Product Management for AI and Data Science
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Today, data has become an essential element for innovation and sustainable growth in the wake of a digital economy. We are established at a point where without data, any business would come to a halt. Like a British Famous data-journalist and writer David McCandless said
“Data is not new oil, Data Is the New Soil”
So that’s why every company should have core data team that could provide anything needed about data. So here we go, I wanna sharing my thought about Product Management for AI and Data Science from my experience as APM of Data Team and from what I learned from various sources, such as online course platform.
Product manager’s job has always been to ensure that companies are building the right things at the right time and in the right way. However product manager working with A.I. and data are a different with other product manager in general. The skills of product manager in the field of data science and AI are harder to come by, this is because it requires product managers to both understand everything it takes to develop a product and also to possess the skills and to understand data.
So I will you the 3 different this between Product Manager in general and product manager in the field of data science and AI:
First
If product management is at the intersection of technical, UX, and business domains. Well, in A.I. and data product manager is at the intersection of one more domain. the domain of data. Like the picture below:
A.I. and data product managers, unlike PMS working on other products, have to juggle yet another component as part of their role. This means a product manager in A.I. and data has an additional responsibility of considering the collection, security, variety and accuracy of the data being used in the algorithms or models. This can include working on projects that help collect more data from users working with third parties to acquire or annotate data, or simply working with analysts to clean and prepare the data for use.
Second
There is uncertainty of time and performance of projects and data products are very similar to research and development, also known as R&D. R&D is common in fields such as pharmaceutical research, where an upfront investment is required to develop a new drug without the certainty of how long it will take to develop this drug and without the certainty that the drug will even work. This is very similar to A.I. and data products. As a product manager in this field, you have the added challenge of working with uncertain timelines, uncertainty of the total investment required and uncertainty of the end performance of this model or algorithm.
Third
There’s an added challenge of communicating A.I. to your stakeholders. Artificial intelligence is a new field, and data science is still very little understood by executive and leadership teams without technical knowledge. A lot of the time, business people see AI as the magical solution for solving business problems. As a product manager and A.I. and data, you’ll have to do the additional work and educating a sizable portion of your company on how I really works and how that impacts the project’s timeline, Budget, and performance.
Conclusion
Although the three different things I explain above looks so difficult, but for me it’s really fun. I enjoy every challenge in each project of Data. As a Data product manager, you can drive the direction for a team and influence the cutting-edge strategy for an organization. Other organizations like Microsoft likewise realized the critical need for Data and AI product management when it hired Kurl DelBene to help Microsoft build its”AI factory”. As a data science and AI continues to mature, it’s become ever more integrated with operationalized systems, the role of data the science and AI product manager is becoming more critical.
