The major difference between data analysis and data analytics is the need for subject knowledge. Typical statisticians specialize in data procedures and have little-to-no knowledge of other fields of study. They must consult with others who have subject-specific expertise to know which data to look for and to help find meaning in that data. Data analysts, on the other hand, must understand their subject matter. They seek to gain important insights that they can use with their subject-matter expertise to make meaning of those insights. Below is a list of ways that subject matter experts use analytics to enhance performance in their areas:
• Engineering analysts use data analytics with building designs.
• Clinical data analysts use predictive methods to foresee future health issues.
• Marketing data analysts use regression data to predict and moderate customer turnover.
• Data journalists search databases for patterns that may be worth investigating.
• Crime data analysts develop spatial models to identify patterns and predict future crimes.
• Disaster relief data analysts work to organize and explain important data about the effects of disasters, which is then used to determine the types of assistance needed.