In a small city in the Northeast, Dye-or-Die, Inc., a shampoo manufacturer, prided itself on its elaborate production methods. However, their intense focus on how they made shampoo left them disconnected from their true audience.
One day, Rachel, the new marketing director, attended a local fair and overheard customers discussing their hair struggles. Inspired, she realized the company was missing vital insights from the very people they served. The team began hosting feedback sessions, inviting loyal customers to share their experiences.
Armed with this feedback, Dye-or-Die, Inc. shifted its focus. They reimagined their products based on client needs rather than production bragging rights. Their campaigns evolved to highlight real customer transformations through testimonials, creating a sense of community and connection.
Sales soared as customers appreciated the genuine engagement. Dye-or-Die, Inc. transformed from a brand emphasizing its manufacturing process into a beloved name recognized for listening to and prioritizing its clients—a key driver of true innovation and satisfaction.
The Disconnect in Data & Analytics (D&A) Management
Much like Dye-or-Die, Inc., many Data & Analytics (D&A) managers are primarily focused on the production process of data products. They emphasize data governance and data dictionaries and follow the ongoing debates between Bill Inmon and Ralph Kimball with keen interest. While keeping up with innovations in strategic data management is essential, an overemphasis on how data is produced, governed, and maintained misses a critical factor: consumer engagement.
Let me ask you: How many D&A leaders consider the foundational work of Philip Kotler, the founding father, and leader in marketing thought, when developing data products? Not many, I guess. Yet, there’s a reason Gartner, Inc. asserts that “Data products are defined by how they are consumed, rather than by how they are produced or the functionality they offer” in their paper “3 Competitive Differentiators of Successful D&A Leaders” (published on September 18, 2024).
Two Critical Marketing Principles for D&A Managers
In our Dye-or-Die, Inc. example, the company initially focused on its production process—but customers simply didn’t care. This is where marketing best practices provide valuable lessons for D&A leaders. Two essential principles stand out:
1. Implementing a Feedback Loop to fuel product innovation
In marketing, it is standard practice to implement product feedback loops when introducing new offerings. This continuous cycle involves collecting customer feedback, analyzing it, and making necessary improvements to the product or service.
For data products, establishing a robust feedback loop is crucial. It ensures that offerings truly meet the needs of stakeholders and continuously improve. Without this, D&A managers risk building products that lack adoption and miss opportunities to innovate and enhance their products.
2. Measuring Success Through Key Metrics
Marketers measure the success of their feedback loops using key metrics such as Feedback Response Rate, Customer Satisfaction Score (CSAT), and Net Promoter Score (NPS). These insights help them refine messaging, enhance engagement, and reduce wasted marketing spend.
Similarly, D&A managers should track adoption rates, user satisfaction, and data product effectiveness. By leveraging these metrics, they can ensure the power and potential of their data products are understood, and usage of these data products drives meaningful business impact.
Analytics Asset Management: The Missing Piece
In 2019, the concept of Data as a Product gained popularity, largely driven by Zhamak Dehghani’s introduction of data mesh. Data products are designed for analytical use cases, featuring domain-centric ownership and cross-domain usability. Their value is based on consumption, with governance automated through federated computational mechanisms. These products are provisioned via self-service platforms like PowerBI, Tableau, and Qlik, making them easily accessible to business analysts and power users.
However, a critical component is missing—one that marketers inherently understand. This is where Analytics Asset Management® (AAM) plays a vital role.
To build effective consumer feedback loops in data products, domain teams must understand what assets are being created on these data products, how they are used, and the adoption trends over time. Usage patterns and shifts in adoption provide crucial insights for improving data products. Additionally, understanding the purpose, user groups and asset types helps establish appropriate service level agreements (SLAs).
An Example of AAM in Action
Consider a skilled D&A manager who aims to transform the HR department by developing a new data product. While analyzing reports and dashboards, she discovered that many HR analysts relied on manually uploaded Excel files to enrich their data—indicating a gap in the data offered.
Further investigation revealed that the existing organizational chart was insufficient, not only leading to additional work for analysts but also to inaccuracies in reporting. Some analysts uploaded old data or did not maintain it. To address this, she analyzed the missing information and enhanced her ETL processes to incorporate additional data points. Armed with insights on the volatility of the analytics assets that should be improved, she informed self-service users about the updated data product and identified which analysts needed additional support.
This overhaul not only improved efficiency but also ensured reports were accurate and reflected the correct organizational structure. Teams could generate reliable insights faster, fostering trust in the data they depended on for decision-making. This example underscores the vital role of automated feedback loops in driving organizational success.
Why D&A Leaders Should Embrace Analytics Asset Management
Analytics Asset Management provides the framework and the toolset for D&A managers to tailor their offerings, ensuring data products align closely with the key performance indicators (KPIs) that business partners rely on. By leveraging AAM, D&A leaders can:
- Gain real-time insights into data product adoption and usage.
- Ensure stakeholders receive actionable insights faster.
- Streamline efforts for efficiency and effectiveness of self-service analytics.
- Strengthen collaboration and cultivate a data-driven culture.
Conclusion
The lessons from marketing are clear—understanding your audience, incorporating feedback loops, and measuring effectiveness are essential for success. Just as Dye-or-Die, Inc. revolutionized its approach by listening to customers, D&A managers must shift from production-focused thinking to consumption-driven strategies.
By integrating marketing principles and adopting Analytics Asset Management, data leaders can build truly valuable data products that drive business success. The key to innovation isn’t just in how data is produced but in how it is consumed and continuously improved.
