We began our journey by asking what the most challenging variables were that hindered growth and what to do to help shape predictions, strategic decisions, and operational efficiency. We did this because we had painful memories of the Great Recession. Also, given that the Science report estimated that 97% of all electronically recorded information had already been in digital bits in 2007, there are additional questions about how and where one can uncover a piece of insight to spur growth from the uncertainty that manifests itself as patterns.

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Subsequently, it became clear that major economic and geopolitical events or circumstances, trade, tariff rates, market dynamics, regulatory changes, monetary policy, a wide range of corporate development, digital transformation, business combinations, and many other factors can all contribute to aggregate demand and drive growth.

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To tackle these challenges, we decided to dig deep into why the world's most renowned organizations continue to drive top-line growth in value and outcomes, notwithstanding global crises, health epidemics or pandemics, and the worst economic downturns.

 

Developing new models presents itself with several unique requirements that are daunting.

 

To experiment, we took risks, even if it meant failing along the way. Leveraging Google Cloud Services, we developed comprehensive cost-saving and ambitious strategic objectives to store massive amounts of raw data and accelerate the hyper-automation process for annotation and labeling. Then, from a model-centric to a data-centric AI approach, we built scaled machine learning models to identify trends, draw, find, understand, and explain patterns, correlations, associations, relationships, outliers, and arbitrage out of distributed data systems in inconsistent, inefficient, and rapidly changing environments. The resultant outputs are staggering.

 

You want an explanation of extracted signals from noisy data to find out what the probability of an event is, to get a complex question answered, or perhaps you want to know what is behind LHMV, Apple, L'Oréal, ExxonMobil, and Walmart's strong growth to support predictions, strategic decisions, and operational efficiency.

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And we do.

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It's only the start. We believe that we have the building blocks in place to help deliver tangible benefits and outcomes of safe AI systems by enabling greater use of data, more intelligent software, and super- automation.

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