Today, it is possible to shorten the lead time to building and launching a product. Twenty years ago, it would have been a mad job to think of going from a beginner to a pro in product management in less than 24 months – but it can be done in 2022.
How?
By using AI and automation for business analysis of product vision, strategies, customer analysis, market research, revenue forecasts, and hiring streams. It’s like going on an adrenaline rush with constant help from a reliable technology. A large number of companies rely on data science techniques to capitalize on the potential of business intelligence, bringing cutting edge insights and analysis driven decision making to every aspect of product management. If you are thinking of having a home run with your product management skills, adding data science courses to your list of interests would serve you just right in 2022.
In this article, we will highlight the importance of data science in product management, and what benefits and opportunities you could extract from doing a course in data science subjects.
Why does data science work in product management? What’s the catch?
Data and analytics mean a lot to every organization, particularly for those companies that are into developing and marketing products and services to new markets and customers. In the last 10 to 15 years, we have seen how the business has moved to online channels, and the demands of doing business have changed totally with customers expecting whatever they buy from online to match their needs and requirements without going to a physical store or showroom. Big organizations like Amazon, Alibaba, Walmart, and others have managed to satisfy their customers to a great extent. They use the advanced data science technologies to extract hidden insights from data and analytics which allow them to exactly point to what the customers are looking for, and what kind of products would make them satisfied. The success of these companies has attracted other companies toward data science applications. Today, 93 percent of the business leaders from product development and B2B marketing companies that support these development companies rely on data science to further their revenue streams.
The remaining 7 percent are either mulling over the promise of data or are looking to invest in data science capabilities in the next 3 years. If they fail to capitalize on the opportunity to invest in data science today, they will be wiped out. The only way to succeed with the demands of the market is to follow data science trends and hope that they are able to hire top talent from the best data science courses with relevant certification in AI, Machine learning, and business analytics. If this does not happen, they will begin to lag behind, and eventually, lose their existential identity.
Are business owners happy with what they invest in data science people?
A majority of the business owners are aware of the transformational qualities of having a data science team in their organization. Yet, only 3 percent of the global companies have a sustainable business analytics roadmap for their data science outcomes. The biggest reason for the lag is the lack of trust and governance, which is partly resulting due to the lack of a clear understanding of how data science actually works in real life scenarios. For instance, we all know that AI and robotics can simplify manual work, but there is no clear benchmark on to what level this automation is possible, and if data science tools can be completely trusted to take over human tasks, without any kind of supervision. Also, the outcomes are still far from real desirable results. So, business owners who are liking the idea of having AI and other smart technologies in their operations are also wary that the ROI may not be quite there, at least not in the immediate future. To change this notion, we need a set of an extremely hungry pool of product managers who can turn data on its head to impress business owners and show them the impressive results of having AI do all the work for their departments, including sales and marketing.
Conclusion
So, we should know that data science is not the future – instead, it is already here, and if we don’t train enough with the current set of data science resources, we may all miss the bus to a fantastic future that would bring in more prosperity, trust, and reliability.
Nikhil Kore