Data science is becoming more and more in demand as technology and necessity support its growth. While the field is showing its importance in practically every area of life, its relation to business is an area of particular interest of late. To better understand the importance of data science as it relates to business, we looked to work by Jonathan Cornelissen, founder of the popular data education company DataCamp. Through this investigation, we’ve provided an overview into both the entrepreneur’s professional life and the significance of data science itself.
Before looking at the broader significance of data science, let’s first examine the career of Jonathan Cornelissen to get a better sense of where this information comes from. The entrepreneur first conceived of the idea for his educational data startup while pursuing his Ph.D. in Econometrics. At that time, he saw both the growing demand for data science as a field as well as the lack of resources for those studying it. One experience that drove this point home was his time teaching a class that required the programming language R. Though his students struggled with the language, there wasn’t an engaging educational platform to which he could point them in order to assist their studies.
From that experience, the data scientist partnered with peers to create a resource that could address this shortcoming. Once there was a functional prototype, the team worked with educational institutions to help students and show professors that there could be another way to assist the learning process in a sometimes challenging area. The prototype met with a very positive response and soon the team was able to expand its work to other applications. As more people became aware of the need for data science education, the startup received greater degrees of funding. To date, the company has helped over 4 million learners in their pursuit of increased data literacy.
When looking to the ways in which data science can make a demonstrable impact in business, one of the first areas one must look is to the realm of decision-making. In earlier times, decision-making in business was often more of an intuitive and descriptive process. Though there were typically some inputs, the field was often more concerned with the experience of a team working on a project. If the quality of the decision-making made sense, less importance was placed upon the quantifiability of any data that may have been used to arrive at the decision.
Recent trends have changed course on this concept and the role of data science has become much more pronounced in modern business decisions. With the increased amount of data that is available these days, companies will often employ a deliberate, data-driven process to help inform important decisions. This reliance on data can be seen in the typical decision-making flowchart, which starts with identifying a problem and then moves directly to data analysis in order to set up next steps. In this context, data enables a higher degree of effectiveness in subsequent problem solving and will eventually lead to a more informed and more applicable end result.
While the above process is relevant for many different areas of business, perhaps one of the most striking is its effect on product development. When a company creates a product it is ostensibly working to solve a problem experienced by customers. In order to best do this, it is important to actually know how customers view their problems and what features may be most important to them in a potential solution.
To this end, many companies rely greatly on customer feedback in order to optimize their products. Companies use this feedback to make changes to existing products depending upon customer desires, or even to create new products to address unresolved issues. In order to engage in this type of development, data science is used to better analyze inputs and help identify important trends in feedback. This process has been greatly helped along by the proliferation of online reviews and other feedback mechanisms available on the internet, which is one of the reasons the field of data science has experienced a surge of late.
Another important area of business that can be greatly affected by data science is the way in which a company manages its internal processes and environment. These methods not only have a direct impact on the way in which the company works, but also can directly impact the satisfaction levels of employees within the company. Employee satisfaction is important not just for the wellbeing of those who work at the company, but can also be a significant driver of business success. Simply put, happy employees are more effective and motivated.
Jonathan Cornelissen has written about this in the past with respect to his own efforts to encourage employee satisfaction at his company. In his case, he used anonymous surveys to gauge how content employees were with various aspects of the way the business was run. These surveys provided a powerful data source from which to extrapolate important decisions on how to change company policies. In part due to these surveys and the data they produced, the data education company was able to make significant improvements in employee satisfaction. This helped lead to a 2018 employee engagement level that was in the top 15% percentile of the field of “new tech.”
When it comes to employees, data science can not only help make existing employees happier, it can also help businesses recruit new employees in a more effective manner. Existing recruitment practices often rely on an inefficient resume submission process which is then analyzed by human resource departments seeking out what they conceive to be the best fit for a position. Not only does this process take up a lot of time and human capital, it is often not an effective way to find the best person for the job. A poor fit in this process can have a strong detrimental impact when the employee begins their work.
By contrast, data science techniques can take much of the guesswork out of the hiring process. For starters, the field can help reduce the burden of resume screening by analyzing resumes as they come in and sorting them according to the potential fit of the candidate. Analytical algorithms can be of additional assistance here by taking the pre-screened resumes and further determining which of them represents the closest match to the work and culture requirements of a given position. This greatly reduces the burden on a company’s human resources department and can ultimately result in an employee that is happier and more effective in a company’s environment.
Data science can also be useful when applied retroactively to assess decisions that have already been made. After an in-depth process is undergone for developing a product or coming to a conclusion, data techniques can be used to look back on how successful that process really was. In the case of a new product, a business might look at sales or new customer feedback, in decision-making, a business might look at how effectively a problem was resolved.
No matter the case, this type of retroactive application of data science accomplishes numerous items of benefit to a business. It can help further refine a decision or product so that it comes closer to meeting and exceeding its goals. It can also be used to shine a light on the development process itself. For example, if an analysis of multiple instances of the same process is showing substandard results, a company may determine that the process needs to be improved upon. If this type of analysis is used to look at multiple different processes, it can help make the determination as to which process is best suited for use in future situations.
From the above overview of data science applications in business, it is clear that the field has much to offer any organization seeking to optimize its results. The field’s usefulness in decision-making, product development, employee satisfaction, and retrospective analysis also help to explain why it has grown so starkly in demand of late. This increased demand reflects the type of spike that Jonathan Cornelissen anticipated when he first created his company. As more and more businesses realize the potential for data science to improve their efforts, they will undoubtedly turn to educational efforts such as those developed by the entrepreneur. Keep an eye on further efforts to come in order to track the directions the field may take moving forward.