of use and clear value in mind.
These products often rely on data pipelines to ensure the data used for insights is accurate and up-to-date.
But what truly defines a data product?
Let’s look at some examples:
| Feature | Data Product (Example) | Similar Asset (Not a Product) |
|---|---|---|
| User-friendly Interface | Sales forecasting tool | Raw sales data |
| Clear Value Proposition | Customer churn prediction model | Data warehouse containing customer data |
| Actionable Insights | Recommendation engine | Historical user behaviour data |
As you can see, data products go beyond just providing access to data. They offer a complete solution, transforming raw information into actionable insights that can be readily used for decision-making.
However, a critical element remains – trust.
For any data product to be widely adopted, quality is paramount.
Inaccurate or misleading data will quickly erode trust and limit its usefulness.
That’s why we, Master Data Management, are passionate about creating trusted data products.
Want to learn more?
Our comprehensive eBook: “ A Blueprint for Building Trusted Data Products in Financial Services,” dives deep into the key principles and best practices for creating data products that users can rely on.
Interested in the financial services industry?
Join us at our upcoming talk at Fintech Summit Africa on 27 June 2024, where we’ll be discussing the specific challenges and opportunities of data products in the financial sector.
Use our code SPEAKER25FNT for a 25% discount!
Click here to visit Master Data Management.
