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Big Data Boom: How to Capture and Leverage Insights

The Northridge Group

To make it worse, the pace of this information continues to increase; data (lots of it) is being produced more rapidly than ever before. How can you capture this data and use it to help you and your organization make informed decisions, and ideally to predict future behavior and events?

Big data 131
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Shaping the AI Future: Key Trends and Insights from the AI & Big Data Expo’24

Helpware

The AI and Big Data Expo Conference, held annually in Santa Clara, California, has become a landmark event in the industry, drawing professionals from diverse sectors to explore the latest advancements in artificial intelligence and big data.

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Guest Blog: The Evolving Nature of Digital Customer Experience – Are You Ready for the Future?

ShepHyken

Companies are swimming in customer data and often have the tools to understand that data. However, Kristen Engelhardt, Vice President, Voice of the Customer, Salesforce questions that “data without the story behind it is just numbers. How will organizations drive an effective experience…?”.

Big data 189
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Where Is Gamification Going? Some ‘New Rules’; Or, Stated Another Way….

Beyond Philosophy

However, like the hype around big data, enthusiasm for gamification has somewhat plateaued, and has needed to be rethought, especially regarding how it can drive more brand bonding and positive financial results. This gave gaming something of a Philosopher’s Stone, or magic wand, aura.

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Guest Blog: How Does AI Marketing Help Drive Customer Engagement?

ShepHyken

Artificial intelligence marketing (AIM) is an innovative way for marketers to bridge the gap between data science and execution. These solutions are constantly combing the internet for relevant conversations and events, meaning, when appropriate, brands can jump in, join and engage with their customer base in a very organic fashion.

Marketing 533
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What Do The Pioneers of Customer Experience See for the Future: And What Should You Do About It?

Beyond Philosophy

It appeals to me because it uses a data-driven approach. Customer Science is a product of a perfect storm of artificial intelligence, the information provided by Big Data, and the interpretation of that data through Behavioral Science. You might recall that I recently discussed Customer Science on a podcast.

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Bundesliga Match Fact Win Probability: Quantifying the effect of in-game events on winning chances using machine learning on AWS

AWS Machine Learning

But what are the events that led to these surprising in-game swings of win probability? Win Probability consumes event data from an ongoing match (goal events, fouls, red cards, and more) as well as data produced by other Match Facts, such as xGoals. How it is implemented?