IoT is driving demand
When I was hired as a data scientist in 2014, it was still a relatively new field. The growth of connected devices, sensors, and better Internet access globally, however, has created an abundance of messy data—driving the demand for data scientists across industries.
When I say data is messy, I’m referring to data quality. Think of it as missing fields from manual entry. To bring it to a consumer level, fitness trackers are a perfect example of disorganized data. When you enter information into a fitness tracker, you tend to do input it quickly. For example, after you ride a bike or go for a run, you may input the distance you traveled; however, there is so much additional information that could have also been added. How many minutes did you exercise? Did you ride a road bike, a mountain bike, or a beach cruiser? Did you run on a treadmill or a trail? At what resistance or pace did you ride? What about your age, weight and activity level? All of these factors help improve the data quality and inform a more complete story about your fitness and health.
When it comes to enterprise-level initiatives, data science teams tackle the challenge of identifying and developing ways to produce measureable outputs of value from data of variable quality originating from disparate sources. Decision-makers want to see summary numbers presented in an informative and consumable way. In the desire to see whole numbers, users do not always understand the importance of also looking at the statistical certainty around data measurements. It is my team’s job to take statistical validity into account while evaluating metrics for both data quality and for performance benchmarking. The data science team will scour through data in order to create and measure benchmarks for tracking improvement efforts and for identifying trends or opportunities for growth.
Every organization’s data might start messy, but it all holds valuable insights that can effect the bottom line. Data scientists can help organizations transform the data being collected in ways that will ultimately help achieve business objectives.