Money never sleeps, dataflows never slow, and a data leader’s job is never done. You aren’t just thinking about how to make sense of the data
With so much data in your own stores, it’s tempting to think you have all you need to start producing great predictive insights. This might be
Many organizations see data acquisition and management as a business expense. You may hear questions like, what does it cost to collect and capture this data?
Congratulations! You’ve embraced machine learning and data science and your organization is well on its way to building a system that helps you deploy predictive analytics
As you know, every second of every day, we’re generating and acquiring new data. As you read this, someone is collecting data on the fact that
Pinpointing the most useful machine learning data and figuring out how to combine sources to create accurate, meaningful models is one of the most business-critical areas
We’d all like to imagine that the machines, systems, and algorithms we create are objective and neutral, devoid of prejudice, free from pesky human weaknesses like
There are some insights that can only come from the data you produce or collect in-house. Historical sales figures, for example. Foot traffic through your store.
Data science websites overflow with tips for organizing and handling your data, improving visibility and gaining insight into your company’s performance. The question is, though: how
