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Data Science for Decision Making

Across the world, the demand for data scientists is skyrocketing — and so far shows no sign of slowing down. Experts predict that in the United States alone, there will be more than a quarter of a million open positions for data scientists by 2024 as data scientists continue to become an essential component of the modern workforce. Businesses without a proper data science operation may soon look as out-of-date as companies that rely on fax machines and floppy disks.

That’s because data scientists are helping organizations solve problems and make decisions through scientific analysis backed by clearly defined insights. The right data science operation can steer companies away from taking unnecessary risks or making huge mistakes thanks to a team of experts on hand ready to connect the dots and comb through every piece of usable data to come to draw a proper conclusion. 

But building a quality data science operation isn’t as simple as hiring a data scientist and calling it a day. Increasingly, organizations are having trouble making the most of their data science operations because they haven’t made the proper structural or organizational changes to their company.

new report from the Harvard Business Review points to data teams armed with incredible insights that aren’t able to properly communicate their findings to non-technical audiences, such as executives. Valuable research and analysis that aren’t properly conveyed ultimately leave people confused and unable to comprehend the scope or conclusion of the work. The result is a fundamentally misunderstood data operation that leaves decision makers questioning the value of their investment. 

The solution to creating a functional and productive data operation is to think beyond the data scientist alone and build a team of experts with complementary skills. Bringing data to life means working with designers, subject matter experts and storytellers who can properly convey the message that lives within your information. Working collaboratively, a properly assembled data science operation can help fix blind spots, make data seem as compelling as possible and convince stakeholders of the necessity of your work.

At Algo.ai, we’re proud to be doing just that. Our team — comprised of some of the world’s leading artificial intelligence experts, software engineers and industry domain experts — works together to bring data science to life for our clients. By embedding data into the DNA of our work, our team members can share their collective talents with our data scientists to help make the best data-driven decisions possible.

Prescriptive Analytics

What Is Prescriptive Analytics?

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Descriptive analytics is one of the first steps in data processing: summarizing historical data to produce useful information and prepare data for further analysis. Predictive analytics utilizes statistical techniques such as data mining, predictive modeling and machine learning to analyze historical facts and make future predictions. Prescriptive analytics takes elements of both descriptive analytics and predictive analytics to turn data into optimized predictions.
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