What the heck is Data as a Product (DaaP), and how do you use DaaP to drive business results?
The team at Pathfinder Product, a Test Double company, wanted answers. So they teamed up with Women in Analytics (WIA) and the DataConnect Conference to create a first-of-its-kind study on the state of DaaP.
“We wanted to clear the muddy water and make Data as a Product more accessible and understandable,” said Jen Tedrow, the report’s co-author and executive director at Pathfinder Product. “It’s exactly the kind of insight I wish I had when I first started working in data management more than a decade ago.”
The comprehensive study examines how real businesses are using data to drive business results, with insights from more than 170 professionals across various industries.
Before drafting the survey, the authors consulted with experts in various data fields.
The overwhelmingly feedback was that Data as a Product means different things to different people, so the survey would need to level set and define what DaaP means in real businesses.
Here’s how the authors define Data as a Product:
The term “Data as a Product” (DaaP) might be relatively new to some. In essence, it represents a paradigm shift where data is no longer viewed merely as a byproduct of business operations, but rather as a valuable asset. This concept calls for planning, strategizing, and employing specific tactics to enhance the quality, accessibility, and usability of data.
The primary purpose of DaaP is to maximize its utility — this could include supporting internal decision-making processes, product development, data products such as dashboards or AI/ML models, or generating revenue through data monetization.
The 46-page study is packed with actionable insights from data experts who work in banking and finance, health, technology, and more industries.
Select highlights include:
81 percent of respondents reported that treating data as a valuable and usable asset had a positive impact on their organization.
The Top 3 use cases for data: to inform strategic decision-making, improve operational efficiency, and enhance customer service.
The bottom three use cases for data: machine learning (ML), artificial intelligence (AI), and data monetization.
The Top 3 challenges when implementing a Data as a Product strategy: integrating data products into existing workflows and systems (63.2%), ensuring data quality and reliability (62.6%), and aligning data products with business value and goals (54.6%).
The study also includes a breakdown of key takeaways and actionable insights by role: data stakeholders, enterprise leaders, SMB leaders, data team members, and data strategy decision-makers.
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