People who don’t work directly in the business intelligence and data science domains tend to get the two confused rather frequently. They both make sense of data and make important business decisions based on that data, but how they do so is completely different from one another. This post from Big Data Collective looks at some of the major differences between the two positions, and how companies can synthesize the two roles to make the most insightful decisions and predictions.

At the most basic level, business intelligence analysts look at data retroactively, while data scientists make predictions, or BI professionals work backwards and data scientists look towards the future. BI professionals analyze pasta data and trends to make decisions for upcoming quarters, like looking at quarterly sales results to determine what changes need to be made in the upcoming quarters. Conversely, data scientists make predictions for future business decisions, without necessarily relying only on actual historical data. Data scientists are more interested in industry trends and cycles to come up with business predictions. For a company to see ultimate success, hiring a candidate for both roles would allow the company to synthesize past data with future predictions for more insightful business and financial decisions. For more on these two roles, keep reading.

Read the full article here: The Difference Between Business Intelligence and Real Data Science

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