My name is Nikita Ivanov and I am a committed Product Owner at Revolut with expertise in data analytics, personalisation tactics and product management. In this article, I'll discuss the critical role data analysis plays in product development and operation and demonstrate how, when used wisely, it may improve the company’s image in the market. Additionally, I will reflect on how a rising amount of data is impacting the profession of product manager and which new categories of jobs evolved out of this.
The rise of data-driven Product Management
Rapid changes in machine learning and artificial intelligence have caused drastic changes in product management in recent years. Initially, product owners were focused on user experience (UX), paying close attention to both - the visual elements and the underlying workflow. It was mainly an interface-focused position that required a lot of UX/UI expertise.
The expenditure of the product management field has happened as a result of the appearance of new analytical techniques and large data sets. Nowadays, in order to stay ahead of the competition and develop digital products that satisfy consumer demands, product managers need to be proficient in data analytic techniques. This is why there is a greater need for product executives on the current market who can use raw data to extract insights and then use those insights to guide decisions about product development.
Discussing further the importance of product managers in handling and familiarizing themselves with data, I want to highlight the emergence of the data product manager role.
Data product management has evolved significantly over the years. Initially, companies like LinkedIn, Netflix, and Uber faced challenges managing vast amounts of data without a clear strategy. This led to the creation of the data product manager role, which focuses on treating data as a product. Data product managers bridge the gap between data science and product management, ensuring data products align with business goals, enhance data accessibility, and provide actionable insights. They have become essential in driving data-driven decision-making and optimizing data usage across organizations.
As a result, a new profession emerged: data product manager. According to Harvard business publishing, “Data product managers, like product managers of other types, don’t have all the technical or analytical expertise to create the model or engineer the data for it. They are unlikely to be gifted at redesigning business processes or retraining workers either. What they do need to have is the ability to manage a cross-functional product development and deployment process, and a team of people with diverse skills to perform the needed tasks.”
The business impact of Data Analysis in Product Development
Product managers have a huge variety of options and concepts to choose from while working on new projects. However, consumer preferences might still be neglected due to the lack of data analysis, thus customer validation plays a crucial role while managing a product.
Achieving success in product development requires monitoring consumer behavior and categorizing customers based on feature use cases. Product managers can learn crucial data about the aspects that consumers value most and redirect their efforts towards creating new products by properly categorizing customers based on their analysis of user behavior.
Furthermore, data analysis makes it possible for the product line to be continuously improved and developed. Product teams can discover areas that need improvement to enhance the customer experience by making data-driven decisions via a data analytical technique and regularly monitoring customer feedback and behavior. In the constantly changing industry, this guarantees that products will remain fresh and innovative.
One of the primary duties of a product manager includes correctly identifying target audience and securing effective internal collaboration between the departments within the company.
To maximize the benefits of data analytics, product specialists need to be able to assess and use data by formulating the right questions. Understanding internal procedures and effectively communicating ideas to other teams - especially the data analytics team - require a high degree of communication.
Product management and data science must work together collaboratively while it is nearly impossible to create customized, data-driven solutions without effective collaboration between the data science and product teams. This collaboration, in which the product manager takes the lead, ensures the creation of solutions that fully satisfy customer needs.
Additionally, it is critical for product managers to be able to perform basic analytical tasks like funnel analysis, business case modeling or A/B testing in order to foster clear communication, prevent misunderstandings and boost the success of product development initiatives.
Product managers' ability to analyze and present data using advanced tools like Looker, Tableau or PowerBI can greatly improve their decision-making processes. Visualization of complex sets of data enables them to identify trends and gain insights for creating new product development strategies. Additionally, comprehensive data analysis and the development of successful business models also require proficiency in spreadsheet programmes like Google Sheets or Excel.
In order to fully appreciate the potential of data analytics product managers also need to be proficient in Python or SQL. These programming languages allow exploration of deeper insights, more complex statistical modeling and sophisticated data analysis. Recent data suggests that approximately 40% of product managers possess technical skills such as Python and SQL. This proficiency helps them manage large datasets, collaborate effectively with data scientists, and make informed decisions that drive product innovation and growth.
Smart usage of modern tools and consistent improvement of data analytical skills will help product managers to gain an advantage and promote innovation in the data-driven industry such as product development.
Conclusion
Data analytics is essential to today's product management since it allows them to validate hypotheses, identify consumer preferences and enhance creative thinking while working on a new project. Product teams with proficiency in data analytics languages and technologies are able to visualize large datasets, carry out complex analyses and derive insightful conclusions that inspire growth and creativity.
An advanced knowledge of data analytics also boosts cross-team collaboration and improves communication with data analytical teams, resulting in a clear understanding and implementation of project goals. Product managers must always improve their data analysis skills in order to stay competitive, foster innovation and provide outstanding user experiences that will boost the business's overall performance.
