Pandata Blog

AI design and development for high risk industries

The data revolution of the 2010’s is very similar to the IT revolution of the 1990’s. Just as the workforce required more computer literate professionals in the 90’s, today’s workforce increasingly calls for professionals that are comfortable making informed decisions based on large amounts of information. The explosion of e-commerce, mobile devices, and SaaS businesses has led to an unprecedented growth in the information available to organization. The types of information that organizations commonly leverage have grown beyond traditional databases to encompass social media, natural language, images, geospatial information, and even more complex data structures.

Enter the Data Scientist

The presence of larger volumes and more complex data has called for a new type of professional. Data Scientists are not only able to collect and access different types of data, they are also able to tell organizations what it all means. “Think of them as a hybrid of data hacker, analyst, communicator, and trusted advisor. The combination is extremely powerful—and rare,” said Thomas Davenport in his article for the Harvard Business Journal called Data Scientist: The Sexiest Job of the 21st Century.  

What exactly IS the difference between data analytics and data science?

Although they are highly related, data analytics deals with the world where we have complete information whereas data science deals with situations where we know we have imperfect or incomplete information and need to make some well-grounded assumptions. The art of data science is knowing how and when certain assumptions make sense.

Why should we care?

Data science can significantly improve an organization’s ability to compete and stay relevant by allowing them to collect more accurate and detailed performance data. Sophisticated analytics allow for better decision making which can maximize efficiencies and ultimately lead to an upturn in customer retention, streamline the operations management processes and allows organizations to more accurately profile customers with laser-focused accuracy.  And that just scratches the surface on how data science can benefit your organization.

Want to learn more about the difference between data science and data analytics, watch this brief video

You can also contact us directly, we are more than happy to tell you ourselves!


Cal Al-Dhubaib is a Partner & Chief Data Scientist at Pandata / Nicole Ponstingle is a Partner & Chief Strategy Officer at Pandata