Had Hergee been into businesses, this article would have been a point of interest to him. And he could have avoided the Big Data errors in his 'Tintin' enterprise
"Are you willing to follow down the line too? Not yet intrigued? #Sigh# Thought so! Let's get you hooked!
Here's the modern business landscape - Data data everywhere, not a drop to waste! Data has become considerably crucial for modern businesses. In this age even AI is getting powered by Big Data. The secret lies in the capability to collect, sort through, and collate data from diverse sources,This brings in the capability to increase the insight-level and make data-based decisions that enhance business enablement. The leverages extend from marketing, internal workflow to sales for businesses.
Now, where does Big Data come into the business realm? Let's get to the root of this, shall we?..."
Business leaders today often refer to the growing demand for Big Data as the new oil boom. In truth, right now data may be more valuable than black gold, especially to marketers
"In helping to transform marketing, data is used for everything from targeted advertising to user profiling. It's tremendously valuable because it generates actionable insights that lead to commercial benefits. Data makes it possible to personalize user experience and automate processes across the board.
But data is actually a lot easier to procure than oil. Consumers regularly give data away for free, despite often knowing exactly how valuable it is to business. Consider how many of us freely give Facebook Pixel access to our browsing habits. The next time you see an ad after talking about going on a holiday, you're seeing what a complex data-driven advertising network predicts you want..."
Can a do-it-all Data Scientist really be more effective at delivering new value from data? While it might sound exhausting, important efficiencies can exist that might bring better value to the business even faster
"Recently, I came across a Reddit thread on the different roles in data science and machine learning: data scientist, decision scientist, product data scientist, data engineer, machine learning engineer, machine learning tooling engineer, AI architect, etc.
I found this worrying. It's difficult to be effective when the data science process (problem framing, data engineering, ML, deployment/maintenance) is split across different people. It leads to coordination overhead, diffusion of responsibility, and lack of a big picture view.
IMHO, I believe data scientists can be more effective by being end-to-end. Here, I'll discuss the benefits and counter-arguments, how to become end-to-end, and the experiences of Stitch Fix and Netflix..."
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