Our AI Impact

 for the health of people



 Our AI Impact

 for the health of planet



 Our AI Impact

 for the health of business



“The work [with Synaptiq] is unprecedented in its scale and potential impact,” Mortenson Center’s Managing Director Laura MacDonald MacDonald said. “It ties together our center’s strengths in impact evaluation and sensor deployment to generate evidence that informs development tools, policy, and practice.” 
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    ⇲ Implement & Scale
    A startup in digital health trained a risk model to open up a robust, precise, and scalable processing pipeline so providers could move faster, and patients could move with confidence after spinal surgery. 
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      Thwart errors, relieve in-take form exhaustion, and build a more accurate data picture for patients in chronic pain? Those who prefer the natural albeit comprehensive path to health and wellness said: sign me up. 
      Read the Case Study ⇢ 


        Using a dynamic machine vision solution for detecting plaques in the carotid artery and providing care teams with rapid answers, saves lives with early disease detection and monitoring. 
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          This global law firm needed to be fast, adaptive, and provide unrivaled client service under pressure, intelligent automation did just that plus it made time for what matters most: meaningful human interactions. 
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            Mushrooms, Goats, and Machine Learning: What do they all have in common? You may never know unless you get started exploring the fundamentals of Machine Learning with Dr. Tim Oates, Synaptiq's Chief Data Scientist. You can read and visualize his new book in Python, tinker with inputs, and practice machine learning techniques for free. 

            Start Chapter 1 Now ⇢ 


              How Should My Company Prioritize AIQ™ Capabilities?





                Start With Your AIQ Score

                  3 min read

                  AIQ: What Data Product Management Means for You

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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 in the realms of data and artificial intelligence, 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.

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                  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

                  Additional Reading:

                  Ask Tim: Using Machine Learning to Detect Objects with No Data

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