How to Know When You Need an AI Expert vs DIY
Knowledge is power. Knowledge is important in AI because it takes knowledge to effectively deploy AI solutions, with...
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Synaptiq has spent the last decade studying data strategy and AI readiness across sectors and industries. We’ve consulted with clients, conferred with partners, collaborated with competitors, and conducted research to understand how and why organizations fail or succeed in their data and AI endeavors.
We channeled our expertise into AIQ: an innovative framework for AI-ready data strategy and AI implementation, focusing on 11 critical capabilities proven to effectively enhance workflow integration and return on investment.
In this blog post, we'll explore in detail one of these 11 capabilities: Data Product Management. For a broader understanding of AIQ, refer to our blog post titled "AIQ: What We Mean & What You Stand to Gain."
Data Product Management
Traditional product management focuses on launching products to the market, encompassing various elements such as market analysis, managing requirements, user experience, managing releases, and capturing value throughout the product lifecycle. Data Product Management builds upon this traditional approach, with a key distinction: in this realm, data is the primary source of value. This approach places a strong emphasis on technology and data literacy, recognizing them as pivotal components in managing data as a product.
As one of the foundational capabilities within the AIQ™ framework, Data Product Management plays a crucial role. It underpins and informs several other capabilities. This makes it an indispensable element in the AIQ™ suite, essential for the effective implementation and success of data-driven strategies and initiatives.
Data Product Management Done Right
Effective Data Product Managers excel at pinpointing potential business opportunities that can be enhanced through data-driven automation, advanced analytics, and the creation of valuable data assets. They possess a scientific mindset, which drives them to undertake experiments or conduct "feasibility studies" to validate whether existing data aligns with their envisioned goals, rather than making assumptions about data suitability.
Crucially, Data Product Managers have a deep understanding of data and its potential. They are adept at utilizing contemporary technologies, such as relational databases, machine learning models, APIs, and data visualization tools, to create and extract value. Their expertise enables them to not only manage data effectively but also to transform it into a strategic asset that drives business growth and innovation.
Why Data Product Management Matters
In an era marked by rapid and groundbreaking technological advancements, early adopters have been able to disrupt entire industries, setting a fast-paced tempo that others struggle to match. Data Product Management is crucial for organizations aiming to keep pace with the increasing adoption of data-driven applications and processes across various industries and to maximize the return on investment from these endeavors. A proficient Data Product Manager plays a vital role in ensuring that an organization's investments in data assets are both viable and profitable, preventing the squandering of resources on unproductive data initiatives.
Photo by Pawel Czerwinski on Unsplash
About Synaptiq
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
Contact us if you have a problem to solve, a process to refine, or a question to ask.
You can learn more about our story through our past projects, blog, or podcast.