Computer Science has been developing as a field in a rapid pace over the last decades. And even now, over 70 years from the invention of the computer, the number of computer science jobs far exceeds the number of computer scientists. This is especially true for the role of data scientists. As computers start invading more and more of our daily lives, it has been made clear that computer programs are becoming necessary for all industries in order to sustain competitive growth. Data Science, especially, provides companies with a competitive edge to stay relevant in the market.
The task of data scientists is to take masses of data and turn it into useful information. This field is becoming more relevant due to the total mass of data stored on the internet and its function in driving up revenue. For example, a fashion company can look at Google searches relevant to clothing and see what kind of trends are becoming prevalent and take action on it. Data science is used in this fashion of taking data, finding trends, and making decisions based on the retrieved information. Furthermore, the function of data science can be utilized in any other field of study and this makes it a powerful tool in any industry. Some examples of this are search engine optimization (SEO) in Information Technology, high-frequency trading algorithms in Finance, disease prevention in Genome Research, and more. Data science is inching into every field of study, but it has especially found a foothold in marketing for businesses.
Businesses can use data science in each part of a product’s lifecycle. Because of this, it is essential for Product Managers to understand the purpose of data science and to employ it to their benefit. Product Managers should work with Data Scientists in order to first find the features and product that people want. Based on Google searches, people often search up how to solve their problems. Using this data, a data scientist can find the most common problems found in a given community or society. Product Managers can use this information in order to develop a product that offers a solution to the community. With this, Product Managers can create and enter into the beginning of a product’s lifecycle, but the utilization of data science should not end there. Through the latter stages of a product’s lifecycle, the Product Manager should continue to employ data science in order to assess any possible improvements to the product. This is especially true for software and services, which are more straightforward in deploying changes compared to physical products. Finally, at the end of a product’s lifecycle, Product Managers can use information from data science to understand when a product has reached market maturity and has no room for growth or will induce losses with further investment.
Throughout the stages of a product, Product Managers can, and should, utilize data science in order to optimize profit and customer satisfaction.
About Paul Warren:
Paul manages the data science team, products, and technical roadmap at Arthena, a YC-backed Art Hedge Fund. He is currently co-authoring a data science textbook with a Professor of Data Science in South Korea and has done several years of data science projects for companies, research labs, and competitions. He studied Computer Science at Stanford for three years before dropping out to travel the world.
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