experts and domain experts encourages knowledge sharing on both sides In an

Experts and domain experts encourages knowledge

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experts and domain experts encourages knowledge sharing on both sides. In an article from McKinsey Quarterly , Cameron Davies, Head of Corporate Decision Sciences at NBCUniversal shared a use case that involved annual forecasting. They decided to build and surface a set of machine-learning algorithms to help augment the process. In the early stages, they brought in a researcher to contribute to the project. He ended up becoming an evangelist in the business units and trained other people how to interpret the recommendations. It will be the domain experts that help ensure that AI is adopted and trusted in an organization.” — Richard Tibbetts, Principal Product Manager for AI, Tableau Data experts gain useful knowledge into how the business uses data while domain experts uplift non-technical roles as advocates for data proficiency. Based on their relationships and expertise, domain experts will play a key role in putting the results of these AI projects into practice across departments and teams—ushering in a new wave of maturity for AI use cases. RELATED CONTENT Artificial Intelligence and the Analyst: Redefining roles in the age of augmented analytics Three common machine learning misconceptions Webinar: Machine Learning, Explainable AI, and Data Visualization
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Brands apply narratives to consumer data for more authentic and enlightening engagements What were your most liked photographs in 2019? What music did you listen to most? How much did you exercise? For consumers in the 21st century, these personal data stories are more accessible, and inspiring, than ever before. We’ve grown accustomed to companies collecting our data and we even anticipate their recaps of our behavior in weekly or yearly summaries. And now, with these insights more pervasive than ever, brands are boosting consumer engagement by making our data stories more meaningful, convenient, and interactive. Data storytelling is an effective way for companies to illustrate our habits and engage us in ways that pure facts and figures could never achieve. Consider your personal banking transactions: you have ready access to detailed histories of all payments, deposits, and charges associated with your account. But do you spend any time analyzing these lists for insights? Then, at the end of the calendar year the bank sends you an aggregated summary of your purchases, organized into categories and presented visually. Instantly, this different view of your data helps you spot trends and inspires you to ask meaningful questions: “Did I spend too much money on clothes when I could be traveling or investing more in my hobbies?” A quick glance of your spending is a simple example, but this type of annual recap demonstrates the power of contextualizing data to help us extract insights. In a Gartner blog post, Use Data and Analytics to Tell a Story , author Christy Pettey writes, “It’s the context around the data that provides value and that’s what will make people listen and engage.” As the stars of these stories, interacting with this data becomes a lot more exciting. And
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