statistics experts

statistics experts

The Role of Statistics Experts in Data-Driven Decision Making

1. Introduction to Statistics Experts

An expert in the field of statistics is someone who, as a result of education or experience, is regarded as having skills and knowledge representing mastery of a particular subject. Although several prominent individuals have offered definitions of the term, no one definition has been adopted. Terms related to the field include: Academic Statistician, Actuarial Science, Any Statistician, Bayesian, Data Analyst, Data Scientist, Dempster-Shafer Theory, Econometrician, Fiducial Inference, Frequentist Statistician, Official Statistician, List of Important Publications in Statistics, Machine Learning, Nuisance Parameter, Statistical Model, P-Value, Statistic Astrologer, and Statistician.

A professional in the field of statistics is known as a Statistician. Some statistics — such as a simple average — are used in the everyday analysis of data. However, more complex techniques require the use of statistics specialists. These experts generate relevant questions and gather the data to answer them. The experts design experiments, surveys, and observation studies to collect the data. After they have collected the data, the experts summarize their findings, evaluate implications, and communicate the results to others. They apply statistical methodology such as probability and statistical inference to provide this life skill. These professionals encourage good user practices and proper directions as well. Expertise in the field is more extensive than other forms of statistics. For example, Mathematical Statistics is a requirement for licensing. Thus, experts provide a clear understanding and interpretation of data.

2. Key Skills and Qualifications of Statistics Experts

The role of statistics in data-driven decision making

The role of statistics experts in data-driven decision making is not only to conduct statistical analyses for decision makers, but also to work with decision makers to guide and inform their data-driven decision processes to ensure that the results are properly understood and used.

To achieve this aim, statistics experts must not only have technical skills, but also communicate and liaise with decision makers effectively. They should be able to ask a question in answerable form, understand business processes and manage client relations; support deliberative processes effectively, including understanding uncertainty and trade-offs, estimating reserves and liabilities, and predicting resource availability; spot and reduce omissions, check data quality, and use graphics and simpler model techniques to enhance understanding. The Community of Practice (COP) for Statistics for Data-Intensive Decision Making is dedicated to developing and sharing a rich knowledge base about the role of statistics in data-driven decision making, including ways of developing and evaluating the related skills.

3. Applications of Statistics in Various Industries

Data analytics has had a great impact in recent years, and specifically, the role of statistical experts has been highly emphasized in a number of research papers in the literature. By manipulating data, extracting information, and conducting various analyses, statisticians are able to assist decision-makers in addressing real-world problems. This chapter summarizes some applications of statistical methods to research problems in various industries, providing readers with a basic understanding of these techniques, especially for graduates who have not yet applied any statistical methods beyond introductory statistics courses.

In the advertising industry, panel data is collected to examine how broadcasting advertising, price, and other factors affect consumer purchasing behavior. The statistical model used in this research is the difference-in-difference regression model. This model allows us to isolate the impact of advertising by comparing the changes in both barriers in the treatment market after switching to broadcast advertising with the changes in both bears in the control market after their respective cut-in dates.

4. Challenges and Opportunities for Statistics Experts

Statistics experts can play an essential role in data-driven decision making. However, remaining relevant requires new skills, an understanding of business-related issues, and addressing significant challenges. This paper outlines these areas that will be increasingly significant in the future of statistics and statistical research.

Data-driven decision making involves making decisions that rely on the output from data analysis. The reliance on data distinguishes data-driven decision making from other modes of decision making. Data-driven decision making spans many areas of activity, for example, business, the Web, social networks, and scientific investigation. In business, this could involve predicting the number of customers who may be interested in a particular product, the time it takes to manufacture a product, or the results of a clinical trial, to mention a few examples.

There are many roles where someone with statistical expertise can be a statistics “expert” such as being a data analyst, biostatistician, marketing analyst, web analyst, quality control expert, or a decision analyst. Such an individual can work in many different domains of application such as labor statistics, drug quality, marketing, manufacturing, programming languages, and be involved in many types of decisions such as user data, web tagging, safety standards, the design of digital products, distribution of ad content, or clinical development decisions.

They can have different sponsors for their work—for example, business operations, business analysts, web intelligence teams, executives, software engineers, hardware design engineers, CRM teams, business customers, or development programs—and be part of different teams—such as clinical teams—and these situations can be in different company structures. They could work as an independent consultant, a permanent employee in a large or small company, a contractor, a temporary worker, a collaborator from an academic or contract research organization, and work either supervised, or unsupervised, or help a business to outsource analytics.

5. Conclusion and Future Trends

We end this paper with the following insights into the role of statistical experts in decision making in the era of big (and dirty) datasets. First, we highlight a distinction between experts who are working proactively to identify information that is needed for solving problems within their decision making, and experts called upon primarily to directly facilitate decision making when needed. Both roles will be vital in the short term, with increasing emphasis on proactively identifying information. Second, we expect to see a continued blurring between traditional statistical experts and experts from other disciplines, given data by definition have both variables and measurements, and the resulting explosion in “big” data. As a result, new training programs will need to develop scientific knowledge that resonates with decision making dynamics, and emphasize the importance of communication and problem-solving skills that facilitate decision making. Finally, future work will be needed to develop and test new, more tangible tools and products that leverage big datasets to address a diverse array of problems rapidly, systematically highlighting how they inform decisions.