What important process plays a key role in product stewardship and helps ensure that end user products are safe for consumer use as well as safe for the environment? How do scientists tease out answers from a mass of seemingly confusing data? Or develop actionable predictions that aid in business decisions? The answer to all of these questions, is, of course, Data Analytics. 

The process of examining raw data to find patterns, draw conclusions, and make informed decisions that involves the use of various tools, models and methodologies, Data Analytics help us gather meaningful project insights that support informed data driven decision-making. Both a project time and budget-saver, Data Analytics has helped Waterborne’s scientists’ strategy and decision making across a wide variety of industries and projects, particularly those involving consumer care products, veterinary medicine products, and agricultural chemical products. 

Identifying useful data conclusions and developing new steps requires a keen understanding of both the data in its original form and the end-goal one is trying to accomplish. Compounding the data chaos is determining which version of Data Analytics is best suited to the project at hand. While well-versed in many Data Analytics forms, our Waterborne scientists have found these three types particularly useful: 

Predictive Analytics.  Predictive Analytics uses algorithms and historical data as predictive tools to evaluate patterns and the probability of a future outcome. For example, these predictive tools (or models) can be used to determine the likelihood of a specific chemical exposure in the environment exceeding the risk level of concern based on a particular use. This information is useful in making informed product stewardship and operational decisions.

Inferential Analytics. Inferential Analytics uses algorithms and historical data to evaluate patterns, however, instead of using it to make future predictions, it is used to find and measure factor/variable importance. For example, these models can be used to determine which factors are most significant to a specific chemical exposure in the environment. Inferential Analytics is useful in improving predictive tools used in environmental risk assessments.

Measured and Modeled Dataset Comparisons. Data analytics can assess the confidence in data by systematically comparing measured and modeled datasets. This comparison facilitates the quantification of agreement between datasets and identifies potential biases or uncertainties in the models. Such analysis enhances the reliability of decision-making by providing a clearer understanding of the accuracy and precision of predictions.

A workhorse and, at times, a savior, Data Analytics is truly an unsung hero of the scientific world!