The Synergistic MethodTM makes Synergy Data Science of Arizona unique.
We understand there is more than one way to find accurate, insightful, and financially meaningful answers to data science questions. The most obvious approach may not always be the best one. The newly emerging landscape of data science too often follows a path that can be unnecessarily complicated with black box approaches that are executed by the unqualified. We call this sub-optimal side of the "new school" approach. Or, data science can fall back on statistical or other tools that have been in use for decades even when newer methods offer better predictions, less chance of mistakes, and can handle the most vexing Big Data. We call this sub-optimal use of the "old school" approach. This is what sets us apart: we create the magic of positive synergy by blending the best of new school and the old school. The method we use is our Synergistic MethodTM.
The Synergistic MethodTM works in several ways.
Data science may be new, but its roots as a discipline go back decades. We combine those time-tested approaches driven by the scientific method with a modern focus on artificial intelligence, machine learning, and big data. These newer approaches can respond to business challenges dynamically, rapidly adjusting to changing market conditions and other factors. By bringing the old-school approach together with a new-school perspective, our data science is more than the sum of its parts. This is based on the reality of positive synergy where increasing returns accrue to illuminated intellectual and business interactions.
Business scenarios can often be approached with perspectives from a range of disciplines such as statistics, decision science, economics, or engineering. The most effective solutions can come from unexpected areas, so we consider numerous approaches for each client's problem, not just the ones that seem most obvious. Synergistic results like these leap past the dangerous one dimensional "The Answer" box to a far more accurate multi-dimensional "answer set" of consensus approaches.
Your actions can be guided more strongly when several techniques converge on a similar solution. When those techniques diverge, we equip you with tools to examine best and worst scenarios of possible solutions. We provide our degree of confidence in competing forecasts. We draw on data mining and visualization results to be sure the problem is being tackled the right way. Sometimes, solutions come in self-defining clusters or networks or can self-learn in real-time.
Data science is heavily scientific with "new school" grounded in computer science, engineering, and computational mathematics along with specialized fields such as genetics and language processing. The scientific basis for the "Old school" comes from centuries old classic disciplines such as statistics, economics, and medicine but includes behavioral sciences such as psychology, consumer behavior, and
Whether it is financial services, beauty, telecommunications, or agriculture, every industry has developed an effective set of tools and techniques. We leverage these industry-specific approaches while bringing in diverse perspectives from other fields, leading to an out-of-the-box, thorough, and ultimately more effective work product targeted exclusively to you.
The integrative nature of the Synergistic Method™ sets us apart. Our own founder’s vision shows other unique facets to Synergy Data Science of Arizona: like 10% giving back to non-profits, ability to ID sources of increasing economic returns, and an understanding of time compression (Internet Years are like dog years, 7:1----time is moving faster). Unlike most data science shops, Synergy Data Science is hard-wired with intense, constant validation and automated quality control to ensure data and results stand scrutiny. That helps keep our projects on time. Our solid emphasis (earned from years of practice on highly regulated, audited finance analytics) makes us stand out to protect you and empower your business in the confusing arena of data science.
Sometimes we ask why and sometimes we just need a prediction or to have the algorithm decide what questions we should be asking instead of assuming cumbersome, non-scalable statistical hypotheses. Other times, how a human learns and not a machine is the important thing so that proven modeling based on human behavior, meaningful patterns, and the ability to say why, how much, and how confident we are in answers and forecasts. The magic occurs when BOTH come together to ignite increasing returns (an interaction effect) where 2+2 > 4. Typically, each method itself has identified the increasing returns sweet spot to quantify benefits or “lift”. The combination of the two can take advantage known sweet spots and find new ones so that we usually see a multiplicative or exponential benefit instead of a linear or constant returns.
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Why is Big Data Big Business? 2018
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