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Data Scientist and Software Development

Introduction

With the technological revolution of the 21st century, data has become an important tool in decision-making. However, to derive meaning out of raw data, it has to be analyzed – a task done by data scientists. These professionals transform data into insight, thus giving elaborate guidance for users of such information to make informed decisions and take action. Initially, data scientists were mainly found within software teams working on data-intensive products, such as advertising and Internet search. However, contemporarily, the majority of software teams are adopting data-driven decision-making, hence the inclusion of data scientists in product development, and they operate at different levels. This paper summarizes the article, “The Emerging Role of Data Scientists on Software Development Teams,” to highlight the various working styles of data scientists and the development lifecycle.

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Working Styles

According to Kim et al. (2016), data scientists have five distinct working styles including insight providers, modeling specialists, platform builders, polymaths, and team leaders. Insight providers generate insights, support, and guide their managers in making the appropriate decisions. Companies have set goals and objectives that should be achieved with a specified period, and thus insight providers collaborate with managers to take the right actions informed by results gathered from analyzing the available data. On their part, modeling specialists are machine learning experts, and they mostly work as consultants building predictive models. Kim et al. (2016) state that modeling specialists’ main task is to “build predictive models that can be instantiated as new software features (e.g., server -telemetry anomaly detection) or to support other team’s data-driven decision making” (p. 102). At times, they work together with insight providers to assess the veracity of their predictive models.

Platform builders create data platforms by carefully incorporating data analytics and engineering skills to produce software systems that could be used to achieve various business goals and for different products. These professionals combine a number of skills to refine data as a way of increasing the confidence in the analysis results, which allows managers to take the most appropriate actions. Polymaths are data scientists “who ‘do it all,’ e.g., forming a business goal, instrumenting a system to collect the required data, doing necessary analyses or experiments, and communicating the results to business leaders” (Kim et al., 2016, p. 103). Therefore, this working style involves the application of both data scientist and software engineering skills as their roles intertwine. Team leaders are senior data scientists overseeing various groups to achieve set objectives and ensure the adoption of best practices. They also champion the adoption of data-driven decision-making in businesses by communicating the importance of such practices (Gilal et al., 2016). Mostly, these professionals work with senior management in companies at advisory levels to inform broad organizational decisions.

Development Lifecycle

The software development lifecycle (SDLC) is the process that is followed in the creation of high-quality software (Kaur, 2015). The process is systematic with a detailed plan delineating how software should be developed, and it involves planning, defining, designing, building, testing, and deployment (Kaur, 2015). In the planning stage, data collected from different sources is used to lay down the rudimentary project approach together with conducting a feasibility study on a certain product in various areas. Insight providers play a major role at this stage. The defining stage involves conducting a thorough analysis to delineate and document all the requirements needed for product development. In the designing phase, engineers come up with the most suitable blueprint for the product being developed. Mostly, multiple designs are proposed and presented before the involved teams to select the best-suited one. Team leaders, modeling specialists, and insight providers give their input during this stage.

The actual product development takes place in the building phase, where the programming code is generated. The design selected in the earlier stages of the cycle is executed with specific details following coding guidelines of the entire process. Various data scientist working styles are needed at this point. In the testing stage, the product is analyzed and tested to identify, track, fix, and retest defects to attain the desired quality standards. In the final stage, the product is released to the market. Normally, this process takes place in stages whereby a limited segment in the market is given the opportunity to use and test the product. Insight providers could be used at this point to collect and analyze data from this user acceptance testing and generate meaningful insights for the refinement of the product.

Conclusion

Data scientists have become an important part of product development as they play various critical roles. These professionals could work as insight providers, modeling specialists, platform builders, polymaths, and team leaders. Data scientists collaborate with one another and other specialists in product development to ensure that the right decisions are made based on the available data. In the contemporary software development environment, data-based decision-making has become an operational standard. The software development cycle has different stages, such as planning, defining, designing, building, testing, and deployment. Each phase is executed carefully to contribute positively to the final product.

References

Gilal, A. R., Jaafar, J., Omar, M., Basri, S., & Waqas, A. (2016). A rule-based model for software development team composition: Team leader role with personality types and gender classification. Information and Software Technology, 74, 105-113.

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Kaur, S. (2015). A review of software development life cycle models. International Journal of Advanced Research in Computer Science and Software Engineering, 5(11), 107-113.

Kim, M., Zimmermann, T., DeLine, R., & Begel, A. (2016). The emerging role of data scientists on software development teams: IEEE/ACM 38th International Conference on Software Engineering. IEEE.

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StudyCorgi. (2022, January 13). Data Scientist and Software Development. Retrieved from https://studycorgi.com/data-scientist-and-software-development/

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StudyCorgi. (2022, January 13). Data Scientist and Software Development. https://studycorgi.com/data-scientist-and-software-development/

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"Data Scientist and Software Development." StudyCorgi, 13 Jan. 2022, studycorgi.com/data-scientist-and-software-development/.

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StudyCorgi. 2022. "Data Scientist and Software Development." January 13, 2022. https://studycorgi.com/data-scientist-and-software-development/.

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StudyCorgi. (2022) 'Data Scientist and Software Development'. 13 January.

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