2019 Trends Report: Seeing the Unseen with AI and data-driven decision making 2019 Trends Report: Seeing the Unseen with AI and data-driven decision making
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2019 Trends Report: Seeing the Unseen with AI and data-driven decision making

Thanks to the emergence of new technologies and the democratization of information, 2019 will be the year of better decision-making in our personal and professional lives alike.

In Jeff Bezos’ 20-year history of penning shareholder letters, he’s focused much of his word count discussing Amazon’s approach to decision-making with sage advice such as “Most decisions should be made with only 70 per cent of the information you need; if you wait for 90 per cent or more, you’re moving too slowly.”

To Bezos’ point, decision making isn’t easy. Companies left behind by digital transformation in 2018 (Toys R Us, David’s Bridal, and soon, Sears) fell victim to the pitfalls of human decision-making errors and internal bias that led them to their untimely downfalls.

Yet as advanced applied AI and analytics become an everyday reality, simplifying our decision-making complexity (and doing away with unconscious bias) is increasingly within reach — so much so that we believe our best decisions are yet to come.

Human decision-making is shaped by a dizzying array of subconscious biases and emotion mixed with rational thought — and not always in the right ratio.

Real Time (Finally) Becomes a Reality

Walmart has its hands full stocking 17,000,000-plus products around the world, posing a logistical challenge that no traditional Business Intelligence (BI) tool has been able to solve.

Enter a partnership with Elastic, which focuses on streamlining the process of tackling unstructured data. The partnership means that Walmart can now tell you in real-time how many bananas it sold as you read this sentence (about 468).

From CPG to transportation, it is now easier than ever to factor what is happening “now” into decisions.

Flying Like a NASA Astronaut

Dr. Steven W. Lockley, a NASA scientist and sleep researcher, recently set the travel world abuzz this summer when he co-launched Timeshifter, an app that draws on his NASA research and provides users with an individualized plan designed to combat jet lag.

The democratization of data is revolutionizing more than our sleep schedules. DataRobot, an ML platform that helps enterprises build predictive models at speed, features hundreds of open-source ML algorithms, allowing even novice users to build predictive models for their companies.

 


Google Introduces Our First Chief Decision Officer

Thanks to the rise and use of algorithms that make decisions on our behalf, our ability to weigh options will soon have exponential implications, a fact that was not lost on Google in 2018. It recently appointed its first-ever chief decision officer, Cassie Kozyrkov, to help Google augment its data-backed decision-making with psychology, neuroscience, economics, and managerial science.

Kozyrkov, one of the top TED speakers of 2018, helps Google augment data science with psychology, neuroscience, economics, and managerial science. So far, she has trained 17,000 Google employees to reduce human bias and avoid blind spots, all in tandem with some of the most cutting-edge machine-learning technology being adopted across the organization.

 

Take Action

The lifeblood of any organization is the quality and speed of its data-driven decision making, and as AI technology continues to advance, those decisions will need to happen faster than ever. The combination of human experience and interpretation, AI and analytics, and the application of EI (emotional intelligence) will move the needle in 2019.

  • Prioritize a reassessment of your data-to-insights process. Many firms vastly underutilize rich data sources such as unstructured data.          
  • Identify the use cases with the greatest near-term impact. Real-time pricing, personalization, and recommendations are among the most compelling on the front-end of the customer journey.
  • Consider new or emerging analytical approaches like a “digital twin” to run simulations before devices are built and deployed.

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