Tag: machine learning

How Are You Analyzing and Adjusting to the Mobile Shopper?

Every retailer is facing a similar challenge. If you are a retailer and constantly feel the pinch from online giants like Amazon and Google, you have an opportunity to gain back control and competitive advantage with more personalized products and services, building that intimate relationship that these giants simply cannot provide.

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What AI Means to a Data Scientist

How many times have you wished for more hours in the day so you can complete more tasks? A key goal of AI or machine learning automation is to have machines complete tasks for you, freeing up time so you can focus on the more complex, higher-value tasks. However, there are simply not enough data scientists in the world to deliver on the AI potential. Data scientists building AI applications require numerous skills – data visualization, data cleansing, artificial intelligence algorithm selection and diagnostics. What if some of these data science tasks could be automated using AI, increasing data science productivity to tackle more AI use cases?

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What AI Means to a Retailer Dedicated to Customer Experience

Retailers are focused more than ever on quickly adjusting to changing customer preferences and demand. Specialty’s Café and Bakery is a great example of a retailer that is using data to drive decisions related to product development and selection, inventories, staffing, and more to attract and keep customers.

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Will AI Increase the Reach of BI and Analytics?

The business intelligence (BI) assembly line is broken, with adoption or utilization rates of only 30 percent in a typical organization, according to Gartner[1]. These adoption rates include all the users of the BI system – administrators who manage the system, analysts who build reports, and business users who consume reports for better decision making. This underutilization ignores employees outside the BI system who could be using valuable data to make better decisions. No one can argue that business performance and innovation are negatively impacted when data assets are not fully leveraged for decision making.

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Making decisions with data – finding a practical role for Machine Learning

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Machine Learning has gone from a niche area of technology to being the savior of companies across multiple industries. Others see it as a technology that is overhyped and doesn’t deliver. Indeed, the Gartner Hype Cycle for Emerging Technologies has Machine Learning, or ML, at the Peak of Inflated Expectations. This is where technologies end up when there are huge amounts of attention and marketing exuberance on display but not necessarily the results to show adoption.

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