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. 
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        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

                  2 min read

                  AIQ: What Data Product Management Means for You

                  Featured Image
                  Synaptiq has spent the last decade studying data and artificial intelligence (AI) across a wide range of industries. We’ve consulted with clients, conferred with partners, collaborated with competitors, and conducted research to understand how and why organizations leverage these technologies.


                  Our ultimate takeaway?

                  AIQ: a novel approach to data and AI, centered on 11 key capabilities shown to facilitate successful workflow integration and return on investment (ROI).

                  This blog post will deep-dive into one of the 11 capabilities: Data Product Management. We’ll discuss what it is, how it’s done (when it’s done right), and why it matters. Or, you can read an overview of AIQ, including all 11 capabilities, in our blog post, “AIQ: What We Mean & What You Stand to Gain.“


                  What is Data Product Management?

                  Traditional product management is the function that brings products to market. It includes elements such as market analysis, requirements management, user experience, release management, and value capture across the product lifecycle. Data Product Management is an extension of traditional product management, wherein data is the primary value and technology and data literacy are crucial. 

                  Data Product Management is one of the foundational AIQ™ capabilities. It informs Data Sourcing, Data Engineering, Business Intelligence, and Modeling capabilities, making it particularly essential.


                  Data Product Management Done Right

                  Successful Data Product Managers know how to identify viable business opportunities for data-driven automation, “deep” analytics (i.e., beyond typical spreadsheet functions), and data asset creation.  Additionally, they have a scientific mindset that leads them to approach product development efforts by conducting experiments or “feasibility studies” before assuming that existing data will meet their vision. 

                  Overall, Data Product Managers understand data. They know how to leverage modern technologies, such as relational databases, machine learning modeling, APIs, and data visualizations to generate value.


                  Why Data Product Management Matters

                  Huge, unprecedented technological advances have allowed early-adopters to disrupt industries, leaving the rest of the world playing catch-up. An organization needs Data Product Management to (i) guide investment in data-driven applications and processes to keep up with growing rates of adoption across industries and (ii) drive maximum return on investment. A Data Product Manager ensures that their organization does not waste money on data assets that are neither viable nor profitable. Ultimately, their purpose is to capitalize on data-related opportunities as the “CEO” of data products in their organization.

                  You can learn how Data Product Management fits into AIQ™ by reading our blog. Or, take our AIQ assessment to determine where your organization stands for each of the 11 capabilities.

                  Additional Reading:

                  Are 'Lionfish' Swimming in Your Digital Ecosystem? How Large Language Models Threaten Enterprise Security

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                  Transparency or Complexity: Understanding the Powers and Pitfalls of Black Box AI

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                  Whisking Words: Unlock the Power of LLMs for Your Business

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