Quality Control Systems at ROP for
Unbiased Data Analysis

The Consequences of Bias in Data Analysis Process

There are various types of bias that can affect data analysis, and the potential for human error needs to be eliminated to establish best practices regarding the data analysis process.

There are various types of bias that can impact data analysis. The following examples indicate the key factors in bias, all which Data Scientists and Data Analysts must prevent against:

• Confirmation Bias Occurs when an analyst relies on data to prove a hypothesis, which tends to skew the results in favor of a preconceived judgment.
• Availability Bias Occurs when analysts make decisions solely based on the data that’s easily available to them, which neglects others key factors in data.
• Selection Bias Occurs when data samples are not accurately representative of a population, and only utilize certain people in the analysis.
• Confounding Variables Occurs when two or more factors are affecting the outcome, making it difficult to determine which variable produced the results. Data Analysts need to ensure that the variables aren’t influenced by anything external.

ROP’s Quality Control Systems was designed to ensure that these forms of bias don’t factor into the Data Analysis process. We eliminate the potential for bias to factor in our analysis procedures, effectively meeting the unique needs and requirements of our clients.

ROP Quality Control Standards

ROP has established and implemented quality assurance and compliance practices to facilitate unbiased data analysis. Through the development of quality control and quality data management best practices, we assure that businesses can derive the right insights from accurate data.

Key features in ROP’s Quality Control Systems:

• Director of Quality Assurance and Data Analysis

  Responsible for creating and implementing quality control standards and compliance processes, and establishing data management procedures. Ensures that all practices related to quality control policies are followed by all team members.

• IT Quality Control Analyst

  Tests data materials and completed results samples. Is engaged in overseeing quality control practices. Reject results that do not meet ROP standards and report testing results to the Director of Quality Assurance and makes suggestions and strategies for improvement.

• Data Quality Management

  ROP follows robust Data Quality Management methods to ensure that data collection, processing, analysis, and distribution is handled effectively. This serves to promote quality data, which builds a foundation for valuable insights that are free from bias.

• Data Quality Assurance

  ROP performs a precise data profiling procedure to locate any inconsistencies or deviations in data, and conducts data cleansing to improve the quality of data.

Stringent Quality Control for Unbiased Data Analysis

Through the utilization of advanced, automated digital technologies and software, and the experience of a highly trained team of analysts, ROP provides consistent, trusted insights based on unbiased data analysis.

Research Optimus has established rigorous Quality Control Systems with the aim of providing businesses with unbiased data analysis for over a decade. Being able to target key areas that propel the success of business strategies and produce consistent results is the key differentiating factor for driving businesses toward success. Contact us today to know more about our data analysis services and engagement models.