⇲ Implement & Scale
DATA STRATEGY
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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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    PREDICTIVE ANALYTICS
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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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      MACHINE VISION
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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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        INTELLIGENT AUTOMATION
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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. 

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            How Should My Company Prioritize AIQ™ Capabilities?

             

               

               

               

              Start With Your AIQ Score

                4 min read

                Reality-Checking AI Hype: Two More Things You Should Know

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                Here's the Truth about AI & Data Strategy

                There’s a lot of hype about artificial intelligence (AI). We published a blog post in June warning business leaders that, although they’ve likely heard grandiose rumors about this technology, a reality check is in order. AI can significantly benefit your business, but only if it’s approached as a tool rather than a magic wand.

                Our June blog explained two things about the reality of AI in any industry:

                •  Low AIQ™ is not necessarily bad.
                •  High AIQ™ does not guarantee success.

                (What is AIQ™? You can read about our method of evaluating an organization’s data maturity here).

                This blog will dive deeper into the reality of AI through the lens of data strategy: “[the] process employed to support the acquisition, organization, analysis, and delivery of data in support of business objectives.” [1] Early this year, market research giant McKinsey & Co. published a report predicting that data strategy will shortly become a requirement for doing business: “[...] most employees will use data to optimize nearly every aspect of their work.” [2] Other sources, like Gartner, agree: “data [has] become a primary driver of business strategy.” [3

                Every business with a stake in the future should be thinking about its data strategy. But you should also be interested in thinking about your data strategy the right way — and that starts with the following insights:

                • Data strategy is more than a mindset.
                • You’re never “not ready” to start applying AIQ™.

                #1. Data Strategy is More than a Mindset

                The belief that “data-first” is a mindset rather than a set of tools and practices is a common pitfall. It’s easy to understand that data makes your business more competitive and, therefore, deserves priority. It’s harder to establish an internal system that collects, organizes, and analyzes data in support of business objectives.

                Take a free AIQ™ assessment to evaluate your business's data maturity. We guarantee that you'll find room for improvement, even if you already consider your business “data-first.” Don’t be discouraged by that revelation! Room for improvement is good news. A mindset is fixed, whereas tools and practices can be honed and optimized. A mindset is vague, whereas tools and practices can be assessed objectively and improved measurably.

                #2. You’re Never “Not Ready” to Apply AIQ™

                So, you’ve taken our AIQ™ assessment and now know (i) where your organization can improve its data maturity and (ii) where it’s already strong. The next step is putting that information into action. But when? It may be tempting to wait a while. Fear of failure can hold you back. What if your leadership, budget, or company culture isn’t ready for a big change? Whether or not your anxiety is valid, you're asking yourself the wrong question.

                Applying AIQ™ isn’t always a big change. It can be a series of small steps toward a better (not perfect!) data strategy. If your organization still keeps its data in filing cabinets, lacks a technology team (besides the IT guy), and hasn’t heard of a “data scientist,” let alone hired one — don't expect to turn into Stark Industries overnight. 

                The second major pitfall that companies encounter on the road to a successful data strategy is believing that they “aren’t ready.” You may not be ready for a big change, but you’re ready for something. For example, imagine that you want your business to transition from using filing cabinets to digital storage. Should you start by hiring an expensive, in-house team of professionals? Or, should you outsource a low-cost feasibility test to establish a basic understanding of your data's potential for digital storage? Obviously, the latter. Small steps.

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                Photo by Jordan Whitt on Unsplash


                 

                About Synaptiq

                Synaptiq is an Oregon-based AI and data science consulting firm. We engage our clients in a collaborative approach to developing custom, human-centered solutions with a 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

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